@inproceedings{chu-etal-2025-llm,
title = "{LLM} Agents for Education: Advances and Applications",
author = "Chu, Zhendong and
Wang, Shen and
Xie, Jian and
Zhu, Tinghui and
Yan, Yibo and
Ye, Jingheng and
Zhong, Aoxiao and
Hu, Xuming and
Liang, Jing and
Yu, Philip S. and
Wen, Qingsong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.743/",
pages = "13782--13810",
ISBN = "979-8-89176-335-7",
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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<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.</abstract>
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%0 Conference Proceedings
%T LLM Agents for Education: Advances and Applications
%A Chu, Zhendong
%A Wang, Shen
%A Xie, Jian
%A Zhu, Tinghui
%A Yan, Yibo
%A Ye, Jingheng
%A Zhong, Aoxiao
%A Hu, Xuming
%A Liang, