higher education articles 2022

4 trends that will shape the future of higher education

Higher education needs to address the problems it faces by moving towards active learning, and teaching skills that will endure in a changing world. Image:  Vasily Koloda for Unsplash

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• Measures adopted during the pandemic do not address the root causes of the problems facing higher education.
• Institutions need to undertake true reform, moving towards active learning, and teaching skills that will endure in a changing world.
• Formative assessment is more effective than high-stakes exams in equipping students with the skills they need to succeed.

Since the onset of the recent pandemic, schools and universities have been forced to put a lot of their teaching online. On the surface, this seems to have spurred a series of innovations in the education sector. Colleges around the world embraced more flexibility, offering both virtual and physical classrooms. Coding is making its way into more school curricula , and the SAT exam for college admission in the US has recently been shortened and digitized , making it easier to take and less stressful for students.

These changes might give the illusion that education is undergoing some much-needed reform. However, if we look closely, these measures do not address the real problems facing higher education. In most countries, higher education is inaccessible to the socio-economically underprivileged, certifies knowledge rather than nurtures learning, and focuses on easily-outdated knowledge. In brief, it is failing on both counts of quality and access.

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Four ways universities can future-proof education, the global education crisis is even worse than we thought. here's what needs to happen, covid-19’s impact on jobs and education: using data to cushion the blow, higher education trends.

In the last year, we have started to see examples of true reform, addressing the root causes of the education challenge. Below are four higher education trends we see taking shape in 2022.

1. Learning from everywhere

There is recognition that as schools and universities all over the world had to abruptly pivot to online teaching, learning outcomes suffered across the education spectrum . However, the experiment with online teaching did force a reexamination of the concepts of time and space in the education world. There were some benefits to students learning at their own pace, and conducting science experiments in their kitchens . Hybrid learning does not just mean combining a virtual and physical classroom, but allowing for truly immersive and experiential learning, enabling students to apply concepts learned in the classroom out in the real world.

So rather than shifting to a “learn from anywhere ” approach (providing flexibility), education institutions should move to a “learn from everywhere ” approach (providing immersion). One of our partners, the European business school, Esade, launched a new bachelor’s degree in 2021, which combines classes conducted on campus in Barcelona, and remotely over a purpose-designed learning platform, with immersive practical experiences working in Berlin and Shanghai, while students create their own social enterprise. This kind of course is a truly hybrid learning experience.

2. Replacing lectures with active learning

Lectures are an efficient way of teaching and an ineffective way of learning. Universities and colleges have been using them for centuries as cost-effective methods for professors to impart their knowledge to students.

However, with digital information being ubiquitous and free, it seems ludicrous to pay thousands of dollars to listen to someone giving you information you can find elsewhere at a much cheaper price. School and college closures have shed light on this as bad lectures made their way into parents’ living rooms, demonstrating their ineffectiveness.

Education institutions need to demonstrate effective learning outcomes, and some are starting to embrace teaching methods that rely on the science of learning. This shows that our brains do not learn by listening, and the little information we learn that way is easily forgotten (as shown by the Ebbinghaus forgetting curve , below). Real learning relies on principles such as spaced learning, emotional learning, and the application of knowledge.

The educational establishment has gradually accepted this method, known as 'fully active learning'. There is evidence that it not only improves learning outcomes but also reduces the education gap with socio-economically disadvantaged students. For example, Paul Quinn College, an HBCU based in Texas, launched an Honors Program using fully active learning in 2020, combined with internships at regional employers. This has given students from traditionally marginalised backgrounds the opportunity to apply the knowledge gained at university in the real world.

3. Teaching skills that remain relevant in a changing world

According to a recent survey, 96% of Chief Academic Officers at universities think they are doing a good job preparing young people for the workforce . Less than half (41%) of college students and only 11% of business leaders shared that view. Universities continue to focus on teaching specific skills involving the latest technologies, even though these skills and the technologies that support them are bound to become obsolete. As a result, universities are forever playing catch up with the skills needed in the future workplace.

What we need to teach are skills that remain relevant in new, changing, and unknown contexts. For example, journalism students might once have been taught how to produce long-form stories that could be published in a newspaper; more recently, they would have been taught how to produce shorter pieces and post content for social media. More enduring skills would be: how to identify and relate to readers, how to compose a written piece; how to choose the right medium for your target readership. These are skills that cross the boundaries of disciplines, applying equally to scientific researchers or lawyers.

San Francisco-based Minerva University, which shares a founder with the Minerva Project, has broken down competencies such as critical thinking or creative thinking into foundational concepts and habits of mind . It teaches these over the four undergraduate years and across disciplines, regardless of the major a student chooses to pursue.

4. Using formative assessment instead of high-stake exams

If you were to sit the final exam of the subject you majored in today, how would you fare? Most of us would fail, as that exam did not measure our learning, but rather what information we retained at that point in time. Equally, many of us hold certifications in subject matters we know little about.

Many people gain admission to higher education based on standardized tests that skew to a certain socio-economic class , rather than measure any real competency level. Universities then try to rectify this bias by imposing admission quotas, rather than dissociating their evaluation of competence from income level. Many US universities are starting to abandon standardized tests, with Harvard leading the charge , and there have been some attempts to replace high-stake exams with other measures that not only assess learning outcomes but actually improve them.

Formative assessment, which entails both formal and informal evaluations through the learning journey, encourages students to actually improve their performance rather than just have it evaluated. The documentation and recording of this assessment includes a range of measures, replacing alphabetical or numerical grades that are uni-dimensional.

The COVID-19 pandemic and recent social and political unrest have created a profound sense of urgency for companies to actively work to tackle inequity.

The Forum's work on Diversity, Equality, Inclusion and Social Justice is driven by the New Economy and Society Platform, which is focused on building prosperous, inclusive and just economies and societies. In addition to its work on economic growth, revival and transformation, work, wages and job creation, and education, skills and learning, the Platform takes an integrated and holistic approach to diversity, equity, inclusion and social justice, and aims to tackle exclusion, bias and discrimination related to race, gender, ability, sexual orientation and all other forms of human diversity.

The Platform produces data, standards and insights, such as the Global Gender Gap Report and the Diversity, Equity and Inclusion 4.0 Toolkit , and drives or supports action initiatives, such as Partnering for Racial Justice in Business , The Valuable 500 – Closing the Disability Inclusion Gap , Hardwiring Gender Parity in the Future of Work , Closing the Gender Gap Country Accelerators , the Partnership for Global LGBTI Equality , the Community of Chief Diversity and Inclusion Officers and the Global Future Council on Equity and Social Justice .

The International School in Geneva just launched its Learner Passport that includes measures of creativity, responsibility and citizenship. In the US, a consortium of schools have launched the Mastery Transcript Consortium that has redesigned the high school transcript to show a more holistic picture of the competencies acquired by students.

Education reform requires looking at the root cause of some of its current problems. We need to look at what is being taught (curriculum), how (pedagogy), when and where (technology and the real world) and whom we are teaching (access and inclusion). Those institutions who are ready to address these fundamental issues will succeed in truly transforming higher education.

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What's the best state for you », how online learning is reshaping higher education.

As the pandemic eases, many institutions are realizing that properly planned online platforms will allow them to better serve all students, including nontraditional learners.

Online Learning Is Reshaping Higher Ed

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“The nice thing about online education is that it can actually escape geographical boundaries,” said Don Kilburn, the CEO of UMass Online.

Two years ago, as COVID-19 caused campuses to close, some institutions were able to shift their students to already robust online learning programs. But many other colleges and universities scrambled to build online education curricula from scratch. Students and faculty often found themselves logging onto Zoom or other platforms for the first time, with little knowledge of how to navigate a new world of virtual learning.

“When the pandemic hit, it was a provocation, as well as a demand for innovation,” said Caroline Levander, the vice president for global and digital strategy at Rice University in Houston, during a recent webinar on the future of online learning hosted by U.S. News & World Report.

While the changes were challenging for many, faculty members at Rice and elsewhere embraced the new opportunities that online learning offered. Levander shared an example of a Rice physics professor, Jason Hafner, who capitalized on the virtual environment to find compelling new ways to teach concepts to students.

“He had been innovating with online delivery in our non-credit offerings before the pandemic,” said Levander. But once COVID-19 spread, Hafner moved beyond the walls of his classroom and took advantage of Rice’s physical campus to enhance his teaching with video-recorded experiments conducted outside of normal class times. For example, in one lesson, he climbed atop a rock edifice in Rice’s engineering quad to drop two equally sized spheres – one made of aluminum and the other of steel – to demonstrate that they would fall with the same acceleration despite their different densities.

Now, many educators are reassessing how virtual learning can further enhance the student experience by offering greater flexibility than in-class options, particularly for hybrid and all-virtual instruction models. During the early days of the pandemic, “people stood up Zoom classrooms” and “they put a lot of video lectures up online,” said Jeff Borden, the chief academic officer for D2L, a company that creates online learning software. “That’s fine. That was important to get people through.” Now, however, Borden stressed, colleges and universities have the opportunity to move beyond these makeshift models. They can work to build more durable online learning platforms that meet the needs of a range of learners who must access coursework at different times and in different formats to suit their particular goals and lifestyles.

While a four-year college education can be thought of as a default for many, there are a lot of people for whom “that’s not the right path,” said Borden. In fact, some students may be looking simply to gain credentials or to upskill, rather than get traditional degrees. “There are tens of millions of other people in our society who have needs that are other than that, who have desires that are different than that,” Borden noted. Online learning now enables older students, working adults, people from nontraditional backgrounds and those who might be neurodiverse to access content more easily than ever before, Borden added.

The multitude of options also extends to graduate and professional schools, many of which have rolled out fully or partially online programs in recent years. In fact, applicants to Rice’s fully online master’s degree program are “much more diverse in every way than students who apply to the residential counterpart,” Levander said, because access is made easier and more compatible to students who may be juggling work and family obligations.

“The nice thing about online education is that it can actually escape geographical boundaries,” said Don Kilburn, the CEO of UMass Online, which has offerings across the five University of Massachusetts schools. Kilburn agreed with his fellow panelists that online learning models play a critical role in broadening access. He also emphasized the potential added benefit of lessening the financial burden on students, since online programs can often cost a fraction of in-person ones. “Part of accessibility is affordability,” he said. “I do think there are ways to actually deliver fully online programs that have a lower cost structure and may actually reduce the cost of education significantly.”

