Large Language Models in Education: A Systematic Review of Empirical Applications, Benefits, and Challenges
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The rapid advancement of Large Language Models (LLMs), particularly following the release of ChatGPT in November 2022, has significantly transformed educational methodologies. This systematic review aims to synthesize empirical studies published between November 2022 and March 2025, examining the implementation and effectiveness of LLMs in educational settings. 88 empirical studies identified key applications, benefits, and challenges associated with LLM integration in education. Our findings reveal that LLMs are utilized across various educational contexts in six primary applications, with Intelligent Tutoring Systems being particularly prominent. The benefits include improved academic performance, increased student engagement, enhanced accessibility, optimized resource utilization, and strengthened cognitive and skill development. However, challenges such as student over-reliance on AI, technical reliability issues, assessment fairness, and privacy concerns were identified. This review provides educators, researchers, and policymakers with evidence-based insights and practical guidance for effective LLM integration, contributing to the ongoing transformation of teaching and learning in the era of Generative Artificial Intelligence (GenAI) technology.
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Fewer studies have focused on systematic evaluations of LLM performance in actual educational contexts.
“While research on AI in education has expanded in recent years, fewer studies have focused on systematic evaluations of LLM performance in actual educational contexts [10,11].”
From An Integrated AHP–Fuzzy AHP Evaluation Framework for Large Language Models in Software Engineering Education (2026) · cites this paper as [10]
Most existing research focuses on the general capabilities, opportunities, and risks of using LLMs in education rather than systematically evaluating the quality of the learning content they generate.
“Most existing research focuses on the general capabilities, opportunities, and risks of using LLMs in education rather than systematically evaluating the quality of the learning content they generate [4,10,16].”
From An Integrated AHP–Fuzzy AHP Evaluation Framework for Large Language Models in Software Engineering Education (2026) · cites this paper as [10]
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- Authors
- Shi, Y., Yu, K., Dong, Y., & Chen, F.
- Affiliations
- University of Technology Sydney (AU)
- Corresponding author
- Shi, Yuhong
- ORCID
- Shi, Yuhong
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2026-06
- Volume
- 10
- Pages
- 100529
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W7115077557
- Cited by
- 80
- References
- 36
- Citations by year
- 2026: 79 · 2025: 1
- FWCI
- 12.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by)
- Funders
- Commonwealth Scientific and Industrial Research Organisation
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- large language models, generative AI in education, intelligent tutoring systems, ChatGPT, student engagement, academic performance, accessibility, overreliance on AI, assessment fairness, privacy concerns, technical reliability, AI in teaching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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