a literature map of the research

How to Master at Literature Mapping: 5 Most Recommended Tools to Use

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After putting in a lot of thought, time, and effort, you’ve finally selected a research topic . As the first step towards conducting a successful and impactful research is completed, what follows it is the gruesome process of literature review . Despite the brainstorming, the struggle of understanding how much literature is enough for your research paper or thesis is very much real. Unlike the old days of flipping through pages for hours in a library, literature has come easy to us due to its availability on the internet through Open Access journals and other publishing platforms. This ubiquity has made it even more difficult to cover only significant data! Nevertheless, an ultimate solution to this problem of conglomerating relevant data is literature mapping .

Table of Contents

What is Literature Mapping?

Literature mapping is one of the key strategies when searching literature for your research. Since writing a literature review requires following a systematic method to identify, evaluate, and interpret the work of other researchers, academics, and practitioners from the same research field, creating a literature map proves beneficial. Mapping ideas, arguments, and concepts in a literature is an imperative part of literature review. Additionally, it is stated as an established method for externalizing knowledge and thinking processes. A map of literature is a “graphical plan”, “diagrammatic representation”, or a “geographical metaphor” of the research topic.

Researchers are often overwhelmed by the large amount of information they encounter and have difficulty identifying and organizing information in the context of their research. It is recommended that experts in their fields develop knowledge structures that are richer not only in terms of knowledge, but also in terms of the links between this knowledge. This knowledge linking process is termed as literature mapping .

How Literature Mapping Helps Researchers?

Literature mapping helps researchers in following ways:

• It provides concrete evidence of a student’s understanding and interpretation of the research field to share with both peers and professors.
• Switching to another modality helps researchers form patterns to see what might otherwise be hidden in the research area.
• Furthermore, it helps in identifying gaps in pertinent research.
• Finally, t lets researchers identify potential original areas of study and parameters of their work.

How to Make a Literature Map?

Literature mapping is not only an organizational tool, but also a reflexive tool. Furthermore, it distinguishes between declarative knowledge shown by identifying key concepts, ideas and methods, and procedural knowledge shown through classifying these key concepts and establishing links or relationships between them. The literature review conceptualizes research structures as a “knowledge production domain” that defines a productive and ongoing constructive element. Thus, the approaches emphasize the identity of different scientific institutions from different fields, which can be mapped theoretically, methodologically, or fundamentally.

The two literature mapping approaches are:

• Mapping with key ideas or descriptors: This is developed from keywords in research topics.
• Author mapping: This is also termed as citation matching that identifies key experts in the field and may include the use of citations to interlink them.

Generally, literature maps can be subdivided by categorization processes based on theories, definitions, or chronology, and cross-reference between the two types of mapping. Furthermore, researchers use mind maps as a deductive process, general concept-specific mapping (results in a right triangle), or an inductive process mapping to specific concepts (results in an inverted triangle).

What are Different Literature Mapping Methods?

The different types of literature mapping and representations are as follows:

1. Feature Mapping:

Argument structures developed from summary registration pages.

2. Topic Tree Mapping:

Summary maps showing the development of the topic in sub-themes up to any number of levels.

3. Content Mapping:

Linear structure of organization of content through hierarchical classification.

4. Taxonomic Mapping:

Classification through standardized taxonomies.

5. Concept Mapping:

Linking concepts and processes allows procedural knowledge from declarative information. With a basic principle of cause and effect and problem solving, concept maps can show the relationship between theory and practice.

6. Rhetorical Mapping:

The use of rhetoric communication to discuss, influence, or persuade is particularly important in social policy and political science and can be considered a linking strategy. A number of rhetorical tools have been identified that can be used to present a case, including ethos, metaphor, trope, and irony.

7. Citation Mapping:

Citation mapping or matching is a research process established to specifically establish links between authors by citing their articles. Traditional manual citation indexes have been replaced by automated databases that allow visual mapping methods (e.g. ISI Web of Science). In conclusion, citation matching in a subject area can be effective in determining the frequency of authors and specific articles.

5 Most Useful Literature Mapping Tools

Technology has made the literature mapping process easier now. However, with numerous options available online, it does get difficult for researchers to select one tool that is efficient. These tools are built behind explicit metadata and citations when coupled with some new machine learning techniques. Here are the most recommended literature mapping tools to choose from:

1. Connected Papers

a. Connected Papers is a simple, yet powerful, one-stop visualization tool that uses a single starter article.

b. It is easy to use tool that quickly identifies similar papers with just one “Seed paper” (a relevant paper).

c. Furthermore, it helps to detect seminal papers as well as review papers.

d. It creates a similarity graph not a citation graph and connecting lines (based on the similarity metric).

e. Does not necessarily show direct citation relationships.

f. The identified papers can then be exported into most reference managers like Zotero, EndNote, Mendeley, etc.

2. Inciteful

a. Inciteful is a customizable tool that can be used with multiple starter articles in an iterative process.

b. Results from multiple seed papers can be imported in a batch with a BibTex file.

c. Inciteful produces the following lists of papers by default:

• Similar papers (uses Adamic/Adar index)
• “Most Important Papers in the Graph” (based on PageRank)
• Recent Papers by the Top 100 Authors
• The Most Important Recent Papers

d. It allows filtration of results by keywords.

e. Importantly, seed papers can also be directly added by title or DOI.

a. Litmaps follows an iterative process and creates visualizations for found papers.

b. It allows importing of papers using BibTex format which can be exported from most reference managers like Zotero, EndNote, Mendeley. In addition, it allows paper imports from an ORCID profile.

c. Keywords search method is used to find Litmaps indexed papers.

d. Additionally, it allows setting up email updates of “emergent literature”.

e. Its unique feature that allows overlay of different maps helps to look for overlaps of papers.

f. Lastly, its explore function allows finding related papers to add to the map.

4. Citation-based Sites

a. CoCites is a citation-based method for researching scientific literature.

b. Citation Gecko is a tool for visualizing links between articles.

c. VOSviewer is a software tool for creating and visualizing bibliometric networks. These networks are for example journals, may include researchers or individual publications, which can be generated based on citation, bibliographic matching , co-citation, or co-authorship relationships. VOSviewer also offers text mining functionality that can be used to create and visualize networks of important terms extracted from a scientific literature.

5. Citation Context Tools

a. Scite allow users to see how a publication has been cited by providing the context of the citation and a classification describing whether it provides supporting or contrasting evidence for the cited claim.

b. Semantic Scholar is a freely available, AI-powered research tool for scientific literature.

Have you ever mapped your literature? Did you use any of these tools before? Lastly, what are the strategies and methods you use for literature mapping ? Let us know how this article helped you in creating a hassle-free and comprehensive literature map.

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Literature Mapping in Scientific Research: A Comprehensive Review

Accelerate scientific research with Literature Mapping: a comprehensive tool for knowledge discovery and data-driven insights.

Literature mapping is a process that involves analyzing and visualizing the scientific literature on a particular topic to identify research gaps, improve collaboration, and inform decision-making.

In this article, we list five benefits of literature mapping for scientists and researchers and show you types and tools to save your time and help you find better evidence.

What is Literature Mapping?

Literature mapping is a process that involves analyzing and visualizing the scientific literature on a particular topic. It includes systematically searching, collecting, and reviewing relevant studies, articles, and books published in a specific field or discipline.

The purpose of literature mapping is to provide a comprehensive overview of the current state of knowledge on a topic, identify gaps in the literature, and potential areas for future research. It can be useful for those who seek to conduct a systematic review, develop a research proposal, or explore new research areas.

Benefits of Literature Mapping

Here are five benefits of literature mapping for scientists and researchers:

• Identify research gaps : Literature mapping helps researchers to identify gaps in the existing research and to determine areas that require further investigation.
• Visualize the research landscape : By creating visualizations of the scientific literature, researchers can see the relationships between different research topics.
• Save time : Literature mapping can help researchers save time by providing an overview of the literature on a particular topic, including relevant studies and duplicated work.
• Improve collaboration : Literature mapping can help researchers to collaborate more effectively by providing a shared understanding of the research landscape. This improves communication, and facilitates the workflow between different disciplines.
• Inform decision-making : Literature mapping can help researchers to make assertive decisions. This can be especially useful for policymakers and other decision-makers who need to make decisions based on scientific evidence.

Types of Literature Mapping

Feature mapping.

Feature mapping is a technique used primarily in data analysis and machine learning to identify patterns and relationships between features of a dataset. It involves analyzing the data and plotting the relationships between different features of the dataset on a map or chart.

Some of the main features include:

• Identification of relationships : Feature mapping can help identify the relationships between different features or variables in a dataset. This can allow for better modeling and prediction of outcomes.
• Pattern recognition : By plotting the relationships between features of a dataset, feature mapping can help identify patterns and anomalies that may not be immediately apparent in the raw data.
• Visualization : Feature mapping often involves creating visual representations of the relationships between features of a dataset. This can help make the data easier to understand and interpret.
• Dimension reduction : When dealing with large datasets with many features, feature mapping can help reduce the dimensionality of the data. This can help simplify the data and make it easier to analyze.
• Data clustering : Feature mapping can also help identify groups or clusters of data points that share similar features. This can allow for more targeted analysis and modeling of specific groups within the dataset.
• Feature selection : Feature mapping can aid in the selection of the most important features from a dataset. By identifying the relationships between features, researchers can determine which features are most relevant to the outcomes they are trying to predict.

Topic Tree Mapping

Topic tree mapping is a technique used to visualize and organize the relationships between different topics or themes within a larger subject area. It involves creating a hierarchical structure of topics, with more general topics at the top and more specific subtopics branching out below.

Content Mapping

Content mapping is the process of creating a visual representation or map of the content of a document, website, or other information source. It involves breaking down the content into its constituent parts, organizing it according to a logical structure, and presenting it in a user-friendly and easily accessible way.

Taxonomic Mapping

Taxonomic mapping is the process of assigning different taxonomic categories to specific objects or organisms based on their characteristics, traits, and other distinguishing features. This mapping enables the organization and identification of different species and helps researchers and scientists to conduct various studies and experiments related to their classification, evolution, and diversity.

Concept Mapping

Concept Mapping is a visual representation of the relationships between concepts and ideas in a particular field. It involves identifying key concepts, and organizing them into a hierarchical structure. It can help to identify gaps in knowledge and aid in the development of new theories.

Rhetorical Mapping

Rhetorical mapping is a process used in communication studies and critical discourse analysis to analyze the structure and content of discourse. It involves creating a visual representation or diagram of a text or speech that identifies its various components, such as arguments, claims, evidence, and rhetorical strategies used to persuade the audience. Rhetorical mapping allows researchers to understand how the speaker or writer uses language and persuasion techniques to influence the audience’s beliefs and attitudes.

Citation Mapping

Citation Mapping involves tracing the citation history of a particular article, and identifying the articles that have cited it. This can help to identify the impact of the article on the field, and identify related research.

