Key research papers on artificial intelligence in education
The most-cited studies on AI and ChatGPT in education: pre-2021 reviews of AIEd, the 2023 wave of generative AI papers and the systematic reviews that followed.
This literature has two layers. The older one, AI in education (AIEd), is about intelligent tutoring, prediction and adaptive systems, and its most-cited work is the systematic review by Zawacki-Richter et al. (2019). The newer one began when ChatGPT was launched in November 2022: all eight of the most-cited papers since 2021 on this page were published in 2023, and they are position papers, surveys of student views, tests of ChatGPT on medical licensing exams and one review of the field.
How the literature is organised
Research on AI in education predates generative AI by decades. Roll and Wylie (2016) looked back over twenty-five years of the field by analysing papers from 1994, 2004 and 2014 in the International Journal of Artificial Intelligence in Education, and Zawacki-Richter et al. (2019) systematically reviewed AI applications in higher education. They found four areas of application: profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. The review's subtitle asks where the educators are. Chen, Chen and Lin (2020), Hwang et al. (2020) and Popenici and Kerr (2017) are the other general overviews from this period, and medical education has its own early strand (Chan and Zary, 2019; Paranjape et al., 2019).
ChatGPT was launched in November 2022 (Lo, 2023). Every one of the eight most-cited papers since 2021 on this page was published in 2023.
Main debates
The 2023 papers set out the main arguments. Kasneci et al. list opportunities and risks of large language models for education. Rudolph, Tan and Tan ask whether ChatGPT ends traditional assessment. Tlili et al. treat it as a case study of chatbots in education, and Baidoo-Anu and Ansah weigh its benefits for teaching and learning. The reviews record concerns as well as uses. Sallam (2023) found concerns stated in 58 of the 60 records reviewed, among them risk of bias, plagiarism, inaccurate content and incorrect citations. Lo (2023), a rapid review of the first three months after release, reports that ChatGPT could assist instructors and tutor students but also generated incorrect or fake information.
Where recent work is heading
Crompton and Burke (2023) map AI research in higher education, Chiu et al. (2023) and Wang et al. (2024) are broad systematic reviews, and Çelik et al. (2022) review what AI offers teachers specifically. Questions a student project could take up are empirical: effects on learning outcomes over a full course, effects on students' own regulation of study, and what assessment designs hold up when every student has access to a capable model.
Most-cited foundational papers
Published before 2021 and ranked by how often later work cites them. Read the abstract of each and the full text of the three or four closest to your question. Citation count measures attention, not quality, so treat this as a map of what the field has argued about rather than a ranking of what is true.
Hui Luan and 9 others (2020). Frontiers in Psychology.
Cited by 646Open accessdoi:10.3389/fpsyg.2020.580820
Most-cited papers since 2021
Primary studies and conceptual papers from 2021 onwards. A paper published in 2024 has had a few years to accumulate citations where the works in the section above have had decades, so compare these counts with each other rather than with the ones above.
Jürgen Rudolph, Samson Tan, Shannon Tan (2023). Journal of Applied Learning & Teaching.
Cited by 1,771Open accessdoi:10.37074/jalt.2023.6.1.9
Recent reviews and meta-analyses
The fastest way into a literature. A good review gives you the structure of the field, a reference list to mine and, in its limitations section, the gaps other researchers have already spotted.
Shan Wang and 5 others (2024). Expert Systems with Applications.
Cited by 909Open accessdoi:10.1016/j.eswa.2024.124167
How big the literature is, and where it is published
OpenAlex indexes 52,722 works whose title matches this topic. The chart shows how many were published each year from 2000 to 2025; the current year is left out because it is incomplete.
Show the numbers as a table
Year
Works
2000
9
2001
4
2002
6
2003
9
2004
7
2005
10
2006
11
2007
10
2008
17
2009
32
2010
26
2011
25
2012
18
2013
26
2014
26
2015
22
2016
41
2017
64
2018
139
2019
309
2020
738
2021
934
2022
1,235
2023
3,763
2024
8,654
2025
15,926
Journals behind the most-cited work
Counted across the 185 most-cited works on the topic, not across everything published. Browsing recent issues of the first two or three is a reliable way to find current work that has not yet been cited much.