Part of serving the needs of those who choose to attend classes online means understanding why they do so and how their needs differ from those who choose traditional, in-person options, said Nancy Gonzales, the executive vice president and university provost at Arizona State University , whose online programs will reach approximately 84,000 students this year.

Many online students choose to take fewer courses at a time and may take semesters off to accommodate other aspects of their lives like taking care of children or work responsibilities – part of why the flexibility of online learning is so appealing, Gonzales said. “We’ve been trying to really try to understand what is the cadence of attendance and how do we meet the needs of students, because they are a very different population,” said Gonzales.

At the same time, for Gonzales, part of what makes an online education model successful is providing students with comparable support and services to what they might receive through in-person instruction. Such services might range from financial aid counseling to ensuring that students can interact with their peers on discussion boards, in order to ensure that interactions with classmates are not lost when attending class online.

But the promise of online education, the panelists agreed, is great. “I think we are just at the beginning of the digital transformation,” said Kilburn. “I can’t tell you when, but at some point you will see a revolution in education like you will in everything else.”

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How technology is shaping learning in higher education

About the authors.

This article is a collaborative effort by Claudio Brasca, Charag Krishnan , Varun Marya , Katie Owen, Joshua Sirois, and Shyla Ziade, representing views from McKinsey’s Education Practice.

The COVID-19 pandemic forced a shift to remote learning overnight for most higher-education students, starting in the spring of 2020. To complement video lectures and engage students in the virtual classroom, educators adopted technologies that enabled more interactivity and hybrid models of online and in-person activities. These tools changed learning, teaching, and assessment in ways that may persist after the pandemic. Investors have taken note. Edtech start-ups raised record amounts of venture capital in 2020 and 2021, and market valuations for bigger players soared.

A study conducted by McKinsey in 2021 found that to engage most effectively with students, higher-education institutions can focus on eight dimensions  of the learning experience. In this article, we describe the findings of a study of the learning technologies that can enable aspects of several of those eight dimensions (see sidebar “Eight dimensions of the online learning experience”).

Eight dimensions of the online learning experience

Leading online higher-education institutions focus on eight key dimensions of the learning experience across three overarching principles.

Seamless journey

Clear education road map: “My online program provides a road map to achieve my life goals and helps me structure my day to day to achieve steady progress.”

Seamless connections: “I have one-click access to classes and learning resources in the virtual learning platform through my laptop or my phone.”

Engaging teaching approach

Range of learning formats: “My program offers a menu of engaging courses with both self-guided and real-time classes, and lots of interaction with instructors and peers.”

Captivating experiences: “I learn from the best professors and experts. My classes are high quality, with up-to-date content.”

Adaptive learning: “I access a personalized platform that helps me practice exercises and exams and gives immediate feedback without having to wait for the course teacher.”

Real-world skills application: “My online program helps me get hands-on practice using exciting virtual tools to solve real-world problems.”

Caring network

Timely support: “I am not alone in my learning journey and have adequate 24/7 support for academic and nonacademic issues.”

Strong community: “I feel part of an academic community and I’m able to make friends online.”

In November 2021, McKinsey surveyed 600 faculty members and 800 students from public and private nonprofit colleges and universities in the United States, including minority-serving institutions, about the use and impact of eight different classroom learning technologies (Exhibit 1). (For more on the learning technologies analyzed in this research, see sidebar “Descriptions of the eight learning technologies.”) To supplement the survey, we interviewed industry experts and higher-education professionals who make decisions about classroom technology use. We discovered which learning tools and approaches have seen the highest uptake, how students and educators view them, the barriers to higher adoption, how institutions have successfully adopted innovative technologies, and the notable impacts on learning (for details about our methodology, see sidebar “About the research”).

Double-digit growth in adoption and positive perceptions

Descriptions of the eight learning technologies.

• Classroom interactions: These are software platforms that allow students to ask questions, make comments, respond to polls, and attend breakout discussions in real time, among other features. They are downloadable and accessible from phones, computers, and tablets, relevant to all subject areas, and useful for remote and in-person learning.
• Classroom exercises: These platforms gamify learning with fun, low-stakes competitions, pose problems to solve during online classes, allow students to challenge peers to quizzes, and promote engagement with badges and awards. They are relevant to all subject areas.
• Connectivity and community building: A broad range of informal, opt-in tools, these allow students to engage with one another and instructors and participate in the learning community. They also include apps that give students 24/7 asynchronous access to lectures, expanded course materials, and notes with enhanced search and retrieval functionality.
• Group work: These tools let students collaborate in and out of class via breakout/study rooms, group preparation for exams and quizzes, and streamlined file sharing.
• Augmented reality/virtual reality (AR/VR): Interactive simulations immerse learners in course content, such as advanced lab simulations for hard sciences, medical simulations for nursing, and virtual exhibit tours for the liberal arts. AR can be offered with proprietary software on most mobile or laptop devices. VR requires special headsets, proprietary software, and adequate classroom space for simultaneous use.
• AI adaptive course delivery: Cloud-based, AI-powered software adapts course content to a student’s knowledge level and abilities. These are fully customizable by instructors and available in many subject areas, including business, humanities, and sciences.
• Machine learning–powered teaching assistants: Also known as chatbot programs, machine learning–powered teaching assistants answer student questions and explain course content outside of class. These can auto-create, deliver, and grade assignments and exams, saving instructors’ time; they are downloadable from mobile app stores and can be accessed on personal devices.
• Student progress monitoring: These tools let instructors monitor academic progress, content mastery, and engagement. Custom alerts and reports identify at-risk learners and help instructors tailor the content or their teaching style for greater effectiveness. This capability is often included with subscriptions to adaptive learning platforms.

Survey respondents reported a 19 percent average increase in overall use of these learning technologies since the start of the COVID-19 pandemic. Technologies that enable connectivity and community building, such as social media–inspired discussion platforms and virtual study groups, saw the biggest uptick in use—49 percent—followed by group work tools, which grew by 29 percent (Exhibit 2). These technologies likely fill the void left by the lack of in-person experiences more effectively than individual-focused learning tools such as augmented reality and virtual reality (AR/VR). Classroom interaction technologies such as real-time chatting, polling, and breakout room discussions were the most widely used tools before the pandemic and remain so; 67 percent of survey respondents said they currently use these tools in the classroom.

About the research

In November 2021, McKinsey surveyed 634 faculty members and 818 students from public, private, and minority-serving colleges and universities over a ten-day period. The survey included only students and faculty who had some remote- or online-learning experience with any of the eight featured technologies. Respondents were 63 percent female, 35 percent male, and 2 percent other gender identities; 69 percent White, 18 percent Black or African American, 8 percent Asian, and 4 percent other ethnicities; and represented every US region. The survey asked respondents about their:

• experiences with technology in the classroom pre-COVID-19;
• experiences with technology in the classroom since the start of the COVID-19 pandemic; and
• desire for future learning experiences in relation to technology.

The shift to more interactive and diverse learning models will likely continue. One industry expert told us, “The pandemic pushed the need for a new learning experience online. It recentered institutions to think about how they’ll teach moving forward and has brought synchronous and hybrid learning into focus.” Consequently, many US colleges and universities are actively investing to scale up their online and hybrid program offerings .

Differences in adoption by type of institution observed in the research

• Historically Black colleges and universities (HBCUs) and tribal colleges and universities made the most use of classroom interactions and group work tools (55 percent) and the least use of tools for monitoring student progress (15 percent).
• Private institutions used classroom interaction technologies (84 percent) more than public institutions (63 percent).
• Public institutions, often associated with larger student populations and course sizes, employed group work and connectivity and community-building tools more often than private institutions.
• The use of AI teaching-assistant technologies increased significantly more at public institutions (30 percent) than at private institutions (9 percent), though overall usage remained comparatively higher at private institutions.
• The use of tools for monitoring student progress increased by 14 percent at private institutions, versus no growth at public institutions.

Some technologies lag behind in adoption. Tools enabling student progress monitoring, AR/VR, machine learning–powered teaching assistants (TAs), AI adaptive course delivery, and classroom exercises are currently used by less than half of survey respondents. Anecdotal evidence suggests that technologies such as AR/VR require a substantial investment in equipment and may be difficult to use at scale in classes with high enrollment. Our survey also revealed utilization disparities based on size. Small public institutions use machine learning–powered TAs, AR/VR, and technologies for monitoring student progress at double or more the rates of medium and large public institutions, perhaps because smaller, specialized schools can make more targeted and cost-effective investments. We also found that medium and large public institutions made greater use of connectivity and community-building tools than small public institutions (57 to 59 percent compared with 45 percent, respectively). Although the uptake of AI-powered tools was slower, higher-education experts we interviewed predict their use will increase; they allow faculty to tailor courses to each student’s progress, reduce their workload, and improve student engagement at scale (see sidebar “Differences in adoption by type of institution observed in the research”).

While many colleges and universities are interested in using more technologies to support student learning, the top three barriers indicated are lack of awareness, inadequate deployment capabilities, and cost (Exhibit 3).

Students want entertaining and efficient tools

More than 60 percent of students said that all the classroom learning technologies they’ve used since COVID-19 began had improved their learning and grades (Exhibit 4). However, two technologies earned higher marks than the rest for boosting academic performance: 80 percent of students cited classroom exercises, and 71 percent cited machine learning–powered teaching assistants.

Although AR/VR is not yet widely used, 37 percent of students said they are “most excited” about its potential in the classroom. While 88 percent of students believe AR/VR will make learning more entertaining, just 5 percent said they think it will improve their ability to learn or master content (Exhibit 5). Industry experts confirmed that while there is significant enthusiasm for AR/VR, its ability to improve learning outcomes is uncertain. Some data look promising. For example, in a recent pilot study, 1 “Immersive biology in the Alien Zoo: A Dreamscape Learn software product,” Dreamscape Learn, accessed October 2021. students who used a VR tool to complete coursework for an introductory biology class improved their subject mastery by an average of two letter grades.

Faculty embrace new tools but would benefit from more technical support and training

Faculty gave learning tools even higher marks than students did, for ease of use, engagement, access to course resources, and instructor connectivity. They also expressed greater excitement than students did for the future use of technologies. For example, while more than 30 percent of students expressed excitement for AR/VR and classroom interactions, more than 60 percent of faculty were excited about those, as well as machine learning–powered teaching assistants and AI adaptive technology.