Tools for Literature Mapping

• Citation Gecko : Citation Gecko is a web-based tool that allows users to quickly and easily search for and download citation data from various academic databases. It streamlines and simplifies the process of finding and organizing citations for research projects.
• Inciteful : Inciteful is a literature-mapping tool that visualizes citation networks and identifies key authors and articles within a particular field of research. It can be used to explore the literature on a specific topic, as well as to identify gaps in current research.
• OpenKnowledge : OpenKnowledge is an online platform for sharing and discovering research papers and other scholarly materials. It enables users to search for and download documents, as well as to connect with other researchers who are working in the same field.
• ConnectedPapers : ConnectedPapers is a search engine that allows users to explore citation networks and discover the most influential papers and authors in a particular field. It uses citation information to uncover relationships between different papers and to suggest potentially relevant articles to read.
• LitMaps : LitMaps is a mapping tool that allows users to explore the relationships between different articles and concepts within a particular field of study. It visualizes the connections between different scholarly articles and helps users to develop a deeper understanding of the underlying themes and concepts within a particular field.
• Local Citation Network : Local Citation Network is a tool for mapping the relationships between different articles and authors within a particular geographic area. It allows users to explore the research in progress in a particular region and to identify potential collaborators and sources of funding.
• CoCites : CoCites is a literature-mapping tool that identifies the most frequently cited articles and authors within a particular field. It allows users to explore the relationships between different papers and to identify key areas of research.
• VOSviewer : VOSviewer is a tool for visualizing citation networks and identifying key authors, papers, and concepts within a particular field of research. It allows users to explore the relationships between different papers and to identify areas of overlap and potential collaboration.
• ResearchRabbit : ResearchRabbit is a web-based research tool that allows users to search for and collect scholarly articles and other research materials. It streamlines the research process by helping users to find relevant articles and to organize and annotate their findings.

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The platform’s vast library of illustrations, user-friendly interface, and collaboration features make it an essential tool for researchers who want to communicate their research findings effectively and efficiently.

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LITERATURE REVIEW SOFTWARE FOR BETTER RESEARCH

“Litmaps is a game changer for finding novel literature... it has been invaluable for my productivity.... I also got my PhD student to use it and they also found it invaluable, finding several gaps they missed”

Varun Venkatesh

Austin Health, Australia

As a full-time researcher, Litmaps has become an indispensable tool in my arsenal. The Seed Maps and Discover features of Litmaps have transformed my literature review process, streamlining the identification of key citations while revealing previously overlooked relevant literature, ensuring no crucial connection goes unnoticed. A true game-changer indeed!

Ritwik Pandey

Doctoral Research Scholar – Sri Sathya Sai Institute of Higher Learning

Using Litmaps for my research papers has significantly improved my workflow. Typically, I start with a single paper related to my topic. Whenever I find an interesting work, I add it to my search. From there, I can quickly cover my entire Related Work section.

David Fischer

Research Associate – University of Applied Sciences Kempten

“It's nice to get a quick overview of related literature. Really easy to use, and it helps getting on top of the often complicated structures of referencing”

Christoph Ludwig

Technische Universität Dresden, Germany

“This has helped me so much in researching the literature. Currently, I am beginning to investigate new fields and this has helped me hugely”

Aran Warren

Canterbury University, NZ

“I can’t live without you anymore! I also recommend you to my students.”

Professor at The Chinese University of Hong Kong

“Seeing my literature list as a network enhances my thinking process!”

Katholieke Universiteit Leuven, Belgium

“Incredibly useful tool to get to know more literature, and to gain insight in existing research”

KU Leuven, Belgium

“As a student just venturing into the world of lit reviews, this is a tool that is outstanding and helping me find deeper results for my work.”

Franklin Jeffers

South Oregon University, USA

“Any researcher could use it! The paper recommendations are great for anyone and everyone”

Swansea University, Wales

“This tool really helped me to create good bibtex references for my research papers”

Ali Mohammed-Djafari

Director of Research at LSS-CNRS, France

“Litmaps is extremely helpful with my research. It helps me organize each one of my projects and see how they relate to each other, as well as to keep up to date on publications done in my field”

Daniel Fuller

Clarkson University, USA

As a person who is an early researcher and identifies as dyslexic, I can say that having research articles laid out in the date vs cite graph format is much more approachable than looking at a standard database interface. I feel that the maps Litmaps offers lower the barrier of entry for researchers by giving them the connections between articles spaced out visually. This helps me orientate where a paper is in the history of a field. Thus, new researchers can look at one of Litmap's "seed maps" and have the same information as hours of digging through a database.

Baylor Fain

Postdoctoral Associate – University of Florida

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Creating a Comprehensive Literature Review Map: A Step-by-Step Example

• backlinkworks
• Writing Articles & Reviews
• October 16, 2023

A literature review is an essential component of any academic research paper or thesis. IT involves examining existing literature, scholarly articles, books, and other sources related to your research topic. A literature review map acts as a visual representation of the concepts, studies, and theories that have been covered in the literature. In this article, we will guide you through the process of creating a comprehensive literature review map, step-by-step, to help you structure and organize your literature review effectively.

Step 1: Define Your Research Topic

The first step in creating a literature review map is to clearly define your research topic. Be specific and narrow down your focus to ensure that you have a manageable scope for your literature review. Take into consideration the research objectives or guiding questions that will shape your review.

Step 2: Identify Relevant Keywords

Once you have defined your research topic, identify the keywords and search terms that are most relevant to your study. Brainstorm a list of potential keywords that are commonly used in the literature related to your topic. These keywords will help you locate relevant sources during your literature search.

Step 3: Conduct a Thorough Literature Search

Using databases and search engines specific to your field of study, begin conducting a thorough literature search using the identified keywords. Take note of the key articles, books, and studies that are relevant to your research topic. In this step, IT is important to evaluate the credibility and quality of the sources to ensure that you are referring to reputable and reliable information.

Step 4: Read and Analyze the Literature

After collecting a substantial number of sources, carefully read and analyze each one. Highlight key concepts, methodologies, and findings that are relevant to your research. As you progress, make notes or annotations to help you remember important details and connections between different sources.

Step 5: Organize the Literature

Now that you have read and analyzed the literature, IT ‘s time to organize the information into a coherent structure. One effective way to do this is by using a literature review map. Start by creating categories or themes based on the concepts or theories that emerge from the literature. Group together similar ideas or findings to create a visual representation of the interconnectedness of the sources.

Step 6: Create the Literature Review Map

With your categorized information, you can now create the literature review map. This can be done using software such as Microsoft Word, PowerPoint, or dedicated mind mapping tools. Start with your main research topic in the center and branch out with subcategories based on the themes or concepts identified earlier. Connect relevant sources to each subcategory, illustrating how they contribute to the overall understanding of your research topic.

Step 7: Revise and Refine

Review your literature review map for coherence and completeness. Ensure that all the key sources are accurately placed within the appropriate category or subcategory. Check for any gaps in your coverage and make sure that the map represents a comprehensive overview of the literature on your research topic.

Q: How many sources should I include in my literature review map?

A: The number of sources you include will depend on the requirements of your research and the depth of analysis you aim to achieve. However, IT is generally recommended to thoroughly examine a range of sources, including both seminal texts and recent publications, to ensure a well-rounded and comprehensive literature review.

Q: How do I determine the credibility of the sources for my literature review?

A: Evaluating the credibility of your sources is crucial to ensure that you are basing your review on reputable information. Consider the author’s qualifications, the credibility and reputation of the publishing outlet, the presence of citations within the article, and the overall coherence and consistency of the research findings.

Q: Can I use a literature review map for disciplines outside of the humanities and social sciences?

A: Absolutely! While literature reviews are commonly associated with humanities and social sciences, they are applicable to any academic field. Whether you are conducting research in the sciences, engineering, or any other discipline, a literature review map will help you organize and present the relevant scholarly literature specific to your research topic.

By following these step-by-step guidelines, you can create a comprehensive literature review map that will serve as a valuable tool throughout your research. Remember to regularly update and refine your map as you progress in your studies. A well-organized literature review will not only demonstrate your knowledge and understanding of the field, but also provide a solid foundation for your own research and contribute to the wider scholarly conversation.

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Literature Mapping Tools

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Literature Reviews: A Working Definition

A literature review is a methodical or organized review of the published literature on a specific topic or research question designed to analyze--not just summarize--scholarly writings that are related directly to your research question. That is, it represents the literature that provides the context for your research and shows a correspondence between those writings and your own work.

Before you get started...

The past few years have seen an explosion of online tools designed to automate the process of doing literature reviews. These tools generally work by asking you to identify a relevant article (often called a "seed article") and use the metadata attached to articles (such as authors and keywords), or citations and reference lists to find related articles. Most tools offer some type of visualization feature to trace the connections between papers, and increasingly, tools offer summaries of the research content. These tools provide researchers with an option to at least partially automate some of their literature review work which can save a lot of time.

Things to keep in mind:

• Very little independent research has been done to test the reliability, scope, and accuracy of these tools
• In our own testing of tools that provide summaries of articles, we have sometimes found that summaries do not reflect the same key take-aways that we have identified
• Reproducibility of searches is questionable so they may not be the best choice for things like systematic reviews
• Because of the reliance on citation chaining, there is a built in bias towards heavily cited works which ends up creating a feedback loop that may cause you to miss relevant and/or newer materials
• Not everything is indexed in the data sets used by a given tool; this is particularly the case in the arts and humanities which are more oriented towards books
• Both the tools themselves, many of which are open access projects, and the indices they rely on may stop being updated/maintained, or go offline for a variety of reasons
• You still need to use a library to access full text in a majority of cases

NOTE: This is a rapidly evolving field and we will be updating this guide on a regular basis.

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• Last Updated: Feb 23, 2024 1:40 PM
• URL: https://guides.lib.udel.edu/litmap

Howard Aldrich

Kenan professor of sociology, dept of sociology @ unc chapel hill.

Powerful Tools for Mapping a Research Literature

Professor Courtney Page Tan , Assistant Professor of Human Resilience in the Department of Security and Emergency Services at Embry-Riddle Aeronautical University, has compiled a list of powerful literature mapping tools. You can use these tools to increase the scale and scope of the literature for your projects. Many provide stunning graphical displays of search results (Edward Tufte would approve).

Connected Papers lets you explore connected papers in a visual graph, beginning with a starter paper you select. You can start with a DOI, URL, or paper title. Purposes: (1) Get a visual overview of a new academic field; (2) Make sure you haven’t missed an important paper; (3) Create the bibliography to your thesis; and (4) Discover the most relevant prior and derivative works.

scite_ Smart Citations for Intelligent Research . Smart Citations allow users to see how a scientific paper has been cited by providing the context of the citation and a classification describing whether it provides supporting or disputing evidence for the cited claim. They claim a database of over 23 million full-text articles.

Open Knowledge Maps . Calling themselves a “visual interface to the world’s scientific community,” their tool allows you to start with a few keywords to search for literature on a topic. Results display the main areas at a glance, and papers related to each area. In addition to giving you an overview of the area, it helps you identify important concepts. They highlight open access papers in their search results.

Local Citation Network . You input an article using its DOI or a scanned copy containing DOIs and the program shows you suggested articles for you to follow up.

They explain that “This web app aims to help scientists with their literature review using metadata from Microsoft Academic and Crossref . Academic papers cite one another, thus creating a citation network (= graph) . Each node (= vertex) represents an article and each edge (= link / arrow) represents a reference / citation. Citation graphs are a topic of bibliometrics, for which other great software exists as well .

