CoChat
Sign Up
Cited by 101Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Large language models in education: a systematic review of empirical applications, benefits, and challenges

Open access
Shi, Yuhong, Yu, Kun, Dong, Yifei, Chen, Fang·2026·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2025.100529
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.caeai.2025.100529

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 25

  1. Adipat, 2025, Challenging conventions: Chatgpt’s controversial impacts on educational and English language teaching practices, Educational Process: International Journal, 14
  2. Alshammari, 2025, An investigation into chatgpt-enhanced adaptive e-learning systems, TEM Journal, 14, 503
  3. Alvarez, 2024, Evaluating the impact of AI–powered tutors Mathgpt and flexi 2.0 in enhancing calculus learning, Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 8, 495
  4. Bezirhan, 2023, Automated reading passage generation with openai’s large language model, Computers and Education: Artificial Intelligence, 5
  5. Cambaz, 2024, Use of AI-driven code generation models in teaching and learning programming: A systematic literature review, 172
  6. Chen, 2023, Exploring the benefits of chatbots game-based learning in science learning outcomes and motivation, Vol. 2, 400
  7. Chhina, 2023, Navigating the terrain of large language models in higher education: A systematic literature review
  8. Chun, 2025, A comparative analysis of on-device AI-driven, self-regulated learning and traditional pedagogy in university health sciences education, Applied Sciences (Switzerland), 15
  9. Elmourabit, 2024, The impact of generative artificial intelligence on education: A comparative study, Vol. 23, 470
  10. Întorsureanu, 2025, Generative AI in education: Perspectives through an academic lens, Electronics (Switzerland), 14
  11. Kutty, 2024, Generative AI in higher education: Perspectives of students, educators and administrators, Journal of Applied Learning and Teaching, 7, 47, 10.37074/jalt.2024.7.2.27 · DOI · Paper page
  12. Lai, 2025, Analysis of learning behaviors and outcomes for students with different knowledge levels: A case study of intelligent tutoring system for coding and learning (ITS-CAL), Applied Sciences (Switzerland), 15
  13. Lin, 2024, Preliminary systematic review of open-source large language models in education, 68
  14. Liu, 2024, Advancing generative intelligent tutoring systems with GPT-4: Design, evaluation, and a modular framework for future learning platforms, Electronics (Switzerland), 13
  15. Melisa, 2025, Critical thinking in the age of AI: A systematic review of ai’s effects on higher education, Educational Process: International Journal, 14
  16. Meyer, 2024, Using llms to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions, Computers and Education: Artificial Intelligence, 6
  17. Norberg, 2024, Rewriting content with GPT-4 to support emerging readers in adaptive mathematics software, International Journal of Artificial Intelligence in Education
  18. Sekli, 2024, Generative AI solutions for faculty and students: A review of literature and roadmap for future research, Journal of Information Technology Education: Research, 23, 014
  19. Teng, 2024, Investigating the utilization and impact of large language model-based intelligent teaching assistants in flipped classrooms, Education and Information Technologies
  20. Vygotsky, 1978, vol. 86
  21. Wang, 2025, Integrating feedback mechanisms and Chatgpt for VR-based experiential learning: Impacts on reflective thinking and aiot physical hands-on tasks, Interactive Learning Environments, 33, 1770
  22. Wang, 2024, Opportunities and challenges of using Chatgpt as a teaching assistant in English language teaching: A systematic literature review, 375
  23. Xu, 2024, Leveraging artificial intelligence and large language models for enhanced teaching and learning: A systematic literature review, 73
  24. Yang, 2024, Enhancing Python learning with pytutor: Efficacy of a chatgpt-based intelligent tutoring system in programming education, Computers and Education: Artificial Intelligence, 7
  25. Zarei, 2025, Chatgpt, a friend or a foe in medical education: A review of strengths, challenges, and opportunities, Shiraz E-Medical Journal

Cited by

Sorted by citations

See all 101

Related

See all

Metadata

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)
DOI
10.1016/j.caeai.2025.100529
OpenAlex
W7115077557
Cited by
80
References
36
Citations by year
2026: 79
FWCI
12.30
Type
journal article
Language
en
Open access
Licence
Locations
Computers and Education Artificial Intelligence (publishedVersion, cc-by)
Funders
Commonwealth Scientific and Industrial Research Organisation
Topics
  • Social Sciences › Education › 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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review