Eighty-one percent or more of faculty said they feel the eight learning technology tools are a good investment of time and effort relative to the value they provide (Exhibit 6). Expert interviews suggest that employing learning technologies can be a strain on faculty members, but those we surveyed said this strain is worthwhile.

While faculty surveyed were enthusiastic about new technologies, experts we interviewed stressed some underlying challenges. For example, digital-literacy gaps have been more pronounced since the pandemic because it forced the near-universal adoption of some technology solutions, deepening a divide that was unnoticed when adoption was sporadic. More tech-savvy instructors are comfortable with interaction-engagement-focused solutions, while staff who are less familiar with these tools prefer content display and delivery-focused technologies.

According to experts we interviewed, learning new tools and features can bring on general fatigue. An associate vice president of e-learning at one university told us that faculty there found designing and executing a pilot study of VR for a computer science class difficult. “It’s a completely new way of instruction. . . . I imagine that the faculty using it now will not use it again in the spring.” Technical support and training help. A chief academic officer of e-learning who oversaw the introduction of virtual simulations for nursing and radiography students said that faculty holdouts were permitted to opt out but not to delay the program. “We structured it in a ‘we’re doing this together’ way. People who didn’t want to do it left, but we got a lot of support from vendors and training, which made it easy to implement simulations.”

Reimagining higher education in the United States

Takeaways from our research.

Despite the growing pains of digitizing the classroom learning experience, faculty and students believe there is a lot more they can gain. Faculty members are optimistic about the benefits, and students expect learning to stay entertaining and efficient. While adoption levels saw double-digit growth during the pandemic, many classrooms have yet to experience all the technologies. For institutions considering the investment, or those that have already started, there are several takeaways to keep in mind.

• It’s important for administration leaders, IT, and faculty to agree on what they want to accomplish by using a particular learning technology. Case studies and expert interviews suggest institutions that seek alignment from all their stakeholders before implementing new technologies are more successful. Is the primary objective student engagement and motivation? Better academic performance? Faculty satisfaction and retention? Once objectives are set, IT staff and faculty can collaborate more effectively in choosing the best technology and initiating programs.
• Factor in student access to technology before deployment. As education technology use grows, the digital divide for students puts access to education at risk. While all the institution types we surveyed use learning technologies in the classroom, they do so to varying degrees. For example, 55 percent of respondents from historically Black colleges and universities and tribal colleges and universities use classroom interaction tools. This is lower than public institutions’ overall utilization rate of 64 percent and private institutions’ utilization rate of 84 percent. Similarly, 15 percent of respondents from historically Black colleges and universities and tribal colleges and universities use tools for monitoring student progress, while the overall utilization rate for both public and private institutions is 25 percent.
• High-quality support eases adoption for students and faculty. Institutions that have successfully deployed new learning technologies provided technical support and training for students and guidance for faculty on how to adapt their course content and delivery. For example, institutions could include self-service resources, standardize tools for adoption, or provide stipend opportunities for faculty who attend technical training courses. One chief academic officer told us, “The adoption of platforms at the individual faculty level can be very difficult. Ease of use is still very dependent upon your IT support representative and how they will go to bat to support you.”
• Agree on impact metrics and start measuring in advance of deployment. Higher-education institutions often don’t have the means to measure the impact of their investment in learning technologies, yet it’s essential for maximizing returns. Attributing student outcomes to a specific technology can be complex due to the number of variables involved in academic performance. However, prior to investing in learning technologies, the institution and its faculty members can align on a core set of metrics to quantify and measure their impact. One approach is to measure a broad set of success indicators, such as tool usage, user satisfaction, letter grades, and DFW rates (the percentage of students who receive a D, F, or Withdraw) each term. The success indicators can then be correlated by modality—online versus hybrid versus in-class—to determine the impact of specific tools. Some universities have offered faculty grants of up to $20,000 for running pilot programs that assess whether tools are achieving high-priority objectives. “If implemented properly, at the right place, and with the right buy-in, education technology solutions are absolutely valuable and have a clear ROI,” a senior vice president of academic affairs and chief technology officer told us.

In an earlier article , we looked at the broader changes in higher education that have been prompted by the pandemic. But perhaps none has advanced as quickly as the adoption of digital learning tools. Faculty and students see substantial benefits, and adoption rates are a long way from saturation, so we can expect uptake to continue. Institutions that want to know how they stand in learning tech adoption can measure their rates and benchmark them against the averages in this article and use those comparisons to help them decide where they want to catch up or get ahead.

Claudio Brasca is a partner in McKinsey’s Bay Area office, where Varun Marya is a senior partner; Charag Krishnan is a partner in the New Jersey office; Katie Owen is an associate partner in the St. Louis office, where Joshua Sirois is a consultant; and Shyla Ziade is a consultant in the Denver office.

The authors wish to thank Paul Kim, chief technology officer and associate dean at Stanford School of Education, and Ryan Golden for their contributions to this article.

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Higher Education Trends, Opportunities, and Challenges in 2022

Trends impacting higher education organizations are expected to create both opportunities and challenges in 2022 and beyond.

Those trends include revenue growth, tuition increases, and cost-cutting. Tuition increases also affect the competition between community colleges, colleges, and universities.

Revenue Growth

Colleges and universities are projected to see slower revenue growth through 2027.

Related Trends

This slower growth could lead to a few changes in direction.

Retention Rates

The labor market is expected to expand which could discourage people from starting or staying in school. Combined with a stagnant high school retention rate, college enrollment is expected to decelerate.

Online Education

Growth in online education could enable colleges to serve more nontraditional students at lower costs. Online courses may increase interest in industry institutions, boosting enrollment and revenue.

Appropriations

State and local appropriations are poised to grow as unemployment likely declines and the government recovers lost tax revenue.

Tuition and Community Colleges

Continued tuition increases could make it more difficult for some students to pursue higher education.

As a result of the trends below, the number of colleges and universities will likely increase an annualized 1% to 2,120 enterprises over the next five years.

Rising tuition costs could change students’ plans for their educations.

Student Loans

While the federal government took steps to increase student loan availability, the rising cost of a four-year college education could steer students toward more affordable options.

That could lead to competition from community colleges and trade schools. The federal government has taken measures to support these institutions.

Competition

Competition from community colleges will likely remain moderate as traditional colleges and universities dominate the higher education market.

Because many students transfer to a four-year institution after completing two years at a community college, lower tuition for community colleges may ultimately result in higher demand for industry institutions.

Cutting Costs

According to IBIS World, industry employment is projected to increase an annualized 1.2% to 3.3 million workers over five years. Additionally, the transition to relatively inexpensive forms of labor is expected to bolster industry profit over the coming years.

However, industry institutions operate on a not-for-profit basis, and colleges and universities could continue to cut costs to bolster the long-term sustainability of higher education.

Consolidation

Universities are expected to merge redundant departments and courses to reduce administrative costs.

Tenured Versus Part-time Positions

The number of tenure-track positions, which typically have higher salaries, will also likely be reduced. Instead, industry institutions could continue to hire part-time lecturers and nontenured professors at lower wages.

Institutions will also likely further implement online education programs to lower costs and compete with community colleges and for-profit universities.

Online education reduces costs because classrooms aren’t required and a small number of professors can instruct a substantial number of students. By increasing the number of courses available online, universities could reach new markets.

Massive Open Online Courses (MOOC)

Start-ups that provide a platform for universities to offer online courses could continue to gain traction over the coming years.

While some MOOC services may pull students away from traditional universities by offering free and low-cost education services, traditional universities provide benefits not found in online education services such as access to professors, contact with peers, and accreditation from an established institution.

These factors limit direct competition between industry institutions and MOOC services. While MOOCs and similar online education services could make higher education more accessible, they aren’t expected to significantly reduce demand for industry services.

Future Opportunities and Challenges

Based on the above trends, higher education organizations could face several challenges over the next two to three years. However, there are several opportunities as well.

Opportunities

• Mergers and acquisitions (M&A). Mergers, acquisitions , alliances, and consortiums are becoming commonplace. Small, faith-based institutions are a prime target for acquisitions. Many institutions are aligning with other colleges or universities to share services such as digital platforms to save on costs.
• Personal wealth and market performance. Institutions for fiscal year-end (FYE) 2021 posted record market performance and endowments, and investments pools are at all-time highs providing purchasing and institutional aid power.
• Federal and state funding. Public and private schools will continue to see infusions of government funding. The Biden administration seeks affordable college and free community college for all. Conversely, as funding increases in the public sector, the private sector will continue to feel pressure to compete and keep tuition rates low.
• Construction. As institutions emerge from the COVID-19 pandemic and lockdowns, they’ll likely move forward with campus expansions and deferred maintenance plans.
• Digital transformations. Many institutions transformed their digital programs due to COVID-19. Some schools outside of major East or West Coast cities are using hybrid models. Many students and faculty prefer a return to campus and in-person learning, however.
• Inflation. Personnel costs are a major operating expense in higher education and will have a great impact as they rise. Cost-of-living adjustments face double-digit growth. The increased cost of goods, especially construction materials, will delay capital expansion and deferred maintenance.
• Workforce shortages. The shortage of personnel and availability of candidates in the workforce will drive costs up. Baby boomers are retiring at record rates. Additionally, the shortages will create an inability to deliver services to students. Also, those shortages could prevent institutions from effectively implementing strategic plans.
• Compliance and regulations. Continued and increased compliance and state and federal regulations on higher education are driving overhead and personnel costs up.

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• Published: 24 April 2023

Artificial intelligence in higher education: the state of the field

• Helen Crompton   ORCID: orcid.org/0000-0002-1775-8219 1 , 3 &
• Diane Burke 2  

International Journal of Educational Technology in Higher Education volume  20 , Article number:  22 ( 2023 ) Cite this article

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This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.

A systematic review examining AIEd in higher education (HE) up to the end of 2022.

Unique findings in the switch from US to China in the most studies published.

A two to threefold increase in studies published in 2021 and 2022 to prior years.

AIEd was used for: Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring System, and Managing Student Learning.

Introduction

The use of artificial intelligence (AI) in higher education (HE) has risen quickly in the last 5 years (Chu et al., 2022 ), with a concomitant proliferation of new AI tools available. Scholars (viz., Chen et al., 2020 ; Crompton et al., 2020 , 2021 ) report on the affordances of AI to both instructors and students in HE. These benefits include the use of AI in HE to adapt instruction to the needs of different types of learners (Verdú et al., 2017 ), in providing customized prompt feedback (Dever et al., 2020 ), in developing assessments (Baykasoğlu et al., 2018 ), and predict academic success (Çağataylı & Çelebi, 2022 ). These studies help to inform educators about how artificial intelligence in education (AIEd) can be used in higher education.