This web app visualizes subsets of the global citation network that I call “local citation networks,” defined by the references of a given set of input articles. In addition, the most cited references missing in the set of input articles are suggested for further review.”

Citation Gecko Gecko is designed to help you find the most relevant papers to your research and give you a more complete sense of the research landscape. Users start from a small set of ‘seed papers’ that define an area you are interested. Gecko will search the citation network for connected papers allowing you to quickly identify important papers you may have missed.

PRISMA Flow Diagram Generator . This is the most complex of the tools. It generates a graphical representation of the flow of citations reviewed in the course of a Systematic Review. Click here for an example.

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3 Innovative Literature Mapping Tools for Citation Maps 

In the ever-evolving landscape of academic research, navigating through vast amounts of literature can be daunting. Enter innovative literature mapping tools, packed with unique features.

They simplify and revolutionise the way researchers interact with scientific literature, enhancing the efficiency and depth of literature reviews.

Let’s dive into how these tools are reshaping the approach to academic research.

Innovative Literature Mapping Tools

What is a literature mapping tool.

A citation mapping tool is a game-changer. Think of it as a detective tool that uncovers the intricate web of connections within scientific literature.

These tools visualise citation relationships, creating a citation map or literature map that guides you through the complex maze of scholarly papers.

One popular example is Inciteful, offering innovative literature mapping tools that not only track citation links but also analyse the context of the citation, revealing direct citation relationships and co-citation networks.

Imagine you have a ‘seed paper,’ a starting point in your literature review. A literature mapping tool then take this seed paper and branch out, finding papers:

• That cite it (direct citation relationships) or
• Those that share a thematic connection (co-citation). 

This forms a citation tree or network, showing you not just one paper but a cluster of similar papers, interconnected by their citation relationships.

More modern citation mapping tool also integrated AI. They not only map out citation relationships but also delve into the citation context or sentiment, offering a richer, more nuanced understanding of how papers are interconnected. 

Litmaps is a cutting-edge citation mapping tool that offers a unique approach to visualising the connections in scientific literature.

It’s designed to simplify and enhance the process of conducting a literature review, especially for researchers looking to map out the citation landscape of a specific topic.

At its core, Litmaps lets you visualise citation relationships in a dynamic, interactive manner. It works by creating a literature map that shows how different papers are connected through citations. 

You start with a ‘seed paper,’ and Litmaps builds a citation network around it, by:

• Identifying seminal papers,
• similar papers, and 
• Other papers that cite your chosen article.

This is particularly helpful for understanding the context and development of research in a given field.

One of the key features of Litmaps is its ability to create a citation tree. This tree not only shows direct citation relationships but also highlights co-citations. This gives you a deeper insight into how ideas and research are interconnected.

In terms of visualisation, Litmaps excels. It uses a similarity graph, not just a standard citation graph, to display connections.

This means you’re seeing a more nuanced representation of the literature, based on the similarity metric of papers, rather than just citation counts.

Litmaps also allows for a high level of customisation. You can filter papers based on:

• The number of citations,
• Publication date, and even
• Specific keywords.

This makes it a highly flexible tool for conducting systematic reviews and meta-analysis.

Litmaps also have a more user-friendly interface, and additional features like tracking the latest papers on a specific topic or a random set of systematic reviews.

Inciteful is an innovative literature mapping tool that stands out in the field of academic research for its unique approach to visualizing citation networks.

This tool is designed to make the process of literature review more intuitive and insightful, especially for researchers and scholars delving into new or complex fields.

When you use Inciteful, you start by selecting a ‘seed paper’. From this single paper, Inciteful creates a citation network, branching out to reveal not only papers that cite your chosen article but also those that are contextually related through co-citation and citation relationships.

This forms a comprehensive citation map, allowing you to see how various research pieces interconnect.

A standout feature of Inciteful is its visualization capabilities. The tool presents a citation graph, where each node represents a paper, and connecting lines indicate citation links.

This visualization helps you grasp the structure of scientific discourse in a field, revealing seminal papers, emerging trends, and key authors. You can then filter and sort papers based on keywords, number of citations, or publication date.

Inciteful isn’t just about numbers of citations; it delves deeper. The tool analyzes the context of citations, bringing to light the sentiment and relevance of each citation relationship.

This adds an extra layer of depth to your literature review, offering insights that go beyond traditional citation counting. Inciteful Incorporates metadata from various sources like:

• Google Scholar,
• Web of Science, and
• Microsoft Academic

Inciteful also ensures that its citation network is rich and current. The tool also supports importing bibliographic data in BibTeX format, making it flexible and adaptable to various research needs.

This makes Inciteful not just a powerful research tool but also a highly customizable one, suited for everything from quick overviews to in-depth systematic reviews.

Connected Papers

Connected Papers is a cool literature mapping tool that offers researchers and scholars an intuitive way to explore the citation network of a specific paper or topic.

It stands out compared to the other mapping tools for its user-friendly design and effective visualisation techniques.

Connected Papers takes a ‘seed paper’ of your choice, then generates a citation graph based on the seed paper, producing a visual network that displays how this paper is connected to others through direct citations and co-citations.

This network reveals the most relevant papers, showing you the ‘big picture’ of research trends and developments related to your topic.

The citation graph in Connected Papers isn’t just a simple map; it’s a detailed visualisation tool. Each node represents a paper, and the lines between them indicate citation relationships.

This visualisation allows you to easily identify:

• Research papers,
• Citations, and even 
• Emerging trends in the field.

You can see at a glance which papers are most cited and how they interlink, providing a comprehensive overview of the scientific landscape.

Connected Papers uses metadata and bibliographic information from databases like Google Scholar, Web of Science, and Microsoft Academic. This ensures that the citation network you’re exploring is both extensive and up-to-date.

It also supports importing data in BibTeX format, making it versatile for different research needs.

This tool is particularly valuable for researchers who are looking to map out the landscape of a new or complex field. It helps in identifying related papers that might not be immediately obvious, providing a deeper understanding of the subject matter.

Literature Review Made Easy, With Citation Map Tools

Litmaps, Inciteful, and Connected Papers represent the forefront of academic research tools, each bringing a unique approach to literature mapping.

They empower researchers with advanced visualisation, comprehensive citation networks, and user-friendly interfaces, making literature reviews more efficient and insightful.

As the landscape of scientific research continues to grow, these tools are invaluable allies in navigating and understanding the complex web of academic knowledge.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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You are here, structuring your ideas: creating a literature map.

It is important to have a plan of the areas to be discussed, using this to indicated how these will link together. In the overall structure of the literature review, there should be a logical flow of ideas and within each paragraph there should be a clear theme, around which related ideas are explored and developed. A literature map can be useful for this purpose as it enables you to create a visual representation of the themes and how they could relate to one another.

A literature map (Cresswell, 2011) is a two dimensional diagrammatic representation of information where links are made between concepts by drawing arrows (which could be annotated to define the nature of these links). Constructing a literature map helps you to:

• develop your understanding of the key issues and research findings in the literature
• to organise ideas in your mind
• to see more clearly how different research studies relate to one another and to group those with similar findings.

Your map can then be used as a plan for your literature review.

As well has helping you to organise the literature for your review, a literature map can be used to help you analyse the information in a particular journal article, supporting the exploration of strengths and weaknesses of the methodology and the resultant findings and enabling you to explore how key themes and concepts in the article link together.

It is important to represent the different views and any conflicting research findings that exist in the literature (Newby, 2014). There is a danger of selective referencing, only including literature that supports your own beliefs and findings, disregarding alternative views. This should be avoided as it is based on the assumption that your views are the correct ones, and it is possible that you could miss key ideas and findings that could take your research in new and exciting directions.

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This LibGuide will help you locate resources for constructing a literature map.  If you need more assistance please click on the 24/7 Chat service or contact me using the links on the left hand side of the page.

Resources for Creating Literature Maps

• Owl English's Resource for Literature Mapping This resource provides an overview of stasis theory and what you can do with it to help you conduct research, compose documents, and work in teams.
• Wilson Library's "Sage Research Methods" Resources on Literature Mapping This resource provides suggestions for books, articles, videos and more for designing literature maps.

Visual of How to Create a Literature Map

• Sample Look at a Literature Map (scroll to bottom of page) This is an example of the literature review process, and in particular, literature mapping (scroll to the bottom of the page).

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We joined Open Knowledge Maps as a Supporting Member because it is an innovative tool for literature search and we are eager to support the further development of Open Knowledge Maps.

Dr. David Johann, Head of Group Knowledge Management, ETH Library, ETH Zurich

I love how OKMaps breaks down the papers into clusters allowing me to identify themes in the literature and focus on papers that are most pertinent for my work.

Girija Goyal, ReFigure Co-Founder, Staff Scientist at Wyss Institute for Biologically Inspired Engineering at Harvard University, USA

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How to Create a Literature Map

How to create a literature map. The Literature Map helps researchers review literature for gaps and points of impact. They are useful in both academic and industry related research projects to help gain traction and market interest.

Book a seat for our upcoming Literature Mapping Webinar Workshop

Learn More about Literature Mapping

Research projects usually start with a Literature Review which involves using tools such as search engines ( e.g. google scholar) and document management and reference systems (e.g. Endnote and Mandalay).

The literature review will attempt to create a space for the research project that has not been covered or is yet to be developed.

Literature Mapping uses graphical methods to plot your literature in a graphical format. There are many types of graphical method from mind mapping to infographic formats.

See our Research Gate Forum where leading experts have discussed the various graphical literature tools from Mind Maps through to Quiqqa and other methods.

Dr Jonathan Drane has developed a unique but simple literature mapping method which streamlines your literature review and helps you refine your topic and its place in the literature universe.

‘In our method we prefer to use a ‘cards on desktop’ graphical logic.  It uses cards (like the icons on your desktop) and allocates identifiers to the cards including different colours as well as other key information points. Think of each card as if it was a library card which is also linked back to the actual publication it refers to’. Dr Jonathan Drane

In the method there is also an X-Y axis to allow for key concept themes to be pinned to the axis. From there each card is positioned based on its alignment to the theme. In the chart below this method is applied to City Growth Dynamics themes from Dr Drane’s doctorate.

‘ As I spent weeks in the literature mapping phase of my doctorate I realised that it was made clearer by using graphical representation of the various themes and concepts.’ Dr Jonathan Drane

An example of his literature map system is shown above which is extracted from Dr Drane’s Doctorate .

Impact and Strategic Importance

Research occurs in a huge range of endeavours from academic research to competitive analysis, market and corporate strategy. A central activity in these is to make sure you know what the current literature, articles and books are in the relevant strategic arena.

The use of literature review is essential to maintaining a strategic advantage and identifying the gaps in the theory or in corporate offerings.

We recommed that you take some time out and attend our upcoming webinar on this topic . Whether you are an academic or a business person or government researcher, this is important.

We look forward to seeing you at this webinar.

Dr Jonathan Drane

Download this article and other related resources (free).

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Other readings and links:

Link to Jonathan’s Research Gate page page on Literature Mapping

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Literature Review: A Self-Guided Tutorial

Using concept maps.

• Literature Reviews: A Recap
• Peer Review
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• 5. Critically analyze and evaluate
• How to Review the Literature
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• 7. Write literature review

Concept maps or mind maps visually represent relationships of different concepts. In research, they can help you make connections between ideas. You can use them as you are formulating your research question, as you are reading a complex text, and when you are creating a literature review. See the video and examples below.

