AI in higher education: A literature review of ChatGPT and guidelines for responsible implementation
Open accessAbstract
Specifically, natural language models like ChatGPT present numerous advantages and disadvantages for higher education. It was trained on a massive dataset of text from the internet, allowing it to respond to a broad range of prompts. This article covers the use and potential implications of ChatGPT since its release in November 2022. One of the main benefits is the potential of Artificial Intelligence (AI) to address challenges in learning, such as improving the transfer of knowledge, dispelling misconceptions, and promoting critical thinking skills among students. It also acknowledges concerns about its use in assessments and the potential for academic dishonesty, integrity, and malpractices. In this article, the authors discuss the high level of interest in ChatGPT and its use in education by reviewing the literature about ChatGPT usage. The article provides a set of guidelines and emphasizes the need for further research to fully understand the current practices, challenges, and opportunities of ChatGPT in higher education. The authors conducted a thorough and systematic review of peer-reviewed journal articles to present a theoretical and conceptual perspective on ChatGPT. They acknowledge the possibility of ChatGPT’s hype mirroring that of previous advancements in artificial intelligence, automation, and AI algorithms. The article offers a summary of the research findings, highlighting both the benefits and drawbacks, while also providing practical guidelines for students and teachers on how to utilize ChatGPT effectively.
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Cites
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Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531 · see citing works
A systematic review of <scp>ChatGPT</scp> use in K‐12 education
Zhang, P., & Tur, G. (2024) · European Journal of Education · doi:10.1111/ejed.12599 · Cited by 280 · see citing works
Yang, Y., Luo, J., Yang, M., Yang, R., & Chen, J. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2327003 · Cited by 129 · see citing works
Perera, P., & Lankathilake, M. (2023) · Journal of Advances in Education and Philosophy · doi:10.36348/jaep.2023.v07i08.001 · Cited by 66 · see citing works
Self-citationLund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025) · AI in Education · doi:10.3390/aieduc1010002 · Cited by 64 · see citing works
“…emphasizes, the path forward is not resistance, but regulation—anchored by institutional policies, ethical guidelines, and shared expectations (Batista et al., 2024; Cordero et al., 2025; Kurtz et al., 2024; D. Lee et al., 2024; Perera & Lankathilaka, 2023; H. Wang et al., 2024; Zhang & Xu, 2025).”
Xia, L., Shen, K., Sun, H., An, X., & Dong, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13137-5 · Cited by 38 · see citing works
- Authors
- Perera, P., & Lankathilaka, M.
- Venue
- International Journal of Research and Innovation in Social Science
- Publisher
- RSIS International
- Published
- 2023
- Volume
- VII
- Issue
- VI
- Pages
- 306-314
- ISSN
- 2454-6186 (ISSN-L)
- OpenAlex
- W4382518736
- Cited by
- 85
- References
- 0
- Citations by year
- 2026: 22 · 2025: 29 · 2024: 25 · 2023: 9
- FWCI
- 19.95
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- cc-by
- Locations
- International Journal of Research and Innovation in Social Science (publishedVersion)
- RePEc: Research Papers in Economics (acceptedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- ChatGPT, large language models, artificial intelligence in education, higher education, academic integrity, academic dishonesty, assessment, critical thinking, knowledge transfer
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