Nonetheless, a gap has been highlighted by scholars (viz., Hrastinski et al., 2019 ; Zawacki-Richter et al., 2019 ) regarding an understanding of the collective affordances provided through the use of AI in HE. Therefore, the purpose of this study is to examine extant research from 2016 to 2022 to provide an up-to-date systematic review of how AI is being used in the HE context.

Artificial intelligence has become pervasive in the lives of twenty-first century citizens and is being proclaimed as a tool that can be used to enhance and advance all sectors of our lives (Górriz et al., 2020 ). The application of AI has attracted great interest in HE which is highly influenced by the development of information and communication technologies (Alajmi et al., 2020 ). AI is a tool used across subject disciplines, including language education (Liang et al., 2021 ), engineering education (Shukla et al., 2019 ), mathematics education (Hwang & Tu, 2021 ) and medical education (Winkler-Schwartz et al., 2019 ),

Artificial intelligence

The term artificial intelligence is not new. It was coined in 1956 by McCarthy (Cristianini, 2016 ) who followed up on the work of Turing (e.g., Turing, 1937 , 1950 ). Turing described the existence of intelligent reasoning and thinking that could go into intelligent machines. The definition of AI has grown and changed since 1956, as there has been significant advancements in AI capabilities. A current definition of AI is “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and the use of data for complex processing tasks” (Popenici et al., 2017 , p. 2). The interdisciplinary interest from scholars from linguistics, psychology, education, and neuroscience who connect AI to nomenclature, perceptions and knowledge in their own disciplines could create a challenge when defining AI. This has created the need to create categories of AI within specific disciplinary areas. This paper focuses on the category of AI in Education (AIEd) and how AI is specifically used in higher educational contexts.

As the field of AIEd is growing and changing rapidly, there is a need to increase the academic understanding of AIEd. Scholars (viz., Hrastinski et al., 2019 ; Zawacki-Richter et al., 2019 ) have drawn attention to the need to increase the understanding of the power of AIEd in educational contexts. The following section provides a summary of the previous research regarding AIEd.

Extant systematic reviews

This growing interest in AIEd has led scholars to investigate the research on the use of artificial intelligence in education. Some scholars have conducted systematic reviews to focus on a specific subject domain. For example, Liang et. al. ( 2021 ) conducted a systematic review and bibliographic analysis the roles and research foci of AI in language education. Shukla et. al. ( 2019 ) focused their longitudinal bibliometric analysis on 30 years of using AI in Engineering. Hwang and Tu ( 2021 ) conducted a bibliometric mapping analysis on the roles and trends in the use of AI in mathematics education, and Winkler-Schwartz et. al. ( 2019 ) specifically examined the use of AI in medical education in looking for best practices in the use of machine learning to assess surgical expertise. These studies provide a specific focus on the use of AIEd in HE but do not provide an understanding of AI across HE.

On a broader view of AIEd in HE, Ouyang et. al. ( 2022 ) conducted a systematic review of AIEd in online higher education and investigated the literature regarding the use of AI from 2011 to 2020. The findings show that performance prediction, resource recommendation, automatic assessment, and improvement of learning experiences are the four main functions of AI applications in online higher education. Salas-Pilco and Yang ( 2022 ) focused on AI applications in Latin American higher education. The results revealed that the main AI applications in higher education in Latin America are: (1) predictive modeling, (2) intelligent analytics, (3) assistive technology, (4) automatic content analysis, and (5) image analytics. These studies provide valuable information for the online and Latin American context but not an overarching examination of AIEd in HE.

Studies have been conducted to examine HE. Hinojo-Lucena et. al. ( 2019 ) conducted a bibliometric study on the impact of AIEd in HE. They analyzed the scientific production of AIEd HE publications indexed in Web of Science and Scopus databases from 2007 to 2017. This study revealed that most of the published document types were proceedings papers. The United States had the highest number of publications, and the most cited articles were about implementing virtual tutoring to improve learning. Chu et. al. ( 2022 ) reviewed the top 50 most cited articles on AI in HE from 1996 to 2020, revealing that predictions of students’ learning status were most frequently discussed. AI technology was most frequently applied in engineering courses, and AI technologies most often had a role in profiling and prediction. Finally, Zawacki-Richter et. al. ( 2019 ) analyzed AIEd in HE from 2007 to 2018 to reveal four primary uses of AIEd: (1) profiling and prediction, (2) assessment and evaluation, (3) adaptive systems and personalization, and (4) intelligent tutoring systems. There do not appear to be any studies examining the last 2 years of AIEd in HE, and these authors describe the rapid speed of both AI development and the use of AIEd in HE and call for further research in this area.

Purpose of the study

The purpose of this study is in response to the appeal from scholars (viz., Chu et al., 2022 ; Hinojo-Lucena et al., 2019 ; Zawacki-Richter et al., 2019 ) to research to investigate the benefits and challenges of AIEd within HE settings. As the academic knowledge of AIEd HE finished with studies examining up to 2020, this study provides the most up-to-date analysis examining research through to the end of 2022.

The overarching question for this study is: what are the trends in HE research regarding the use of AIEd? The first two questions provide contextual information, such as where the studies occurred and the disciplines AI was used in. These contextual details are important for presenting the main findings of the third question of how AI is being used in HE.

In what geographical location was the AIEd research conducted, and how has the trend in the number of publications evolved across the years?

What departments were the first authors affiliated with, and what were the academic levels and subject domains in which AIEd research was being conducted?

Who are the intended users of the AI technologies and what are the applications of AI in higher education?

A PRISMA systematic review methodology was used to answer three questions guiding this study. PRISMA principles (Page et al., 2021 ) were used throughout the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P; Moher et al., 2015 ) were utilized in this study to provide an a priori roadmap to conduct a rigorous systematic review. Furthermore, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Page et al., 2021 ) were used to search, identify, and select articles to be included in the research were used for searching, identifying, and selecting articles, then in how to read, extract, and manage the secondary data gathered from those studies (Moher et al., 2015 , PRISMA Statement, 2021 ). This systematic review approach supports an unbiased synthesis of the data in an impartial way (Hemingway & Brereton, 2009 ). Within the systematic review methodology, extracted data were aggregated and presented as whole numbers and percentages. A qualitative deductive and inductive coding methodology was also used to analyze extant data and generate new theories on the use of AI in HE (Gough et al., 2017 ).

The research begins with the search for the research articles to be included in the study. Based on the research question, the study parameters are defined including the search years, quality and types of publications to be included. Next, databases and journals are selected. A Boolean search is created and used for the search of those databases and journals. Once a set of publications are located from those searches, they are then examined against an inclusion and exclusion criteria to determine which studies will be included in the final study. The relevant data to match the research questions is then extracted from the final set of studies and coded. This method section is organized to describe each of these methods with full details to ensure transparency.

Search strategy

Only peer-reviewed journal articles were selected for examination in this systematic review. This ensured a level of confidence in the quality of the studies selected (Gough et al., 2017 ). The search parameters narrowed the search focus to include studies published in 2016 to 2022. This timeframe was selected to ensure the research was up to date, which is especially important with the rapid change in technology and AIEd.

The data retrieval protocol employed an electronic and a hand search. The electronic search included educational databases within EBSCOhost. Then an additional electronic search was conducted of Wiley Online Library, JSTOR, Science Direct, and Web of Science. Within each of these databases a full text search was conducted. Aligned to the research topic and questions, the Boolean search included terms related to AI, higher education, and learning. The Boolean search is listed in Table 1 . In the initial test search, the terms “machine learning” OR “intelligent support” OR “intelligent virtual reality” OR “chatbot” OR “automated tutor” OR “intelligent agent” OR “expert system” OR “neural network” OR “natural language processing” were used. These were removed as they were subcategories of terms found in Part 1 of the search. Furthermore, inclusion of these specific AI terms resulted in a large number of computer science courses that were focused on learning about AI and not the use of AI in learning.

Part 2 of the search ensured that articles involved formal university education. The terms higher education and tertiary were both used to recognize the different terms used in different countries. The final Boolean search was “Artificial intelligence” OR AI OR “smart technologies” OR “intelligent technologies” AND “higher education” OR tertiary OR graduate OR undergraduate. Scholars (viz., Ouyang et al., 2022 ) who conducted a systematic review on AIEd in HE up to 2020 noted that they missed relevant articles from their study, and other relevant journals should intentionally be examined. Therefore, a hand search was also conducted to include an examination of other journals relevant to AIEd that may not be included in the databases. This is important as the field of AIEd is still relatively new, and journals focused on this field may not yet be indexed in databases. The hand search included: The International Journal of Learning Analytics and Artificial Intelligence in Education, the International Journal of Artificial Intelligence in Education, and Computers & Education: Artificial Intelligence.

Electronic and hand searches resulted in 371 articles for possible inclusion. The search parameters within the electronic database search narrowed the search to articles published from 2016 to 2022, per-reviewed journal articles, and duplicates. Further screening was conducted manually, as each of the 138 articles were reviewed in full by two researchers to examine a match against the inclusion and exclusion criteria found in Table 2 .

The inter-rater reliability was calculated by percentage agreement (Belur et al., 2018 ). The researchers reached a 95% agreement for the coding. Further discussion of misaligned articles resulted in a 100% agreement. This screening process against inclusion and exclusion criteria resulted in the exclusion of 237 articles. This included the duplicates and those removed as part of the inclusion and exclusion criteria, see Fig.  1 . Leaving 138 articles for inclusion in this systematic review.

(From: Page et al., 2021 )

PRISMA flow chart of article identification and screening

The 138 articles were then coded to answer each of the research questions using deductive and inductive coding methods. Deductive coding involves examining data using a priori codes. A priori are pre-determined criteria and this process was used to code the countries, years, author affiliations, academic levels, and domains in the respective groups. Author affiliations were coded using the academic department of the first author of the study. First authors were chosen as that person is the primary researcher of the study and this follows past research practice (e.g., Zawacki-Richter et al., 2019 ). Who the AI was intended for was also coded using the a priori codes of Student, Instructor, Manager or Others. The Manager code was used for those who are involved in organizational tasks, e.g., tracking enrollment. Others was used for those not fitting the other three categories.