How to Create a Concept Map

Credit: Penn State Libraries ( CC-BY ) Run Time: 3:13

• Bubbl.us Free version allows 3 mind maps, image export, and sharing.
• MindMeister Free version allows 3 mind maps, sharing, collaborating, and importing. No image-based exporting.

Mind Map of a Text Example

Credit: Austin Kleon. A map I drew of John Berger’s Ways of Seeing in 2008. Tumblr post. April 14, 2016. http://tumblr.austinkleon.com/post/142802684061#notes

Literature Review Mind Map Example

This example shows the different aspects of the author's literature review with citations to scholars who have written about those aspects.

Credit: Clancy Ratliff, Dissertation: Literature Review. Culturecat: Rhetoric and Feminism [blog]. 2 October 2005. http://culturecat.net/node/955 .

• << Previous: Reading the Literature
• Next: 1. Identify the question >>
• Last Updated: Feb 22, 2024 10:53 AM
• URL: https://libguides.williams.edu/literature-review

Application of literature maps and literature matrices for quick and effective literature reviews

What is a literature review, and why do we need a map, what is a literature map, how does a literature map solve the problem .

understanding the critical issues, proper methodologies and research findings in the literature,

organising the flow of ideas using a structured document. 

Understanding the relationships between different studies and groupings according to similar findings or methodologies. 

How does one prepare a literature map?

Write your main topic in a text box at the top. 

Decide on a few important subtopics related to the main topic, and write them down below it. 

For each subtopic, decide on a few crucial points of research and one by one, write them down below the relevant subtopic, as shown in Figure 1. 

Search Google Scholar or your favorite publication repository for good research articles pertaining to each point and record the primary author's name and the year of publication. 

Download those articles and save them in your Mendeley library or at your desired location.  

You may need to spend a reasonable amount of time preparing this map. Once it has been finalised, the map can be used to conduct a planned and structured literature review.

Citation mapping;

Concept mapping;

Feature mapping;

Topic-tree mapping;

Content mapping; or

Taxonomic mapping. 

Connected papers ;

Inciteful ;

Scite ; and

Semantic Scholar .

What is a Literature matrix, and how can it be useful?

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The four building blocks of change

Large-scale organizational change has always been difficult, and there’s no shortage of research showing that a majority of transformations continue to fail. Today’s dynamic environment adds an extra level of urgency and complexity. Companies must increasingly react to sudden shifts in the marketplace, to other external shocks, and to the imperatives of new business models. The stakes are higher than ever.

So what’s to be done? In both research and practice, we find that transformations stand the best chance of success when they focus on four key actions to change mind-sets and behavior: fostering understanding and conviction, reinforcing changes through formal mechanisms, developing talent and skills, and role modeling. Collectively labeled the “influence model,” these ideas were introduced more than a dozen years ago in a McKinsey Quarterly article, “ The psychology of change management .” They were based on academic research and practical experience—what we saw worked and what didn’t.

Digital technologies and the changing nature of the workforce have created new opportunities and challenges for the influence model (for more on the relationship between those trends and the model, see this article’s companion, “ Winning hearts and minds in the 21st century ”). But it still works overall, a decade and a half later (exhibit). In a recent McKinsey Global Survey, we examined successful transformations and found that they were nearly eight times more likely to use all four actions as opposed to just one. 1 1. See “ The science of organizational transformations ,” September 2015. Building both on classic and new academic research, the present article supplies a primer on the model and its four building blocks: what they are, how they work, and why they matter.

Fostering understanding and conviction

We know from research that human beings strive for congruence between their beliefs and their actions and experience dissonance when these are misaligned. Believing in the “why” behind a change can therefore inspire people to change their behavior. In practice, however, we find that many transformation leaders falsely assume that the “why” is clear to the broader organization and consequently fail to spend enough time communicating the rationale behind change efforts.

This common pitfall is predictable. Research shows that people frequently overestimate the extent to which others share their own attitudes, beliefs, and opinions—a tendency known as the false-consensus effect. Studies also highlight another contributing phenomenon, the “curse of knowledge”: people find it difficult to imagine that others don’t know something that they themselves do know. To illustrate this tendency, a Stanford study asked participants to tap out the rhythms of well-known songs and predict the likelihood that others would guess what they were. The tappers predicted that the listeners would identify half of the songs correctly; in reality, they did so less than 5 percent of the time. 2 2. Chip Heath and Dan Heath, “The curse of knowledge,” Harvard Business Review , December 2006, Volume 8, Number 6, hbr.org.

Therefore, in times of transformation, we recommend that leaders develop a change story that helps all stakeholders understand where the company is headed, why it is changing, and why this change is important. Building in a feedback loop to sense how the story is being received is also useful. These change stories not only help get out the message but also, recent research finds, serve as an effective influencing tool. Stories are particularly effective in selling brands. 3 3. Harrison Monarth, “The irresistible power of storytelling as a strategic business tool,” Harvard Business Review , March 11, 2014, hbr.org.

Even 15 years ago, at the time of the original article, digital advances were starting to make employees feel involved in transformations, allowing them to participate in shaping the direction of their companies. In 2006, for example, IBM used its intranet to conduct two 72-hour “jam sessions” to engage employees, clients, and other stakeholders in an online debate about business opportunities. No fewer than 150,000 visitors attended from 104 countries and 67 different companies, and there were 46,000 posts. 4 4. Icons of Progress , “A global innovation jam,” ibm.com. As we explain in “Winning hearts and minds in the 21st century,” social and mobile technologies have since created a wide range of new opportunities to build the commitment of employees to change.

Reinforcing with formal mechanisms

Psychologists have long known that behavior often stems from direct association and reinforcement. Back in the 1920s, Ivan Pavlov’s classical conditioning research showed how the repeated association between two stimuli—the sound of a bell and the delivery of food—eventually led dogs to salivate upon hearing the bell alone. Researchers later extended this work on conditioning to humans, demonstrating how children could learn to fear a rat when it was associated with a loud noise. 5 5. John B. Watson and Rosalie Rayner, “Conditioned emotional reactions,” Journal of Experimental Psychology , 1920, Volume 3, Number 1, pp. 1–14. Of course, this conditioning isn’t limited to negative associations or to animals. The perfume industry recognizes how the mere scent of someone you love can induce feelings of love and longing.

Reinforcement can also be conscious, shaped by the expected rewards and punishments associated with specific forms of behavior. B. F. Skinner’s work on operant conditioning showed how pairing positive reinforcements such as food with desired behavior could be used, for example, to teach pigeons to play Ping-Pong. This concept, which isn’t hard to grasp, is deeply embedded in organizations. Many people who have had commissions-based sales jobs will understand the point—being paid more for working harder can sometimes be a strong incentive.

Despite the importance of reinforcement, organizations often fail to use it correctly. In a seminal paper “On the folly of rewarding A, while hoping for B,” management scholar Steven Kerr described numerous examples of organizational-reward systems that are misaligned with the desired behavior, which is therefore neglected. 6 6. Steven Kerr, “On the folly of rewarding A, while hoping for B,” Academy of Management Journal , 1975, Volume 18, Number 4, pp. 769–83. Some of the paper’s examples—such as the way university professors are rewarded for their research publications, while society expects them to be good teachers—are still relevant today. We ourselves have witnessed this phenomenon in a global refining organization facing market pressure. By squeezing maintenance expenditures and rewarding employees who cut them, the company in effect treated that part of the budget as a “super KPI.” Yet at the same time, its stated objective was reliable maintenance.

Even when organizations use money as a reinforcement correctly, they often delude themselves into thinking that it alone will suffice. Research examining the relationship between money and experienced happiness—moods and general well-being—suggests a law of diminishing returns. The relationship may disappear altogether after around $75,000, a much lower ceiling than most executives assume. 7 7. Belinda Luscombe, “Do we need $75,000 a year to be happy?” Time , September 6, 2010, time.com.

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Money isn’t the only motivator, of course. Victor Vroom’s classic research on expectancy theory explained how the tendency to behave in certain ways depends on the expectation that the effort will result in the desired kind of performance, that this performance will be rewarded, and that the reward will be desirable. 8 8. Victor Vroom, Work and motivation , New York: John Wiley, 1964. When a Middle Eastern telecommunications company recently examined performance drivers, it found that collaboration and purpose were more important than compensation (see “Ahead of the curve: The future of performance management,” forthcoming on McKinsey.com). The company therefore moved from awarding minor individual bonuses for performance to celebrating how specific teams made a real difference in the lives of their customers. This move increased motivation while also saving the organization millions.

How these reinforcements are delivered also matters. It has long been clear that predictability makes them less effective; intermittent reinforcement provides a more powerful hook, as slot-machine operators have learned to their advantage. Further, people react negatively if they feel that reinforcements aren’t distributed fairly. Research on equity theory describes how employees compare their job inputs and outcomes with reference-comparison targets, such as coworkers who have been promoted ahead of them or their own experiences at past jobs. 9 9. J. S. Adams, “Inequity in social exchanges,” Advances in Experimental Social Psychology , 1965, Volume 2, pp. 267–300. We therefore recommend that organizations neutralize compensation as a source of anxiety and instead focus on what really drives performance—such as collaboration and purpose, in the case of the Middle Eastern telecom company previously mentioned.

Developing talent and skills

Thankfully, you can teach an old dog new tricks. Human brains are not fixed; neuroscience research shows that they remain plastic well into adulthood. Illustrating this concept, scientific investigation has found that the brains of London taxi drivers, who spend years memorizing thousands of streets and local attractions, showed unique gray-matter volume differences in the hippocampus compared with the brains of other people. Research linked these differences to the taxi drivers’ extraordinary special knowledge. 10 10. Eleanor Maguire, Katherine Woollett, and Hugo Spires, “London taxi drivers and bus drivers: A structural MRI and neuropsychological analysis,” Hippocampus , 2006, Volume 16, pp. 1091–1101.

Despite an amazing ability to learn new things, human beings all too often lack insight into what they need to know but don’t. Biases, for example, can lead people to overlook their limitations and be overconfident of their abilities. Highlighting this point, studies have found that over 90 percent of US drivers rate themselves above average, nearly 70 percent of professors consider themselves in the top 25 percent for teaching ability, and 84 percent of Frenchmen believe they are above-average lovers. 11 11. The art of thinking clearly, “The overconfidence effect: Why you systematically overestimate your knowledge and abilities,” blog entry by Rolf Dobelli, June 11, 2013, psychologytoday.com. This self-serving bias can lead to blind spots, making people too confident about some of their abilities and unaware of what they need to learn. In the workplace, the “mum effect”—a proclivity to keep quiet about unpleasant, unfavorable messages—often compounds these self-serving tendencies. 12 12. Eliezer Yariv, “‘Mum effect’: Principals’ reluctance to submit negative feedback,” Journal of Managerial Psychology , 2006, Volume 21, Number 6, pp. 533–46.

