essay advantages of technology in education

How Important Is Technology in Education? Benefits, Challenges, and Impact on Students

Many of today’s high-demand jobs were created in the last decade, according to the International Society for Technology in Education (ISTE). As advances in technology drive globalization and digital transformation, teachers can help students acquire the necessary skills to succeed in the careers of the future.

How important is technology in education? The COVID-19 pandemic is quickly demonstrating why online education should be a vital part of teaching and learning. By integrating technology into existing curricula, as opposed to using it solely as a crisis-management tool, teachers can harness online learning as a powerful educational tool.

The effective use of digital learning tools in classrooms can increase student engagement, help teachers improve their lesson plans, and facilitate personalized learning. It also helps students build essential 21st-century skills.

Virtual classrooms, video, augmented reality (AR), robots, and other technology tools can not only make class more lively, they can also create more inclusive learning environments that foster collaboration and inquisitiveness and enable teachers to collect data on student performance.

Still, it’s important to note that technology is a tool used in education and not an end in itself. The promise of educational technology lies in what educators do with it and how it is used to best support their students’ needs.

Educational Technology Challenges

BuiltIn reports that 92 percent of teachers understand the impact of technology in education. According to Project Tomorrow, 59 percent of middle school students say digital educational tools have helped them with their grades and test scores. These tools have become so popular that the educational technology market is projected to expand to $342 billion by 2025, according to the World Economic Forum.

However, educational technology has its challenges, particularly when it comes to implementation and use. For example, despite growing interest in the use of AR, artificial intelligence, and other emerging technology, less than 10 percent of schools report having these tools in their classrooms, according to Project Tomorrow. Additional concerns include excessive screen time, the effectiveness of teachers using the technology, and worries about technology equity.

Prominently rising from the COVID-19 crisis is the issue of content. Educators need to be able to develop and weigh in on online educational content, especially to encourage students to consider a topic from different perspectives. The urgent actions taken during this crisis did not provide sufficient time for this. Access is an added concern — for example, not every school district has resources to provide students with a laptop, and internet connectivity can be unreliable in homes.

Additionally, while some students thrive in online education settings, others lag for various factors, including support resources. For example, a student who already struggled in face-to-face environments may struggle even more in the current situation. These students may have relied on resources that they no longer have in their homes.

Still, most students typically demonstrate confidence in using online education when they have the resources, as studies have suggested. However, online education may pose challenges for teachers, especially in places where it has not been the norm.

Despite the challenges and concerns, it’s important to note the benefits of technology in education, including increased collaboration and communication, improved quality of education, and engaging lessons that help spark imagination and a search for knowledge in students.

The Benefits of Technology in Education

Teachers want to improve student performance, and technology can help them accomplish this aim. To mitigate the challenges, administrators should help teachers gain the competencies needed to enhance learning for students through technology. Additionally, technology in the classroom should make teachers’ jobs easier without adding extra time to their day.

Technology provides students with easy-to-access information, accelerated learning, and fun opportunities to practice what they learn. It enables students to explore new subjects and deepen their understanding of difficult concepts, particularly in STEM. Through the use of technology inside and outside the classroom, students can gain 21st-century technical skills necessary for future occupations.

Still, children learn more effectively with direction. The World Economic Forum reports that while technology can help young students learn and acquire knowledge through play, for example, evidence suggests that learning is more effective through guidance from an adult, such as a teacher.

Leaders and administrators should take stock of where their faculty are in terms of their understanding of online spaces. From lessons learned during this disruptive time, they can implement solutions now for the future. For example, administrators could give teachers a week or two to think carefully about how to teach courses not previously online. In addition to an exploration of solutions, flexibility during these trying times is of paramount importance.

Below are examples of how important technology is in education and the benefits it offers to students and teachers.

Increased Collaboration and Communication

Educational technology can foster collaboration. Not only can teachers engage with students during lessons, but students can also communicate with each other. Through online lessons and learning games, students get to work together to solve problems. In collaborative activities, students can share their thoughts and ideas and support each other. At the same time, technology enables one-on-one interaction with teachers. Students can ask classroom-related questions and seek additional help on difficult-to-understand subject matter. At home, students can upload their homework, and teachers can access and view completed assignments using their laptops.

Personalized Learning Opportunities

Technology allows 24/7 access to educational resources. Classes can take place entirely online via the use of a laptop or mobile device. Hybrid versions of learning combine the use of technology from anywhere with regular in-person classroom sessions. In both scenarios, the use of technology to tailor learning plans for each student is possible. Teachers can create lessons based on student interests and strengths. An added benefit is that students can learn at their own pace. When they need to review class material to get a better understanding of essential concepts, students can review videos in the lesson plan. The data generated through these online activities enable teachers to see which students struggled with certain subjects and offer additional assistance and support.

Curiosity Driven by Engaging Content

Through engaging and educational content, teachers can spark inquisitiveness in children and boost their curiosity, which research says has ties to academic success. Curiosity helps students get a better understanding of math and reading concepts. Creating engaging content can involve the use of AR, videos, or podcasts. For example, when submitting assignments, students can include videos or interact with students from across the globe.

Improved Teacher Productivity and Efficiency

Teachers can leverage technology to achieve new levels of productivity, implement useful digital tools to expand learning opportunities for students, and increase student support and engagement. It also enables teachers to improve their instruction methods and personalize learning. Schools can benefit from technology by reducing the costs of physical instructional materials, enhancing educational program efficiency, and making the best use of teacher time.

Become a Leader in Enriching Classrooms through Technology

Educators unfamiliar with some of the technology used in education may not have been exposed to the tools as they prepared for their careers or as part of their professional development. Teachers looking to make the transition and acquire the skills to incorporate technology in education can take advantage of learning opportunities to advance their competencies. For individuals looking to help transform the education system through technology, American University’s School of Education online offers a Master of Arts in Teaching and a Master of Arts in Education Policy and Leadership to prepare educators with essential tools to become leaders. Courses such as Education Program and Policy Implementation and Teaching Science in Elementary School equip graduate students with critical competencies to incorporate technology into educational settings effectively.

Learn more about American University’s School of Education online and its master’s degree programs.

Virtual Reality in Education: Benefits, Tools, and Resources

Data-Driven Decision Making in Education: 11 Tips for Teachers & Administration

Helping Girls Succeed in STEM

BuiltIn, “Edtech 101”

EdTech, “Teaching Teachers to Put Tech Tools to Work”

International Society for Technology in Education, “Preparing Students for Jobs That Don’t Exist”

The Journal, “How Teachers Use Technology to Enrich Learning Experiences”

Pediatric Research, “Early Childhood Curiosity and Kindergarten Reading and Math Academic Achievement”

Project Tomorrow, “Digital Learning: Peril or Promise for Our K-12 Students”

World Economic Forum, “The Future of Jobs Report 2018”

World Economic Forum, “Learning through Play: How Schools Can Educate Students through Technology”

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Essay on Impact of Technology on Education

Students are often asked to write an essay on Impact of Technology on Education in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Impact of Technology on Education

Introduction.

Technology has greatly influenced education. It has changed the way we learn and teach, making education more accessible and engaging.

Interactive Learning

Technology has introduced interactive learning tools like smart boards and tablets. They make lessons more engaging and fun, helping students understand better.

Online Education

With the internet, learning is not limited to classrooms. Online courses, video lectures, and digital libraries have made education accessible to everyone, everywhere.

Improved Communication

Technology has improved communication between students and teachers. Emails, chats, and video calls make it easier to discuss and solve doubts.

In conclusion, technology’s impact on education is profound. It has made learning more interactive, accessible, and communicative.

250 Words Essay on Impact of Technology on Education

The advent of technology in education.

The advent of technology has revolutionized various sectors, with education being one of the most impacted. It has transformed traditional teaching methods, making learning more engaging, accessible, and efficient.

Enhancing Accessibility and Flexibility

Technology has democratized education, breaking down geographical barriers. Online learning platforms and digital libraries provide easy access to a vast range of resources. This flexibility allows students to learn at their own pace, fostering a self-driven learning environment.

Interactive Learning Experience

Technological tools like virtual reality, digital simulations, and gamified learning apps have made education more interactive. These tools cater to different learning styles, enhancing comprehension, and retention of knowledge.

Collaborative Learning

Tools like cloud-based applications and social media platforms promote collaborative learning. They enable students to work together on projects, share ideas, and gain diverse perspectives, fostering critical thinking and problem-solving skills.

Challenges Posed by Technology

Despite its benefits, technology also poses challenges. The digital divide, where some students lack access to technology, can exacerbate educational inequalities. Additionally, over-reliance on technology might hinder the development of interpersonal skills and critical thinking.

In conclusion, the impact of technology on education is profound, offering immense benefits while posing certain challenges. It’s crucial to balance the use of technology in education, maximizing its advantages while mitigating its potential drawbacks.

500 Words Essay on Impact of Technology on Education

The advent of technology has dramatically transformed various sectors globally, and education is no exception. Over the years, technology has played a pivotal role in reshaping educational landscapes, creating new opportunities for both students and educators. This essay explores the impact of technology on education, focusing on its benefits, challenges, and future implications.

The Benefits of Technology in Education

One of the most significant benefits of technology in education is the democratization of knowledge. Digital platforms such as online libraries, e-books, and educational websites have made information accessible to anyone with an internet connection, breaking down geographical and socio-economic barriers.

Technology has also fostered a more personalized learning experience. Adaptive learning systems and educational apps can tailor content to individual students’ needs, enhancing their understanding and engagement. Furthermore, technology facilitates collaborative learning through platforms that allow students to work together remotely, fostering teamwork and communication skills.

The Challenges of Technology in Education

Despite the numerous benefits, technology’s integration into education is not without challenges. One of the primary issues is the digital divide, which refers to the disparity in access to technology between different socioeconomic groups. This divide exacerbates educational inequalities, as students who lack access to digital resources are disadvantaged.

Another challenge is the potential for distraction. With the proliferation of digital devices, students may be tempted to use them for non-educational purposes, which can hinder their academic progress. Additionally, the over-reliance on technology may diminish critical thinking and problem-solving skills, as students may resort to quick online solutions rather than engaging in deep, thoughtful analysis.

Future Implications

As we look towards the future, the role of technology in education is set to grow even more prominent. Virtual Reality (VR) and Augmented Reality (AR) are expected to revolutionize the classroom experience, making learning more immersive and engaging. Artificial Intelligence (AI) will likely automate administrative tasks, freeing up teachers’ time to focus more on instruction and student interaction.

However, as technology continues to evolve, it is crucial to address its challenges. Policymakers and educators must work together to bridge the digital divide, ensuring that all students can benefit from technological advancements. Additionally, digital literacy programs should be implemented to teach students how to use technology responsibly and effectively.

In conclusion, technology has had a profound impact on education, offering numerous benefits but also presenting significant challenges. As we navigate the digital age, it is essential to harness technology’s potential to enhance education while mitigating its drawbacks. This balanced approach will ensure that technology serves as a powerful tool in shaping a more equitable, engaging, and efficient educational landscape.

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Technology In Education Essay

Essay On Technology In Education- Technology makes education very easy. Technology is now very essential to maintaining society, and it will definitely have an impact on education. In today's life, technology has made study easier. Here are 100, 200 and 500 word essays on Technology In Education

Technology plays a huge part in education. The students' learning process gets simpler as technology advances. Students can easily learn the concepts thanks to technologies utilised in schools and universities, such as computer labs and high-end equipment and instruments. In today's life, technology has made study easier. Here are some sample essays on Technology In Education

100 Words Essay On Technology In Education

Technology makes education very easy. Technology is now essential to maintaining society, and it will definitely have an impact on education. Previously teachers didn't allow students to use technology in education. Today's everything is connected to technology including education,communication, etc. Although technology has been a part of our lives for many years, the development and use of technology in education have only lately started to take shape. One of the most crucial things we have now that can help students perform better academically is technology. As technology advances, it creates new opportunities for students to interact and learn through a variety of sources. Online classes are the best example of technology.

200 Words Essay On Technology In Education

The word "technology" is derived from the Greek word "tekhnologia," where "tekh" signifies an art, a skill, etc., and "logy" defines a subject of interest. Technology makes our tasks easy and makes life easy. Today, technology plays a significant role in our lives and offers a digital platform. The term "smart classes" is being used increasingly in schools and colleges, and these classes are the best use of technology.

Technology And Education

Technology made education easy and attractive. Students study because of technology with their mobile phones and laptops.

By using technology, online classes have started, and students love doing smart classes.

Technology keeps students updated on the world and shows the right direction to do good in education.

Through technology, students can read newspapers daily wise. Technology made education easy and attractive.

From technology, schools make their app and take attendance online, which helps the environment also by not using paper and pen.

Technology attracts children more, which helps them to choose their path.

Education should not be done with only books; students should get a chance to explore their knowledge and try something new. Technology is the best thing to explore. By using technology, students' knowledge will grow faster than before.

500 Words Essay On Technology In Education

Technology has become an integral part of education because of different apps and websites. Nowadays, if you want to clear your doubts or to know your syllabus, everything is available online. Nowadays, education is nothing without technology.

Is Technology Helpful In Education?

Yes, technology is helpful to education. Nowadays, you will see the difference in how technology has changed teaching. In older days, students read from their books, and if they faced any problem, they would ask their teachers the next day at school or for tuition.

But nowadays, students clear their doubts by using apps and websites. Due to technology, they can also ask a question or can have live interaction with their teachers personally. Education has progressed a lot.

Technology has made education easy, and today we have multiple options to clear our doubts and interact online with our teachers. Nowadays, we have easy access to the internet, and other helping apps have made education accessible and exciting.

Technology is essential for students. Parents and teachers should permit their children to use technology for their students because time has changed, and the mode of education should also be changed. Students should be given a chance to learn something new and exciting and technology makes it possible.

Different Technologies for Education

Many devices make education easier for students and clear students' doubts. Some of them are-

Laptops | One of the best tools for learning is a laptop. You can obtain information on the Internet either in written form, video form, or audio form. On several applications and websites, you can find tutors who can give you a thorough explanation. Students can acquire extensive information and have their questions answered thanks to it. You may effortlessly visit several educational portals using a laptop.

Smartphone | Smartphones are smaller versions of laptops; you can use them more easily than laptops and take them with you wherever you go. It is user-friendly due to its compact size and simple internet connection. Students can speak with their teacher about questions using a smartphone. Many students have smartphones, which they use for academic purposes. Numerous apps were available for students on mobile devices.

Kindle for Textbooks | Kindle Textbooks are a type of online book. Kindle books are available at half the price of paper books. This helps to reduce the production of paper, which allows our environment and online books to be easily stored. Kindle Textbooks are popular these days. Many students use them.

My Experience

From the 12th standard, I used a smartphone and laptop for education. Technology makes study easier. When I didn't understand something from school, I used to look for those online and try to clear all my doubts by watching topic specific videos. In my school days, I learned different crafts and drawing skills by watching videos online. I used to take help from online videos to understand many science experiments and easy tricks to solve various mathematical questions. Technology in education is perfect for the future because the use of technology in education will bring a drastic change in our education system.

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Advantages & Disadvantages of Using Technology in Education Essay

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Numerous advantages accrue to teachers who utilize technology not only in their teaching but also as a tool to interact with students across space and time. One of the significant advantages of using technology is that it enables teachers to design and implement interactive course materials that could be used to enhance learning experiences that are more pleasurable and meaningful to students.

There are millions of readily available applications and downloadable programs that teachers could use to design self-assessment tests to be administered to students via online protocols, not mentioning that science teachers often benefit immensely from the use of these free programs to design animations and simulations that could be used to elaborate complex scientific content to students.

From own experience, it can be stated that using simulations designed to teach students about the blood circulation system not only expose learners to a more pleasurable and exciting learning experience, but also ensure that learning outcomes are grasped and internalized with relative ease.

The second advantage of using technology, especially the World Wide Web, is that it has the capacity to bring isolated learners together in “virtual” groups without the limitations of space or time. Unlike in a traditional classroom context, teachers using the Web for teaching purposes enjoy the opportunity to teach and interact with disparate groups of learners located in diverse locations around the world.

Recent discoveries in Web-based technologies ensure that teachers are now able to interact with their students in real-time and to share resources across the network. In my teaching experience, I have made use of virtual classrooms to reach out to numerous students without necessarily making physical contact, not mentioning that I have also made use of the Web to get learning materials from my course instructors.

The third advantage is rested on the premise that it is indeed possible to not only protect intellectual property through the application of passwords and access codes but also to support confidential exchange of learning materials and information between registered users (Cantillon et al. 2003).

This implies that online technologies avail a safe and secure framework where learning, communication, and exchange of information can take place.

Using technology in education has obvious disadvantages. First, it is a well-known fact that teachers who use online assessments have no capacity to control the students’ unauthorized use of online resources and content to complete their assignments. Many teachers often face this hurdle and are unable to provide competitive assessments that could then be used to grade students on their performance.

However, in my view, performance in teaching should not be emphasized at the expense of task comprehension, implying that these assignments should be given in a manner that the teacher is able to evaluate the task comprehension of each student despite their use of online content to complete the tasks.

The second disadvantage arises from the fact that technology, if not well designed and adopted, will definitely provide students with the opportunity to collaborate with each other in taking their tests, therefore adversely affecting the core principles and justifications of good educational practice.

However, from own experience, it is indeed clear that teachers can now use the latest applications and plagiarism software not only to set availability dates and times for all assessments but also discourage students who would want to copy other people’s work and pass it as their own.

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How technology is reinventing education.

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of  Stanford Graduate School of Education  (GSE), who is also a professor of educational technology at the GSE and faculty director of the  Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately  worried  that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or  coach  students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the  AI + Education initiative  at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of  CRAFT  (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the  Digital Learning initiative  at the Stanford Accelerator for Learning, which runs a program exploring the use of  virtual field trips  to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Is technology good or bad for learning?

Subscribe to the brown center on education policy newsletter, saro mohammed, ph.d. smp saro mohammed, ph.d. partner - the learning accelerator @edresearchworks.

May 8, 2019

I’ll bet you’ve read something about technology and learning recently. You may have read that device use enhances learning outcomes . Or perhaps you’ve read that screen time is not good for kids . Maybe you’ve read that there’s no link between adolescents’ screen time and their well-being . Or that college students’ learning declines the more devices are present in their classrooms .

If ever there were a case to be made that more research can cloud rather than clarify an issue, technology use and learning seems to fit the bill. This piece covers what the research actually says, some outstanding questions, and how to approach the use of technology in learning environments to maximize opportunities for learning and minimize the risk of doing harm to students.

In my recent posts , I have frequently cited the mixed evidence about blended learning, which strategically integrates in-person learning with technology to enable real-time data use, personalized instruction, and mastery-based progression. One thing that this nascent evidence base does show is that technology can be linked to improved learning . When technology is integrated into lessons in ways that are aligned with good in-person teaching pedagogy, learning can be better than without technology.

A 2018 meta-analysis of dozens of rigorous studies of ed tech , along with the executive summary of a forthcoming update (126 rigorous experiments), indicated that when education technology is used to individualize students’ pace of learning, the results overall show “ enormous promise .” In other words, ed tech can improve learning when used to personalize instruction to each student’s pace.

Further, this same meta-analysis, along with other large but correlational studies (e.g., OECD 2015 ), also found that increased access to technology in school was associated with improved proficiency with, and increased use of, technology overall. This is important in light of the fact that access to technology outside of learning environments is still very unevenly distributed across ethnic, socio-economic, and geographic lines. Technology for learning, when deployed to all students, ensures that no student experiences a “21st-century skills and opportunity” gap.