Inductive coding was used for the overarching question of this study in examining how the AI was being used in HE. Researchers of extant systematic reviews on AIEd in HE (viz., Chu et al., 2022 ; Zawacki-Richter et al., 2019 ) often used an a priori framework as researchers matched the use of AI to pre-existing frameworks. A grounded coding methodology (Strauss & Corbin, 1995 ) was selected for this study to allow findings of the trends on AIEd in HE to emerge from the data. This is important as it allows a direct understanding of how AI is being used rather than how researchers may think it is being used and fitting the data to pre-existing ideas.

Grounded coding process involved extracting how the AI was being used in HE from the articles. “In vivo” (Saldana, 2015 ) coding was also used alongside grounded coding. In vivo codes are when codes use language directly from the article to capture the primary authors’ language and ensure consistency with their findings. The grounded coding design used a constant comparative method. Researchers identified important text from articles related to the use of AI, and through an iterative process, initial codes led to axial codes with a constant comparison of uses of AI with uses of AI, then of uses of AI with codes, and codes with codes. Codes were deemed theoretically saturated when the majority of the data fit with one of the codes. For both the a priori and the grounded coding, two researchers coded and reached an inter-rater percentage agreement of 96%. After discussing misaligned articles, a 100% agreement was achieved.

Findings and discussion

The findings and discussion section are organized by the three questions guiding this study. The first two questions provide contextual information on the AIEd research, and the final question provides a rigorous investigation into how AI is being used in HE.

RQ1. In what geographical location was the AIEd research conducted, and how has the trend in the number of publications evolved across the years?

The 138 studies took place across 31 countries in six of seven continents of the world. Nonetheless, that distribution was not equal across continents. Asia had the largest number of AIEd studies in HE at 41%. Of the seven countries represented in Asia, 42 of the 58 studies were conducted in Taiwan and China. Europe, at 30%, was the second largest continent and had 15 countries ranging from one to eight studies a piece. North America, at 21% of the studies was the continent with the third largest number of studies, with the USA producing 21 of the 29 studies in that continent. The 21 studies from the USA places it second behind China. Only 1% of studies were conducted in South America and 2% in Africa. See Fig.  2 for a visual representation of study distribution across countries. Those continents with high numbers of studies are from high income countries and those with low numbers have a paucity of publications in low-income countries.

Geographical distribution of the AIEd HE studies

Data from Zawacki-Richter et. al.’s ( 2019 ) 2007–2018 systematic review examining countries found that the USA conducted the most studies across the globe at 43 out of 146, and China had the second largest at eleven of the 146 papers. Researchers have noted a rapid trend in Chinese researchers publishing more papers on AI and securing more patents than their US counterparts in a field that was originally led by the US (viz., Li et al., 2021 ). The data from this study corroborate this trend in China leading in the number of AIEd publications.

With the accelerated use of AI in society, gathering data to examine the use of AIEd in HE is useful in providing the scholarly community with specific information on that growth and if it is as prolific as anticipated by scholars (e.g., Chu et al., 2022 ). The analysis of data of the 138 studies shows that the trend towards the use of AIEd in HE has greatly increased. There is a drop in 2019, but then a great rise in 2021 and 2022; see Fig.  3 .

Chronological trend in AIEd in HE

Data on the rise in AIEd in HE is similar to the findings of Chu et. al. ( 2022 ) who noted an increase from 1996 to 2010 and 2011–2020. Nonetheless Chu’s parameters are across decades, and the rise is to be anticipated with a relatively new technology across a longitudinal review. Data from this study show a dramatic rise since 2020 with a 150% increase from the prior 2 years 2020–2019. The rise in 2021 and 2022 in HE could have been caused by the vast increase in HE faculty having to teach with technology during the pandemic lockdown. Faculty worldwide were using technologies, including AI, to explore how they could continue teaching and learning that was often face-to-face prior to lockdown. The disadvantage of this rapid adoption of technology is that there was little time to explore the possibilities of AI to transform learning, and AI may have been used to replicate past teaching practices, without considering new strategies previously inconceivable with the affordances of AI.

However, in a further examination of the research from 2021 to 2022, it appears that there are new strategies being considered. For example, Liu et. al.’s, 2022 study used AIEd to provide information on students’ interactions in an online environment and examine their cognitive effort. In Yao’s study in 2022, he examined the use of AI to determine student emotions while learning.

RQ2. What departments were the first authors affiliated with, and what were the academic levels and subject domains in which AIEd research was being conducted?

Department affiliations

Data from the AIEd HE studies show that of the first authors were most frequently from colleges of education (28%), followed by computer science (20%). Figure  4 presents the 15 academic affiliations of the authors found in the studies. The wide variety of affiliations demonstrate the variety of ways AI can be used in various educational disciplines, and how faculty in diverse areas, including tourism, music, and public affairs were interested in how AI can be used for educational purposes.

Research affiliations

In an extant AIED HE systematic review, Zawacki-Richter et. al.’s ( 2019 ) named their study Systematic review of research on artificial intelligence applications in higher education—where are the educators? In this study, the authors were keen to highlight that of the AIEd studies in HE, only six percent were written by researchers directly connected to the field of education, (i.e., from a college of education). The researchers found a great lack in pedagogical and ethical implications of implementing AI in HE and that there was a need for more educational perspectives on AI developments from educators conducting this work. It appears from our data that educators are now showing greater interest in leading these research endeavors, with the highest affiliated group belonging to education. This may again be due to the pandemic and those in the field of education needing to support faculty in other disciplines, and/or that they themselves needed to explore technologies for their own teaching during the lockdown. This may also be due to uptake in professors in education becoming familiar with AI tools also driven by a societal increased attention. As the focus of much research by education faculty is on teaching and learning, they are in an important position to be able to share their research with faculty in other disciplines regarding the potential affordances of AIEd.

Academic levels

The a priori coding of academic levels show that the majority of studies involved undergraduate students with 99 of the 138 (72%) focused on these students. This was in comparison to the 12 of 138 (9%) for graduate students. Some of the studies used AI for both academic levels: see Fig.  5

Academic level distribution by number of articles

This high percentage of studies focused on the undergraduate population was congruent with an earlier AIED HE systematic review (viz., Zawacki-Richter et al., 2019 ) who also reported student academic levels. This focus on undergraduate students may be due to the variety of affordances offered by AIEd, such as predictive analytics on dropouts and academic performance. These uses of AI may be less required for graduate students who already have a record of performance from their undergraduate years. Another reason for this demographic focus can also be convenience sampling, as researchers in HE typically has a much larger and accessible undergraduate population than graduates. This disparity between undergraduates and graduate populations is a concern, as AIEd has the potential to be valuable in both settings.

Subject domains

The studies were coded into 14 areas in HE; with 13 in a subject domain and one category of AIEd used in HE management of students; See Fig.  6 . There is not a wide difference in the percentages of top subject domains, with language learning at 17%, computer science at 16%, and engineering at 12%. The management of students category appeared third on the list at 14%. Prior studies have also found AIEd often used for language learning (viz., Crompton et al., 2021 ; Zawacki-Richter et al., 2019 ). These results are different, however, from Chu et. al.’s ( 2022 ) findings that show engineering dramatically leading with 20 of the 50 studies, with other subjects, such as language learning, appearing once or twice. This study appears to be an outlier that while the searches were conducted in similar databases, the studies only included 50 studies from 1996 to 2020.

Subject domains of AIEd in HE

Previous scholars primarily focusing on language learning using AI for writing, reading, and vocabulary acquisition used the affordances of natural language processing and intelligent tutoring systems (e.g., Liang et al., 2021 ). This is similar to the findings in studies with AI used for automated feedback of writing in a foreign language (Ayse et al., 2022 ), and AI translation support (Al-Tuwayrish, 2016 ). The large use of AI for managerial activities in this systematic review focused on making predictions (12 studies) and then admissions (three studies). This is positive to see this use of AI to look across multiple databases to see trends emerging from data that may not have been anticipated and cross referenced before (Crompton et al., 2022 ). For example, to examine dropouts, researchers may consider examining class attendance, and may not examine other factors that appear unrelated. AI analysis can examine all factors and may find that dropping out is due to factors beyond class attendance.

RQ3. Who are the intended users of the AI technologies and what are the applications of AI in higher education?

Intended user of AI

Of the 138 articles, the a priori coding shows that 72% of the studies focused on Students, followed by a focus on Instructors at 17%, and Managers at 11%, see Fig.  7 . The studies provided examples of AI being used to provide support to students, such as access to learning materials for inclusive learning (Gupta & Chen, 2022 ), provide immediate answers to student questions, self-testing opportunities (Yao, 2022 ), and instant personalized feedback (Mousavi et al., 2020 ).

Intended user

The data revealed a large emphasis on students in the use of AIEd in HE. This user focus is different from a recent systematic review on AIEd in K-12 that found that AIEd studies in K-12 settings prioritized teachers (Crompton et al., 2022 ). This may appear that HE uses AI to focus more on students than in K-12. However, this large number of student studies in HE may be due to the student population being more easily accessibility to HE researchers who may study their own students. The ethical review process is also typically much shorter in HE than in K-12. Therefore, the data on the intended focus should be reviewed while keeping in mind these other explanations. It was interesting that Managers were the lowest focus in K-12 and also in this study in HE. AI has great potential to collect, cross reference and examine data across large datasets that can allow data to be used for actionable insight. More focus on the use of AI by managers would tap into this potential.

How is AI used in HE

Using grounded coding, the use of AIEd from each of the 138 articles was examined and six major codes emerged from the data. These codes provide insight into how AI was used in HE. The five codes are: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. For each of these codes there are also axial codes, which are secondary codes as subcategories from the main category. Each code is delineated below with a figure of the codes with further descriptive information and examples.

Assessment/evaluation

Assessment and Evaluation was the most common use of AIEd in HE. Within this code there were six axial codes broken down into further codes; see Fig.  8 . Automatic assessment was most common, seen in 26 of the studies. It was interesting to see that this involved assessment of academic achievement, but also other factors, such as affect.

Codes and axial codes for assessment and evaluation

Automatic assessment was used to support a variety of learners in HE. As well as reducing the time it takes for instructors to grade (Rutner & Scott, 2022 ), automatic grading showed positive use for a variety of students with diverse needs. For example, Zhang and Xu ( 2022 ) used automatic assessment to improve academic writing skills of Uyghur ethnic minority students living in China. Writing has a variety of cultural nuances and in this study the students were shown to engage with the automatic assessment system behaviorally, cognitively, and affectively. This allowed the students to engage in self-regulated learning while improving their writing.