Even when people overcome such biases and actually want to improve, they can handicap themselves by doubting their ability to change. Classic psychological research by Martin Seligman and his colleagues explained how animals and people can fall into a state of learned helplessness—passive acceptance and resignation that develops as a result of repeated exposure to negative events perceived as unavoidable. The researchers found that dogs exposed to unavoidable shocks gave up trying to escape and, when later given an opportunity to do so, stayed put and accepted the shocks as inevitable. 13 13. Martin Seligman and Steven Maier, “Failure to escape traumatic shock,” Journal of Experimental Psychology , 1967, Volume 74, Number 1, pp. 1–9. Like animals, people who believe that developing new skills won’t change a situation are more likely to be passive. You see this all around the economy—from employees who stop offering new ideas after earlier ones have been challenged to unemployed job seekers who give up looking for work after multiple rejections.

Instilling a sense of control and competence can promote an active effort to improve. As expectancy theory holds, people are more motivated to achieve their goals when they believe that greater individual effort will increase performance. 14 14. Victor Vroom, Work and motivation , New York: John Wiley, 1964. Fortunately, new technologies now give organizations more creative opportunities than ever to showcase examples of how that can actually happen.

Role modeling

Research tells us that role modeling occurs both unconsciously and consciously. Unconsciously, people often find themselves mimicking the emotions, behavior, speech patterns, expressions, and moods of others without even realizing that they are doing so. They also consciously align their own thinking and behavior with those of other people—to learn, to determine what’s right, and sometimes just to fit in.

While role modeling is commonly associated with high-power leaders such as Abraham Lincoln and Bill Gates, it isn’t limited to people in formal positions of authority. Smart organizations seeking to win their employees’ support for major transformation efforts recognize that key opinion leaders may exert more influence than CEOs. Nor is role modeling limited to individuals. Everyone has the power to model roles, and groups of people may exert the most powerful influence of all. Robert Cialdini, a well-respected professor of psychology and marketing, examined the power of “social proof”—a mental shortcut people use to judge what is correct by determining what others think is correct. No wonder TV shows have been using canned laughter for decades; believing that other people find a show funny makes us more likely to find it funny too.

Today’s increasingly connected digital world provides more opportunities than ever to share information about how others think and behave. Ever found yourself swayed by the number of positive reviews on Yelp? Or perceiving a Twitter user with a million followers as more reputable than one with only a dozen? You’re not imagining this. Users can now “buy followers” to help those users or their brands seem popular or even start trending.

The endurance of the influence model shouldn’t be surprising: powerful forces of human nature underlie it. More surprising, perhaps, is how often leaders still embark on large-scale change efforts without seriously focusing on building conviction or reinforcing it through formal mechanisms, the development of skills, and role modeling. While these priorities sound like common sense, it’s easy to miss one or more of them amid the maelstrom of activity that often accompanies significant changes in organizational direction. Leaders should address these building blocks systematically because, as research and experience demonstrate, all four together make a bigger impact.

Tessa Basford is a consultant in McKinsey’s Washington, DC, office; Bill Schaninger is a director in the Philadelphia office.

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Winning hearts and minds in the 21st century

Changing change management

Digital hives: Creating a surge around change

A systematic literature review of empirical research on ChatGPT in education

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• Volume 3 , article number  60 , ( 2024 )

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Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli’s (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell’s (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

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1 Introduction

Educational technology, a rapidly evolving field, plays a crucial role in reshaping the landscape of teaching and learning [ 82 ]. One of the most transformative technological innovations of our era that has influenced the field of education is Artificial Intelligence (AI) [ 50 ]. Over the last four decades, AI in education (AIEd) has gained remarkable attention for its potential to make significant advancements in learning, instructional methods, and administrative tasks within educational settings [ 11 ]. In particular, a large language model (LLM), a type of AI algorithm that applies artificial neural networks (ANNs) and uses massively large data sets to understand, summarize, generate, and predict new content that is almost difficult to differentiate from human creations [ 79 ], has opened up novel possibilities for enhancing various aspects of education, from content creation to personalized instruction [ 35 ]. Chatbots that leverage the capabilities of LLMs to understand and generate human-like responses have also presented the capacity to enhance student learning and educational outcomes by engaging students, offering timely support, and fostering interactive learning experiences [ 46 ].

The ongoing and remarkable technological advancements in chatbots have made their use more convenient, increasingly natural and effortless, and have expanded their potential for deployment across various domains [ 70 ]. One prominent example of chatbot applications is the Chat Generative Pre-Trained Transformer, known as ChatGPT, which was introduced by OpenAI, a leading AI research lab, on November 30th, 2022. ChatGPT employs a variety of deep learning techniques to generate human-like text, with a particular focus on recurrent neural networks (RNNs). Long short-term memory (LSTM) allows it to grasp the context of the text being processed and retain information from previous inputs. Also, the transformer architecture, a neural network architecture based on the self-attention mechanism, allows it to analyze specific parts of the input, thereby enabling it to produce more natural-sounding and coherent output. Additionally, the unsupervised generative pre-training and the fine-tuning methods allow ChatGPT to generate more relevant and accurate text for specific tasks [ 31 , 62 ]. Furthermore, reinforcement learning from human feedback (RLHF), a machine learning approach that combines reinforcement learning techniques with human-provided feedback, has helped improve ChatGPT’s model by accelerating the learning process and making it significantly more efficient.

This cutting-edge natural language processing (NLP) tool is widely recognized as one of today's most advanced LLMs-based chatbots [ 70 ], allowing users to ask questions and receive detailed, coherent, systematic, personalized, convincing, and informative human-like responses [ 55 ], even within complex and ambiguous contexts [ 63 , 77 ]. ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [ 16 ] with an estimated 13 million daily queries [ 49 ], surpassing all other applications [ 64 ]. This remarkable growth can be attributed to the unique features and user-friendly interface that ChatGPT offers. Its intuitive design allows users to interact seamlessly with the technology, making it accessible to a diverse range of individuals, regardless of their technical expertise [ 78 ]. Additionally, its exceptional performance results from a combination of advanced algorithms, continuous enhancements, and extensive training on a diverse dataset that includes various text sources such as books, articles, websites, and online forums [ 63 ], have contributed to a more engaging and satisfying user experience [ 62 ]. These factors collectively explain its remarkable global growth and set it apart from predecessors like Bard, Bing Chat, ERNIE, and others.

In this context, several studies have explored the technological advancements of chatbots. One noteworthy recent research effort, conducted by Schöbel et al. [ 70 ], stands out for its comprehensive analysis of more than 5,000 studies on communication agents. This study offered a comprehensive overview of the historical progression and future prospects of communication agents, including ChatGPT. Moreover, other studies have focused on making comparisons, particularly between ChatGPT and alternative chatbots like Bard, Bing Chat, ERNIE, LaMDA, BlenderBot, and various others. For example, O’Leary [ 53 ] compared two chatbots, LaMDA and BlenderBot, with ChatGPT and revealed that ChatGPT outperformed both. This superiority arises from ChatGPT’s capacity to handle a wider range of questions and generate slightly varied perspectives within specific contexts. Similarly, ChatGPT exhibited an impressive ability to formulate interpretable responses that were easily understood when compared with Google's feature snippet [ 34 ]. Additionally, ChatGPT was compared to other LLMs-based chatbots, including Bard and BERT, as well as ERNIE. The findings indicated that ChatGPT exhibited strong performance in the given tasks, often outperforming the other models [ 59 ].

Furthermore, in the education context, a comprehensive study systematically compared a range of the most promising chatbots, including Bard, Bing Chat, ChatGPT, and Ernie across a multidisciplinary test that required higher-order thinking. The study revealed that ChatGPT achieved the highest score, surpassing Bing Chat and Bard [ 64 ]. Similarly, a comparative analysis was conducted to compare ChatGPT with Bard in answering a set of 30 mathematical questions and logic problems, grouped into two question sets. Set (A) is unavailable online, while Set (B) is available online. The results revealed ChatGPT's superiority in Set (A) over Bard. Nevertheless, Bard's advantage emerged in Set (B) due to its capacity to access the internet directly and retrieve answers, a capability that ChatGPT does not possess [ 57 ]. However, through these varied assessments, ChatGPT consistently highlights its exceptional prowess compared to various alternatives in the ever-evolving chatbot technology.

The widespread adoption of chatbots, especially ChatGPT, by millions of students and educators, has sparked extensive discussions regarding its incorporation into the education sector [ 64 ]. Accordingly, many scholars have contributed to the discourse, expressing both optimism and pessimism regarding the incorporation of ChatGPT into education. For example, ChatGPT has been highlighted for its capabilities in enriching the learning and teaching experience through its ability to support different learning approaches, including adaptive learning, personalized learning, and self-directed learning [ 58 , 60 , 91 ]), deliver summative and formative feedback to students and provide real-time responses to questions, increase the accessibility of information [ 22 , 40 , 43 ], foster students’ performance, engagement and motivation [ 14 , 44 , 58 ], and enhance teaching practices [ 17 , 18 , 64 , 74 ].

On the other hand, concerns have been also raised regarding its potential negative effects on learning and teaching. These include the dissemination of false information and references [ 12 , 23 , 61 , 85 ], biased reinforcement [ 47 , 50 ], compromised academic integrity [ 18 , 40 , 66 , 74 ], and the potential decline in students' skills [ 43 , 61 , 64 , 74 ]. As a result, ChatGPT has been banned in multiple countries, including Russia, China, Venezuela, Belarus, and Iran, as well as in various educational institutions in India, Italy, Western Australia, France, and the United States [ 52 , 90 ].

Clearly, the advent of chatbots, especially ChatGPT, has provoked significant controversy due to their potential impact on learning and teaching. This indicates the necessity for further exploration to gain a deeper understanding of this technology and carefully evaluate its potential benefits, limitations, challenges, and threats to education [ 79 ]. Therefore, conducting a systematic literature review will provide valuable insights into the potential prospects and obstacles linked to its incorporation into education. This systematic literature review will primarily focus on ChatGPT, driven by the aforementioned key factors outlined above.

However, the existing literature lacks a systematic literature review of empirical studies. Thus, this systematic literature review aims to address this gap by synthesizing the existing empirical studies conducted on chatbots, particularly ChatGPT, in the field of education, highlighting how ChatGPT has been utilized in educational settings, and identifying any existing gaps. This review may be particularly useful for researchers in the field and educators who are contemplating the integration of ChatGPT or any chatbot into education. The following research questions will guide this study:

What are students' and teachers' initial attempts at utilizing ChatGPT in education?

What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?

2 Methodology

To conduct this study, the authors followed the essential steps of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) and Okoli’s [ 54 ] steps for conducting a systematic review. These included identifying the study’s purpose, drafting a protocol, applying a practical screening process, searching the literature, extracting relevant data, evaluating the quality of the included studies, synthesizing the studies, and ultimately writing the review. The subsequent section provides an extensive explanation of how these steps were carried out in this study.

2.1 Identify the purpose

Given the widespread adoption of ChatGPT by students and teachers for various educational purposes, often without a thorough understanding of responsible and effective use or a clear recognition of its potential impact on learning and teaching, the authors recognized the need for further exploration of ChatGPT's impact on education in this early stage. Therefore, they have chosen to conduct a systematic literature review of existing empirical studies that incorporate ChatGPT into educational settings. Despite the limited number of empirical studies due to the novelty of the topic, their goal is to gain a deeper understanding of this technology and proactively evaluate its potential benefits, limitations, challenges, and threats to education. This effort could help to understand initial reactions and attempts at incorporating ChatGPT into education and bring out insights and considerations that can inform the future development of education.