Computers and Education Artificial Intelligence21 papers
International Journal of Educational Technology in Higher Education14 papers
Education Sciences9 papers
Sustainability8 papers
Education and Information Technologies6 papers
Interactive Learning Environments6 papers
Computers & Education5 papers
JMIR Medical Education5 papers
Search strings to copy
Written for databases that accept Boolean operators (Scopus, Web of Science, ERIC, PubMed, EBSCO). Quotation marks keep a phrase together, an asterisk stands in for the end of a word, and OR groups go in brackets. British and American spellings are written out with OR rather than covered by a single-character wildcard, because those wildcards differ between databases: PubMed’s help page documents the asterisk only, and asks for at least four characters before it. Limit the search to title and abstract first; widen it only if you get too little. Our guide to starting a literature review covers how to record what you searched.
Generative AI and ChatGPT in higher education
(ChatGPT OR "generative AI" OR "generative artificial intelligence" OR "large language model*") AND ("higher education" OR universit* OR undergraduate*) AND (learning OR assessment OR "academic integrity")
AI in education before ChatGPT
("artificial intelligence in education" OR AIEd OR "intelligent tutoring system*" OR "adaptive learning") AND (review OR "systematic review")
Teachers and AI
("artificial intelligence" OR ChatGPT) AND (teacher* OR educator* OR faculty) AND (attitude* OR readiness OR "professional development" OR literacy)
Whether AI tools support or replace students' own regulation of learning.
How to cite these papers
Every paper above has a DOI, a part of the reference that is easy to leave out. Here is one of them, “Artificial Intelligence in Education: A Review” (2020), in the two styles students ask about most:
APA 7th edition
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/access.2020.2988510
MLA 9th edition
Chen, Lijia, et al. “Artificial Intelligence in Education: A Review.” IEEE Access, vol. 8, 2020, pp. 75264–78, https://doi.org/10.1109/access.2020.2988510.
Check the details against the article itself before you submit: databases, including the one behind this page, sometimes carry the online-first year rather than the volume year. Full rules and more examples are in our guides to APA, MLA, Chicago, Harvard, Vancouver and ABNT, with the rest in the citation guides. You can also format a reference from its DOI with our free citation tools.
Frequently asked questions
What is the most cited paper on AI in education?
In OpenAlex it is the systematic review of AI applications in higher education by Zawacki-Richter et al. (2019), with Kasneci et al. (2023), ChatGPT for good? On opportunities and challenges of large language models for education, a few dozen citations behind. Both had passed 6,500 citations by September 2026.
Are 2023 papers on ChatGPT already out of date?
Check what period each one covers. Lo (2023), for example, reviews only the first three months after release, December 2022 to February 2023, and the tests of ChatGPT on exams describe the model available at the time. Cite the 2023 papers for the arguments they introduced about assessment and academic integrity, and look for newer empirical work when you need evidence about current tools.
Is there enough literature for a systematic review on generative AI in education?
Yes. OpenAlex indexes more than 15,000 works published in 2025 alone with AI and education terms in the title. The practical problem is the opposite: you need narrow inclusion criteria, such as one educational level, one subject and empirical studies only.
How this page was made
The lists come from OpenAlex, an open index of scholarly works whose data are published under a CC0 licence, queried on September 21, 2026 for works whose title matches (("artificial intelligence" OR chatgpt OR "generative ai" OR "generative artificial intelligence") AND (education OR educational OR students OR teaching OR teachers OR classroom)). Only works with a DOI are listed. Each one was checked against the publisher’s own record at Crossref or DataCite (title, year, first author, journal, volume and pages), and in three cases, where the publisher deposited no byline, against PubMed; anything OpenAlex or Crossref flags as retracted was left out, and an editor took out results that matched the words but not the subject. Citation counts are OpenAlex’s on that date and are usually lower than Google Scholar’s, which counts more kinds of document. Ranking by citations tells you what a field has relied on, not what is correct; several heavily cited papers on any topic are cited because later work disputes them. Books without a DOI are missing, which matters in fields where the founding text is a book.
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