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Cited by 96Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

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LLM Agents for Education: Advances and Applications

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Chu, Zhendong, Wang, Shen, Xie, Jian, Zhu, Tinghui, Yan, Yibo, Ye, Jingheng, Zhong, Aoxiao, Hu, Xuming, Liang, Jing, Yu, Philip S., Wen, Qingsong·2025·Findings of the Association for Computational Linguistics: EMNLP 2025
DOI 10.18653/v1/2025.findings-emnlp.743
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Abstract

Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes.In this survey, we present a systematic review of recent advances in applying LLM agents to address key challenges in educational settings, such as feedback comment generation, curriculum design, etc.We analyze the technologies enabling these agents, including representative datasets, benchmarks, and algorithmic frameworks.Additionally, we highlight key challenges in deploying LLM agents in educational settings, including ethical issues, hallucination and overreliance, and integration with existing educational ecosystems.Beyond the core technical focus, we include in Appendix A a comprehensive overview of domain-specific educational agents, covering areas such as science learning, language learning, and professional development.

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Authors
Chu, Z., Wang, S., Xie, J., Zhu, T., Yan, Y., Ye, J., Zhong, A., Hu, X., Liang, J., Yu, P. S., & Wen, Q.
Affiliations
  • Fudan University (CN)
  • The Hong Kong University of Science and Technology (Guangzhou) (CN)
  • Tsinghua University (CN)
  • University of Illinois Chicago (US)
ORCID
Venue
Findings of the Association for Computational Linguistics: EMNLP 2025
Publisher
Association for Computational Linguistics
Published
2025
Pages
13782-13810
DOI
10.18653/v1/2025.findings-emnlp.743
OpenAlex
W4416033994
Cited by
96
References
3
Citations by year
2026: 71 · 2025: 25
FWCI
87.21
Type
conference paper
Language
en
Open access
Locations
Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion)
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, LLM agents, educational technology, artificial intelligence in education, generative AI applications

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 ›

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