2.2 Draft the protocol

The next step is formulating the protocol. This protocol serves to outline the study process in a rigorous and transparent manner, mitigating researcher bias in study selection and data extraction [ 88 ]. The protocol will include the following steps: generating the research question, predefining a literature search strategy, identifying search locations, establishing selection criteria, assessing the studies, developing a data extraction strategy, and creating a timeline.

2.3 Apply practical screen

The screening step aims to accurately filter the articles resulting from the searching step and select the empirical studies that have incorporated ChatGPT into educational contexts, which will guide us in answering the research questions and achieving the objectives of this study. To ensure the rigorous execution of this step, our inclusion and exclusion criteria were determined based on the authors' experience and informed by previous successful systematic reviews [ 21 ]. Table 1 summarizes the inclusion and exclusion criteria for study selection.

2.4 Literature search

We conducted a thorough literature search to identify articles that explored, examined, and addressed the use of ChatGPT in Educational contexts. We utilized two research databases: Dimensions.ai, which provides access to a large number of research publications, and lens.org, which offers access to over 300 million articles, patents, and other research outputs from diverse sources. Additionally, we included three databases, Scopus, Web of Knowledge, and ERIC, which contain relevant research on the topic that addresses our research questions. To browse and identify relevant articles, we used the following search formula: ("ChatGPT" AND "Education"), which included the Boolean operator "AND" to get more specific results. The subject area in the Scopus and ERIC databases were narrowed to "ChatGPT" and "Education" keywords, and in the WoS database was limited to the "Education" category. The search was conducted between the 3rd and 10th of April 2023, which resulted in 276 articles from all selected databases (111 articles from Dimensions.ai, 65 from Scopus, 28 from Web of Science, 14 from ERIC, and 58 from Lens.org). These articles were imported into the Rayyan web-based system for analysis. The duplicates were identified automatically by the system. Subsequently, the first author manually reviewed the duplicated articles ensured that they had the same content, and then removed them, leaving us with 135 unique articles. Afterward, the titles, abstracts, and keywords of the first 40 manuscripts were scanned and reviewed by the first author and were discussed with the second and third authors to resolve any disagreements. Subsequently, the first author proceeded with the filtering process for all articles and carefully applied the inclusion and exclusion criteria as presented in Table  1 . Articles that met any one of the exclusion criteria were eliminated, resulting in 26 articles. Afterward, the authors met to carefully scan and discuss them. The authors agreed to eliminate any empirical studies solely focused on checking ChatGPT capabilities, as these studies do not guide us in addressing the research questions and achieving the study's objectives. This resulted in 14 articles eligible for analysis.

2.5 Quality appraisal

The examination and evaluation of the quality of the extracted articles is a vital step [ 9 ]. Therefore, the extracted articles were carefully evaluated for quality using Fink’s [ 24 ] standards, which emphasize the necessity for detailed descriptions of methodology, results, conclusions, strengths, and limitations. The process began with a thorough assessment of each study's design, data collection, and analysis methods to ensure their appropriateness and comprehensive execution. The clarity, consistency, and logical progression from data to results and conclusions were also critically examined. Potential biases and recognized limitations within the studies were also scrutinized. Ultimately, two articles were excluded for failing to meet Fink’s criteria, particularly in providing sufficient detail on methodology, results, conclusions, strengths, or limitations. The review process is illustrated in Fig.  1 .

The study selection process

2.6 Data extraction

The next step is data extraction, the process of capturing the key information and categories from the included studies. To improve efficiency, reduce variation among authors, and minimize errors in data analysis, the coding categories were constructed using Creswell's [ 15 ] coding techniques for data extraction and interpretation. The coding process involves three sequential steps. The initial stage encompasses open coding , where the researcher examines the data, generates codes to describe and categorize it, and gains a deeper understanding without preconceived ideas. Following open coding is axial coding , where the interrelationships between codes from open coding are analyzed to establish more comprehensive categories or themes. The process concludes with selective coding , refining and integrating categories or themes to identify core concepts emerging from the data. The first coder performed the coding process, then engaged in discussions with the second and third authors to finalize the coding categories for the first five articles. The first coder then proceeded to code all studies and engaged again in discussions with the other authors to ensure the finalization of the coding process. After a comprehensive analysis and capturing of the key information from the included studies, the data extraction and interpretation process yielded several themes. These themes have been categorized and are presented in Table  2 . It is important to note that open coding results were removed from Table  2 for aesthetic reasons, as it included many generic aspects, such as words, short phrases, or sentences mentioned in the studies.

2.7 Synthesize studies

In this stage, we will gather, discuss, and analyze the key findings that emerged from the selected studies. The synthesis stage is considered a transition from an author-centric to a concept-centric focus, enabling us to map all the provided information to achieve the most effective evaluation of the data [ 87 ]. Initially, the authors extracted data that included general information about the selected studies, including the author(s)' names, study titles, years of publication, educational levels, research methodologies, sample sizes, participants, main aims or objectives, raw data sources, and analysis methods. Following that, all key information and significant results from the selected studies were compiled using Creswell’s [ 15 ] coding techniques for data extraction and interpretation to identify core concepts and themes emerging from the data, focusing on those that directly contributed to our research questions and objectives, such as the initial utilization of ChatGPT in learning and teaching, learners' and educators' familiarity with ChatGPT, and the main findings of each study. Finally, the data related to each selected study were extracted into an Excel spreadsheet for data processing. The Excel spreadsheet was reviewed by the authors, including a series of discussions to ensure the finalization of this process and prepare it for further analysis. Afterward, the final result being analyzed and presented in various types of charts and graphs. Table 4 presents the extracted data from the selected studies, with each study labeled with a capital 'S' followed by a number.

This section consists of two main parts. The first part provides a descriptive analysis of the data compiled from the reviewed studies. The second part presents the answers to the research questions and the main findings of these studies.

3.1 Part 1: descriptive analysis

This section will provide a descriptive analysis of the reviewed studies, including educational levels and fields, participants distribution, country contribution, research methodologies, study sample size, study population, publication year, list of journals, familiarity with ChatGPT, source of data, and the main aims and objectives of the studies. Table 4 presents a comprehensive overview of the extracted data from the selected studies.

3.1.1 The number of the reviewed studies and publication years

The total number of the reviewed studies was 14. All studies were empirical studies and published in different journals focusing on Education and Technology. One study was published in 2022 [S1], while the remaining were published in 2023 [S2]-[S14]. Table 3 illustrates the year of publication, the names of the journals, and the number of reviewed studies published in each journal for the studies reviewed.

3.1.2 Educational levels and fields

The majority of the reviewed studies, 11 studies, were conducted in higher education institutions [S1]-[S10] and [S13]. Two studies did not specify the educational level of the population [S12] and [S14], while one study focused on elementary education [S11]. However, the reviewed studies covered various fields of education. Three studies focused on Arts and Humanities Education [S8], [S11], and [S14], specifically English Education. Two studies focused on Engineering Education, with one in Computer Engineering [S2] and the other in Construction Education [S3]. Two studies focused on Mathematics Education [S5] and [S12]. One study focused on Social Science Education [S13]. One study focused on Early Education [S4]. One study focused on Journalism Education [S9]. Finally, three studies did not specify the field of education [S1], [S6], and [S7]. Figure  2 represents the educational levels in the reviewed studies, while Fig.  3 represents the context of the reviewed studies.

Educational levels in the reviewed studies

Context of the reviewed studies

3.1.3 Participants distribution and countries contribution

The reviewed studies have been conducted across different geographic regions, providing a diverse representation of the studies. The majority of the studies, 10 in total, [S1]-[S3], [S5]-[S9], [S11], and [S14], primarily focused on participants from single countries such as Pakistan, the United Arab Emirates, China, Indonesia, Poland, Saudi Arabia, South Korea, Spain, Tajikistan, and the United States. In contrast, four studies, [S4], [S10], [S12], and [S13], involved participants from multiple countries, including China and the United States [S4], China, the United Kingdom, and the United States [S10], the United Arab Emirates, Oman, Saudi Arabia, and Jordan [S12], Turkey, Sweden, Canada, and Australia [ 13 ]. Figures  4 and 5 illustrate the distribution of participants, whether from single or multiple countries, and the contribution of each country in the reviewed studies, respectively.

The reviewed studies conducted in single or multiple countries

The Contribution of each country in the studies

3.1.4 Study population and sample size

Four study populations were included: university students, university teachers, university teachers and students, and elementary school teachers. Six studies involved university students [S2], [S3], [S5] and [S6]-[S8]. Three studies focused on university teachers [S1], [S4], and [S6], while one study specifically targeted elementary school teachers [S11]. Additionally, four studies included both university teachers and students [S10] and [ 12 , 13 , 14 ], and among them, study [S13] specifically included postgraduate students. In terms of the sample size of the reviewed studies, nine studies included a small sample size of less than 50 participants [S1], [S3], [S6], [S8], and [S10]-[S13]. Three studies had 50–100 participants [S2], [S9], and [S14]. Only one study had more than 100 participants [S7]. It is worth mentioning that study [S4] adopted a mixed methods approach, including 10 participants for qualitative analysis and 110 participants for quantitative analysis.

3.1.5 Participants’ familiarity with using ChatGPT

The reviewed studies recruited a diverse range of participants with varying levels of familiarity with ChatGPT. Five studies [S2], [S4], [S6], [S8], and [S12] involved participants already familiar with ChatGPT, while eight studies [S1], [S3], [S5], [S7], [S9], [S10], [S13] and [S14] included individuals with differing levels of familiarity. Notably, one study [S11] had participants who were entirely unfamiliar with ChatGPT. It is important to note that four studies [S3], [S5], [S9], and [S11] provided training or guidance to their participants before conducting their studies, while ten studies [S1], [S2], [S4], [S6]-[S8], [S10], and [S12]-[S14] did not provide training due to the participants' existing familiarity with ChatGPT.

3.1.6 Research methodology approaches and source(S) of data

The reviewed studies adopted various research methodology approaches. Seven studies adopted qualitative research methodology [S1], [S4], [S6], [S8], [S10], [S11], and [S12], while three studies adopted quantitative research methodology [S3], [S7], and [S14], and four studies employed mixed-methods, which involved a combination of both the strengths of qualitative and quantitative methods [S2], [S5], [S9], and [S13].

In terms of the source(s) of data, the reviewed studies obtained their data from various sources, such as interviews, questionnaires, and pre-and post-tests. Six studies relied on interviews as their primary source of data collection [S1], [S4], [S6], [S10], [S11], and [S12], four studies relied on questionnaires [S2], [S7], [S13], and [S14], two studies combined the use of pre-and post-tests and questionnaires for data collection [S3] and [S9], while two studies combined the use of questionnaires and interviews to obtain the data [S5] and [S8]. It is important to note that six of the reviewed studies were quasi-experimental [S3], [S5], [S8], [S9], [S12], and [S14], while the remaining ones were experimental studies [S1], [S2], [S4], [S6], [S7], [S10], [S11], and [S13]. Figures  6 and 7 illustrate the research methodologies and the source (s) of data used in the reviewed studies, respectively.