More practically, technology has been shown to scale and sustain instructional practices that would be too resource-intensive to work in exclusively in-person learning environments, especially those with the highest needs. In multiple , large-scale studies where technology has been incorporated into the learning experiences of hundreds of students across multiple schools and school systems, they have been associated with better academic outcomes than comparable classrooms that did not include technology. Added to these larger bodies of research are dozens, if not hundreds, of smaller , more localized examples of technology being used successfully to improve students’ learning experiences. Further, meta-analyses and syntheses of the research show that blended learning can produce greater learning than exclusively in-person learning.

All of the above suggest that technology, used well, can drive equity in learning opportunities. We are seeing that students and families from privileged backgrounds are able to make choices about technology use that maximize its benefits and minimize its risks , while students and families from marginalized backgrounds do not have opportunities to make the same informed choices. Intentional, thoughtful inclusion of technology in public learning environments can ensure that all students, regardless of their ethnicity, socioeconomic status, language status, special education status, or other characteristics, have the opportunity to experience learning and develop skills that allow them to fully realize their potential.

On the other hand, the evidence is decidedly mixed on the neurological impact of technology use. In November 2016, the American Association of Pediatrics updated their screen time guidelines for parents, generally relaxing restrictions and increasing the recommended maximum amount of time that children in different age groups spend interacting with screens. These guidelines were revised not because of any new research, but for two far more practical reasons. First, the nuance of the existing evidence–especially the ways in which recommendations change as children get older–was not adequately captured in the previous guidelines. Second, the proliferation of technology in our lives had made the previous guidelines almost impossible to follow.

The truth is that infants, in particular, learn by interacting with our physical world and with other humans, and it is likely that very early (passive) interactions with devices–rather than humans–can disrupt or misinform neural development . As we grow older, time spent on devices often replaces time spent engaging in physical activity or socially with other people, and it can even become a substitute for emotional regulation, which is detrimental to physical, social, and emotional development.

In adolescence and young adulthood, the presence of technology in learning environments has also been associated with (but has not been shown to be the cause of) negative variables such as attention deficits or hyperactivity , feeling lonely , and lower grades . Multitasking is not something our brains can do while learning , and technology often represents not just one more “task” to have to attend to in a learning environment, but multiple additional tasks due to the variety of apps and programs installed on and producing notifications through a single device.

The pragmatic

The current takeaway from the research is that there are potential benefits and risks to deploying technology in learning environments. While we can’t wrap this topic up with a bow just yet–there are still more questions than answers–there is evidence that technology can amplify effective teaching and learning when in the hands of good teachers. The best we can do today is understand how technology can be a valuable tool for educators to do the complex, human work that is teaching by capitalizing on the benefits while remaining fully mindful of the risks as we currently understand them.

We must continue to build our understanding of both the risks and benefits as we proceed. With that in mind, here are some “Dos” and “Don’ts” for using technology in learning environments:

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Saro Mohammed, Ph.D.

November 3, 2017

June 16, 2017

September 27, 2017

Education Technology K-12 Education

Governance Studies

Brown Center on Education Policy

Jing Liu, Cameron Conrad, David Blazar

May 1, 2024

Hannah C. Kistler, Shaun M. Dougherty

April 9, 2024

Darrell M. West, Joseph B. Keller

February 12, 2024

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• Published: 12 February 2024

Education reform and change driven by digital technology: a bibliometric study from a global perspective

• Chengliang Wang 1 ,
• Xiaojiao Chen 1 ,
• Teng Yu   ORCID: orcid.org/0000-0001-5198-7261 2 , 3 ,
• Yidan Liu 1 , 4 &
• Yuhui Jing 1  

Humanities and Social Sciences Communications volume  11 , Article number:  256 ( 2024 ) Cite this article

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• Development studies
• Science, technology and society

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

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Introduction.

Digital technology has become an essential component of modern education, facilitating the extension of temporal and spatial boundaries and enriching the pedagogical contexts (Selwyn and Facer, 2014 ). The advent of mobile communication technology has enabled learning through social media platforms (Szeto et al. 2015 ; Pires et al. 2022 ), while the advancement of augmented reality technology has disrupted traditional conceptions of learning environments and spaces (Perez-Sanagustin et al., 2014 ; Kyza and Georgiou, 2018 ). A wide range of digital technologies has enabled learning to become a norm in various settings, including the workplace (Sjöberg and Holmgren, 2021 ), home (Nazare et al. 2022 ), and online communities (Tang and Lam, 2014 ). Education is no longer limited to fixed locations and schedules, but has permeated all aspects of life, allowing learning to continue at any time and any place (Camilleri and Camilleri, 2016 ; Selwyn and Facer, 2014 ).

The advent of digital technology has led to the creation of several informal learning environments (Greenhow and Lewin, 2015 ) that exhibit divergent form, function, features, and patterns in comparison to conventional learning environments (Nygren et al. 2019 ). Consequently, the associated teaching and learning processes, as well as the strategies for the creation, dissemination, and acquisition of learning resources, have undergone a complete overhaul. The ensuing transformations have posed a myriad of novel issues, such as the optimal structuring of teaching methods by instructors and the adoption of appropriate learning strategies by students in the new digital technology environment. Consequently, an examination of the principles that underpin effective teaching and learning in this environment is a topic of significant interest to numerous scholars engaged in digital technology education research.

Over the course of the last two decades, digital technology has made significant strides in the field of education, notably in extending education time and space and creating novel educational contexts with sustainability. Despite research attempts to consolidate the application of digital technology in education, previous studies have only focused on specific aspects of digital technology, such as Pinto and Leite’s ( 2020 ) investigation into digital technology in higher education and Mustapha et al.’s ( 2021 ) examination of the role and value of digital technology in education during the pandemic. While these studies have provided valuable insights into the practical applications of digital technology in particular educational domains, they have not comprehensively explored the macro-mechanisms and internal logic of digital technology implementation in education. Additionally, these studies were conducted over a relatively brief period, making it challenging to gain a comprehensive understanding of the macro-dynamics and evolutionary process of digital technology in education. Some studies have provided an overview of digital education from an educational perspective but lack a precise understanding of technological advancement and change (Yang et al. 2022 ). Therefore, this study seeks to employ a systematic scientific approach to collate relevant research from 2000 to 2022, comprehend the internal logic and development trends of digital technology in education, and grasp the outstanding contribution of digital technology in promoting the sustainability of education in time and space. In summary, this study aims to address the following questions:

RQ1: Since the turn of the century, what is the productivity distribution of the field of digital technology education application research in terms of authorship, country/region, institutional and journal level?

RQ2: What is the development trend of research on the application of digital technology in education in the past two decades?

RQ3: What are the current frontiers of research on the application of digital technology in education?

Literature review

Although the term “digital technology” has become ubiquitous, a unified definition has yet to be agreed upon by scholars. Because the meaning of the word digital technology is closely related to the specific context. Within the educational research domain, Selwyn’s ( 2016 ) definition is widely favored by scholars (Pinto and Leite, 2020 ). Selwyn ( 2016 ) provides a comprehensive view of various concrete digital technologies and their applications in education through ten specific cases, such as immediate feedback in classes, orchestrating teaching, and community learning. Through these specific application scenarios, Selwyn ( 2016 ) argues that digital technology encompasses technologies associated with digital devices, including but not limited to tablets, smartphones, computers, and social media platforms (such as Facebook and YouTube). Furthermore, Further, the behavior of accessing the internet at any location through portable devices can be taken as an extension of the behavior of applying digital technology.

The evolving nature of digital technology has significant implications in the field of education. In the 1890s, the focus of digital technology in education was on comprehending the nuances of digital space, digital culture, and educational methodologies, with its connotations aligned more towards the idea of e-learning. The advent and subsequent widespread usage of mobile devices since the dawn of the new millennium have been instrumental in the rapid expansion of the concept of digital technology. Notably, mobile learning devices such as smartphones and tablets, along with social media platforms, have become integral components of digital technology (Conole and Alevizou, 2010 ; Batista et al. 2016 ). In recent times, the burgeoning application of AI technology in the education sector has played a vital role in enriching the digital technology lexicon (Banerjee et al. 2021 ). ChatGPT, for instance, is identified as a novel educational technology that has immense potential to revolutionize future education (Rospigliosi, 2023 ; Arif, Munaf and Ul-Haque, 2023 ).

Pinto and Leite ( 2020 ) conducted a comprehensive macroscopic survey of the use of digital technologies in the education sector and identified three distinct categories, namely technologies for assessment and feedback, mobile technologies, and Information Communication Technologies (ICT). This classification criterion is both macroscopic and highly condensed. In light of the established concept definitions of digital technology in the educational research literature, this study has adopted the characterizations of digital technology proposed by Selwyn ( 2016 ) and Pinto and Leite ( 2020 ) as crucial criteria for analysis and research inclusion. Specifically, this criterion encompasses several distinct types of digital technologies, including Information and Communication Technologies (ICT), Mobile tools, eXtended Reality (XR) Technologies, Assessment and Feedback systems, Learning Management Systems (LMS), Publish and Share tools, Collaborative systems, Social media, Interpersonal Communication tools, and Content Aggregation tools.

Methodology and materials

Research method: bibliometric.

The research on econometric properties has been present in various aspects of human production and life, yet systematic scientific theoretical guidance has been lacking, resulting in disorganization. In 1969, British scholar Pritchard ( 1969 ) proposed “bibliometrics,” which subsequently emerged as an independent discipline in scientific quantification research. Initially, Pritchard defined bibliometrics as “the application of mathematical and statistical methods to books and other media of communication,” however, the definition was not entirely rigorous. To remedy this, Hawkins ( 2001 ) expanded Pritchard’s definition to “the quantitative analysis of the bibliographic features of a body of literature.” De Bellis further clarified the objectives of bibliometrics, stating that it aims to analyze and identify patterns in literature, such as the most productive authors, institutions, countries, and journals in scientific disciplines, trends in literary production over time, and collaboration networks (De Bellis, 2009 ). According to Garfield ( 2006 ), bibliometric research enables the examination of the history and structure of a field, the flow of information within the field, the impact of journals, and the citation status of publications over a longer time scale. All of these definitions illustrate the unique role of bibliometrics as a research method for evaluating specific research fields.

This study uses CiteSpace, VOSviewer, and Charticulator to analyze data and create visualizations. Each of these three tools has its own strengths and can complement each other. CiteSpace and VOSviewer use set theory and probability theory to provide various visualization views in fields such as keywords, co-occurrence, and co-authors. They are easy to use and produce visually appealing graphics (Chen, 2006 ; van Eck and Waltman, 2009 ) and are currently the two most widely used bibliometric tools in the field of visualization (Pan et al. 2018 ). In this study, VOSviewer provided the data necessary for the Performance Analysis; Charticulator was then used to redraw using the tabular data exported from VOSviewer (for creating the chord diagram of country collaboration); this was to complement the mapping process, while CiteSpace was primarily utilized to generate keyword maps and conduct burst word analysis.

Data retrieval

This study selected documents from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) in the Web of Science Core Collection as the data source, for the following reasons:

(1) The Web of Science Core Collection, as a high-quality digital literature resource database, has been widely accepted by many researchers and is currently considered the most suitable database for bibliometric analysis (Jing et al. 2023a ). Compared to other databases, Web of Science provides more comprehensive data information (Chen et al. 2022a ), and also provides data formats suitable for analysis using VOSviewer and CiteSpace (Gaviria-Marin et al. 2019 ).

(2) The application of digital technology in the field of education is an interdisciplinary research topic, involving technical knowledge literature belonging to the natural sciences and education-related literature belonging to the social sciences. Therefore, it is necessary to select Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) as the sources of research data, ensuring the comprehensiveness of data while ensuring the reliability and persuasiveness of bibliometric research (Hwang and Tsai, 2011 ; Wang et al. 2022 ).

After establishing the source of research data, it is necessary to determine a retrieval strategy (Jing et al. 2023b ). The choice of a retrieval strategy should consider a balance between the breadth and precision of the search formula. That is to say, it should encompass all the literature pertaining to the research topic while excluding irrelevant documents as much as possible. In light of this, this study has set a retrieval strategy informed by multiple related papers (Mustapha et al. 2021 ; Luo et al. 2021 ). The research by Mustapha et al. ( 2021 ) guided us in selecting keywords (“digital” AND “technolog*”) to target digital technology, while Luo et al. ( 2021 ) informed the selection of terms (such as “instruct*,” “teach*,” and “education”) to establish links with the field of education. Then, based on the current application of digital technology in the educational domain and the scope of selection criteria, we constructed the final retrieval strategy. Following the general patterns of past research (Jing et al. 2023a , 2023b ), we conducted a specific screening using the topic search (Topics, TS) function in Web of Science. For the specific criteria used in the screening for this study, please refer to Table 1 .

Literature screening

Literature acquired through keyword searches may contain ostensibly related yet actually unrelated works. Therefore, to ensure the close relevance of literature included in the analysis to the research topic, it is often necessary to perform a manual screening process to identify the final literature to be analyzed, subsequent to completing the initial literature search.

The manual screening process consists of two steps. Initially, irrelevant literature is weeded out based on the title and abstract, with two members of the research team involved in this phase. This stage lasted about one week, resulting in 1106 articles being retained. Subsequently, a comprehensive review of the full text is conducted to accurately identify the literature required for the study. To carry out the second phase of manual screening effectively and scientifically, and to minimize the potential for researcher bias, the research team established the inclusion criteria presented in Table 2 . Three members were engaged in this phase, which took approximately 2 weeks, culminating in the retention of 588 articles after meticulous screening. The entire screening process is depicted in Fig. 1 , adhering to the PRISMA guidelines (Page et al. 2021 ).

The process of obtaining and filtering the necessary literature data for research.

Data standardization

Nguyen and Hallinger ( 2020 ) pointed out that raw data extracted from scientific databases often contains multiple expressions of the same term, and not addressing these synonymous expressions could affect research results in bibliometric analysis. For instance, in the original data, the author list may include “Tsai, C. C.” and “Tsai, C.-C.”, while the keyword list may include “professional-development” and “professional development,” which often require merging. Therefore, before analyzing the selected literature, a data disambiguation process is necessary to standardize the data (Strotmann and Zhao, 2012 ; Van Eck and Waltman, 2019 ). This study adopted the data standardization process proposed by Taskin and Al ( 2019 ), mainly including the following standardization operations:

Firstly, the author and source fields in the data are corrected and standardized to differentiate authors with similar names.

Secondly, the study checks whether the journals to which the literature belongs have been renamed in the past over 20 years, so as to avoid the influence of periodical name change on the analysis results.

Finally, the keyword field is standardized by unifying parts of speech and singular/plural forms of keywords, which can help eliminate redundant entries in the knowledge graph.

Performance analysis (RQ1)

This section offers a thorough and detailed analysis of the state of research in the field of digital technology education. By utilizing descriptive statistics and visual maps, it provides a comprehensive overview of the development trends, authors, countries, institutions, and journal distribution within the field. The insights presented in this section are of great significance in advancing our understanding of the current state of research in this field and identifying areas for further investigation. The use of visual aids to display inter-country cooperation and the evolution of the field adds to the clarity and coherence of the analysis.

Time trend of the publications

To understand a research field, it is first necessary to understand the most basic quantitative information, among which the change in the number of publications per year best reflects the development trend of a research field. Figure 2 shows the distribution of publication dates.

Time trend of the publications on application of digital technology in education.

From the Fig. 2 , it can be seen that the development of this field over the past over 20 years can be roughly divided into three stages. The first stage was from 2000 to 2007, during which the number of publications was relatively low. Due to various factors such as technological maturity, the academic community did not pay widespread attention to the role of digital technology in expanding the scope of teaching and learning. The second stage was from 2008 to 2019, during which the overall number of publications showed an upward trend, and the development of the field entered an accelerated period, attracting more and more scholars’ attention. The third stage was from 2020 to 2022, during which the number of publications stabilized at around 100. During this period, the impact of the pandemic led to a large number of scholars focusing on the role of digital technology in education during the pandemic, and research on the application of digital technology in education became a core topic in social science research.

Analysis of authors

An analysis of the author’s publication volume provides information about the representative scholars and core research strengths of a research area. Table 3 presents information on the core authors in adaptive learning research, including name, publication number, and average number of citations per article (based on the analysis and statistics from VOSviewer).

Variations in research foci among scholars abound. Within the field of digital technology education application research over the past two decades, Neil Selwyn stands as the most productive author, having published 15 papers garnering a total of 1027 citations, resulting in an average of 68.47 citations per paper. As a Professor at the Faculty of Education at Monash University, Selwyn concentrates on exploring the application of digital technology in higher education contexts (Selwyn et al. 2021 ), as well as related products in higher education such as Coursera, edX, and Udacity MOOC platforms (Bulfin et al. 2014 ). Selwyn’s contributions to the educational sociology perspective include extensive research on the impact of digital technology on education, highlighting the spatiotemporal extension of educational processes and practices through technological means as the greatest value of educational technology (Selwyn, 2012 ; Selwyn and Facer, 2014 ). In addition, he provides a blueprint for the development of future schools in 2030 based on the present impact of digital technology on education (Selwyn et al. 2019 ). The second most productive author in this field, Henderson, also offers significant contributions to the understanding of the important value of digital technology in education, specifically in the higher education setting, with a focus on the impact of the pandemic (Henderson et al. 2015 ; Cohen et al. 2022 ). In contrast, Edwards’ research interests focus on early childhood education, particularly the application of digital technology in this context (Edwards, 2013 ; Bird and Edwards, 2015 ). Additionally, on the technical level, Edwards also mainly prefers digital game technology, because it is a digital technology that children are relatively easy to accept (Edwards, 2015 ).

Analysis of countries/regions and organization

The present study aimed to ascertain the leading countries in digital technology education application research by analyzing 75 countries related to 558 works of literature. Table 4 depicts the top ten countries that have contributed significantly to this field in terms of publication count (based on the analysis and statistics from VOSviewer). Our analysis of Table 4 data shows that England emerged as the most influential country/region, with 92 published papers and 2401 citations. Australia and the United States secured the second and third ranks, respectively, with 90 papers (2187 citations) and 70 papers (1331 citations) published. Geographically, most of the countries featured in the top ten publication volumes are situated in Australia, North America, and Europe, with China being the only exception. Notably, all these countries, except China, belong to the group of developed nations, suggesting that economic strength is a prerequisite for fostering research in the digital technology education application field.

This study presents a visual representation of the publication output and cooperation relationships among different countries in the field of digital technology education application research. Specifically, a chord diagram is employed to display the top 30 countries in terms of publication output, as depicted in Fig. 3 . The chord diagram is composed of nodes and chords, where the nodes are positioned as scattered points along the circumference, and the length of each node corresponds to the publication output, with longer lengths indicating higher publication output. The chords, on the other hand, represent the cooperation relationships between any two countries, and are weighted based on the degree of closeness of the cooperation, with wider chords indicating closer cooperation. Through the analysis of the cooperation relationships, the findings suggest that the main publishing countries in this field are engaged in cooperative relationships with each other, indicating a relatively high level of international academic exchange and research internationalization.

In the diagram, nodes are scattered along the circumference of a circle, with the length of each node representing the volume of publications. The weighted arcs connecting any two points on the circle are known as chords, representing the collaborative relationship between the two, with the width of the arc indicating the closeness of the collaboration.

Further analyzing Fig. 3 , we can extract more valuable information, enabling a deeper understanding of the connections between countries in the research field of digital technology in educational applications. It is evident that certain countries, such as the United States, China, and England, display thicker connections, indicating robust collaborative relationships in terms of productivity. These thicker lines signify substantial mutual contributions and shared objectives in certain sectors or fields, highlighting the interconnectedness and global integration in these areas. By delving deeper, we can also explore potential future collaboration opportunities through the chord diagram, identifying possible partners to propel research and development in this field. In essence, the chord diagram successfully encapsulates and conveys the multi-dimensionality of global productivity and cooperation, allowing for a comprehensive understanding of the intricate inter-country relationships and networks in a global context, providing valuable guidance and insights for future research and collaborations.