Feedback was a description often used in the studies, as students were given text and/or images as feedback as a formative evaluation. Mousavi et. al. ( 2020 ) developed a system to provide first year biology students with an automated personalized feedback system tailored to the students’ specific demographics, attributes, and academic status. With the unique feature of AIEd being able to analyze multiple data sets involving a variety of different students, AI was used to assess and provide feedback on students’ group work (viz., Ouatik et al., 2021 ).

AI also supports instructors in generating questions and creating multiple question tests (Yang et al., 2021 ). For example, (Lu et al., 2021 ) used natural language processing to create a system that automatically created tests. Following a Turing type test, researchers found that AI technologies can generate highly realistic short-answer questions. The ability for AI to develop multiple questions is a highly valuable affordance as tests can take a great deal of time to make. However, it would be important for instructors to always confirm questions provided by the AI to ensure they are correct and that they match the learning objectives for the class, especially in high value summative assessments.

The axial code within assessment and evaluation revealed that AI was used to review activities in the online space. This included evaluating student’s reflections, achievement goals, community identity, and higher order thinking (viz., Huang et al., 2021 ). Three studies used AIEd to evaluate educational materials. This included general resources and textbooks (viz., Koć‑Januchta et al., 2022 ). It is interesting to see the use of AI for the assessment of educational products, rather than educational artifacts developed by students. While this process may be very similar in nature, this shows researchers thinking beyond the traditional use of AI for assessment to provide other affordances.

Predicting was a common use of AIEd in HE with 21 studies focused specifically on the use of AI for forecasting trends in data. Ten axial codes emerged on the way AI was used to predict different topics, with nine focused on predictions regarding students and the other on predicting the future of higher education. See Fig.  9 .

Predicting axial codes

Extant systematic reviews on HE highlighted the use of AIEd for prediction (viz., Chu et al., 2022 ; Hinojo-Lucena et al., 2019 ; Ouyang et al., 2022 ; Zawacki-Richter et al., 2019 ). Ten of the articles in this study used AI for predicting academic performance. Many of the axial codes were often overlapping, such as predicting at risk students, and predicting dropouts; however, each provided distinct affordances. An example of this is the study by Qian et. al. ( 2021 ). These researchers examined students taking a MOOC course. MOOCs can be challenging environments to determine information on individual students with the vast number of students taking the course (Krause & Lowe, 2014 ). However, Qian et al., used AIEd to predict students’ future grades by inputting 17 different learning features, including past grades, into an artificial neural network. The findings were able to predict students’ grades and highlight students at risk of dropping out of the course.

In a systematic review on AIEd within the K-12 context (viz., Crompton et al., 2022 ), prediction was less pronounced in the findings. In the K-12 setting, there was a brief mention of the use of AI in predicting student academic performance. One of the studies mentioned students at risk of dropping out, but this was immediately followed by questions about privacy concerns and describing this as “sensitive”. The use of prediction from the data in this HE systematic review cover a wide range of AI predictive affordances. students Sensitivity is still important in a HE setting, but it is positive to see the valuable insight it provides that can be used to avoid students failing in their goals.

AI assistant

The studies evaluated in this review indicated that the AI Assistant used to support learners had a variety of different names. This code included nomenclature such as, virtual assistant, virtual agent, intelligent agent, intelligent tutor, and intelligent helper. Crompton et. al. ( 2022 ), described the difference in the terms to delineate the way that the AI appeared to the user. For example, if there was an anthropomorphic presence to the AI, such as an avatar, or if the AI appeared to support via other means, such as text prompt. The findings of this systematic review align to Crompton et. al.’s ( 2022 ) descriptive differences of the AI Assistant. Furthermore, this code included studies that provide assistance to students, but may not have specifically used the word assistance. These include the use of chatbots for student outreach, answering questions, and providing other assistance. See Fig.  10 for the axial codes for AI Assistant.

AI assistant axial codes

Many of these assistants offered multiple supports to students, such as Alex , the AI described as a virtual change agent in Kim and Bennekin’s ( 2016 ) study. Alex interacted with students in a college mathematics course by asking diagnostic questions and gave support depending on student needs. Alex’s support was organized into four stages: (1) goal initiation (“Want it”), (2) goal formation (“Plan for it”), (3) action control (“Do it”), and (4) emotion control (“Finish it”). Alex provided responses depending on which of these four areas students needed help. These messages supported students with the aim of encouraging persistence in pursuing their studies and degree programs and improving performance.

The role of AI in providing assistance connects back to the seminal work of Vygotsky ( 1978 ) and the Zone of Proximal Development (ZPD). ZPD highlights the degree to which students can rapidly develop when assisted. Vygotsky described this assistance often in the form of a person. However, with technological advancements, the use of AI assistants in these studies are providing that support for students. The affordances of AI can also ensure that the support is timely without waiting for a person to be available. Also, assistance can consider aspects on students’ academic ability, preferences, and best strategies for supporting. These features were evident in Kim and Bennekin’s ( 2016 ) study using Alex.

Intelligent tutoring system

The use of Intelligent Tutoring Systems (ITS) was revealed in the grounded coding. ITS systems are adaptive instructional systems that involve the use of AI techniques and educational methods. An ITS system customizes educational activities and strategies based on student’s characteristics and needs (Mousavinasab et al., 2021 ). While ITS may be an anticipated finding in AIED HE systematic reviews, it was interesting that extant reviews similar to this study did not always describe their use in HE. For example, Ouyang et. al. ( 2022 ), included “intelligent tutoring system” in search terms describing it as a common technique, yet ITS was not mentioned again in the paper. Zawacki-Richter et. al. ( 2019 ) on the other hand noted that ITS was in the four overarching findings of the use of AIEd in HE. Chu et. al. ( 2022 ) then used Zawacki-Richter’s four uses of AIEd for their recent systematic review.

In this systematic review, 18 studies specifically mentioned that they were using an ITS. The ITS code did not necessitate axial codes as they were performing the same type of function in HE, namely, in providing adaptive instruction to the students. For example, de Chiusole et. al. ( 2020 ) developed Stat-Knowlab, an ITS that provides the level of competence and best learning path for each student. Thus Stat-Knowlab personalizes students’ learning and provides only educational activities that the student is ready to learn. This ITS is able to monitor the evolution of the learning process as the student interacts with the system. In another study, Khalfallah and Slama ( 2018 ) built an ITS called LabTutor for engineering students. LabTutor served as an experienced instructor in enabling students to access and perform experiments on laboratory equipment while adapting to the profile of each student.

The student population in university classes can go into the hundreds and with the advent of MOOCS, class sizes can even go into the thousands. Even in small classes of 20 students, the instructor cannot physically provide immediate unique personalize questions to each student. Instructors need time to read and check answers and then take further time to provide feedback before determining what the next question should be. Working with the instructor, AIEd can provide that immediate instruction, guidance, feedback, and following questioning without delay or becoming tired. This appears to be an effective use of AIEd, especially within the HE context.

Managing student learning

Another code that emerged in the grounded coding was focused on the use of AI for managing student learning. AI is accessed to manage student learning by the administrator or instructor to provide information, organization, and data analysis. The axial codes reveal the trends in the use of AI in managing student learning; see Fig.  11 .

Learning analytics was an a priori term often found in studies which describes “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Long & Siemens, 2011 , p. 34). The studies investigated in this systematic review were across grades and subject areas and provided administrators and instructors different types of information to guide their work. One of those studies was conducted by Mavrikis et. al. ( 2019 ) who described learning analytics as teacher assistance tools. In their study, learning analytics were used in an exploratory learning environment with targeted visualizations supporting classroom orchestration. These visualizations, displayed as screenshots in the study, provided information such as the interactions between the students, goals achievements etc. These appear similar to infographics that are brightly colored and draw the eye quickly to pertinent information. AI is also used for other tasks, such as organizing the sequence of curriculum in pacing guides for future groups of students and also designing instruction. Zhang ( 2022 ) described how designing an AI teaching system of talent cultivation and using the digital affordances to establish a quality assurance system for practical teaching, provides new mechanisms for the design of university education systems. In developing such a system, Zhang found that the stability of the instructional design, overcame the drawbacks of traditional manual subjectivity in the instructional design.

Another trend that emerged from the studies was the use of AI to manage student big data to support learning. Ullah and Hafiz ( 2022 ) lament that using traditional methods, including non-AI digital techniques, asking the instructor to pay attention to every student’s learning progress is very difficult and that big data analysis techniques are needed. The ability to look across and within large data sets to inform instruction is a valuable affordance of AIEd in HE. While the use of AIEd to manage student learning emerged from the data, this study uncovered only 19 studies in 7 years (2016–2022) that focused on the use of AIEd to manage student data. This lack of the use was also noted in a recent study in the K-12 space (Crompton et al., 2022 ). In Chu et. al.’s ( 2022 ) study examining the top 50 most cited AIEd articles, they did not report the use of AIEd for managing student data in the top uses of AIEd HE. It would appear that more research should be conducted in this area to fully explore the possibilities of AI.

Gaps and future research

From this systematic review, six gaps emerged in the data providing opportunities for future studies to investigate and provide a fuller understanding of how AIEd can used in HE. (1) The majority of the research was conducted in high income countries revealing a paucity of research in developing countries. More research should be conducted in these developing countries to expand the level of understanding about how AI can enhance learning in under-resourced communities. (2) Almost 50% of the studies were conducted in the areas of language learning, computer science and engineering. Research conducted by members from multiple, different academic departments would help to advance the knowledge of the use of AI in more disciplines. (3) This study revealed that faculty affiliated with schools of education are taking an increasing role in researching the use of AIEd in HE. As this body of knowledge grows, faculty in Schools of Education should share their research regarding the pedagogical affordances of AI so that this knowledge can be applied by faculty across disciplines. (4) The vast majority of the research was conducted at the undergraduate level. More research needs to be done at the graduate student level, as AI provides many opportunities in this environment. (5) Little study was done regarding how AIEd can assist both instructors and managers in their roles in HE. The power of AI to assist both groups further research. (6) Finally, much of the research investigated in this systematic review revealed the use of AIEd in traditional ways that enhance or make more efficient current practices. More research needs to focus on the unexplored affordances of AIEd. As AI becomes more advanced and sophisticated, new opportunities will arise for AIEd. Researchers need to be on the forefront of these possible innovations.