Research methodologies in the reviewed studies

Source of data in the reviewed studies

3.1.7 The aim and objectives of the studies

The reviewed studies encompassed a diverse set of aims, with several of them incorporating multiple primary objectives. Six studies [S3], [S6], [S7], [S8], [S11], and [S12] examined the integration of ChatGPT in educational contexts, and four studies [S4], [S5], [S13], and [S14] investigated the various implications of its use in education, while three studies [S2], [S9], and [S10] aimed to explore both its integration and implications in education. Additionally, seven studies explicitly explored attitudes and perceptions of students [S2] and [S3], educators [S1] and [S6], or both [S10], [S12], and [S13] regarding the utilization of ChatGPT in educational settings.

3.2 Part 2: research questions and main findings of the reviewed studies

This part will present the answers to the research questions and the main findings of the reviewed studies, classified into two main categories (learning and teaching) according to AI Education classification by [ 36 ]. Figure  8 summarizes the main findings of the reviewed studies in a visually informative diagram. Table 4 provides a detailed list of the key information extracted from the selected studies that led to generating these themes.

The main findings in the reviewed studies

4 Students' initial attempts at utilizing ChatGPT in learning and main findings from students' perspective

4.1 virtual intelligent assistant.

Nine studies demonstrated that ChatGPT has been utilized by students as an intelligent assistant to enhance and support their learning. Students employed it for various purposes, such as answering on-demand questions [S2]-[S5], [S8], [S10], and [S12], providing valuable information and learning resources [S2]-[S5], [S6], and [S8], as well as receiving immediate feedback [S2], [S4], [S9], [S10], and [S12]. In this regard, students generally were confident in the accuracy of ChatGPT's responses, considering them relevant, reliable, and detailed [S3], [S4], [S5], and [S8]. However, some students indicated the need for improvement, as they found that answers are not always accurate [S2], and that misleading information may have been provided or that it may not always align with their expectations [S6] and [S10]. It was also observed by the students that the accuracy of ChatGPT is dependent on several factors, including the quality and specificity of the user's input, the complexity of the question or topic, and the scope and relevance of its training data [S12]. Many students felt that ChatGPT's answers were not always accurate and most of them believed that it requires good background knowledge to work with.

4.2 Writing and language proficiency assistant

Six of the reviewed studies highlighted that ChatGPT has been utilized by students as a valuable assistant tool to improve their academic writing skills and language proficiency. Among these studies, three mainly focused on English education, demonstrating that students showed sufficient mastery in using ChatGPT for generating ideas, summarizing, paraphrasing texts, and completing writing essays [S8], [S11], and [S14]. Furthermore, ChatGPT helped them in writing by making students active investigators rather than passive knowledge recipients and facilitated the development of their writing skills [S11] and [S14]. Similarly, ChatGPT allowed students to generate unique ideas and perspectives, leading to deeper analysis and reflection on their journalism writing [S9]. In terms of language proficiency, ChatGPT allowed participants to translate content into their home languages, making it more accessible and relevant to their context [S4]. It also enabled them to request changes in linguistic tones or flavors [S8]. Moreover, participants used it to check grammar or as a dictionary [S11].

4.3 Valuable resource for learning approaches

Five studies demonstrated that students used ChatGPT as a valuable complementary resource for self-directed learning. It provided learning resources and guidance on diverse educational topics and created a supportive home learning environment [S2] and [S4]. Moreover, it offered step-by-step guidance to grasp concepts at their own pace and enhance their understanding [S5], streamlined task and project completion carried out independently [S7], provided comprehensive and easy-to-understand explanations on various subjects [S10], and assisted in studying geometry operations, thereby empowering them to explore geometry operations at their own pace [S12]. Three studies showed that students used ChatGPT as a valuable learning resource for personalized learning. It delivered age-appropriate conversations and tailored teaching based on a child's interests [S4], acted as a personalized learning assistant, adapted to their needs and pace, which assisted them in understanding mathematical concepts [S12], and enabled personalized learning experiences in social sciences by adapting to students' needs and learning styles [S13]. On the other hand, it is important to note that, according to one study [S5], students suggested that using ChatGPT may negatively affect collaborative learning competencies between students.

4.4 Enhancing students' competencies

Six of the reviewed studies have shown that ChatGPT is a valuable tool for improving a wide range of skills among students. Two studies have provided evidence that ChatGPT led to improvements in students' critical thinking, reasoning skills, and hazard recognition competencies through engaging them in interactive conversations or activities and providing responses related to their disciplines in journalism [S5] and construction education [S9]. Furthermore, two studies focused on mathematical education have shown the positive impact of ChatGPT on students' problem-solving abilities in unraveling problem-solving questions [S12] and enhancing the students' understanding of the problem-solving process [S5]. Lastly, one study indicated that ChatGPT effectively contributed to the enhancement of conversational social skills [S4].

4.5 Supporting students' academic success

Seven of the reviewed studies highlighted that students found ChatGPT to be beneficial for learning as it enhanced learning efficiency and improved the learning experience. It has been observed to improve students' efficiency in computer engineering studies by providing well-structured responses and good explanations [S2]. Additionally, students found it extremely useful for hazard reporting [S3], and it also enhanced their efficiency in solving mathematics problems and capabilities [S5] and [S12]. Furthermore, by finding information, generating ideas, translating texts, and providing alternative questions, ChatGPT aided students in deepening their understanding of various subjects [S6]. It contributed to an increase in students' overall productivity [S7] and improved efficiency in composing written tasks [S8]. Regarding learning experiences, ChatGPT was instrumental in assisting students in identifying hazards that they might have otherwise overlooked [S3]. It also improved students' learning experiences in solving mathematics problems and developing abilities [S5] and [S12]. Moreover, it increased students' successful completion of important tasks in their studies [S7], particularly those involving average difficulty writing tasks [S8]. Additionally, ChatGPT increased the chances of educational success by providing students with baseline knowledge on various topics [S10].

5 Teachers' initial attempts at utilizing ChatGPT in teaching and main findings from teachers' perspective

5.1 valuable resource for teaching.

The reviewed studies showed that teachers have employed ChatGPT to recommend, modify, and generate diverse, creative, organized, and engaging educational contents, teaching materials, and testing resources more rapidly [S4], [S6], [S10] and [S11]. Additionally, teachers experienced increased productivity as ChatGPT facilitated quick and accurate responses to questions, fact-checking, and information searches [S1]. It also proved valuable in constructing new knowledge [S6] and providing timely answers to students' questions in classrooms [S11]. Moreover, ChatGPT enhanced teachers' efficiency by generating new ideas for activities and preplanning activities for their students [S4] and [S6], including interactive language game partners [S11].

5.2 Improving productivity and efficiency

The reviewed studies showed that participants' productivity and work efficiency have been significantly enhanced by using ChatGPT as it enabled them to allocate more time to other tasks and reduce their overall workloads [S6], [S10], [S11], [S13], and [S14]. However, three studies [S1], [S4], and [S11], indicated a negative perception and attitude among teachers toward using ChatGPT. This negativity stemmed from a lack of necessary skills to use it effectively [S1], a limited familiarity with it [S4], and occasional inaccuracies in the content provided by it [S10].

5.3 Catalyzing new teaching methodologies

Five of the reviewed studies highlighted that educators found the necessity of redefining their teaching profession with the assistance of ChatGPT [S11], developing new effective learning strategies [S4], and adapting teaching strategies and methodologies to ensure the development of essential skills for future engineers [S5]. They also emphasized the importance of adopting new educational philosophies and approaches that can evolve with the introduction of ChatGPT into the classroom [S12]. Furthermore, updating curricula to focus on improving human-specific features, such as emotional intelligence, creativity, and philosophical perspectives [S13], was found to be essential.

5.4 Effective utilization of CHATGPT in teaching

According to the reviewed studies, effective utilization of ChatGPT in education requires providing teachers with well-structured training, support, and adequate background on how to use ChatGPT responsibly [S1], [S3], [S11], and [S12]. Establishing clear rules and regulations regarding its usage is essential to ensure it positively impacts the teaching and learning processes, including students' skills [S1], [S4], [S5], [S8], [S9], and [S11]-[S14]. Moreover, conducting further research and engaging in discussions with policymakers and stakeholders is indeed crucial for the successful integration of ChatGPT in education and to maximize the benefits for both educators and students [S1], [S6]-[S10], and [S12]-[S14].

6 Discussion

The purpose of this review is to conduct a systematic review of empirical studies that have explored the utilization of ChatGPT, one of today’s most advanced LLM-based chatbots, in education. The findings of the reviewed studies showed several ways of ChatGPT utilization in different learning and teaching practices as well as it provided insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of the reviewed studies came from diverse fields of education, which helped us avoid a biased review that is limited to a specific field. Similarly, the reviewed studies have been conducted across different geographic regions. This kind of variety in geographic representation enriched the findings of this review.

In response to RQ1 , "What are students' and teachers' initial attempts at utilizing ChatGPT in education?", the findings from this review provide comprehensive insights. Chatbots, including ChatGPT, play a crucial role in supporting student learning, enhancing their learning experiences, and facilitating diverse learning approaches [ 42 , 43 ]. This review found that this tool, ChatGPT, has been instrumental in enhancing students' learning experiences by serving as a virtual intelligent assistant, providing immediate feedback, on-demand answers, and engaging in educational conversations. Additionally, students have benefited from ChatGPT’s ability to generate ideas, compose essays, and perform tasks like summarizing, translating, paraphrasing texts, or checking grammar, thereby enhancing their writing and language competencies. Furthermore, students have turned to ChatGPT for assistance in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks, which fosters a supportive home learning environment, allowing them to take responsibility for their own learning and cultivate the skills and approaches essential for supportive home learning environment [ 26 , 27 , 28 ]. This finding aligns with the study of Saqr et al. [ 68 , 69 ] who highlighted that, when students actively engage in their own learning process, it yields additional advantages, such as heightened motivation, enhanced achievement, and the cultivation of enthusiasm, turning them into advocates for their own learning.

Moreover, students have utilized ChatGPT for tailored teaching and step-by-step guidance on diverse educational topics, streamlining task and project completion, and generating and recommending educational content. This personalization enhances the learning environment, leading to increased academic success. This finding aligns with other recent studies [ 26 , 27 , 28 , 60 , 66 ] which revealed that ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities. Ultimately, fostering students' performance, engagement, and motivation, leading to increase students' academic success [ 14 , 44 , 58 ]. This ultimate outcome is in line with the findings of Saqr et al. [ 68 , 69 ], which emphasized that learning strategies are important catalysts of students' learning, as students who utilize effective learning strategies are more likely to have better academic achievement.

Teachers, too, have capitalized on ChatGPT's capabilities to enhance productivity and efficiency, using it for creating lesson plans, generating quizzes, providing additional resources, generating and preplanning new ideas for activities, and aiding in answering students’ questions. This adoption of technology introduces new opportunities to support teaching and learning practices, enhancing teacher productivity. This finding aligns with those of Day [ 17 ], De Castro [ 18 ], and Su and Yang [ 74 ] as well as with those of Valtonen et al. [ 82 ], who revealed that emerging technological advancements have opened up novel opportunities and means to support teaching and learning practices, and enhance teachers’ productivity.