An in-depth examination of the publishing institutions is provided in Table 5 , showcasing the foremost 10 institutions ranked by their publication volume. Notably, Monash University and Australian Catholic University, situated in Australia, have recorded the most prolific publications within the digital technology education application realm, with 22 and 10 publications respectively. Moreover, the University of Oslo from Norway is featured among the top 10 publishing institutions, with an impressive average citation count of 64 per publication. It is worth highlighting that six institutions based in the United Kingdom were also ranked within the top 10 publishing institutions, signifying their leading position in this area of research.

Analysis of journals

Journals are the main carriers for publishing high-quality papers. Some scholars point out that the two key factors to measure the influence of journals in the specified field are the number of articles published and the number of citations. The more papers published in a magazine and the more citations, the greater its influence (Dzikowski, 2018 ). Therefore, this study utilized VOSviewer to statistically analyze the top 10 journals with the most publications in the field of digital technology in education and calculated the average citations per article (see Table 6 ).

Based on Table 6 , it is apparent that the highest number of articles in the domain of digital technology in education research were published in Education and Information Technologies (47 articles), Computers & Education (34 articles), and British Journal of Educational Technology (32 articles), indicating a higher article output compared to other journals. This underscores the fact that these three journals concentrate more on the application of digital technology in education. Furthermore, several other journals, such as Technology Pedagogy and Education and Sustainability, have published more than 15 articles in this domain. Sustainability represents the open access movement, which has notably facilitated research progress in this field, indicating that the development of open access journals in recent years has had a significant impact. Although there is still considerable disagreement among scholars on the optimal approach to achieve open access, the notion that research outcomes should be accessible to all is widely recognized (Huang et al. 2020 ). On further analysis of the research fields to which these journals belong, except for Sustainability, it is evident that they all pertain to educational technology, thus providing a qualitative definition of the research area of digital technology education from the perspective of journals.

Temporal keyword analysis: thematic evolution (RQ2)

The evolution of research themes is a dynamic process, and previous studies have attempted to present the developmental trajectory of fields by drawing keyword networks in phases (Kumar et al. 2021 ; Chen et al. 2022b ). To understand the shifts in research topics across different periods, this study follows past research and, based on the significant changes in the research field and corresponding technological advancements during the outlined periods, divides the timeline into four stages (the first stage from January 2000 to December 2005, the second stage from January 2006 to December 2011, the third stage from January 2012 to December 2017; and the fourth stage from January 2018 to December 2022). The division into these four stages was determined through a combination of bibliometric analysis and literature review, which presented a clear trajectory of the field’s development. The research analyzes the keyword networks for each time period (as there are only three articles in the first stage, it was not possible to generate an appropriate keyword co-occurrence map, hence only the keyword co-occurrence maps from the second to the fourth stages are provided), to understand the evolutionary track of the digital technology education application research field over time.

2000.1–2005.12: germination period

From January 2000 to December 2005, digital technology education application research was in its infancy. Only three studies focused on digital technology, all of which were related to computers. Due to the popularity of computers, the home became a new learning environment, highlighting the important role of digital technology in expanding the scope of learning spaces (Sutherland et al. 2000 ). In specific disciplines and contexts, digital technology was first favored in medical clinical practice, becoming an important tool for supporting the learning of clinical knowledge and practice (Tegtmeyer et al. 2001 ; Durfee et al. 2003 ).

2006.1–2011.12: initial development period

Between January 2006 and December 2011, it was the initial development period of digital technology education research. Significant growth was observed in research related to digital technology, and discussions and theoretical analyses about “digital natives” emerged. During this phase, scholars focused on the debate about “how to use digital technology reasonably” and “whether current educational models and school curriculum design need to be adjusted on a large scale” (Bennett and Maton, 2010 ; Selwyn, 2009 ; Margaryan et al. 2011 ). These theoretical and speculative arguments provided a unique perspective on the impact of cognitive digital technology on education and teaching. As can be seen from the vocabulary such as “rethinking”, “disruptive pedagogy”, and “attitude” in Fig. 4 , many scholars joined the calm reflection and analysis under the trend of digital technology (Laurillard, 2008 ; Vratulis et al. 2011 ). During this phase, technology was still undergoing dramatic changes. The development of mobile technology had already caught the attention of many scholars (Wong et al. 2011 ), but digital technology represented by computers was still very active (Selwyn et al. 2011 ). The change in technological form would inevitably lead to educational transformation. Collins and Halverson ( 2010 ) summarized the prospects and challenges of using digital technology for learning and educational practices, believing that digital technology would bring a disruptive revolution to the education field and bring about a new educational system. In addition, the term “teacher education” in Fig. 4 reflects the impact of digital technology development on teachers. The rapid development of technology has widened the generation gap between teachers and students. To ensure smooth communication between teachers and students, teachers must keep up with the trend of technological development and establish a lifelong learning concept (Donnison, 2009 ).

In the diagram, each node represents a keyword, with the size of the node indicating the frequency of occurrence of the keyword. The connections represent the co-occurrence relationships between keywords, with a higher frequency of co-occurrence resulting in tighter connections.

2012.1–2017.12: critical exploration period

During the period spanning January 2012 to December 2017, the application of digital technology in education research underwent a significant exploration phase. As can be seen from Fig. 5 , different from the previous stage, the specific elements of specific digital technology have started to increase significantly, including the enrichment of technological contexts, the greater variety of research methods, and the diversification of learning modes. Moreover, the temporal and spatial dimensions of the learning environment were further de-emphasized, as noted in previous literature (Za et al. 2014 ). Given the rapidly accelerating pace of technological development, the education system in the digital era is in urgent need of collaborative evolution and reconstruction, as argued by Davis, Eickelmann, and Zaka ( 2013 ).

In the domain of digital technology, social media has garnered substantial scholarly attention as a promising avenue for learning, as noted by Pasquini and Evangelopoulos ( 2016 ). The implementation of social media in education presents several benefits, including the liberation of education from the restrictions of physical distance and time, as well as the erasure of conventional educational boundaries. The user-generated content (UGC) model in social media has emerged as a crucial source for knowledge creation and distribution, with the widespread adoption of mobile devices. Moreover, social networks have become an integral component of ubiquitous learning environments (Hwang et al. 2013 ). The utilization of social media allows individuals to function as both knowledge producers and recipients, which leads to a blurring of the conventional roles of learners and teachers. On mobile platforms, the roles of learners and teachers are not fixed, but instead interchangeable.

In terms of research methodology, the prevalence of empirical studies with survey designs in the field of educational technology during this period is evident from the vocabulary used, such as “achievement,” “acceptance,” “attitude,” and “ict.” in Fig. 5 . These studies aim to understand learners’ willingness to adopt and attitudes towards new technologies, and some seek to investigate the impact of digital technologies on learning outcomes through quasi-experimental designs (Domínguez et al. 2013 ). Among these empirical studies, mobile learning emerged as a hot topic, and this is not surprising. First, the advantages of mobile learning environments over traditional ones have been empirically demonstrated (Hwang et al. 2013 ). Second, learners born around the turn of the century have been heavily influenced by digital technologies and have developed their own learning styles that are more open to mobile devices as a means of learning. Consequently, analyzing mobile learning as a relatively novel mode of learning has become an important issue for scholars in the field of educational technology.

The intervention of technology has led to the emergence of several novel learning modes, with the blended learning model being the most representative one in the current phase. Blended learning, a novel concept introduced in the information age, emphasizes the integration of the benefits of traditional learning methods and online learning. This learning mode not only highlights the prominent role of teachers in guiding, inspiring, and monitoring the learning process but also underlines the importance of learners’ initiative, enthusiasm, and creativity in the learning process. Despite being an early conceptualization, blended learning’s meaning has been expanded by the widespread use of mobile technology and social media in education. The implementation of new technologies, particularly mobile devices, has resulted in the transformation of curriculum design and increased flexibility and autonomy in students’ learning processes (Trujillo Maza et al. 2016 ), rekindling scholarly attention to this learning mode. However, some scholars have raised concerns about the potential drawbacks of the blended learning model, such as its significant impact on the traditional teaching system, the lack of systematic coping strategies and relevant policies in several schools and regions (Moskal et al. 2013 ).

2018.1–2022.12: accelerated transformation period

The period spanning from January 2018 to December 2022 witnessed a rapid transformation in the application of digital technology in education research. The field of digital technology education research reached a peak period of publication, largely influenced by factors such as the COVID-19 pandemic (Yu et al. 2023 ). Research during this period was built upon the achievements, attitudes, and social media of the previous phase, and included more elements that reflect the characteristics of this research field, such as digital literacy, digital competence, and professional development, as depicted in Fig. 6 . Alongside this, scholars’ expectations for the value of digital technology have expanded, and the pursuit of improving learning efficiency and performance is no longer the sole focus. Some research now aims to cultivate learners’ motivation and enhance their self-efficacy by applying digital technology in a reasonable manner, as demonstrated by recent studies (Beardsley et al. 2021 ; Creely et al. 2021 ).

The COVID-19 pandemic has emerged as a crucial backdrop for the digital technology’s role in sustaining global education, as highlighted by recent scholarly research (Zhou et al. 2022 ; Pan and Zhang, 2020 ; Mo et al. 2022 ). The online learning environment, which is supported by digital technology, has become the primary battleground for global education (Yu, 2022 ). This social context has led to various studies being conducted, with some scholars positing that the pandemic has impacted the traditional teaching order while also expanding learning possibilities in terms of patterns and forms (Alabdulaziz, 2021 ). Furthermore, the pandemic has acted as a catalyst for teacher teaching and technological innovation, and this viewpoint has been empirically substantiated (Moorhouse and Wong, 2021 ). Additionally, some scholars believe that the pandemic’s push is a crucial driving force for the digital transformation of the education system, serving as an essential mechanism for overcoming the system’s inertia (Romero et al. 2021 ).

The rapid outbreak of the pandemic posed a challenge to the large-scale implementation of digital technologies, which was influenced by a complex interplay of subjective and objective factors. Objective constraints included the lack of infrastructure in some regions to support digital technologies, while subjective obstacles included psychological resistance among certain students and teachers (Moorhouse, 2021 ). These factors greatly impacted the progress of online learning during the pandemic. Additionally, Timotheou et al. ( 2023 ) conducted a comprehensive systematic review of existing research on digital technology use during the pandemic, highlighting the critical role played by various factors such as learners’ and teachers’ digital skills, teachers’ personal attributes and professional development, school leadership and management, and administration in facilitating the digitalization and transformation of schools.

The current stage of research is characterized by the pivotal term “digital literacy,” denoting a growing interest in learners’ attitudes and adoption of emerging technologies. Initially, the term “literacy” was restricted to fundamental abilities and knowledge associated with books and print materials (McMillan, 1996 ). However, with the swift advancement of computers and digital technology, there have been various attempts to broaden the scope of literacy beyond its traditional meaning, including game literacy (Buckingham and Burn, 2007 ), information literacy (Eisenberg, 2008 ), and media literacy (Turin and Friesem, 2020 ). Similarly, digital literacy has emerged as a crucial concept, and Gilster and Glister ( 1997 ) were the first to introduce this concept, referring to the proficiency in utilizing technology and processing digital information in academic, professional, and daily life settings. In practical educational settings, learners who possess higher digital literacy often exhibit an aptitude for quickly mastering digital devices and applying them intelligently to education and teaching (Yu, 2022 ).

The utilization of digital technology in education has undergone significant changes over the past two decades, and has been a crucial driver of educational reform with each new technological revolution. The impact of these changes on the underlying logic of digital technology education applications has been noticeable. From computer technology to more recent developments such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), the acceleration in digital technology development has been ongoing. Educational reforms spurred by digital technology development continue to be dynamic, as each new digital innovation presents new possibilities and models for teaching practice. This is especially relevant in the post-pandemic era, where the importance of technological progress in supporting teaching cannot be overstated (Mughal et al. 2022 ). Existing digital technologies have already greatly expanded the dimensions of education in both time and space, while future digital technologies aim to expand learners’ perceptions. Researchers have highlighted the potential of integrated technology and immersive technology in the development of the educational metaverse, which is highly anticipated to create a new dimension for the teaching and learning environment, foster a new value system for the discipline of educational technology, and more effectively and efficiently achieve the grand educational blueprint of the United Nations’ Sustainable Development Goals (Zhang et al. 2022 ; Li and Yu, 2023 ).

Hotspot evolution analysis (RQ3)

The examination of keyword evolution reveals a consistent trend in the advancement of digital technology education application research. The emergence and transformation of keywords serve as indicators of the varying research interests in this field. Thus, the utilization of the burst detection function available in CiteSpace allowed for the identification of the top 10 burst words that exhibited a high level of burst strength. This outcome is illustrated in Table 7 .

According to the results presented in Table 7 , the explosive terminology within the realm of digital technology education research has exhibited a concentration mainly between the years 2018 and 2022. Prior to this time frame, the emerging keywords were limited to “information technology” and “computer”. Notably, among them, computer, as an emergent keyword, has always had a high explosive intensity from 2008 to 2018, which reflects the important position of computer in digital technology and is the main carrier of many digital technologies such as Learning Management Systems (LMS) and Assessment and Feedback systems (Barlovits et al. 2022 ).

Since 2018, an increasing number of research studies have focused on evaluating the capabilities of learners to accept, apply, and comprehend digital technologies. As indicated by the use of terms such as “digital literacy” and “digital skill,” the assessment of learners’ digital literacy has become a critical task. Scholarly efforts have been directed towards the development of literacy assessment tools and the implementation of empirical assessments. Furthermore, enhancing the digital literacy of both learners and educators has garnered significant attention. (Nagle, 2018 ; Yu, 2022 ). Simultaneously, given the widespread use of various digital technologies in different formal and informal learning settings, promoting learners’ digital skills has become a crucial objective for contemporary schools (Nygren et al. 2019 ; Forde and OBrien, 2022 ).

Since 2020, the field of applied research on digital technology education has witnessed the emergence of three new hotspots, all of which have been affected to some extent by the pandemic. Firstly, digital technology has been widely applied in physical education, which is one of the subjects that has been severely affected by the pandemic (Parris et al. 2022 ; Jiang and Ning, 2022 ). Secondly, digital transformation has become an important measure for most schools, especially higher education institutions, to cope with the impact of the pandemic globally (García-Morales et al. 2021 ). Although the concept of digital transformation was proposed earlier, the COVID-19 pandemic has greatly accelerated this transformation process. Educational institutions must carefully redesign their educational products to face this new situation, providing timely digital learning methods, environments, tools, and support systems that have far-reaching impacts on modern society (Krishnamurthy, 2020 ; Salas-Pilco et al. 2022 ). Moreover, the professional development of teachers has become a key mission of educational institutions in the post-pandemic era. Teachers need to have a certain level of digital literacy and be familiar with the tools and online teaching resources used in online teaching, which has become a research hotspot today. Organizing digital skills training for teachers to cope with the application of emerging technologies in education is an important issue for teacher professional development and lifelong learning (Garzón-Artacho et al. 2021 ). As the main organizers and practitioners of emergency remote teaching (ERT) during the pandemic, teachers must put cognitive effort into their professional development to ensure effective implementation of ERT (Romero-Hall and Jaramillo Cherrez, 2022 ).

The burst word “digital transformation” reveals that we are in the midst of an ongoing digital technology revolution. With the emergence of innovative digital technologies such as ChatGPT and Microsoft 365 Copilot, technology trends will continue to evolve, albeit unpredictably. While the impact of these advancements on school education remains uncertain, it is anticipated that the widespread integration of technology will significantly affect the current education system. Rejecting emerging technologies without careful consideration is unwise. Like any revolution, the technological revolution in the education field has both positive and negative aspects. Detractors argue that digital technology disrupts learning and memory (Baron, 2021 ) or causes learners to become addicted and distracted from learning (Selwyn and Aagaard, 2020 ). On the other hand, the prudent use of digital technology in education offers a glimpse of a golden age of open learning. Educational leaders and practitioners have the opportunity to leverage cutting-edge digital technologies to address current educational challenges and develop a rational path for the sustainable and healthy growth of education.

Discussion on performance analysis (RQ1)

The field of digital technology education application research has experienced substantial growth since the turn of the century, a phenomenon that is quantifiably apparent through an analysis of authorship, country/region contributions, and institutional engagement. This expansion reflects the increased integration of digital technologies in educational settings and the heightened scholarly interest in understanding and optimizing their use.

Discussion on authorship productivity in digital technology education research

The authorship distribution within digital technology education research is indicative of the field’s intellectual structure and depth. A primary figure in this domain is Neil Selwyn, whose substantial citation rate underscores the profound impact of his work. His focus on the implications of digital technology in higher education and educational sociology has proven to be seminal. Selwyn’s research trajectory, especially the exploration of spatiotemporal extensions of education through technology, provides valuable insights into the multifaceted role of digital tools in learning processes (Selwyn et al. 2019 ).

Other notable contributors, like Henderson and Edwards, present diversified research interests, such as the impact of digital technologies during the pandemic and their application in early childhood education, respectively. Their varied focuses highlight the breadth of digital technology education research, encompassing pedagogical innovation, technological adaptation, and policy development.

Discussion on country/region-level productivity and collaboration

At the country/region level, the United Kingdom, specifically England, emerges as a leading contributor with 92 published papers and a significant citation count. This is closely followed by Australia and the United States, indicating a strong English-speaking research axis. Such geographical concentration of scholarly output often correlates with investment in research and development, technological infrastructure, and the prevalence of higher education institutions engaging in cutting-edge research.

China’s notable inclusion as the only non-Western country among the top contributors to the field suggests a growing research capacity and interest in digital technology in education. However, the lower average citation per paper for China could reflect emerging engagement or different research focuses that may not yet have achieved the same international recognition as Western counterparts.

The chord diagram analysis furthers this understanding, revealing dense interconnections between countries like the United States, China, and England, which indicates robust collaborations. Such collaborations are fundamental in addressing global educational challenges and shaping international research agendas.

Discussion on institutional-level contributions to digital technology education

Institutional productivity in digital technology education research reveals a constellation of universities driving the field forward. Monash University and the Australian Catholic University have the highest publication output, signaling Australia’s significant role in advancing digital education research. The University of Oslo’s remarkable average citation count per publication indicates influential research contributions, potentially reflecting high-quality studies that resonate with the broader academic community.

The strong showing of UK institutions, including the University of London, The Open University, and the University of Cambridge, reinforces the UK’s prominence in this research field. Such institutions are often at the forefront of pedagogical innovation, benefiting from established research cultures and funding mechanisms that support sustained inquiry into digital education.

Discussion on journal publication analysis

An examination of journal outputs offers a lens into the communicative channels of the field’s knowledge base. Journals such as Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology not only serve as the primary disseminators of research findings but also as indicators of research quality and relevance. The impact factor (IF) serves as a proxy for the quality and influence of these journals within the academic community.

The high citation counts for articles published in Computers & Education suggest that research disseminated through this medium has a wide-reaching impact and is of particular interest to the field. This is further evidenced by its significant IF of 11.182, indicating that the journal is a pivotal platform for seminal work in the application of digital technology in education.

The authorship, regional, and institutional productivity in the field of digital technology education application research collectively narrate the evolution of this domain since the turn of the century. The prominence of certain authors and countries underscores the importance of socioeconomic factors and existing academic infrastructure in fostering research productivity. Meanwhile, the centrality of specific journals as outlets for high-impact research emphasizes the role of academic publishing in shaping the research landscape.

As the field continues to grow, future research may benefit from leveraging the collaborative networks that have been elucidated through this analysis, perhaps focusing on underrepresented regions to broaden the scope and diversity of research. Furthermore, the stabilization of publication numbers in recent years invites a deeper exploration into potential plateaus in research trends or saturation in certain sub-fields, signaling an opportunity for novel inquiries and methodological innovations.