In addition, empirical exploration is needed for new tools, such as ChatGPT that was available for public use at the end of 2022. With the time it takes for a peer review journal article to be published, ChatGPT did not appear in the articles for this study. What is interesting is that it could fit with a variety of the use codes found in this study, with students getting support in writing papers and instructors using Chat GPT to assess students work and with help writing emails or descriptions for students. It would be pertinent for researchers to explore Chat GPT.

Limitations

The findings of this study show a rapid increase in the number of AIEd studies published in HE. However, to ensure a level of credibility, this study only included peer review journal articles. These articles take months to publish. Therefore, conference proceedings and gray literature such as blogs and summaries may reveal further findings not explored in this study. In addition, the articles in this study were all published in English which excluded findings from research published in other languages.

In response to the call by Hinojo-Lucena et. al. ( 2019 ), Chu et. al. ( 2022 ), and Zawacki-Richter et. al. ( 2019 ), this study provides unique findings with an up-to-date examination of the use of AIEd in HE from 2016 to 2022. Past systematic reviews examined the research up to 2020. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged.

The findings show that of the 138 studies examined, research was conducted in six of the seven continents of the world. In extant systematic reviews showed that the US led by a large margin in the number of studies published. This trend has now shifted to China. Another shift in AIEd HE is that while extant studies lamented the lack of focus on professors of education leading these studies, this systematic review found education to be the most common department affiliation with 28% and computer science coming in second at 20%. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for, 72% of the studies focused on students, 17% instructors, and 11% managers.

Grounded coding was used to answer the overarching question of how AIEd was used in HE. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. Assessment and evaluation had a wide variety of purposes, including assessing academic progress and student emotions towards learning, individual and group evaluations, and class based online community assessments. Predicting emerged as a code with ten axial codes, as AIEd predicted dropouts and at-risk students, innovative ability, and career decisions. AI Assistants were specific to supporting students in HE. These assistants included those with an anthropomorphic presence, such as virtual agents and persuasive intervention through digital programs. ITS systems were not always noted in extant systematic reviews but were specifically mentioned in 18 of the studies in this review. ITS systems in this study provided customized strategies and approaches to student’s characteristics and needs. The final code in this study highlighted the use of AI in managing student learning, including learning analytics, curriculum sequencing, instructional design, and clustering of students.

The findings of this study provide a springboard for future academics, practitioners, computer scientists, policymakers, and funders in understanding the state of the field in AIEd HE, how AI is used. It also provides actionable items to ameliorate gaps in the current understanding. As the use AIEd will only continue to grow this study can serve as a baseline for further research studies in the use of AIEd in HE.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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• Artificial Intelligence
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• Higher education

Higher Education Isn’t the Enemy

Those who threaten academic freedom, from outside or inside campus, are threatening higher education itself—to America’s peril.

I ’ve spent more than five decades making difficult decisions in finance, government, business, and politics. Looking back, what most prepared me for the life I’ve led was the open exchange of ideas that I experienced in college and law school, supported by a society-wide understanding that universities and their faculty should be allowed to pursue areas of study as they see fit, without undue political or financial pressure. More broadly, throughout my career, I have seen firsthand the way America’s higher-education system strengthens our nation.

I cannot recall a time when the country’s colleges and universities, and the wide range of benefits they bring, have faced such numerous or serious threats. Protests over Gaza, Israel, Hamas, and anti-Semitism—and the attempt by certain elected officials and donors to capitalize on these protests and push a broader anti-higher-education agenda—have been the stuff of daily headlines for months. But the challenges facing colleges and universities have been building for years, revealed in conflicts over everything from climate change and curriculum to ideological diversity and academic governance.

But there is a threat that is being ignored, one that goes beyond any single issue or political controversy. Transfixed by images of colleges and universities in turmoil, we risk overlooking the foundational role that higher education plays in American life. With its underlying principles of free expression and academic freedom, the university system is one of the nation’s great strengths. It is not to be taken for granted. Undermining higher education would harm all Americans, weakening our country and making us less able to confront the many challenges we face.

T he most recent upheavals on American campuses—and the threat posed to the underlying principles of higher education—have been well documented.

In some cases, individuals have been silenced or suppressed, not because they were threatening anyone’s physical safety or disrupting the functioning of the university environment, but rather, it seems, because of their opinions. The University of Southern California, for example, recently canceled its valedictorian’s speech at graduation. Although administrators cited safety concerns, many on campus, including the student herself, said they believe that the true cause lay in the speaker’s pro-Palestinian, anti-Israel views . One does not have to agree with the sentiments being expressed by a speaker in order to be troubled by the idea that they would be suppressed because of their content.

Conor Friedersdorf: Columbia University’s impossible position

In other cases, it is the demonstrators themselves who have sought to force their views on others—by breaking university policies regarding shared spaces, occupying buildings, and reportedly imposing ideological litmus tests on students seeking to enter public areas of campus. Some activists have advocated violence against those with whom they disagree. Even before the unrest of recent weeks, I had heard for many years from students and professors that they felt a chilling effect on campuses that rendered true discussion—including exchanges of ideas that might make others uncomfortable—very difficult.

Even as free speech faces serious threats from inside the campus, academic freedom is under assault from outside. To an unprecedented degree, donors have involved themselves in pressure campaigns, explicitly linking financial support to views expressed on campus and the scholarship undertaken by students and faculty. At the University of Pennsylvania, one such effort pressed donors to reduce their annual contribution to $1 to protest the university’s decision to host a Palestinian literary conference. At Yale, Beverly Gage, the head of the prestigious Brady-Johnson Program in Grand Strategy, felt compelled to resign after the program came under increasing pressure from its donors. Among other things, the donors objected to an op-ed by an instructor in the program headlined “How to Protect America From the Next Donald Trump.”

It’s not just donors. Elected officials and candidates for office are also attacking academic freedom. On a Zoom call whose content was subsequently leaked, a Republican member of Congress, Jim Banks of Indiana, characterized recent hearings with the presidents of Harvard, MIT, Penn, and Columbia—along with upcoming ones with the presidents of Rutgers, UCLA, and Northwestern—as part of a strategy to “defund these universities.” In a recent campaign video, former President Trump asserted that colleges are “turning our students into Communists and terrorists and sympathizers,” and promised to retaliate by taxing, fining, and suing private universities if he wins a second term. Senator J. D. Vance of Ohio, a close ally of Trump’s, has introduced a bill that would punish schools that don’t crack down on demonstrators. The bill would tax the endowment of such schools heavily and curb their access to federal funds.

The methods of these donors and politicians—politically motivated subpoenas and hearings, social-media pressure campaigns, campaign-trail threats—may not violate the First Amendment. They do, however, seek to produce a chilling effect on free speech. The goal of these efforts is to force universities to bow to outside pressure and curtail the range of ideas they allow—not because scholars at universities believe those ideas lack merit, but because the ideas are at odds with the political views of those bringing the pressure.

A ll of this needs to be seen against a foreboding backdrop. At a time when trust in many American institutions is at an all-time low, skepticism about higher education is on the rise. Earlier this year, a noteworthy essay by Douglas Belkin in The Wall Street Journal explored “Why Americans Have Lost Faith in the Value of College.” The New York Times wondered last fall whether college might be a “risky bet.” According to Gallup, confidence in higher education has fallen dramatically—from 57 percent in 2015 to 36 percent in 2023. The attacks on free expression and academic freedom on campus are both causes and symptoms of this declining confidence.

It is ironic that, at a moment when higher education faces unprecedented assaults, more Americans than ever have a college diploma. When I graduated from college, in 1960, only 8 percent of Americans held a four-year degree. Today, that number has increased almost fivefold, to 38 percent. Even so, I suspect that many Americans don’t realize just how exceptional the country’s university system actually is. Although the United States can claim less than 5 percent of the world’s population, it is home to 65 percent of the world’s 20 highest-ranked universities (and 28 percent of the world’s top-200 universities). Americans can get a quality education at thousands of academic institutions throughout the country.

Despite the skepticism in some quarters about whether a college degree is really worth it, the financial benefits of obtaining a degree remain clear. At 25, college graduates may earn only about 27 percent more than high-school-diploma holders. However, the college wage premium doubles over the course of their lifetime, jumping to 60 percent by the time they reach age 55. Looking solely at an individual’s financial prospects, the case for attending college remains strong.

David Deming: The college backlash is going too far

But the societal benefits we gain from higher education are far greater—and that’s the larger point. Colleges and universities don’t receive tax exemptions and public funds because of the help they give to specific individuals. We invest in higher education because there’s a broad public purpose.

Our colleges and universities are seen, rightly, as centers of learning, but they are also engines of economic growth. Higher graduation rates among our young people lead to a better-educated workforce for businesses and a larger tax base for the country as a whole. Institutions of higher education spur early-stage research of all kinds, create environments for commercializing that research, provide a base for start-up and technology hubs, and serve as a mentoring incubator for new generations of entrepreneurs and business leaders. In many communities, especially smaller towns and rural areas, campuses also create jobs that would be difficult to replace.

The importance of colleges and universities to the American economy will grow in the coming decades. As the list of industries that can be automated with AI becomes longer, the liberal-arts values and critical-thinking skills taught by colleges and universities will become only more valuable. Machine learning can aid in decision making. It cannot fully replace thoughtfulness and judgment.

Colleges and universities also help the United States maintain a geopolitical edge. We continue to attract the best and brightest from around the world to study here. Although many of these students stay and strengthen the country, many more return home, bringing with them a lifelong positive association with the United States. When I served as Treasury secretary, I found it extremely advantageous that so many of my foreign counterparts had spent their formative years in the U.S. That’s just as true today. In many instances, even the leadership class in unfriendly countries aspires to send its children to study here. In a multipolar world, this kind of soft-power advantage matters more than ever.

At home, higher education helps create the kind of citizenry that is central to a democracy’s ability to function and perhaps even to survive. This impact may be hard to quantify, but that doesn’t make it any less real.

It is not just lawmakers and executives who must make difficult decisions in the face of uncertainty. All of us—from those running civil-society groups that seek to influence policy to the voters who put elected leaders in office in the first place—are called upon to make hard choices as we live our civic lives. All of us are aware that the country is not in its best condition—this is hardly news. Imagine what that condition might be if we set out to undermine the very institutions that nurture rigorous and disciplined thinking and the free exchange of ideas.