In response to RQ2 , "What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?", the findings from this review provide profound insights and raise significant concerns. Starting with the insights, chatbots, including ChatGPT, have demonstrated the potential to reshape and revolutionize education, creating new, novel opportunities for enhancing the learning process and outcomes [ 83 ], facilitating different learning approaches, and offering a range of pedagogical benefits [ 19 , 43 , 72 ]. In this context, this review found that ChatGPT could open avenues for educators to adopt or develop new effective learning and teaching strategies that can evolve with the introduction of ChatGPT into the classroom. Nonetheless, there is an evident lack of research understanding regarding the potential impact of generative machine learning models within diverse educational settings [ 83 ]. This necessitates teachers to attain a high level of proficiency in incorporating chatbots, such as ChatGPT, into their classrooms to create inventive, well-structured, and captivating learning strategies. In the same vein, the review also found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects [ 37 ]. This concern could lead to inequity of access to the benefits of chatbots, including ChatGPT, as individuals who lack the necessary expertise may not be able to harness their full potential, resulting in disparities in educational outcomes and opportunities. Therefore, immediate action is needed to address these potential issues. A potential solution is offering training, support, and competency development for teachers to ensure that all of them can leverage chatbots, including ChatGPT, effectively and equitably in their educational practices [ 5 , 28 , 80 ], which could enhance accessibility and inclusivity, and potentially result in innovative outcomes [ 82 , 83 ].

Additionally, chatbots, including ChatGPT, have the potential to significantly impact students' thinking abilities, including retention, reasoning, analysis skills [ 19 , 45 ], and foster innovation and creativity capabilities [ 83 ]. This review found that ChatGPT could contribute to improving a wide range of skills among students. However, it found that frequent use of ChatGPT may result in a decrease in innovative capacities, collaborative skills and cognitive capacities, and students' motivation to attend classes, as well as could lead to reduced higher-order thinking skills among students [ 22 , 29 ]. Therefore, immediate action is needed to carefully examine the long-term impact of chatbots such as ChatGPT, on learning outcomes as well as to explore its incorporation into educational settings as a supportive tool without compromising students' cognitive development and critical thinking abilities. In the same vein, the review also found that it is challenging to draw a consistent conclusion regarding the potential of ChatGPT to aid self-directed learning approach. This finding aligns with the recent study of Baskara [ 8 ]. Therefore, further research is needed to explore the potential of ChatGPT for self-directed learning. One potential solution involves utilizing learning analytics as a novel approach to examine various aspects of students' learning and support them in their individual endeavors [ 32 ]. This approach can bridge this gap by facilitating an in-depth analysis of how learners engage with ChatGPT, identifying trends in self-directed learning behavior, and assessing its influence on their outcomes.

Turning to the significant concerns, on the other hand, a fundamental challenge with LLM-based chatbots, including ChatGPT, is the accuracy and quality of the provided information and responses, as they provide false information as truth—a phenomenon often referred to as "hallucination" [ 3 , 49 ]. In this context, this review found that the provided information was not entirely satisfactory. Consequently, the utilization of chatbots presents potential concerns, such as generating and providing inaccurate or misleading information, especially for students who utilize it to support their learning. This finding aligns with other findings [ 6 , 30 , 35 , 40 ] which revealed that incorporating chatbots such as ChatGPT, into education presents challenges related to its accuracy and reliability due to its training on a large corpus of data, which may contain inaccuracies and the way users formulate or ask ChatGPT. Therefore, immediate action is needed to address these potential issues. One possible solution is to equip students with the necessary skills and competencies, which include a background understanding of how to use it effectively and the ability to assess and evaluate the information it generates, as the accuracy and the quality of the provided information depend on the input, its complexity, the topic, and the relevance of its training data [ 28 , 49 , 86 ]. However, it's also essential to examine how learners can be educated about how these models operate, the data used in their training, and how to recognize their limitations, challenges, and issues [ 79 ].

Furthermore, chatbots present a substantial challenge concerning maintaining academic integrity [ 20 , 56 ] and copyright violations [ 83 ], which are significant concerns in education. The review found that the potential misuse of ChatGPT might foster cheating, facilitate plagiarism, and threaten academic integrity. This issue is also affirmed by the research conducted by Basic et al. [ 7 ], who presented evidence that students who utilized ChatGPT in their writing assignments had more plagiarism cases than those who did not. These findings align with the conclusions drawn by Cotton et al. [ 13 ], Hisan and Amri [ 33 ] and Sullivan et al. [ 75 ], who revealed that the integration of chatbots such as ChatGPT into education poses a significant challenge to the preservation of academic integrity. Moreover, chatbots, including ChatGPT, have increased the difficulty in identifying plagiarism [ 47 , 67 , 76 ]. The findings from previous studies [ 1 , 84 ] indicate that AI-generated text often went undetected by plagiarism software, such as Turnitin. However, Turnitin and other similar plagiarism detection tools, such as ZeroGPT, GPTZero, and Copyleaks, have since evolved, incorporating enhanced techniques to detect AI-generated text, despite the possibility of false positives, as noted in different studies that have found these tools still not yet fully ready to accurately and reliably identify AI-generated text [ 10 , 51 ], and new novel detection methods may need to be created and implemented for AI-generated text detection [ 4 ]. This potential issue could lead to another concern, which is the difficulty of accurately evaluating student performance when they utilize chatbots such as ChatGPT assistance in their assignments. Consequently, the most LLM-driven chatbots present a substantial challenge to traditional assessments [ 64 ]. The findings from previous studies indicate the importance of rethinking, improving, and redesigning innovative assessment methods in the era of chatbots [ 14 , 20 , 64 , 75 ]. These methods should prioritize the process of evaluating students' ability to apply knowledge to complex cases and demonstrate comprehension, rather than solely focusing on the final product for assessment. Therefore, immediate action is needed to address these potential issues. One possible solution would be the development of clear guidelines, regulatory policies, and pedagogical guidance. These measures would help regulate the proper and ethical utilization of chatbots, such as ChatGPT, and must be established before their introduction to students [ 35 , 38 , 39 , 41 , 89 ].

In summary, our review has delved into the utilization of ChatGPT, a prominent example of chatbots, in education, addressing the question of how ChatGPT has been utilized in education. However, there remain significant gaps, which necessitate further research to shed light on this area.

7 Conclusions

This systematic review has shed light on the varied initial attempts at incorporating ChatGPT into education by both learners and educators, while also offering insights and considerations that can facilitate its effective and responsible use in future educational contexts. From the analysis of 14 selected studies, the review revealed the dual-edged impact of ChatGPT in educational settings. On the positive side, ChatGPT significantly aided the learning process in various ways. Learners have used it as a virtual intelligent assistant, benefiting from its ability to provide immediate feedback, on-demand answers, and easy access to educational resources. Additionally, it was clear that learners have used it to enhance their writing and language skills, engaging in practices such as generating ideas, composing essays, and performing tasks like summarizing, translating, paraphrasing texts, or checking grammar. Importantly, other learners have utilized it in supporting and facilitating their directed and personalized learning on a broad range of educational topics, assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. Educators, on the other hand, found ChatGPT beneficial for enhancing productivity and efficiency. They used it for creating lesson plans, generating quizzes, providing additional resources, and answers learners' questions, which saved time and allowed for more dynamic and engaging teaching strategies and methodologies.

However, the review also pointed out negative impacts. The results revealed that overuse of ChatGPT could decrease innovative capacities and collaborative learning among learners. Specifically, relying too much on ChatGPT for quick answers can inhibit learners' critical thinking and problem-solving skills. Learners might not engage deeply with the material or consider multiple solutions to a problem. This tendency was particularly evident in group projects, where learners preferred consulting ChatGPT individually for solutions over brainstorming and collaborating with peers, which negatively affected their teamwork abilities. On a broader level, integrating ChatGPT into education has also raised several concerns, including the potential for providing inaccurate or misleading information, issues of inequity in access, challenges related to academic integrity, and the possibility of misusing the technology.

Accordingly, this review emphasizes the urgency of developing clear rules, policies, and regulations to ensure ChatGPT's effective and responsible use in educational settings, alongside other chatbots, by both learners and educators. This requires providing well-structured training to educate them on responsible usage and understanding its limitations, along with offering sufficient background information. Moreover, it highlights the importance of rethinking, improving, and redesigning innovative teaching and assessment methods in the era of ChatGPT. Furthermore, conducting further research and engaging in discussions with policymakers and stakeholders are essential steps to maximize the benefits for both educators and learners and ensure academic integrity.

It is important to acknowledge that this review has certain limitations. Firstly, the limited inclusion of reviewed studies can be attributed to several reasons, including the novelty of the technology, as new technologies often face initial skepticism and cautious adoption; the lack of clear guidelines or best practices for leveraging this technology for educational purposes; and institutional or governmental policies affecting the utilization of this technology in educational contexts. These factors, in turn, have affected the number of studies available for review. Secondly, the utilization of the original version of ChatGPT, based on GPT-3 or GPT-3.5, implies that new studies utilizing the updated version, GPT-4 may lead to different findings. Therefore, conducting follow-up systematic reviews is essential once more empirical studies on ChatGPT are published. Additionally, long-term studies are necessary to thoroughly examine and assess the impact of ChatGPT on various educational practices.

Despite these limitations, this systematic review has highlighted the transformative potential of ChatGPT in education, revealing its diverse utilization by learners and educators alike and summarized the benefits of incorporating it into education, as well as the forefront critical concerns and challenges that must be addressed to facilitate its effective and responsible use in future educational contexts. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Data availability

The data supporting our findings are available upon request.

Abbreviations

• Artificial intelligence

AI in education

Large language model

Artificial neural networks

Chat Generative Pre-Trained Transformer

Recurrent neural networks

Long short-term memory

Reinforcement learning from human feedback

Natural language processing

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

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The paper is co-funded by the Academy of Finland (Suomen Akatemia) Research Council for Natural Sciences and Engineering for the project Towards precision education: Idiographic learning analytics (TOPEILA), Decision Number 350560.

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Yazid Albadarin, Mohammed Saqr, Nicolas Pope & Markku Tukiainen

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YA contributed to the literature search, data analysis, discussion, and conclusion. Additionally, YA contributed to the manuscript’s writing, editing, and finalization. MS contributed to the study’s design, conceptualization, acquisition of funding, project administration, allocation of resources, supervision, validation, literature search, and analysis of results. Furthermore, MS contributed to the manuscript's writing, revising, and approving it in its finalized state. NP contributed to the results, and discussions, and provided supervision. NP also contributed to the writing process, revisions, and the final approval of the manuscript in its finalized state. MT contributed to the study's conceptualization, resource management, supervision, writing, revising the manuscript, and approving it.

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See Table  4

The process of synthesizing the data presented in Table  4 involved identifying the relevant studies through a search process of databases (ERIC, Scopus, Web of Knowledge, Dimensions.ai, and lens.org) using specific keywords "ChatGPT" and "education". Following this, inclusion/exclusion criteria were applied, and data extraction was performed using Creswell's [ 15 ] coding techniques to capture key information and identify common themes across the included studies.

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Albadarin, Y., Saqr, M., Pope, N. et al. A systematic literature review of empirical research on ChatGPT in education. Discov Educ 3 , 60 (2024). https://doi.org/10.1007/s44217-024-00138-2

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Published : 26 May 2024

DOI : https://doi.org/10.1007/s44217-024-00138-2

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