Discussion on the evolutionary trends (RQ2)

The evolution of the research field concerning the application of digital technology in education over the past two decades is a story of convergence, diversification, and transformation, shaped by rapid technological advancements and shifting educational paradigms.

At the turn of the century, the inception of digital technology in education was largely exploratory, with a focus on how emerging computer technologies could be harnessed to enhance traditional learning environments. Research from this early period was primarily descriptive, reflecting on the potential and challenges of incorporating digital tools into the educational setting. This phase was critical in establishing the fundamental discourse that would guide subsequent research, as it set the stage for understanding the scope and impact of digital technology in learning spaces (Wang et al. 2023 ).

As the first decade progressed, the narrative expanded to encompass the pedagogical implications of digital technologies. This was a period of conceptual debates, where terms like “digital natives” and “disruptive pedagogy” entered the academic lexicon, underscoring the growing acknowledgment of digital technology as a transformative force within education (Bennett and Maton, 2010 ). During this time, the research began to reflect a more nuanced understanding of the integration of technology, considering not only its potential to change where and how learning occurred but also its implications for educational equity and access.

In the second decade, with the maturation of internet connectivity and mobile technology, the focus of research shifted from theoretical speculations to empirical investigations. The proliferation of digital devices and the ubiquity of social media influenced how learners interacted with information and each other, prompting a surge in studies that sought to measure the impact of these tools on learning outcomes. The digital divide and issues related to digital literacy became central concerns, as scholars explored the varying capacities of students and educators to engage with technology effectively.

Throughout this period, there was an increasing emphasis on the individualization of learning experiences, facilitated by adaptive technologies that could cater to the unique needs and pacing of learners (Jing et al. 2023a ). This individualization was coupled with a growing recognition of the importance of collaborative learning, both online and offline, and the role of digital tools in supporting these processes. Blended learning models, which combined face-to-face instruction with online resources, emerged as a significant trend, advocating for a balance between traditional pedagogies and innovative digital strategies.

The later years, particularly marked by the COVID-19 pandemic, accelerated the necessity for digital technology in education, transforming it from a supplementary tool to an essential platform for delivering education globally (Mo et al. 2022 ; Mustapha et al. 2021 ). This era brought about an unprecedented focus on online learning environments, distance education, and virtual classrooms. Research became more granular, examining not just the pedagogical effectiveness of digital tools, but also their role in maintaining continuity of education during crises, their impact on teacher and student well-being, and their implications for the future of educational policy and infrastructure.

Across these two decades, the research field has seen a shift from examining digital technology as an external addition to the educational process, to viewing it as an integral component of curriculum design, instructional strategies, and even assessment methods. The emergent themes have broadened from a narrow focus on specific tools or platforms to include wider considerations such as data privacy, ethical use of technology, and the environmental impact of digital tools.

Moreover, the field has moved from considering the application of digital technology in education as a primarily cognitive endeavor to recognizing its role in facilitating socio-emotional learning, digital citizenship, and global competencies. Researchers have increasingly turned their attention to the ways in which technology can support collaborative skills, cultural understanding, and ethical reasoning within diverse student populations.

In summary, the past over twenty years in the research field of digital technology applications in education have been characterized by a progression from foundational inquiries to complex analyses of digital integration. This evolution has mirrored the trajectory of technology itself, from a facilitative tool to a pervasive ecosystem defining contemporary educational experiences. As we look to the future, the field is poised to delve into the implications of emerging technologies like AI, AR, and VR, and their potential to redefine the educational landscape even further. This ongoing metamorphosis suggests that the application of digital technology in education will continue to be a rich area of inquiry, demanding continual adaptation and forward-thinking from educators and researchers alike.

Discussion on the study of research hotspots (RQ3)

The analysis of keyword evolution in digital technology education application research elucidates the current frontiers in the field, reflecting a trajectory that is in tandem with the rapidly advancing digital age. This landscape is sculpted by emergent technological innovations and shaped by the demands of an increasingly digital society.

Interdisciplinary integration and pedagogical transformation

One of the frontiers identified from recent keyword bursts includes the integration of digital technology into diverse educational contexts, particularly noted with the keyword “physical education.” The digitalization of disciplines traditionally characterized by physical presence illustrates the pervasive reach of technology and signifies a push towards interdisciplinary integration where technology is not only a facilitator but also a transformative agent. This integration challenges educators to reconceptualize curriculum delivery to accommodate digital tools that can enhance or simulate the physical aspects of learning.

Digital literacy and skills acquisition

Another pivotal frontier is the focus on “digital literacy” and “digital skill”, which has intensified in recent years. This suggests a shift from mere access to technology towards a comprehensive understanding and utilization of digital tools. In this realm, the emphasis is not only on the ability to use technology but also on critical thinking, problem-solving, and the ethical use of digital resources (Yu, 2022 ). The acquisition of digital literacy is no longer an additive skill but a fundamental aspect of modern education, essential for navigating and contributing to the digital world.

Educational digital transformation

The keyword “digital transformation” marks a significant research frontier, emphasizing the systemic changes that education institutions must undergo to align with the digital era (Romero et al. 2021 ). This transformation includes the redesigning of learning environments, pedagogical strategies, and assessment methods to harness digital technology’s full potential. Research in this area explores the complexity of institutional change, addressing the infrastructural, cultural, and policy adjustments needed for a seamless digital transition.

Engagement and participation

Further exploration into “engagement” and “participation” underscores the importance of student-centered learning environments that are mediated by technology. The current frontiers examine how digital platforms can foster collaboration, inclusivity, and active learning, potentially leading to more meaningful and personalized educational experiences. Here, the use of technology seeks to support the emotional and cognitive aspects of learning, moving beyond the transactional view of education to one that is relational and interactive.

Professional development and teacher readiness

As the field evolves, “professional development” emerges as a crucial area, particularly in light of the pandemic which necessitated emergency remote teaching. The need for teacher readiness in a digital age is a pressing frontier, with research focusing on the competencies required for educators to effectively integrate technology into their teaching practices. This includes familiarity with digital tools, pedagogical innovation, and an ongoing commitment to personal and professional growth in the digital domain.

Pandemic as a catalyst

The recent pandemic has acted as a catalyst for accelerated research and application in this field, particularly in the domains of “digital transformation,” “professional development,” and “physical education.” This period has been a litmus test for the resilience and adaptability of educational systems to continue their operations in an emergency. Research has thus been directed at understanding how digital technologies can support not only continuity but also enhance the quality and reach of education in such contexts.

Ethical and societal considerations

The frontier of digital technology in education is also expanding to consider broader ethical and societal implications. This includes issues of digital equity, data privacy, and the sociocultural impact of technology on learning communities. The research explores how educational technology can be leveraged to address inequities and create more equitable learning opportunities for all students, regardless of their socioeconomic background.

Innovation and emerging technologies

Looking forward, the frontiers are set to be influenced by ongoing and future technological innovations, such as artificial intelligence (AI) (Wu and Yu, 2023 ; Chen et al. 2022a ). The exploration into how these technologies can be integrated into educational practices to create immersive and adaptive learning experiences represents a bold new chapter for the field.

In conclusion, the current frontiers of research on the application of digital technology in education are multifaceted and dynamic. They reflect an overarching movement towards deeper integration of technology in educational systems and pedagogical practices, where the goals are not only to facilitate learning but to redefine it. As these frontiers continue to expand and evolve, they will shape the educational landscape, requiring a concerted effort from researchers, educators, policymakers, and technologists to navigate the challenges and harness the opportunities presented by the digital revolution in education.

Conclusions and future research

Conclusions.

The utilization of digital technology in education is a research area that cuts across multiple technical and educational domains and continues to experience dynamic growth due to the continuous progress of technology. In this study, a systematic review of this field was conducted through bibliometric techniques to examine its development trajectory. The primary focus of the review was to investigate the leading contributors, productive national institutions, significant publications, and evolving development patterns. The study’s quantitative analysis resulted in several key conclusions that shed light on this research field’s current state and future prospects.

(1) The research field of digital technology education applications has entered a stage of rapid development, particularly in recent years due to the impact of the pandemic, resulting in a peak of publications. Within this field, several key authors (Selwyn, Henderson, Edwards, etc.) and countries/regions (England, Australia, USA, etc.) have emerged, who have made significant contributions. International exchanges in this field have become frequent, with a high degree of internationalization in academic research. Higher education institutions in the UK and Australia are the core productive forces in this field at the institutional level.

(2) Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology are notable journals that publish research related to digital technology education applications. These journals are affiliated with the research field of educational technology and provide effective communication platforms for sharing digital technology education applications.

(3) Over the past two decades, research on digital technology education applications has progressed from its early stages of budding, initial development, and critical exploration to accelerated transformation, and it is currently approaching maturity. Technological progress and changes in the times have been key driving forces for educational transformation and innovation, and both have played important roles in promoting the continuous development of education.

(4) Influenced by the pandemic, three emerging frontiers have emerged in current research on digital technology education applications, which are physical education, digital transformation, and professional development under the promotion of digital technology. These frontier research hotspots reflect the core issues that the education system faces when encountering new technologies. The evolution of research hotspots shows that technology breakthroughs in education’s original boundaries of time and space create new challenges. The continuous self-renewal of education is achieved by solving one hotspot problem after another.

The present study offers significant practical implications for scholars and practitioners in the field of digital technology education applications. Firstly, it presents a well-defined framework of the existing research in this area, serving as a comprehensive guide for new entrants to the field and shedding light on the developmental trajectory of this research domain. Secondly, the study identifies several contemporary research hotspots, thus offering a valuable decision-making resource for scholars aiming to explore potential research directions. Thirdly, the study undertakes an exhaustive analysis of published literature to identify core journals in the field of digital technology education applications, with Sustainability being identified as a promising open access journal that publishes extensively on this topic. This finding can potentially facilitate scholars in selecting appropriate journals for their research outputs.

Limitation and future research

Influenced by some objective factors, this study also has some limitations. First of all, the bibliometrics analysis software has high standards for data. In order to ensure the quality and integrity of the collected data, the research only selects the periodical papers in SCIE and SSCI indexes, which are the core collection of Web of Science database, and excludes other databases, conference papers, editorials and other publications, which may ignore some scientific research and original opinions in the field of digital technology education and application research. In addition, although this study used professional software to carry out bibliometric analysis and obtained more objective quantitative data, the analysis and interpretation of data will inevitably have a certain subjective color, and the influence of subjectivity on data analysis cannot be completely avoided. As such, future research endeavors will broaden the scope of literature screening and proactively engage scholars in the field to gain objective and state-of-the-art insights, while minimizing the adverse impact of personal subjectivity on research analysis.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/F9QMHY

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This research was supported by the Zhejiang Provincial Social Science Planning Project, “Mechanisms and Pathways for Empowering Classroom Teaching through Learning Spaces under the Strategy of High-Quality Education Development”, the 2022 National Social Science Foundation Education Youth Project “Research on the Strategy of Creating Learning Space Value and Empowering Classroom Teaching under the background of ‘Double Reduction’” (Grant No. CCA220319) and the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202310337023).

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Wang, C., Chen, X., Yu, T. et al. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11 , 256 (2024). https://doi.org/10.1057/s41599-024-02717-y

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New global data reveal education technology’s impact on learning

The promise of technology in the classroom is great: enabling personalized, mastery-based learning; saving teacher time; and equipping students with the digital skills they will need  for 21st-century careers. Indeed, controlled pilot studies have shown meaningful improvements in student outcomes through personalized blended learning. 1 John F. Pane et al., “How does personalized learning affect student achievement?,” RAND Corporation, 2017, rand.org. During this time of school shutdowns and remote learning , education technology has become a lifeline for the continuation of learning.

As school systems begin to prepare for a return to the classroom , many are asking whether education technology should play a greater role in student learning beyond the immediate crisis and what that might look like. To help inform the answer to that question, this article analyzes one important data set: the 2018 Programme for International Student Assessment (PISA), published in December 2019 by the Organisation for Economic Co-operation and Development (OECD).

Every three years, the OECD uses PISA to test 15-year-olds around the world on math, reading, and science. What makes these tests so powerful is that they go beyond the numbers, asking students, principals, teachers, and parents a series of questions about their attitudes, behaviors, and resources. An optional student survey on information and communications technology (ICT) asks specifically about technology use—in the classroom, for homework, and more broadly.

In 2018, more than 340,000 students in 51 countries took the ICT survey, providing a rich data set for analyzing key questions about technology use in schools. How much is technology being used in schools? Which technologies are having a positive impact on student outcomes? What is the optimal amount of time to spend using devices in the classroom and for homework? How does this vary across different countries and regions?

From other studies we know that how education technology is used, and how it is embedded in the learning experience, is critical to its effectiveness. This data is focused on extent and intensity of use, not the pedagogical context of each classroom. It cannot therefore answer questions on the eventual potential of education technology—but it can powerfully tell us the extent to which that potential is being realized today in classrooms around the world.

Five key findings from the latest results help answer these questions and suggest potential links between technology and student outcomes:

• The type of device matters—some are associated with worse student outcomes.
• Geography matters—technology is associated with higher student outcomes in the United States than in other regions.
• Who is using the technology matters—technology in the hands of teachers is associated with higher scores than technology in the hands of students.
• Intensity matters—students who use technology intensely or not at all perform better than those with moderate use.
• A school system’s current performance level matters—in lower-performing school systems, technology is associated with worse results.

This analysis covers only one source of data, and it should be interpreted with care alongside other relevant studies. Nonetheless, the 2018 PISA results suggest that systems aiming to improve student outcomes should take a more nuanced and cautious approach to deploying technology once students return to the classroom. It is not enough add devices to the classroom, check the box, and hope for the best.

What can we learn from the latest PISA results?

How will the use, and effectiveness, of technology change post-covid-19.

The PISA assessment was carried out in 2018 and published in December 2019. Since its publication, schools and students globally have been quite suddenly thrust into far greater reliance on technology. Use of online-learning websites and adaptive software has expanded dramatically. Khan Academy has experienced a 250 percent surge in traffic; smaller sites have seen traffic grow fivefold or more. Hundreds of thousands of teachers have been thrown into the deep end, learning to use new platforms, software, and systems. No one is arguing that the rapid cobbling together of remote learning under extreme time pressure represents best-practice use of education technology. Nonetheless, a vast experiment is underway, and innovations often emerge in times of crisis. At this point, it is unclear whether this represents the beginning of a new wave of more widespread and more effective technology use in the classroom or a temporary blip that will fade once students and teachers return to in-person instruction. It is possible that a combination of software improvements, teacher capability building, and student familiarity will fundamentally change the effectiveness of education technology in improving student outcomes. It is also possible that our findings will continue to hold true and technology in the classroom will continue to be a mixed blessing. It is therefore critical that ongoing research efforts track what is working and for whom and, just as important, what is not. These answers will inform the project of reimagining a better education for all students in the aftermath of COVID-19.

PISA data have their limitations. First, these data relate to high-school students, and findings may not be applicable in elementary schools or postsecondary institutions. Second, these are single-point observational data, not longitudinal experimental data, which means that any links between technology and results should be interpreted as correlation rather than causation. Third, the outcomes measured are math, science, and reading test results, so our analysis cannot assess important soft skills and nonacademic outcomes.

It is also worth noting that technology for learning has implications beyond direct student outcomes, both positive and negative. PISA cannot address these broader issues, and neither does this paper.

But PISA results, which we’ve broken down into five key findings, can still provide powerful insights. The assessment strives to measure the understanding and application of ideas, rather than the retention of facts derived from rote memorization, and the broad geographic coverage and sample size help elucidate the reality of what is happening on the ground.

Finding 1: The type of device matters

The evidence suggests that some devices have more impact than others on outcomes (Exhibit 1). Controlling for student socioeconomic status, school type, and location, 2 Specifically, we control for a composite indicator for economic, social, and cultural status (ESCS) derived from questions about general wealth, home possessions, parental education, and parental occupation; for school type “Is your school a public or a private school” (SC013); and for school location (SC001) where the options are a village, hamlet or rural area (fewer than 3,000 people), a small town (3,000 to about 15,000 people), a town (15,000 to about 100,000 people), a city (100,000 to about 1,000,000 people), and a large city (with more than 1,000,000 people). the use of data projectors 3 A projector is any device that projects computer output, slides, or other information onto a screen in the classroom. and internet-connected computers in the classroom is correlated with nearly a grade-level-better performance on the PISA assessment (assuming approximately 40 PISA points to every grade level). 4 Students were specifically asked (IC009), “Are any of these devices available for you to use at school?,” with the choices being “Yes, and I use it,” “Yes, but I don’t use it,” and “No.” We compared the results for students who have access to and use each device with those who do not have access. The full text for each device in our chart was as follows: Data projector, eg, for slide presentations; Internet-connected school computers; Desktop computer; Interactive whiteboard, eg, SmartBoard; Portable laptop or notebook; and Tablet computer, eg, iPad, BlackBerry PlayBook.

On the other hand, students who use laptops and tablets in the classroom have worse results than those who do not. For laptops, the impact of technology varies by subject; students who use laptops score five points lower on the PISA math assessment, but the impact on science and reading scores is not statistically significant. For tablets, the picture is clearer—in every subject, students who use tablets in the classroom perform a half-grade level worse than those who do not.

Some technologies are more neutral. At the global level, there is no statistically significant difference between students who use desktop computers and interactive whiteboards in the classroom and those who do not.

Finding 2: Geography matters

Looking more closely at the reading results, which were the focus of the 2018 assessment, 5 PISA rotates between focusing on reading, science, and math. The 2018 assessment focused on reading. This means that the total testing time was two hours for each student, of which one hour was reading focused. we can see that the relationship between technology and outcomes varies widely by country and region (Exhibit 2). For example, in all regions except the United States (representing North America), 6 The United States is the only country that took the ICT Familiarity Questionnaire survey in North America; thus, we are comparing it as a country with the other regions. students who use laptops in the classroom score between five and 12 PISA points lower than students who do not use laptops. In the United States, students who use laptops score 17 PISA points higher than those who do not. It seems that US students and teachers are doing something different with their laptops than those in other regions. Perhaps this difference is related to learning curves that develop as teachers and students learn how to get the most out of devices. A proxy to assess this learning curve could be penetration—71 percent of US students claim to be using laptops in the classroom, compared with an average of 37 percent globally. 7 The rate of use excludes nulls. The United States measures higher than any other region in laptop use by students in the classroom. US = 71 percent, Asia = 40 percent, EU = 35 percent, Latin America = 31 percent, MENA = 21 percent, Non-EU Europe = 41 percent. We observe a similar pattern with interactive whiteboards in non-EU Europe. In every other region, interactive whiteboards seem to be hurting results, but in non-EU Europe they are associated with a lift of 21 PISA points, a total that represents a half-year of learning. In this case, however, penetration is not significantly higher than in other developed regions.

Finding 3: It matters whether technology is in the hands of teachers or students

The survey asks students whether the teacher, student, or both were using technology. Globally, the best results in reading occur when only the teacher is using the device, with some benefit in science when both teacher and students use digital devices (Exhibit 3). Exclusive use of the device by students is associated with significantly lower outcomes everywhere. The pattern is similar for science and math.

Again, the regional differences are instructive. Looking again at reading, we note that US students are getting significant lift (three-quarters of a year of learning) from either just teachers or teachers and students using devices, while students alone using a device score significantly lower (half a year of learning) than students who do not use devices at all. Exclusive use of devices by the teacher is associated with better outcomes in Europe too, though the size of the effect is smaller.