O f course , there is much about higher education that needs fixing. Precisely because colleges and universities are so valuable to society, they should do more to engage with it. Bringing down costs can help ensure that talented, qualified young people are not denied higher education for financial reasons. Being clear about the principles and policies regarding the open expression of views—even as we recognize that applying them may require judgment calls, and that it is crucial to protect student safety and maintain an environment where learning and research can be conducted—would help blunt the criticism, not always made in good faith, that universities have an ideological agenda. Communicating more effectively with the public would help more Americans understand what is truly at stake.

But the fact that universities can do more does not change a basic fact: It is harmful to society to put constraints on open discussion or to attack universities for purposes of short-term political gain. Perhaps some of those trying to discourage the open exchange of ideas at universities believe that we can maintain their quality while attacking the culture of academic independence. I disagree. Unfettered discussion and freedom of thought and expression are the foundation upon which the greatness of our higher-education system is built. You cannot undermine the former without damaging the latter. To take one recent example: After Governor Ron DeSantis reshaped Florida’s New College along ideological lines, one-third of the faculty left within a year. This included scholars not only in fields such as gender studies, which many conservatives view with distaste, but in areas such as neuroscience as well.

We can have the world’s greatest higher-education system, with all of the benefits it brings to our country, or we can have colleges and universities in which the open exchange of views is undermined by pressure campaigns from many directions. We can’t have both.

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Some universities struggling to close high-earner gender pay gap

The  analysis finds some uk universities had fewer women in the top pay bracket in 2022-23 than they did in 2014-15.

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The proportion of men in the top pay bracket at UK universities has fallen for the eighth year in a row, analysis shows, but vast gender inequalities still exist at certain institutions and in many fields.

Higher Education Statistics Agency (Hesa) figures  show that about 33,300 UK university staff received at least £65,578 in 2022-23 – 33.6 per cent of which were women.

This was up from 32.4 per cent in the top bracket the year before – a metric that has increased every year since the time series began in 2014-15.

However, Jefferson Frank, professor of economics at Royal Holloway, University of London , said headline measures such as these are progressing too slowly because of underlying issues such as discrimination in promotions and pay, and the under-provision of support for women seeking to advance their careers.

Professor Frank said the  historical under-representation of women  meant that inequality is embedded in the sector, and unless some professors retire or work part-time, it can be difficult to open up new opportunities.

Times Higher Education ’s   analysis found that more women were in the top pay bracket in 2022-23 than in 2014-15 across the vast majority of UK providers.

The University of Exeter and the University of Stirling both saw the proportion of women earning the maximum salaries increase by 16 percentage points over this time – the greatest improvement of the 67 institutions where the gender was known of at least 100 staff.

Campus resource collection: Wisdom from women leaders in higher education

However, four providers ( Northumbria University , Aston University , the University of Hull and Nottingham Trent University ) recorded fewer top-paid women than in 2014-15, and Ulster University saw no improvement.

Professor Frank said pay inequality has worsened with the move towards “managerial universities” – with academics often judged on their contributions to the bureaucratic structure, rather than on their research and teaching productivity.

“While this might in the short run have an effect on cutting the gender pay gap…it has a prolonged negative effect on their careers,” he added.

David Bass, director of equality, diversity and inclusion at Advance HE, said that despite longstanding challenges, the UK sector has made progress to narrow the pay gap.

“However, women academics are over-represented in part-time roles, which tend to be lower paid, and senior academic roles are not often offered on a part-time basis ,” he added.

“The lack of flexible working arrangements in more senior roles also prevents women from applying or getting promoted and accessing those higher wages.”

The figures also show that gender parity has been achieved in education departments – where women make up 51 per cent of the best paid. All academic fields saw  improvements over the eight-year period , with the largest coming in architecture and planning – a 13 percentage point swing.

However, men still make up 83 per cent of the top earners in engineering and technology, and 79 per cent in the biological, mathematical and physical sciences – two fields which saw among the lowest improvements.

Jo Grady, general secretary of the University and College Union (UCU), said institutions should look to universities like Bristol, which agreed a landmark deal to tackle the pay gap.

“Due to years of tireless campaigning by UCU members, the gender pay gap in higher education is closing, however, the pace of change remains too slow,” she added.

Hull said it had increased the proportion of women in the top pay bracket in the last year by investing in professional development, creating new career pathways and peer support networks and strengthening governance and policy.

A Northumbria spokesperson said it has made a strategic commitment to eliminating pay gaps and has increased progression rates for women over the past two years.

Aston said it has made a significant number of senior female appointments over the past 18 months which have yet to be included fully in the data.

Ulster and Nottingham Trent have been approached for comment.

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Guest Essay

Higher Education Needs More Socrates and Plato

By Ezekiel J. Emanuel and Harun Küçük

Dr. Emanuel and Dr. Küçük are on the faculty of the University of Pennsylvania, where Dr. Emanuel is a professor and the vice provost for global initiatives and Dr. Küçük is an associate professor of the history and sociology of science.

The right attacks colleges and universities as leftist and woke. Progressives castigate them as perpetuating patriarchy and white privilege. The burdens of these culture war assaults are compounded by parents worried that the exorbitant costs of higher education aren’t worth it.

No wonder Americans’ faith in universities is at a low. Only 36 percent of Americans have confidence in higher education, according to a survey by Gallup last year, a significant drop from eight years ago. And this was before colleges and universities across the country were swept up in a wave of protests and counter-protests over the war in Gaza.

But the problems facing American higher education are not just the protests and culture war attacks on diversity, course content, speech and speakers. The problem is that higher education is fundamentally misunderstood. In response, colleges and universities must reassert the liberal arts ideals that have made them great but that have been slipping away.

By liberal arts, we mean a broad-based education that aspires to send out into society an educated citizenry prepared to make its way responsibly in an ever-more complex and divided world. We worry that at many schools, students can fulfill all or most of their general education requirements and take any number of electives without having had a single meaningful discussion that is relevant to one’s political life as a citizen.

Over the past century, what made American higher education the best in the world is not its superiority in career training, but educating students for democratic citizenship, cultivating critical thinking and contributing to the personal growth of its students through self-creation. To revive American higher education, we need to reinvigorate these roots.

In Europe and many countries elsewhere, colleges and universities have undergraduates specialize from Day 1, focusing on developing area-specific skills and knowledge. College students are trained to become doctors, lawyers or experts in international relations, English literature or computer science.

In the United States, European-style specialization for medical, legal, business or public policy careers is the purpose of post-collegiate professional schools. Traditionally, the American college has been about imparting a liberal arts education, emphasizing reasoning and problem solving. Those enduring skills are the critical ingredients for flourishing companies and countries.

Historically, students arriving on American college campuses spent a majority of their first two years taking classes outside their projected majors. This exposed them to a common curriculum that had them engage with thoughtful writings of the past to develop the skills and capacity to form sound, independent judgments.

Over the past half century, American colleges and universities have moved away from this ideal , becoming less confident in their ability to educate students for democratic citizenship. This has led to a decline in their commitment to the liberal arts, a trend underscored in the results last year of a survey of chief academic officers at American colleges and universities by Inside Higher Ed. Nearly two-thirds agreed that liberal arts education was in decline, and well over half felt that politicians, college presidents and university boards were increasingly unsympathetic to the liberal arts.

Today, there is almost no emphasis on shared courses among majors that explore and debate big questions about the meaning of equality, justice, patriotism, personal obligations, civic responsibility and the purpose of a human life. Majors that once required only eight or 10 courses now require 14 or more, and students are increasingly double majoring — all of which crowds out a liberal arts education. Ambitious students eager to land a prestigious consulting, finance or tech job will find it too easy to brush aside courses in the arts, humanities and social and natural sciences — the core of a liberal education.

The devaluing of the first two years of a shared liberal arts education has shortchanged our students and our nation. Educating young adults to be citizens is why the first two years of college still matter.

To that end, the so-called Great Books have long been the preferred way to foster citizenship. This approach is not, contrary to critics on the left and right, about sanctifying specific texts for veneration or a mechanism for heritage transmission.

Books by Plato, Aristotle, Hobbes, Locke, Kant, Emerson, Thoreau, Whitman as well as Wollstonecraft, Austen, Woolf, Baldwin, Hurston and Orwell are worthy of introductory collegiate courses for students of all majors. These writers address the fundamental questions of human life. They explore the ideas of self-determination, friendship, virtue, equality, democracy and religious toleration and race that we have all been shaped by.

As students address those big questions, the Great Books authors provide a road map as they challenge and criticize one another and the conventional wisdom of the past. The Socrates of Plato’s dialogues is the exemplar — asking about beliefs and then subjecting them to respectful but critical analysis and skepticism.

These books are best studied in small seminar discussions, which model and inculcate in students democratic behavior. This discourse is an antidote to the grandstanding in today’s media and social media.

The teacher is less an expert in specific writers and more a role model for intellectual curiosity, asking probing questions, offering critical analyses and seeking deeper understanding. In an idealized Socratic fashion, these discussions require listening at length and speaking briefly and, most important, being willing to go where the argument leads.

Parents who are paying for college might question the value of spending $80,000 a year so that their son or daughter can read Plato, Hobbes and Thoreau instead of studying molecular biology or machine learning. But discussing life’s big value questions in seminars gives students personal engagement with professors that can never be reproduced in large lecture halls. Discussions among students on their deepest thoughts cultivates curiosity and empathy, and forges bonds of friendship important for citizenship and fulfilling lives.

Although we like to set ourselves apart from the past by appeals to modernity, the fundamental questions that we find ourselves asking are not always modern, and the latest answer is not always right. But how would you know how to think beyond the readily presented check boxes if you haven’t done the work of laying things out and putting them back together for yourself?

War was no less a concern for Thucydides, Tacitus and Thoreau than it is today. Discussing Great Books allows students to gain distance from the daily noise and allows their reason to roam free among principles and foundations rather than becoming absorbed in contemporary events. Our biggest problems are often best addressed not by leaning in but by stepping away to reflect on enduring perspectives.

Liberal arts education is not value neutral. That is why it is indispensable today. Freedom of thought, critical reasoning, empathy for others and respectful disagreement are paramount for a flourishing democratic society. Without them, we get the unreasoned condemnations so pervasive in today’s malignant public discourse. With them, we have a hope of furthering the shared governance that is vital to America’s pluralistic society.

Ezekiel Emanuel and Harun Küçük are on the faculty of the University of Pennsylvania, where Dr. Emanuel is a professor and the vice provost for global initiatives and Dr. Küçük is an associate professor of the history and sociology of science.

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