Finding 4: Intensity of use matters

PISA also asked students about intensity of use—how much time they spend on devices, 8 PISA rotates between focusing on reading, science, and math. The 2018 assessment focused on reading. This means that the total testing time was two hours for each student, of which one hour was reading focused. both in the classroom and for homework. The results are stark: students who either shun technology altogether or use it intensely are doing better, with those in the middle flailing (Exhibit 4).

The regional data show a dramatic picture. In the classroom, the optimal amount of time to spend on devices is either “none at all” or “greater than 60 minutes” per subject per week in every region and every subject (this is the amount of time associated with the highest student outcomes, controlling for student socioeconomic status, school type, and location). In no region is a moderate amount of time (1–30 minutes or 31–60 minutes) associated with higher student outcomes. There are important differences across subjects and regions. In math, the optimal amount of time is “none at all” in every region. 9 The United States is the only country that took the ICT Familiarity Questionnaire survey in North America; thus, we are comparing it as a country with the other regions. In reading and science, however, the optimal amount of time is greater than 60 minutes for some regions: Asia and the United States for reading, and the United States and non-EU Europe for science.

The pattern for using devices for homework is slightly less clear cut. Students in Asia, the Middle East and North Africa (MENA), and non-EU Europe score highest when they spend “no time at all” on devices for their homework, while students spending a moderate amount of time (1–60 minutes) score best in Latin America and the European Union. Finally, students in the United States who spend greater than 60 minutes are getting the best outcomes.

One interpretation of these data is that students need to get a certain familiarity with technology before they can really start using it to learn. Think of typing an essay, for example. When students who mostly write by hand set out to type an essay, their attention will be focused on the typing rather than the essay content. A competent touch typist, however, will get significant productivity gains by typing rather than handwriting.

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Finding 5: the school systems’ overall performance level matters.

Diving deeper into the reading outcomes, which were the focus of the 2018 assessment, we can see the magnitude of the impact of device use in the classroom. In Asia, Latin America, and Europe, students who spend any time on devices in their literacy and language arts classrooms perform about a half-grade level below those who spend none at all. In MENA, they perform more than a full grade level lower. In the United States, by contrast, more than an hour of device use in the classroom is associated with a lift of 17 PISA points, almost a half-year of learning improvement (Exhibit 5).

At the country level, we see that those who are on what we would call the “poor-to-fair” stage of the school-system journey 10 Michael Barber, Chinezi Chijoke, and Mona Mourshed, “ How the world’s most improved school systems keep getting better ,” November 2010. have the worst relationships between technology use and outcomes. For every poor-to-fair system taking the survey, the amount of time on devices in the classroom associated with the highest student scores is zero minutes. Good and great systems are much more mixed. Students in some very highly performing systems (for example, Estonia and Chinese Taipei) perform highest with no device use, but students in other systems (for example, Japan, the United States, and Australia) are getting the best scores with over an hour of use per week in their literacy and language arts classrooms (Exhibit 6). These data suggest that multiple approaches are effective for good-to-great systems, but poor-to-fair systems—which are not well equipped to use devices in the classroom—may need to rethink whether technology is the best use of their resources.

What are the implications for students, teachers, and systems?

Looking across all these results, we can say that the relationship between technology and outcomes in classrooms today is mixed, with variation by device, how that device is used, and geography. Our data do not permit us to draw strong causal conclusions, but this section offers a few hypotheses, informed by existing literature and our own work with school systems, that could explain these results.

First, technology must be used correctly to be effective. Our experience in the field has taught us that it is not enough to “add technology” as if it were the missing, magic ingredient. The use of tech must start with learning goals, and software selection must be based on and integrated with the curriculum. Teachers need support to adapt lesson plans to optimize the use of technology, and teachers should be using the technology themselves or in partnership with students, rather than leaving students alone with devices. These lessons hold true regardless of geography. Another ICT survey question asked principals about schools’ capacity using digital devices. Globally, students performed better in schools where there were sufficient numbers of devices connected to fast internet service; where they had adequate software and online support platforms; and where teachers had the skills, professional development, and time to integrate digital devices in instruction. This was true even accounting for student socioeconomic status, school type, and location.

COVID-19 and student learning in the United States: The hurt could last a lifetime

Second, technology must be matched to the instructional environment and context. One of the most striking findings in the latest PISA assessment is the extent to which technology has had a different impact on student outcomes in different geographies. This corroborates the findings of our 2010 report, How the world’s most improved school systems keep getting better . Those findings demonstrated that different sets of interventions were needed at different stages of the school-system reform journey, from poor-to-fair to good-to-great to excellent. In poor-to-fair systems, limited resources and teacher capabilities as well as poor infrastructure and internet bandwidth are likely to limit the benefits of student-based technology. Our previous work suggests that more prescriptive, teacher-based approaches and technologies (notably data projectors) are more likely to be effective in this context. For example, social enterprise Bridge International Academies equips teachers across several African countries with scripted lesson plans using e-readers. In general, these systems would likely be better off investing in teacher coaching than in a laptop per child. For administrators in good-to-great systems, the decision is harder, as technology has quite different impacts across different high-performing systems.

Third, technology involves a learning curve at both the system and student levels. It is no accident that the systems in which the use of education technology is more mature are getting more positive impact from tech in the classroom. The United States stands out as the country with the most mature set of education-technology products, and its scale enables companies to create software that is integrated with curricula. 11 Common Core State Standards sought to establish consistent educational standards across the United States. While these have not been adopted in all states, they cover enough states to provide continuity and consistency for software and curriculum developers. A similar effect also appears to operate at the student level; those who dabble in tech may be spending their time learning the tech rather than using the tech to learn. This learning curve needs to be built into technology-reform programs.

Taken together, these results suggest that systems that take a comprehensive, data-informed approach may achieve learning gains from thoughtful use of technology in the classroom. The best results come when significant effort is put into ensuring that devices and infrastructure are fit for purpose (fast enough internet service, for example), that software is effective and integrated with curricula, that teachers are trained and given time to rethink lesson plans integrating technology, that students have enough interaction with tech to use it effectively, and that technology strategy is cognizant of the system’s position on the school-system reform journey. Online learning and education technology are currently providing an invaluable service by enabling continued learning over the course of the pandemic; this does not mean that they should be accepted uncritically as students return to the classroom.

Jake Bryant is an associate partner in McKinsey’s Washington, DC, office; Felipe Child is a partner in the Bogotá office; Emma Dorn is the global Education Practice manager in the Silicon Valley office; and Stephen Hall is an associate partner in the Dubai office.

The authors wish to thank Fernanda Alcala, Sujatha Duraikkannan, and Samuel Huang for their contributions to this article.

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What 126 studies say about education technology

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In recent years, there has been widespread excitement around the transformative potential of technology in education. In the United States alone, spending on education technology has now exceeded $13 billion . Programs and policies to promote the use of education technology may expand access to quality education, support students’ learning in innovative ways, and help families navigate complex school systems.

However, the rapid development of education technology in the United States is occurring in a context of deep and persistent inequality . Depending on how programs are designed, how they are used, and who can access them, education technologies could alleviate or aggravate existing disparities. To harness education technology’s full potential, education decision-makers, product developers, and funders need to understand the ways in which technology can help — or in some cases hurt — student learning.

To address this need, J-PAL North America recently released a new publication summarizing 126 rigorous evaluations of different uses of education technology. Drawing primarily from research in developed countries, the publication looks at randomized evaluations and regression discontinuity designs across four broad categories: (1) access to technology, (2) computer-assisted learning or educational software, (3) technology-enabled nudges in education, and (4) online learning.

This growing body of evidence suggests some areas of promise and points to four key lessons on education technology.

First, supplying computers and internet alone generally do not improve students’ academic outcomes from kindergarten to 12th grade, but do increase computer usage and improve computer proficiency. Disparities in access to information and communication technologies can exacerbate existing educational inequalities. Students without access at school or at home may struggle to complete web-based assignments and may have a hard time developing digital literacy skills.

Broadly, programs to expand access to technology have been effective at increasing use of computers and improving computer skills. However, computer distribution and internet subsidy programs generally did not improve grades and test scores and in some cases led to adverse impacts on academic achievement. The limited rigorous evidence suggests that distributing computers may have a more direct impact on learning outcomes at the postsecondary level.

Second, educational software (often called “computer-assisted learning”) programs designed to help students develop particular skills have shown enormous promise in improving learning outcomes, particularly in math. Targeting instruction to meet students’ learning levels has been found to be effective in improving student learning, but large class sizes with a wide range of learning levels can make it hard for teachers to personalize instruction. Software has the potential to overcome traditional classroom constraints by customizing activities for each student. Educational software programs range from light-touch homework support tools to more intensive interventions that re-orient the classroom around the use of software.

Most educational software that have been rigorously evaluated help students practice particular skills through personalized tutoring approaches. Computer-assisted learning programs have shown enormous promise in improving academic achievement, especially in math. Of all 30 studies of computer-assisted learning programs, 20 reported statistically significant positive effects, 15 of which were focused on improving math outcomes.

Third, technology-based nudges — such as text message reminders — can have meaningful, if modest, impacts on a variety of education-related outcomes, often at extremely low costs. Low-cost interventions like text message reminders can successfully support students and families at each stage of schooling. Text messages with reminders, tips, goal-setting tools, and encouragement can increase parental engagement in learning activities, such as reading with their elementary-aged children.

Middle and high schools, meanwhile, can help parents support their children by providing families with information about how well their children are doing in school. Colleges can increase application and enrollment rates by leveraging technology to suggest specific action items, streamline financial aid procedures, and/or provide personalized support to high school students.

Online courses are developing a growing presence in education, but the limited experimental evidence suggests that online-only courses lower student academic achievement compared to in-person courses. In four of six studies that directly compared the impact of taking a course online versus in-person only, student performance was lower in the online courses. However, students performed similarly in courses with both in-person and online components compared to traditional face-to-face classes.

The new publication is meant to be a resource for decision-makers interested in learning which uses of education technology go beyond the hype to truly help students learn. At the same time, the publication outlines key open questions about the impacts of education technology, including questions relating to the long-term impacts of education technology and the impacts of education technology on different types of learners.

To help answer these questions, J-PAL North America’s Education, Technology, and Opportunity Initiative is working to build the evidence base on promising uses of education technology by partnering directly with education leaders.

Education leaders are invited to submit letters of interest to partner with J-PAL North America through its  Innovation Competition . Anyone interested in learning more about how to apply is encouraged to contact initiative manager Vincent Quan .

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Information and communication technology (ICT) in education

Information and communications technology (ict) can impact student learning when teachers are digitally literate and understand how to integrate it into curriculum..

Schools use a diverse set of ICT tools to communicate, create, disseminate, store, and manage information.(6) In some contexts, ICT has also become integral to the teaching-learning interaction, through such approaches as replacing chalkboards with interactive digital whiteboards, using students’ own smartphones or other devices for learning during class time, and the “flipped classroom” model where students watch lectures at home on the computer and use classroom time for more interactive exercises.

When teachers are digitally literate and trained to use ICT, these approaches can lead to higher order thinking skills, provide creative and individualized options for students to express their understandings, and leave students better prepared to deal with ongoing technological change in society and the workplace.(18)

ICT issues planners must consider include: considering the total cost-benefit equation, supplying and maintaining the requisite infrastructure, and ensuring investments are matched with teacher support and other policies aimed at effective ICT use.(16)

Issues and Discussion

Digital culture and digital literacy: Computer technologies and other aspects of digital culture have changed the ways people live, work, play, and learn, impacting the construction and distribution of knowledge and power around the world.(14) Graduates who are less familiar with digital culture are increasingly at a disadvantage in the national and global economy. Digital literacy—the skills of searching for, discerning, and producing information, as well as the critical use of new media for full participation in society—has thus become an important consideration for curriculum frameworks.(8)

In many countries, digital literacy is being built through the incorporation of information and communication technology (ICT) into schools. Some common educational applications of ICT include:

• One laptop per child: Less expensive laptops have been designed for use in school on a 1:1 basis with features like lower power consumption, a low cost operating system, and special re-programming and mesh network functions.(42) Despite efforts to reduce costs, however, providing one laptop per child may be too costly for some developing countries.(41)
• Tablets: Tablets are small personal computers with a touch screen, allowing input without a keyboard or mouse. Inexpensive learning software (“apps”) can be downloaded onto tablets, making them a versatile tool for learning.(7)(25) The most effective apps develop higher order thinking skills and provide creative and individualized options for students to express their understandings.(18)
• Interactive White Boards or Smart Boards : Interactive white boards allow projected computer images to be displayed, manipulated, dragged, clicked, or copied.(3) Simultaneously, handwritten notes can be taken on the board and saved for later use. Interactive white boards are associated with whole-class instruction rather than student-centred activities.(38) Student engagement is generally higher when ICT is available for student use throughout the classroom.(4)
• E-readers : E-readers are electronic devices that can hold hundreds of books in digital form, and they are increasingly utilized in the delivery of reading material.(19) Students—both skilled readers and reluctant readers—have had positive responses to the use of e-readers for independent reading.(22) Features of e-readers that can contribute to positive use include their portability and long battery life, response to text, and the ability to define unknown words.(22) Additionally, many classic book titles are available for free in e-book form.
• Flipped Classrooms: The flipped classroom model, involving lecture and practice at home via computer-guided instruction and interactive learning activities in class, can allow for an expanded curriculum. There is little investigation on the student learning outcomes of flipped classrooms.(5) Student perceptions about flipped classrooms are mixed, but generally positive, as they prefer the cooperative learning activities in class over lecture.(5)(35)

ICT and Teacher Professional Development: Teachers need specific professional development opportunities in order to increase their ability to use ICT for formative learning assessments, individualized instruction, accessing online resources, and for fostering student interaction and collaboration.(15) Such training in ICT should positively impact teachers’ general attitudes towards ICT in the classroom, but it should also provide specific guidance on ICT teaching and learning within each discipline. Without this support, teachers tend to use ICT for skill-based applications, limiting student academic thinking.(32) To sup­port teachers as they change their teaching, it is also essential for education managers, supervisors, teacher educators, and decision makers to be trained in ICT use.(11)

Ensuring benefits of ICT investments: To ensure the investments made in ICT benefit students, additional conditions must be met. School policies need to provide schools with the minimum acceptable infrastructure for ICT, including stable and affordable internet connectivity and security measures such as filters and site blockers. Teacher policies need to target basic ICT literacy skills, ICT use in pedagogical settings, and discipline-specific uses. (21) Successful imple­mentation of ICT requires integration of ICT in the curriculum. Finally, digital content needs to be developed in local languages and reflect local culture. (40) Ongoing technical, human, and organizational supports on all of these issues are needed to ensure access and effective use of ICT. (21)

Resource Constrained Contexts: The total cost of ICT ownership is considerable: training of teachers and administrators, connectivity, technical support, and software, amongst others. (42) When bringing ICT into classrooms, policies should use an incremental pathway, establishing infrastructure and bringing in sustainable and easily upgradable ICT. (16) Schools in some countries have begun allowing students to bring their own mobile technology (such as laptop, tablet, or smartphone) into class rather than providing such tools to all students—an approach called Bring Your Own Device. (1)(27)(34) However, not all families can afford devices or service plans for their children. (30) Schools must ensure all students have equitable access to ICT devices for learning.

Inclusiveness Considerations

Digital Divide: The digital divide refers to disparities of digital media and internet access both within and across countries, as well as the gap between people with and without the digital literacy and skills to utilize media and internet.(23)(26)(31) The digital divide both creates and reinforces socio-economic inequalities of the world’s poorest people. Policies need to intentionally bridge this divide to bring media, internet, and digital literacy to all students, not just those who are easiest to reach.

Minority language groups: Students whose mother tongue is different from the official language of instruction are less likely to have computers and internet connections at home than students from the majority. There is also less material available to them online in their own language, putting them at a disadvantage in comparison to their majority peers who gather information, prepare talks and papers, and communicate more using ICT. (39) Yet ICT tools can also help improve the skills of minority language students—especially in learning the official language of instruction—through features such as automatic speech recognition, the availability of authentic audio-visual materials, and chat functions. (2)(17)

Students with different styles of learning: ICT can provide diverse options for taking in and processing information, making sense of ideas, and expressing learning. Over 87% of students learn best through visual and tactile modalities, and ICT can help these students ‘experience’ the information instead of just reading and hearing it. (20)(37) Mobile devices can also offer programmes (“apps”) that provide extra support to students with special needs, with features such as simplified screens and instructions, consistent placement of menus and control features, graphics combined with text, audio feedback, ability to set pace and level of difficulty, appropriate and unambiguous feedback, and easy error correction. (24)(29)

Plans and policies

• India [ PDF ]
• Detroit, USA [ PDF ]
• Finland [ PDF ]
• Alberta Education. 2012. Bring your own device: A guide for schools . Retrieved from http://education.alberta.ca/admin/technology/research.aspx
• Alsied, S.M. and Pathan, M.M. 2015. ‘The use of computer technology in EFL classroom: Advantages and implications.’ International Journal of English Language and Translation Studies . 1 (1).
• BBC. N.D. ‘What is an interactive whiteboard?’ Retrieved from http://www.bbcactive.com/BBCActiveIdeasandResources/Whatisaninteractivewhiteboard.aspx
• Beilefeldt, T. 2012. ‘Guidance for technology decisions from classroom observation.’ Journal of Research on Technology in Education . 44 (3).
• Bishop, J.L. and Verleger, M.A. 2013. ‘The flipped classroom: A survey of the research.’ Presented at the 120th ASEE Annual Conference and Exposition. Atlanta, Georgia.
• Blurton, C. 2000. New Directions of ICT-Use in Education . United National Education Science and Culture Organization (UNESCO).
• Bryant, B.R., Ok, M., Kang, E.Y., Kim, M.K., Lang, R., Bryant, D.P. and Pfannestiel, K. 2015. ‘Performance of fourth-grade students with learning disabilities on multiplication facts comparing teacher-mediated and technology-mediated interventions: A preliminary investigation. Journal of Behavioral Education. 24.
• Buckingham, D. 2005. Educación en medios. Alfabetización, aprendizaje y cultura contemporánea, Barcelona, Paidós.
• Buckingham, D., Sefton-Green, J., and Scanlon, M. 2001. 'Selling the Digital Dream: Marketing Education Technologies to Teachers and Parents.'  ICT, Pedagogy, and the Curriculum: Subject to Change . London: Routledge.
• "Burk, R. 2001. 'E-book devices and the marketplace: In search of customers.' Library Hi Tech 19 (4)."
• Chapman, D., and Mählck, L. (Eds). 2004. Adapting technology for school improvement: a global perspective. Paris: International Institute for Educational Planning.
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Home — Essay Samples — Information Science and Technology — Technology in Education — The Importance of Technology in Education

The Importance of Technology in Education: Benefits for Students and Educators

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Published: May 24, 2022

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Introduction, importance of technology in education (essay).

• Himmelsbach, V. (2019). How Does Technology Impact Student Learning? Retrieved November 26, 2020, from https:tophat.combloghow-does-technology-impact-student-learningDikusar, A. (2018).
• How Important is Technology in Education? Retrieved October 22, 2020, from https:xbsoftware.combloghow-important-is-technology-in-educationCox, J. (2019).
• Benefits of Technology in the Classroom. Retrieved November 4, 2020, from https:www.teachhub.comtechnology-in-the-classroom201911benefits-of-technology-in-the-classroom

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

• Published: 21 November 2022
• Volume 28 , pages 6695–6726, ( 2023 )

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• Stella Timotheou 1 ,
• Ourania Miliou 1 ,
• Yiannis Dimitriadis 2 ,
• Sara Villagrá Sobrino 2 ,
• Nikoleta Giannoutsou 2 ,
• Romina Cachia 3 ,
• Alejandra Martínez Monés 2 &
• Andri Ioannou   ORCID: orcid.org/0000-0002-3570-6578 1  

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Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

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1 Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

2 Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table 1 .

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

3.1 Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

3.2 Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

3.3 Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

3.4 Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

3.5 Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

3.5.1 Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

3.5.2 Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

3.5.3 School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

3.5.4 Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

3.5.5 Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

3.5.6 Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

3.5.7 Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

4 Discussion

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

5 Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

6 Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

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Timotheou, S., Miliou, O., Dimitriadis, Y. et al. Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review. Educ Inf Technol 28 , 6695–6726 (2023). https://doi.org/10.1007/s10639-022-11431-8

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Teaching About Technology in Schools Through Technoskeptical Inquiry

June 3, 2024 | victorialynn | Harvard Educational Review Contributors , Voices in Education

By Jacob Pleasants, Daniel G. Krutka, and T. Philip Nichols

New technologies are rapidly transforming our societies, our relationships, and our schools. Look no further than the intense — and often panicked — discourse around generative AI , the metaverse , and the creep of digital media into all facets of civic and social life . How are schools preparing students to think about and respond to these changes?

In various ways, students are taught how to use technologies in school. Most schools teach basic computing skills and many offer elective vocational-technical classes. But outside of occasional conversations around digital citizenship, students rarely wrestle with deeper questions about the effects of technologies on individuals and society.

Decades ago, Neil Postman (1995) argued for a different form of technology education focused on teaching students to critically examine technologies and their psychological and social effects. While Postman’s ideas have arguably never been more relevant, his suggestion to add technology education as a separate subject to a crowded curriculum gained little traction. Alternatively, we argue that technology education could be an interdisciplinary endeavor that occurs across core subject areas. Technology is already a part of English Language Arts (ELA), Science, and Social Studies instruction. What is missing is a coherent vision and common set of practices and principles that educators can use to align their efforts.

To provide a coherent vision, in our recent HER article , we propose “technoskepticism” as an organizing goal for teaching about technology. We define technoskepticism as a critical disposition and practice of investigating the complex relationships between technologies and societies. A technoskeptical person is not necessarily anti-technology, but rather one who deeply examines technological issues from multiple dimensions and perspectives akin to an art critic.

We created the Technoskepticism Iceberg as a framework to support teachers and students in conducting technological inquiries. The metaphor of an iceberg conveys how many important influences of technology lie beneath our conscious awareness. People often perceive technologies as tools (the “visible” layer of the iceberg), but technoskepticism requires that they be seen as parts of systems (with interactions that produce many unintended effects) and embedded with values about what is good and desirable (and for whom). The framework also identifies three dimensions of technology that students can examine. The technical dimension concerns the design and functions of a technology, including how it may work differently for different people. The psychosocial dimension addresses how technologies change our individual cognition and our larger societies. The political dimension considers who makes decisions concerning the terms, rules, or laws that govern technologies.

To illustrate these ideas, how might we use the Technoskeptical Iceberg to interrogate generative AI such as ChatGPT in the core subject areas?

A science/STEM classroom might focus on the technical dimension by investigating how generative AI works and demystifying its ostensibly “intelligent” capabilities. Students could then examine the infrastructures involved in AI systems , such as immense computing power and specialized hardware that in turn have profound environmental consequences. A teacher could ask students to use their values to weigh the costs and potential benefits of ChatGPT.

A social studies class could investigate the psychosocial dimension through the longer histories of informational technologies (e.g., the printing press, telegraph, internet, and now AI) to consider how they shifted people’s lives. They could also explore political questions about what rules or regulations governments should impose on informational systems that include people’s data and intellectual property.

In an ELA classroom, students might begin by investigating the psychosocial dimensions of reading and writing, and the values associated with different literacy practices. Students could consider how the concept of “authorship” shifts when one writes by hand, with word processing software, or using ChatGPT. Or how we are to engage with AI-generated essays, stories, and poetry differently than their human-produced counterparts. Such conversations would highlight how literary values are mediated by technological systems . 

Students who use technoskepticism to explore generative AI technologies should be better equipped to act as citizens seeking to advance just futures in and out of schools. Our questions are, what might it take to establish technoskepticism as an educational goal in schools? What support will educators need? And what might students teach us through technoskeptical inquiries?

Postman, N. (1995). The End of Education: Redefining the Value of School. Vintage Books.

About the Authors

Jacob Pleasants is an assistant professor of science education at the University of Oklahoma. Through his teaching and research, he works to humanize STEM education by helping students engage with issues at the intersection of STEM and society.

Daniel G. Krutka is a dachshund enthusiast, former high school social studies teacher, and associate professor of social studies education at the University of North Texas. His research concerns technology, democracy, and education, and he is the cofounder of the Civics of Technology project ( www.civicsoftechnology.org ).

T. Philip Nichols is an associate professor in the Department of Curriculum and Instruction at Baylor University. He studies the digitalization of public education and the ways science and technology condition the ways we practice, teach, and talk about literacy.

They are the authors of “ What Relationships Do We Want with Technology? Toward Technoskepticism in Schools ” in the Winter 2023 issue of Harvard Educational Review .

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Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

• Research article
• Open access
• Published: 03 June 2024

From concerns to benefits: a comprehensive study of ChatGPT usage in education

• Hyeon Jo   ORCID: orcid.org/0000-0001-7442-4736 1  

International Journal of Educational Technology in Higher Education volume  21 , Article number:  35 ( 2024 ) Cite this article

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Artificial Intelligence (AI) chatbots are increasingly becoming integral components of the digital learning ecosystem. As AI technologies continue to evolve, it is crucial to understand the factors influencing their adoption and use among students in higher education. This study is undertaken against this backdrop to explore the behavioral determinants associated with the use of the AI Chatbot, ChatGPT, among university students. The investigation delves into the role of ChatGPT’s self-learning capabilities and their influence on students’ knowledge acquisition and application, subsequently affecting the individual impact. It further elucidates the correlation of chatbot personalization with novelty value and benefits, underscoring their importance in shaping students’ behavioral intentions. Notably, individual impact is revealed to have a positive association with perceived benefits and behavioral intention. The study also brings to light potential barriers to AI chatbot adoption, identifying privacy concerns, technophobia, and guilt feelings as significant detractors from behavioral intention. However, despite these impediments, innovativeness emerges as a positive influencer, enhancing behavioral intention and actual behavior. This comprehensive exploration of the multifaceted influences on student behavior in the context of AI chatbot utilization provides a robust foundation for future research. It also offers invaluable insights for AI chatbot developers and educators, aiding them in crafting more effective strategies for AI integration in educational settings.

Introduction

In the realm of advanced language models, OpenAI’s Generative Pre-trained Transformer (GPT) has emerged as a groundbreaking tool that has begun to make significant strides in numerous disciplines (Biswas, 2023a , b ; Firat, 2023 ; Kalla, 2023 ). Education, in particular, has become a fertile ground for this innovation (Kasneci et al., 2023 ). A standout instance is ChatGPT, the latest iteration of this transformative technology, which has been rapidly adopted by a large segment of university students across the globe (Rudolph et al., 2023 ). At its core, ChatGPT is armed with the ability to produce cohesive, contextually appropriate responses predicated on preceding dialogues, providing an interactive medium akin to human conversation (King & ChatGPT, 2023 ). This interactive capacity of ChatGPT has the potential to significantly restructure the educational landscape, altering the way students absorb, interact, and engage with academic content (Fauzi et al., 2023 ). Despite the burgeoning intrigue surrounding this technology and its applications, the comprehensive examination of determinants shaping students’ behavior in the adoption and usage of ChatGPT remains a largely uncharted territory. A thorough, systematic understanding of these determinants, both facilitative and inhibitive, is essential for the more effective deployment and acceptance of such a tool in educational settings, thereby contributing to its ultimate success and efficacy. The current study seeks to shed light on this crucial aspect, aiming to provide a nuanced comprehension of the array of factors that influence university students’ behavioral intentions and patterns towards utilizing ChatGPT for their educational pursuits.

ChatGPT is replete with remarkable features courtesy of its underlying technologies: machine learning (Rospigliosi, 2023 ) and natural language processing (Maddigan & Susnjak, 2023 ). Its capabilities span across a broad spectrum, from completing texts, answering questions, to even spawning original content. In the landscape of education, these functionalities morph into a powerful apparatus that could revolutionize traditional learning modalities. Similar to a human tutor, students can converse with ChatGPT—asking questions, eliciting explanations, or diving into profound discussions about their study materials. Beyond its conversational abilities, ChatGPT’s capacity to learn from past interactions enables it to refine its responses over time, catering more accurately to the user’s distinct needs and preferences (McGee, 2023 ). This attribute of continuous learning and adaptation paves the way for a more personalized and efficient learning experience, offering custom-made academic assistance (Aljanabi, 2023 ). This intricate combination of characteristics arguably makes ChatGPT a potent tool in the educational sphere, worthy of systematic examination to optimize its potential benefits and mitigate any challenges.

ChatGPT’s unique features manifest a range of enabling factors that can significantly influence its adoption among university students. One such factor is the self-learning attribute that empowers the AI to progressively enhance its performance (Rijsdijk et al., 2007 ). This feature aligns with the ongoing learning journey of students, potentially fostering a symbiotic learning environment. Another pivotal factor is the scope for knowledge acquisition and application. As ChatGPT assists in knowledge acquisition, students can simultaneously apply their newly garnered knowledge, potentially bolstering their academic performance. Personalization of the AI forms another significant enabling factor, ensuring that the interactions are fine-tuned to each student’s distinctive needs, thereby promoting a more individual-centered learning experience. Lastly, the novelty value that ChatGPT brings to the educational realm can stimulate student engagement, making learning an exciting and enjoyable process. Unraveling the influence of these factors on students’ behavior toward ChatGPT could yield valuable insights, paving the way for the tool’s effective implementation in education.

Alongside the positive influencers, some potential detractors could hinder the adoption of ChatGPT among university students. Among these, privacy concerns stand out as paramount (Lund & Wang, 2023 ; McCallum, 2023 ). With data breaches becoming increasingly common, students might be apprehensive about sharing their academic queries and discussions with an AI tool. Technophobia is another potential inhibitor, as not all students might be comfortable interacting with an advanced AI model. Moreover, some students might experience guilt feelings while using ChatGPT, equating its assistance to a form of ‘cheating’ (Anders, 2023 ). Evaluating these potential inhibitors is crucial to forming a comprehensive understanding of the factors that shape students’ behavior towards ChatGPT. This balanced approach could help identify ways to alleviate these concerns and foster wider adoption of this promising educational tool.

Despite the growing adoption of AI tools like ChatGPT, there is a notable gap in the literature pertaining to how these tools influence behavior in an educational context. Moreover, existing studies often fail to account for both enabling and inhibiting factors. Therefore, this paper endeavors to fill this gap by offering a more balanced and comprehensive exploration. The primary objective of this study is to empirically examine the determinants—both enablers and inhibitors—that influence university students’ behavioral intentions and actual behavior towards using ChatGPT. By doing so, this study seeks to make a valuable contribution to the academic discourse surrounding the use of AI in education, while also offering practical implications for its more effective implementation in educational settings.

The rest of this paper is structured as follows: the next section reviews the relevant literature and formulates the research hypotheses. The subsequent section outlines the research methodology, followed by a presentation and discussion of the results. Finally, the paper concludes with the implications for theory and practice, and suggestions for future research.

Theoretical background and hypothesis development

This study is grounded in several theoretical frameworks that inform the understanding of AI-based language models, like ChatGPT, and their impact on individual impact, benefits, behavioral intention, and behavior.

Figure  1 displays the research mode. The model asserts that the AI’s self-learning capability influences knowledge acquisition and application, which in turn impact individual users. Personalization of the AI and the novelty value it offers are also predicted to have substantial effects on the perceived benefits, influencing the behavioral intention to use AI, culminating in actual behavior. The model also takes into account potential negative influences such as perceived risk, technophobia, and feelings of guilt on the behavioral intention to use the AI. The last two constructs in the model, behavioral intention and innovativeness, are anticipated to influence the actual behavior of the AI.

Research Model

Self-determination theory (SDT)

The SDT is indeed a critical theoretical foundation in this study. The theory, developed by Ryan and Deci ( 2000 ), posits that when tasks are intrinsically motivating and individuals perceive competence in these tasks, it leads to improved performance, persistence, and creativity. This theoretical framework supports the idea that the self-learning capability of AI, like ChatGPT, is vital in fostering intrinsic motivation among its users. Specifically, the self-learning aspect allows ChatGPT to tailor its interactions according to the individual user’s needs, thereby creating a more engaging and personally relevant learning environment. This can enhance users’ perception of their competence, resulting in more effective knowledge acquisition and application (H1).

Further, by promoting effective knowledge acquisition and application, the self-learning capability of ChatGPT can significantly impact individuals positively (H2, H3). That is, students can use the knowledge gained from interactions with ChatGPT to improve their academic performance, deepen their understanding of various subjects, and even stimulate their creativity. This aligns with the SDT’s emphasis on competence perception as a driver of enhanced performance and creativity.

Therefore, in line with the SDT, this study hypothesizes that the self-learning capability of ChatGPT, which promotes intrinsic motivation and perceived competence, can lead to improved knowledge acquisition and application, and consequently, a positive individual impact. This theoretical foundation helps to validate the first three hypotheses of the study and underscores the significance of intrinsic motivation and perceived competence in leveraging the educational potential of AI tools like ChatGPT.

• Self-learning

Self-learning is considered as an inherent capability of ChatGPT, the AI tool (Rijsdijk et al., 2007 ). This unique ability of ChatGPT allows it to conduct continuous learning, improve itself, and solve problems based on big data analysis. The concept of self-learning in AI involves the system’s ability to learn and improve from experience without being explicitly programmed (Bishop & Nasrabadi, 2006 ). Such an AI-based tool, like ChatGPT, can evolve its capabilities over time, making it a highly efficient tool for knowledge acquisition and application among students (Berland et al., 2014 ). Berland et al. ( 2014 ) pointed out that self-learning in AI can provide personalized learning experiences, thereby helping students to acquire and retain knowledge more effectively. This is due to the AI’s ability to adapt its responses based on past interactions and data, allowing for more targeted and relevant knowledge acquisition. For knowledge application, the self-learning capability of AI tools like ChatGPT can play a significant role. D’mello and Graesser ( 2013 ) argued that such AI systems could enhance learners’ ability to apply acquired knowledge, as they can simulate a variety of scenarios and provide immediate feedback. This real-time, contextual problem-solving can further strengthen knowledge application skills in students. Consequently, recognizing the potential of self-learning in AI, such as in ChatGPT, to improve both knowledge acquisition and application among university students, this study suggests the following hypotheses.

H1a. Self-learning has a positive effect on knowledge acquisition.

H1b. Self-learning has a positive effect on knowledge application.

Knowledge Acquisition

Knowledge acquisition is the process in which new information is obtained and subsequently expanded upon when additional insights are gained. A research by Nonaka ( 1994 ) emphasized the role of knowledge acquisition in enhancing organizational productivity. Another study by Alavi and Leidner ( 2001 ) suggested that knowledge acquisition can positively influence individual performance by improving the efficiency and effectiveness of task execution. Several scholars have shown the significance of knowledge acquisition on perceived usefulness in the context of learning (Al-Emran et al., 2018 ; Al-Emran & Teo, 2020 ). Given the capabilities of AI chatbots like ChatGPT in providing users with immediate, accurate, and personalized information, it’s likely that knowledge acquisition from using such a tool could lead to improved task performance or individual impact. Therefore, this study proposes the following hypothesis:

H2. Knowledge acquisition has a positive effect on individual impact.

Knowledge application

Knowledge application, as defined by Al-Emran et al. ( 2020 ), implies the facilitation of individuals to effortlessly utilize knowledge techniques via efficient storage and retrieval systems. With AI-driven chatbots like ChatGPT, this application process can be significantly enhanced. The positive influence of effective knowledge application on individual outcomes has also been corroborated by previous research (Al-Emran & Teo, 2020 ; Alavi & Leidner, 2001 ; Heisig, 2009 ). Therefore, this study suggests the following hypothesis:

H3. Knowledge application has a positive effect on individual impact.

• Personalization

The study’s conceptual framework is further informed by personalization (Maghsudi et al., 2021 ; Yu et al., 2017 ), underscoring the importance of personalized experiences in a learning environment. Literature on personalization advocates that designing experiences tailored to individual needs and preferences enhances the user’s engagement and satisfaction (Desaid, 2020 ; Jo, 2022 ). In the context of AI, personalization refers to the ability of the system, in this case, ChatGPT, to adapt its interactions based on the unique requirements and patterns of the individual user. This capacity to customize interactions could make the learning process more relevant and stimulating for the students, thereby potentially increasing the novelty value they derive from using ChatGPT (H4a).

Furthermore, the personalization theory also suggests that a personalized learning environment can lead to increased perceived benefits, as it allows students to learn at their own pace, focus on areas of interest or difficulty, and receive feedback that is specific to their learning progress (H4b). This aligns with the results of the study, which found a significant positive relationship between personalization and perceived benefits.

Personalization refers to the technology’s ability to tailor interactions based on the user’s preferences, behaviors, and individual characteristics, providing a unique and individualized user experience (Wirtz et al., 2018 ). This capacity has been found to significantly enhance users’ perception of value in their interactions with the technology (Chen et al., 2022 ). Previous research has indicated that the personalization capabilities of AI chatbots, such as ChatGPT, can enhance this novelty value, as the individualized user experience provides a unique and fresh engagement every time (Haleem et al., 2022 ; Koubaa et al., 2023 ). Research has highlighted that personalized interactions with AI chatbots can lead to increased perceived benefits, such as improved efficiency, productivity, and task performance (Makice, 2009 ). Personalization enables the technology to cater to the individual user’s needs effectively, thereby enhancing their perception of the benefits derived from using it (Wirtz et al., 2018 ). Thus, this study proposes the following hypotheses:

H4a. Personalization has a positive effect on novelty value.

H4b. Personalization has a positive effect on benefits.

Diffusion of Innovation

The diffusion of innovations theory offers a valuable lens through which to understand the adoption of new technologies (Rogers, 2010 ). The theory suggests that the diffusion and adoption of an innovation in a social system are influenced by five key factors: the innovation’s relative advantage, compatibility, complexity, trialability, and observability.

One essential aspect of the innovation, which resonates strongly with this study, is its relative advantage – the degree to which an innovation is perceived as being better than the idea it supersedes. This relative advantage often takes the form of novelty value, especially in the early stages of an innovation’s diffusion process. In this context, ChatGPT, with its self-learning capability, personalization, and interactive conversation style, can be seen as a novel approach to traditional learning methods. This novelty can make the learning process more engaging, exciting, and effective, thereby increasing the perceived benefits for students.

Rogers’ theory also highlights that these perceived benefits can significantly influence an individual’s intention to adopt the innovation. The more an individual perceives the benefits of the innovation to outweigh the costs, the more likely they are to adopt it. In the context of this study, the novelty value of using ChatGPT contributes to the perceived benefits (H5), which in turn influences the intention to use it (H7). This underscores the importance of managing the perceived novelty value of AI tools like ChatGPT in a learning environment, as it can be a crucial determinant of their adoption and use. Furthermore, Rogers’ theory stresses the importance of compatibility and simplicity of the innovation. These attributes align with the concepts of personalization and self-learning capabilities of ChatGPT, highlighting that an AI tool that aligns with users’ needs and is easy to use is more likely to be adopted.

Novelty value

Novelty value refers to the unique and new experience that users perceive when interacting with the technology (Moussawi et al., 2021 ). This sense of novelty has been associated with increased interest, engagement, and satisfaction, contributing to a more valuable and enriching user experience (Hund et al., 2021 ). In this context, the novelty value provided by AI artifacts can enhance the perceived value of using the technology (Jo, 2022 ). This relationship has been documented in prior research, showing that the novelty of technology use can lead to increased perceived benefits, possibly due to heightened user interest, engagement, and satisfaction (Huang & Benyoucef, 2013 ). Therefore, this study suggests the following hypothesis:

H5. Novelty value has a positive effect on benefits.

Technology Acceptance Model (TAM)

The TAM has been widely employed to explain user acceptance and usage of technology. TAM posits that two particular perceptions, perceived usefulness (the degree to which a person believes that using a particular system would enhance their job performance) and perceived ease of use (the degree to which a person believes that using a particular system would be free of effort), determine an individual’s attitude toward using the system, which in turn influences their behavioral intention to use it, and ultimately their actual system use.

In the context of this study, TAM offers valuable insight into how individual impact, perceived as the influence of using ChatGPT on a student’s academic performance and outcomes, can affect perceived benefits and behavioral intention (H6a, H6b). Students are more likely to use ChatGPT if they perceive it to have a positive impact on their learning outcomes. This perceived impact can enhance the perceived benefits of using ChatGPT, which can then influence their behavioral intention to use it. This aligns with Davis’s proposition that perceived usefulness positively influences behavioral intention to use a technology (Davis, 1989 ).

Moreover, the relationship between perceived benefits and behavioral intention (H7) is also consistent with TAM. According to Davis ( 1989 ), when users perceive a system as beneficial, they are more likely to form positive intentions to use it. Thus, the more students perceive the benefits of using ChatGPT, such as improved learning outcomes, personalized learning experiences, and increased engagement, the stronger their intention to use ChatGPT.

Individual impact

Individual impact refers to the perceived improvements in task performance and productivity brought about by the use of an IT such as ChatGPT (Aparicio et al., 2017 ). The perceived usefulness, which is closely related to individual impact, has a positive effect on behavioral intention to use a technology (Davis, 1989 ). In the literature, numerous studies have demonstrated that when users perceive a technology to be beneficial and impactful on their personal efficiency or productivity, they are more likely to have a positive behavioral intention towards using it (Gatzioufa & Saprikis, 2022 ; Kelly et al., 2022 ; Venkatesh et al., 2012 ). Similarly, these perceived improvements or benefits can also increase the perceived value of the technology, contributing to its overall appeal (Kim & Benbasat, 2006 ). Therefore, building upon this premise, this study proposes the following hypotheses.

H6a. Individual impact has a positive effect on benefits.

H6b. Individual impact has a positive effect on behavioral intention.

The concept of benefits is the perceived advantage or gain a user experiences from the use of the IT (Al-Fraihat et al., 2020 ). This study employed the individual impact and benefits separately. The rationale for considering individual impact and benefits as separate constructs in this research stems from the subtle differences in their underlying meanings and implications in the context of using AI chatbots like ChatGPT. While they might appear closely related, treating them separately can provide a more nuanced understanding of user experiences and perceptions. Individual impact, as defined in this research, pertains to the direct effects of using ChatGPT on the user’s performance and productivity. It encapsulates the tangible, immediate changes that occur in a user’s work efficiency and task completion speed as a result of using the AI chatbot. Therefore, individual impact serves as a measure of performance enhancement, indicating the ‘output’ side of the user’s interaction with ChatGPT. On the other hand, benefits, as gauged by the proposed survey items, are more encompassing. They extend beyond the immediate task-related effects to include the overall advantages perceived by the users. This could range from acquiring new knowledge to achieving personal or educational goals. Benefits, in essence, capture the ‘outcome’ aspect of using ChatGPT, reflecting a broader range of positive effects that contribute to user satisfaction and perceived value. By differentiating between individual impact and benefits, this research can provide a more detailed and comprehensive analysis of users’ experiences with ChatGPT.

The benefit as a determinant of behavioral intention is firmly established within the extant literature on technology acceptance and adoption. the theory of reasoned action (TRA) posits that the perception of benefits or value is directly linked to behavioral intentions (Fishbein & Ajzen, 1975 ). Similarly, the TAM proposes that perceived usefulness significantly influences behavioral intention to use a technology (Davis, 1989 ). Recent studies further confirm this relationship, which highlighted that the perceived benefit of a technology strongly predicts users’ intentions to use it (Cao et al., 2021 ; Chao, 2019 ; Yang & Wibowo, 2022 ). Given the consistent findings supporting this relationship, the following hypothesis is proposed:

H7. Benefits has a positive effect on behavioral intention.

Privacy theory and Technostress

The privacy paradox theory, formulated by Barnes ( 2006 ), provides insight into the intriguing contradiction that exists between individuals’ expressed concerns about privacy and their actual online behavior. Despite voicing concerns about their privacy, many individuals continue to disclose personal information or engage in activities that may compromise their privacy. However, in the context of AI technologies like ChatGPT, this study posits that privacy concerns may have a more pronounced impact. Particularly in an educational context, where sensitive academic information may be shared, privacy concerns can act as a deterrent, negatively influencing the behavioral intention to use the technology (H8).

On the other hand, the technostress model by Tarafdar et al. ( 2007 ) further illuminates our understanding of technology-induced stress and its impact on behavioral intention. This model proposes that individuals can experience different forms of technostress, including technophobia, which refers to the fear or anxiety experienced by some individuals when faced with new technologies. Technophobia can be particularly prevalent among individuals who lack familiarity or comfort with emerging technologies. In the context of this study, technophobia can potentially inhibit students from adopting and using AI tools like ChatGPT, thus negatively impacting their behavioral intention (H9).

Together, the privacy paradox theory and the technostress model provide a theoretical basis for understanding the potential inhibitors that could affect university students’ behavioral intention to use ChatGPT. By considering these theories, this study offers a more comprehensive perspective on the factors that can influence the adoption and use of AI technologies in an educational setting.

Privacy concerns

The concept of privacy concern refers to an individual’s anxiety or worry about the potential misuse or improper handling of personal data (Barth & de Jong, 2017 ). Privacy concern has been widely studied in the context of technology use, with a substantial body of research suggesting it is a major determinant of user behavior and intention (de Cosmo et al., 2021 ; Lutz & Tamò-Larrieux, 2021 ; Zhu et al., 2022 ). In the era of big data and pervasive digital technology, privacy concerns have increasingly become a pivotal factor shaping user behaviors (Smith et al., 2011 ). A host of empirical studies suggest that privacy concerns have a negative impact on behavioral intention. For example, Dinev and Hart ( 2006 ) observed that online privacy concerns negatively influence intention to use Internet services. Similarly, Li (2012) found that privacy concerns negatively affect users’ intention to adopt mobile apps. In the context of AI chatbots like ChatGPT, this concern could be even more pronounced, given the bot’s capacity for self-learning and data processing. Therefore, following the pattern observed in previous research, this study suggests the following hypothesis:

H8. Privacy concern has a negative effect on behavioral intention.

Technophobia

Technophobia is typically described as a personal fear or anxiety towards interacting with advanced technology, including AI systems such as ChatGPT (Ivanov & Webster, 2017 ). People suffering from technophobia often exhibit avoidance behavior when it comes to using new technology (Khasawneh, 2022 ). Several studies have empirically supported the negative relationship between technophobia and behavioral intention. For instance, Marakas et al. ( 1998 ) demonstrated that technophobia significantly reduces users’ behavioral intention to use a new IT system. Further, Selwyn (2018) found that technophobia can lead to avoidance behaviors and resistance towards adopting and using new technology, thus negatively influencing behavioral intention. Some users may fear losing control to these systems or fear making mistakes while using them, leading to a decline in their intention to use AI systems like ChatGPT. In consideration of these factors and in line with previous studies, this study posits the following hypothesis:

H9. Technophobia has a negative effect on behavioral intention.

Affect theory

The affect theory, proposed by Lazarus ( 1991 ), offers a comprehensive framework for understanding how emotions influence behavior, including technology use. According to this theory, emotional responses are elicited by the cognitive appraisal of a given situation and these emotional responses, in turn, have a significant impact on individuals’ behavior.

Applying this theory to the context of this study, it can be suggested that the use of AI technologies like ChatGPT can elicit a range of emotional responses among students. One particular emotion that is of interest in this study is guilt feeling. Guilt is typically experienced when individuals perceive that they have violated a moral standard. In the case of using AI technologies for learning, some students might perceive this as a form of ‘cheating’ or unfair advantage, thus leading to feelings of guilt.

This study hypothesizes that these guilt feelings can negatively influence students’ behavioral intention to use ChatGPT (H10). That is, students who experience guilt feelings related to using ChatGPT might be less inclined to use this tool for their learning. This underscores the importance of considering emotional factors, in addition to cognitive and behavioral factors, when exploring the determinants of technology use in an educational setting.

By integrating affect theory into the analysis, this study contributes to a more holistic understanding of the factors influencing the adoption and use of AI technologies in education. It also highlights the need for strategies that address potential guilt feelings among students to promote more positive attitudes and intentions towards using these technologies for learning.

Guilt feeling

Guilt feeling as a psychological state has been discussed extensively in consumer behavior research, often in the context of regret after certain consumption decisions (Khan et al., 2005 ; Zeelenberg & Pieters, 2007 ). Recently, guilt feeling has been associated with technology use, particularly when users perceive their technology use as excessive or inappropriate (Masood et al., 2022 ). For instance, guilt feeling could emerge among students when they feel they excessively rely on AI tools, such as ChatGPT, for completing assignments instead of their own effort and intellectual work. In line with the negative state relief model (Cialdini et al., 1973 ), individuals experiencing guilt feelings tend to take actions to alleviate these feelings. If university students feel guilty for using ChatGPT in their assignments, they might reduce their future use of this tool, thereby negatively impacting their behavioral intention to use ChatGPT. This finding is consistent with the results of a study conducted by Masood et al. ( 2022 ), which found that feelings of guilt resulting from excessive use of social networking sites predicted discontinuation. Thus, in the light of previous research suggesting a potential link between guilt feelings and behavioral intentions related to technology use, this study proposes the following hypothesis:

H10. Guilt feeling has a negative effect on behavioral intention.

• Innovativeness

Lastly, the diffusion of innovation theory (Rogers, 2010 ) lends further support to the hypotheses within the model (H12a, H12b), positing a positive influence of innovativeness on both behavioral intention and behavior. Innovativeness is defined as an individual’s predisposition to adopt new ideas, processes, or products earlier than other members of a social system (Rogers, 1983 ). It plays a critical role in technology adoption research, with prior studies highlighting the positive effects of innovativeness on both behavioral intention and actual behavior. For instance, Agarwal and Prasad ( 1998 ) found that personal innovativeness in the domain of information technology significantly influenced individuals’ intention to use a new technology. Similarly, Varma Citrin et al. ( 2000 ) discovered that consumer innovativeness positively affected both intention to use and actual use of self-service technologies. In the context of AI chatbots like ChatGPT, it is reasonable to propose that innovative university students, open to trying new technologies and applications, would exhibit stronger intentions to use ChatGPT and indeed, more likely to use it in practice. Hence, this study advances the following hypotheses:

H11a. Innovativeness has a positive effect on behavioral intention.

H11b. Innovativeness has a positive effect on behavior.

Theory of planned behavior (TPB)

The TPB, as proposed by Ajzen ( 1991 ), is a widely recognized psychological framework for understanding human behavior in various contexts, including technology adoption (Huang, 2023 ; Meng & Choi, 2019 ; Song & Jo, 2023 ). At the heart of TPB is the principle that individuals’ behavioral intentions are the most immediate and critical determinant of their actual behavior. This principle is predicated on the assumption that individuals are rational actors who make systematic use of the information available to them when deciding their course of action.

In the context of this study, the TPB supports the hypothesis that behavioral intention, influenced by various enabling and inhibiting factors, can significantly predict the actual use of ChatGPT among university students (H11). That is, students who have a stronger intention to use ChatGPT, either due to perceived benefits or other positive factors, are more likely to engage in actual usage behavior. Conversely, those with weaker intentions, perhaps due to privacy concerns or feelings of guilt, are less likely to use ChatGPT.

This hypothesis, grounded in TPB, is instrumental in linking the various enabling and inhibiting factors to the actual use of ChatGPT. It helps to consolidate the theoretical framework of the study, enabling a more holistic understanding of the behavioral dynamics at play. Moreover, it underscores the importance of cultivating positive behavioral intentions among students to encourage the actual use of AI technologies in education.

By integrating the TPB into the analysis, this study not only aligns with established psychological theories of behavior but also expands the application of these theories in the context of AI technology adoption in education.

• Behavioral intention

The relationship between behavioral intention and actual behavior is a fundamental principle in various behavior prediction models, most notably the TPB by Ajzen ( 1991 ). The TPB posits that behavioral intention is the most proximal determinant of actual behavior. The assumption is that individuals act in accordance with their intentions, given that they have sufficient control over the behavior. Empirical studies have provided substantial evidence to support this link (Gatzioufa & Saprikis, 2022 ; Sun & Jeyaraj, 2013 ). In the context of ChatGPT usage among university students, it is therefore expected that higher intentions to use ChatGPT would translate into actual usage behavior. Therefore, drawing on existing literature and theoretical models, this study suggests the following hypothesis:

H12. Behavioral intention has a positive effect on behavior.

Methodology

The instrument for this study was carefully crafted, leveraging a structured questionnaire to assess the influential factors in university students’ behavioral intentions and actual use of ChatGPT. Items in the questionnaire were adopted and adapted from the existing literature, ensuring their validity and relevance in examining the constructs of interest. Each item in the questionnaire was evaluated using a seven-point Likert scale, with 1 representing ‘strongly disagree’ and 7 denoting ‘strongly agree’. This provided a nuanced measurement of the participants’ perceptions and intentions. The survey questions were meticulously designed based on established theories and previous studies in the field, ensuring relevance to our research objectives.

Table A1 outlines a comprehensive list of constructs and their associated items used in the study, detailing how each construct is measured through specific statements. The constructs include self-learning, knowledge acquisition, knowledge application, personalization, novelty value, individual impact, benefits, privacy concern, technophobia, guilt feeling, innovativeness, behavioral intention, and behavior. Each item is attributed to sources from recent research, evidencing the rigorous grounding of these measures in existing literature. For instance, self-learning items are drawn from Chen et al. ( 2022 ), indicating the AI’s capability to enhance its functions through learning. Knowledge acquisition and application items reflect the user’s ability to gain and use knowledge via ChatGPT, sourced from Al-Emran et al. ( 2018 ) and Al-Sharafi et al. ( 2022 ), respectively. Personalization items, derived from Chen et al. ( 2022 ), measure ChatGPT’s ability to tailor responses to individual users. The novelty value construct, sourced from Hasan et al. ( 2021 ), captures the unique experience of using ChatGPT. Individual impact and benefits items, referenced from various authors, assess the practical advantages of using ChatGPT in educational settings. Privacy concern and technophobia items address potential user apprehensions, while guilt feeling items, a novel addition from Masood et al. ( 2022 ), explore emotional reactions to using AI in education. Innovativeness items, cited from Agarwal and Prasad ( 1998 ), examine user openness to new technologies. Behavioral intention and behavior constructs, finally, measure users’ future use intentions and current use patterns, demonstrating the study’s comprehensive approach to understanding AI chatbot utilization among university students.

The survey items underwent a rigorous validation process involving academic experts and a pilot study with a subset of the target population to refine their clarity and applicability. Prior to the actual distribution of the survey, a pretest was carried out with a select group of students to ensure the clarity and comprehensibility of the items. Feedback from this pretest was taken into account, and minor revisions were made to enhance the clarity of certain questions. The refined questionnaire was then disseminated to a broader group of university students, with the collected responses forming the primary data for this study. These responses were meticulously analyzed to understand the role of various factors on students’ behavioral intention and their actual usage of ChatGPT.

Questionnaire Design and Data Collection

The questionnaire was structured in two sections. The first section collected demographic information about the respondents, including their age, gender, and field of study, to capture the heterogeneity of the sample. The second section was dedicated to evaluating the constructs related to the study, each measured using multiple items.

Once the final version of the questionnaire was prepared, it was disseminated among university students. The data for this study was collected via a Google survey form. To reach a broad range of university students, the survey was disseminated through multiple channels. First, it was sent out to a network of professors at several universities. These professors then distributed the survey form to their students. During the survey phase, we explained the purpose of our study to professors across the country, asking for their assistance in distributing the survey if their students were actively using ChatGPT and were willing to participate. Additionally, some professors further shared the survey with their professional networks, expanding our reach. This approach was selected to ensure a diverse and representative sample of students from various disciplines and academic levels. Approximately 400 students were initially approached through this network. This method was chosen over others to leverage the existing academic networks, ensuring a broad yet relevant participant base for our study, reflecting diverse experiences with ChatGPT across different universities. In addition to professors, the survey was also shared within various university communities, such as student groups and clubs. This allowed the study to reach students who might not have been reached through the professors’ networks. Finally, the survey was distributed within ChatGPT community groups. This was especially important as it enabled the study to capture the perspectives of students who were already familiar with or using ChatGPT, thereby enriching the study’s insights into the factors affecting students’ behavioral intentions towards using AI tools like ChatGPT. Responses were collected over a period of several weeks, with a total of 273 responses received. The sample size of this study, 273, is considered adequate and appropriate given the number of latent variables and observation variables in the model. The literature suggests that a higher sample size is always better in SEM-based studies due to its effect on statistical power (Hair et al., 2021 ). In this case, the ratio of sample size to the number of latent variables (approximately 21:1) is within acceptable limits. Moreover, the sample size also exceeds the minimum thresholds suggested by other guidelines. For instance, Hair et al. ( 2021 ) suggested a minimum sample size of 10 times the number of items in the scale with the most items. Considering that the scale with the most items in this study contains 3 items, the sample size of 273 is well above the minimum threshold. The data, then, were analyzed.

Table  1 presents sample details. The gender split among respondents was nearly even, with 123 males (45.1%) and 150 females (54.9%), ensuring diverse gender perspectives. Age-wise, respondents ranged from under 20 to over 23 years. The largest groups were those under 20 (42.9%) and over 23 (37.4%), with 21- and 22-year-olds constituting 9.2% and 10.6%, respectively. Most respondents (89.4%) were undergraduates, with postgraduates making up 10.6%, reflecting the primary academic users of AI tools like ChatGPT. In terms of study majors, Business (44.3%) was predominant, followed by Humanities and Social Sciences (22.7%), and Science (22.0%). Arts, Sports, and Military majors each represented 5.5%. The analysis of descriptive statistics was conducted using the pivot table feature in Excel.

The theoretical framework for this study was evaluated utilizing the Partial Least Squares (PLS) approach via SmartPLS 4 software. The PLS method is a prevalent choice within the Information Systems/Information Technology domain, as substantiated by (Hair et al., 2014 ). One of the principal benefits of PLS lies in its fewer constraints concerning sample size distribution and residuals relative to covariance-based structural equation techniques such as LISREL and AMOS, as highlighted by (Hair et al., 2021 ). Our analysis employed a three-step strategy, encompassing common method bias (CMB), the measurement model, and the structural model.

Common Method Bias (CMB)

CMB refers to the spurious effect that can occur when both predictor and criterion variables are collected from the same source at the same time. It can potentially inflate or deflate the observed relationships between the constructs, leading to biased results. Thus, addressing the issue of CMB is critical to ensuring the validity of the study’s findings. In this study, we took several procedural and statistical steps to address the potential issue of CMB. Procedurally, we ensured the anonymity of the respondents and the confidentiality of their responses. We also used clear and simple language in the survey to minimize any ambiguity in the questions.

Statistically, we employed the Harman’s single factor test, which is a commonly used method to detect CMB (Podsakoff et al., 2003 ). The basic premise of this test is that if a single factor emerges from a factor analysis of all the items in the questionnaire or one general factor accounts for the majority of the covariance among the measures, then common method variance is a problem. In our case, the first of included factors explained 33.88% of the total variance, suggesting that CMB is not a substantial concern in this study. Additionally, we used the marker variable technique, which involves selecting a variable that is theoretically unrelated to other variables in the study and checking for its correlation with them. If significant correlations exist, they might be due to common method variance. Our marker variable showed negligible correlations with the study variables, further indicating that CMB is unlikely to have significantly influenced our results. In conclusion, both procedural and statistical checks suggest that CMB is not a major issue in this study, and the relationships identified between the constructs can be interpreted with confidence.

Measurement model

The measurement model in this study was assessed by examining the reliability, convergent validity, and discriminant validity of the constructs. Reliability was assessed using composite reliability (CR) and Cronbach’s alpha (CA). The values for both CR and CA should exceed the threshold of 0.7, indicating acceptable reliability. In our study, all constructs exceeded this threshold, demonstrating good reliability. Convergent Validity is the extent to which a measure correlates positively with alternate measures of the same construct. It was assessed using the average variance extracted (AVE) and the factor loadings of the indicators. All AVE values should be greater than 0.5, and all factor loadings should be significant and exceed 0.7. In our study, all constructs demonstrated satisfactory convergent validity as they met these criteria. Table  2 describes the test results of reliability and validity.

Discriminant validity refers to the extent to which a construct is truly distinct from other constructs. This was examined using the Fornell-Larcker criterion and the heterotrait-monotrait ratio (HTMT). According to the Fornell-Larcker criterion, the square root of the AVE of each construct should be greater than its highest correlation with any other construct. Table  3 shows the results of Fornell-Larcker criterion.

Table  4 displays the HTMT matrix. Notably, knowledge acquisition and knowledge application showed a 0.954 HTMT value, above the 0.85 norm. Despite exceeding the threshold, these values suggest a moderate overlap. Although related, the constructs are distinct: knowledge acquisition involves generating new knowledge, while knowledge application focuses on immediate knowledge integration. Given their different roles in AI-assisted learning, and supported by literature allowing higher HTMT values within the same network, both constructs are retained in our study. Future research should further investigate their differentiation.

Structural model

A Structural Equation Modeling (SEM) analysis was carried out to scrutinize the hypothesized interconnections among the constructs using Partial Least Squares (PLS). For hypothesis testing and path coefficient determination, this study employed a bootstrapping procedure, setting the subsample size at 5000. Overall, the structural model describes approximately 38.0% of the variability in behavior. Figure  2 ; Table  5 details of the significance test results of SEM.

The Structural Model with Path Coefficients

The results of this study confirmed a positive correlation between self-learning of ChatGPT and knowledge acquisition and application, consistent with prior research on AI-driven learning tools (H1a, H1b) (Jarrahi et al., 2023 ). The results mean that as students engage with ChatGPT, they acquire new knowledge, which is then processed and incorporated into their existing knowledge base. Also, interacting with ChatGPT not only helps students gain new knowledge but also aids them in applying this knowledge in various scenarios, consequently resulting in a higher individual impact. The ability of AI to leverage vast data and improve problem-solving provides a fertile ground for enhanced learning. Yet, the novelty of this finding in the context of AI like ChatGPT calls for more studies to solidify this understanding.

Additionally, the results revealed that knowledge acquisition and application significantly influenced individual impact (H2, H3), mirroring the findings of Arpaci et al., (2020), Al-Emran et al. ( 2018 ), Al-Emran and Teo ( 2020 ), and Alavi and Leidner ( 2001 ). The findings suggest that as learners more actively gain knowledge from ChatGPT, they experience improvements in their productivity, task completion speed, and overall performance. Further, as learners effectively utilize the knowledge acquired from ChatGPT, they can better achieve their goals and enhance their work capabilities. This reinforces the importance of AI tools in achieving task completion and boosting productivity. Nevertheless, the variance in individual impact explained by these two factors indicates that other elements may also be at play.

Personalization was found to significantly correlate with novelty value and benefits (H4a, H4b), supporting Kapoor et al.‘s assertion of personalized experiences driving perceived value (Kapoor & Vij, 2018 ). As well the results are keeping with the observations in previous works (Haleem et al., 2022 ; Koubaa et al., 2023 ). The analysis suggests that the tailored experience delivered by ChatGPT is perceived as novel by the users. As users find these tailored experiences new and intriguing, the novelty value of the AI tool is enhanced. Similarly, as the AI tool continuously learns and adapts to individual users’ needs, it helps learners achieve their learning objectives more effectively and efficiently. This aligns with the perception of increased benefits, as users recognize the tool’s contribution to their learning outcomes. However, the degree of this impact varies, suggesting that personalization’s impact may be contingent on other factors that need exploration.

The positive effect of novelty value on perceived benefits (H5) found in this study aligns with diffusion theory (Rogers, 2010 ) and past study on novelty of AI (Jo, 2022 ). The unique and different learning experience that ChatGPT offers might stimulate users’ curiosity and interest in learning. It could make the learning process more enjoyable, keeping users engaged and motivated, which, in turn, enhances their learning outcomes and the perceived benefits from using the chatbot. Yet, the relative strength of this relationship indicates that novelty may not be the only driver of perceived benefits.

Regarding the relationship between individual impact and benefits, and behavioral intention (H6a, H6b), our findings are in line with the TAM (Davis, 1989 ), further solidifying its relevance in the AI context. These findings imply that when users perceive a higher individual impact from using ChatGPT (i.e., improved efficiency, productivity, or task accomplishment), they also perceive more benefits and express stronger intentions to continue using it. Moreover, the immediate positive effects of using ChatGPT, such as accomplishing tasks more quickly or increasing productivity, directly contribute to users’ willingness to continue using it. Yet, the relative effect sizes suggest that while individual impact affects both outcomes, its influence is stronger on behavioral intention, a facet worthy of further examination.

In line with prior studies (de Cosmo et al., 2021 ; Lutz & Tamò-Larrieux, 2021 ; Zhu et al., 2022 ), privacy concern was negatively associated with behavioral intention (H8). The results coincide with the privacy calculus theory (Dienlin & Metzger, 2016 ; Dinev & Hart, 2006 ), which suggests that when privacy concerns outweigh the perceived benefits, it can deter future usage intention. In congruent with previous findings (Marakas et al., 1998 ; Selwyn, 2013 ), this study found technophobia negatively impacting behavioral intention (H9), adding a novel dimension to our understanding of AI acceptance. This result highlights the need for easy-to-use, user-friendly interfaces and perhaps educational programs to mitigate fear and promote user confidence when engaging with AI chatbots. Similarly, the guilt feeling affecting behavioral intention (H10) provides a new angle for exploration in AI usage context.

Lastly, the results confirmed the strong effect of behavioral intention on actual behavior (H11), as suggested by (Ajzen, 1991 ). Also, innovativeness positively affected behavioral intention and behavior (H12a, H12b), adding to the growing literature on innovation adoption (Rogers, 2010 ). In the context of using AI chatbots, such as ChatGPT, students who are more innovative (who have higher levels of innovativeness) demonstrated a stronger intention to use and an actual higher usage of the chatbot. This could be interpreted to mean that these students are more inclined to explore and make use of new technologies in their learning processes, which in turn influences their behavior in a positive manner. Furthermore, the positive correlation between innovativeness and behavioral intention may also be linked to the tendency of innovative individuals to perceive less risk in trying out new technologies. This lack of perceived risk, coupled with their natural proclivity towards novelty, may increase their intention to utilize AI chatbots. However, the precise mechanisms through which innovativeness operates in the AI context warrant further study.

Theoretical contributions

The current study, in its quest to uncover the relationships between AI chatbot variables and their effects on university students, has made several valuable theoretical contributions to the existing body of literature on AI and education. Prior to this research, much of the literature focused on the use of AI chatbots in commercial and customer service settings, leaving the educational context relatively unexplored (Ashfaq et al., 2020 ; Chung et al., 2020 ; Pillai & Sivathanu, 2020 ). This study has filled this gap by focusing specifically on university students and their interaction with AI chatbots, thereby providing new insights and extending the knowledge boundary of the AI field to encompass the educational sector.

Additionally, this study’s emphasis on the self-learning capabilities of ChatGPT as a significant determinant of knowledge acquisition and application among students is a significant contribution to the existing body of literature. Previous research primarily centered on the chatbot’s features and functionalities (Haleem et al., 2022 ; Hocutt et al., 2022 ), leaving the learning capabilities of these AI systems underexplored. By focusing on the self-learning feature of ChatGPT, this study has expanded the discourse on AI capabilities and their impact on knowledge dissemination in the educational context.

This research significantly contributes to the understanding of innovativeness’s impact on behavioral intention and behavior in AI chatbot usage. While prior studies focused mainly on business contexts (BARIŞ, 2020 ; Heo & Lee, 2018 ; Selamat & Windasari, 2021 ), applying these insights to AI chatbots is relatively new, marking this study as a pioneer. Innovativeness as a determinant in this realm is novel for several reasons. First, it shifts focus from technology attributes to user characteristics, highlighting individual differences in technology adoption often overshadowed by technocentric views. Second, our study provides empirical evidence that innovativeness positively influences both behavioral intention and behavior, challenging previous assertions that limited its influence to early adoption stages (Agarwal & Prasad, 1998 ). By applying innovativeness in an AI chatbot context, with its unique interactional dynamics, the research broadens the theoretical construct of innovativeness, emphasizing the relevance of individual traits in emerging technology usage. Furthermore, this study’s integration of innovativeness into the research model encourages future research to consider other personal traits affecting technology usage, promoting a more comprehensive, user-centric approach to technology adoption. Hence, exploring innovativeness in AI chatbot usage offers significant theoretical insights and opens avenues for future research in technology usage behaviors.

This research makes a notable theoretical contribution by exploring the negative effects of privacy concern, technophobia, and guilt feeling on behavioral intention towards AI chatbot use, areas often overlooked in favor of positive influences (Ashfaq et al.  2020 ; Huang, 2021 ; Nguyen et al., 2021 ; Rafiq et al. 2022 ). Privacy concern, consistent with prior studies (Dinev & Hart, 2006 ), is critically examined in AI chatbots, where data privacy is paramount due to the potential for extensive personal data sharing. This study extends understanding by focusing on AI chatbots, a context where privacy concerns have heightened relevance. Technophobia’s impact, previously explored in technology adoption (Khasawneh, 2022 ; Kotze et al., 2016 ; Koul & Eydgahi, 2020 ), is uniquely applied to AI chatbots, shedding light on how fear and anxiety towards advanced technology can affect usage intentions. This perspective enriches existing knowledge by situating technophobia within the realm of cutting-edge AI technologies. Guilt feeling, a relatively unexplored concept in technology use, especially in AI chatbots, is also investigated. This study reveals how guilt, potentially stemming from AI reliance for learning or work, can deter usage intention, thus addressing a significant gap in technology adoption research.

This research notably contributes to theory by examining the effects of innovativeness on behavioral intention and behavior in AI chatbot usage, a relatively unexplored area, especially outside of business contexts (BARIŞ, 2020 ; Heo & Lee, 2018 ; Selamat & Windasari, 2021 ). This study stands out as one of the first to apply these concepts to AI chatbots, marking a novel approach in several ways. Firstly, it shifts the focus from technology attributes to user characteristics, emphasizing individual differences in technology adoption—a perspective often overshadowed by a technocentric focus. This approach diverges from traditional technology acceptance models, spotlighting the user’s innovativeness. Secondly, by demonstrating empirically that innovativeness positively affects both behavioral intention and behavior, this research underscores the dynamic role of this personal trait in driving technology usage. Furthermore, the study broadens the theoretical construct of innovativeness by applying it to the unique interactional dynamics of AI chatbots, thereby highlighting the relevance of individual traits in using emerging technologies. Lastly, integrating innovativeness into the research model sets a precedent for future studies to include other personal traits or user characteristics that may influence technology usage behavior. This approach promises a more holistic, user-centric understanding of technology adoption and use. Overall, exploring the impact of innovativeness in AI chatbot usage provides valuable theoretical insights and opens avenues for future research, enhancing our understanding of technology adoption in this rapidly evolving field.

The research significantly enhances theoretical understanding by substantiating the relationships between constructs like knowledge acquisition and application, individual impact, and benefits in the AI chatbot context. Previously, educational and organizational literature recognized the importance of knowledge acquisition and application for performance (Al-Emran et al., 2018 ; Al-Emran & Teo, 2020 ; Bhatt, 2001 ; Grant, 1996 ; Heisig, 2009 ). This study extends these principles to AI chatbot usage, underscoring their critical role in effective technology utilization. The study’s innovation lies in demonstrating how these cognitive processes interact with the individual impact construct. This interaction offers a comprehensive view of a user’s learning journey within the AI chatbot environment. Furthermore, the research goes beyond by illustrating that user-perceived benefits are influenced by both AI chatbot personalization and individual impact, the latter being a synergistic result of knowledge acquisition and application. This interplay provides a nuanced view of the factors contributing to perceived benefits, deepening our theoretical grasp of what renders AI chatbot usage beneficial. By presenting a complex, interconnected model of AI chatbot usage, this research contributes to a more thorough understanding of the dynamics involved. It encourages a multi-dimensional approach in examining AI chatbots’ adoption and usage factors. The findings also prompt further scholarly inquiry into how these relationships vary with context, AI chatbot type, or user characteristics. Thus, this study lays a foundation for future research, aiming to enrich the comprehension of AI chatbot usage dynamics.

Lastly, this study’s comprehensive research model, encompassing multiple constructs and their interrelationships, serves as a robust theoretical framework for future research in AI chatbot usage in educational settings. The findings of this study, highlighting the significance of various constructs and their relationships, provide a roadmap for scholars, guiding future research to better understand the dynamics of AI chatbot usage and its effects on students’ learning experiences. Furthermore, our research model takes a holistic approach by combining both positive and negative predictors of behavioral intention. This approach can offer a more comprehensive understanding of the intricate factors influencing AI chatbot use among university students.

Managerial implications

The findings of this study have numerous practical implications, particularly for educators, students, and developers of AI chatbot technologies.

Firstly, the results underscore the potential benefits of employing AI chatbots, like ChatGPT, as supplementary learning tools in educational settings. Given the significant positive effect of ChatGPT’s self-learning capabilities on knowledge acquisition and application among students, educators could consider integrating such AI chatbots into their teaching methods (Essel et al., 2022 ; Mohd Rahim et al., 2022 ; Vázquez-Cano et al., 2021 ). The integration of AI chatbots could take various forms, such as using chatbots to provide additional learning resources, facilitate interactive learning exercises, or offer personalized tutoring. Given the increasingly widespread use of online and blended learning methods in education (Crawford et al., 2020 ), the potential of AI chatbots to enhance students’ learning experiences should not be overlooked.

Secondly, this study’s findings may serve as a guide for students in maximizing their learning outcomes while using AI chatbots. Understanding that the self-learning capabilities of chatbots significantly enhance knowledge acquisition and application, students could be more inclined to utilize these tools for learning (Al-Sharafi et al., 2022 ). Furthermore, recognizing that personalization contributes positively to novelty value and benefits might motivate students to engage more deeply with personalized learning experiences offered by AI chatbots.

Thirdly, the present study’s results offer crucial insights into key features that users find valuable in an AI chatbot within an educational context. The demonstrated significance of self-learning capabilities, personalization, and novelty value indicates distinct areas that developers can focus on to amplify user experience and educational outcomes. Understanding the paramount importance of self-learning capabilities can shape the development strategies of AI chatbots. For instance, developers can focus on improving the ability of AI chatbots like ChatGPT to learn from user interactions, thereby enhancing the quality of responses over time. It would require advanced machine learning algorithms to grasp the context of user inquiries accurately, ensuring a consistent learning experience for the users. As noted by (Komiak & Benbasat, 2006 ), the AI’s ability to learn and adapt based on user input and data over time can lead to improved user satisfaction and ultimately, enhanced learning outcomes. Personalization is another pivotal feature that developers need to consider. This could involve customizing chatbot responses according to the user’s level of understanding, learning pace, and areas of interest. Personalization also extends to recognizing users’ specific learning goals and offering resources or guidance to help them achieve these goals. This feature can increase the value that users derive from their interactions with the chatbot (Jo, 2022 ). Lastly, the novelty value of AI chatbots in educational contexts cannot be underestimated. Developers can capitalize on this by introducing innovative features and interaction modes that keep users engaged and make learning exciting. Such features could include gamified learning activities, integration with other learning resources, and more interactive and responsive dialogue systems. It is well established in literature that perceived novelty can positively impact users’ attitudes and behaviors towards a new technology (Hasan et al., 2021 ; Jo, 2022 ).

Fourthly, the study’s findings related to the negative impact of privacy concerns, technophobia, and guilt feelings on behavioral intentions provide crucial insights for AI chatbot developers and educational institutions. Privacy concerns form a significant barrier to the acceptance of AI technologies in various fields, including education (Belen Saglam et al., 2021 ; Ischen et al., 2020 ; Manikonda et al., 2018 ). The concerns typically arise from the vast amounts of personal data AI systems gather, analyze, and store (Kokolakis, 2017 ). In response, developers can strengthen the privacy features of AI chatbots, clearly communicate their data handling practices to users, and ensure compliance with stringent data protection regulations. For instance, incorporating robust encryption methods, anonymization techniques, and allowing users to control the data they share can allay privacy concerns. Educational institutions can also provide guidance on safe and responsible use of AI technologies to further assuage these concerns. Technophobia, or the fear of technology, can also hinder the acceptance and use of AI chatbots. To address this, developers can design user-friendly interfaces, provide extensive user support, and gradually introduce advanced features to avoid overwhelming users. Moreover, educational institutions can play a vital role in mitigating technophobia by offering appropriate training and support to help students become comfortable with using these technologies. It has been suggested that familiarity and understanding significantly reduce technophobia (Khasawneh, 2018a ; Xi et al., 2022 ). Guilt feeling, as revealed in this study, is another factor that negatively influences behavioral intention. This can occur when users feel guilty about relying heavily on AI chatbots for learning or assignments. To address this, educational institutions should foster an environment that encourages balanced use of AI tools. This could involve providing guidelines on ethical AI use, setting boundaries on AI tool usage in assignments, and integrating AI tools as supplemental rather than primary learning resources.

Finally, the significant impact of perceived benefits and individual impact on behavioral intentions underscores the importance of demonstrating the tangible benefits of AI chatbot use in education to users. Making these benefits clear to users could encourage greater adoption and more effective use of AI chatbots in educational settings.

Limitation and Future Research

While this study makes several important contributions to our understanding of AI chatbots’ use in an educational context, it also opens up avenues for further investigation. The study’s primary limitation is its focus on university students, which may limit the generalizability of the findings. Future studies could explore different demographics, such as younger students, adult learners, or professionals engaging in continuous education, to provide a broader understanding of AI chatbot utilization in diverse learning contexts. The scope of the research could also be extended to include various types of AI chatbot technologies. As AI continues to advance, chatbots are becoming more diverse in their capabilities and functionalities. Hence, research considering different kinds of AI chatbots, their specific features, and their effects on learning outcomes could provide further insight. Moreover, this study primarily focuses on individual impacts and user perceptions. Future research could also look into institutional perspectives and the macro-level impacts of AI chatbot adoption in education. For instance, studies could investigate how the implementation of AI chatbots affects teaching strategies, curriculum development, and institutional resource allocation. Lastly, it would be insightful to conduct longitudinal studies to understand the long-term effects of AI chatbot usage on student performance and attitudes towards AI technologies. This approach could reveal trends and impacts that may not be immediately apparent in cross-sectional studies.

Data availability

The data used in this study are available from the corresponding author upon reasonable request.

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Jo, H. From concerns to benefits: a comprehensive study of ChatGPT usage in education. Int J Educ Technol High Educ 21 , 35 (2024). https://doi.org/10.1186/s41239-024-00471-4

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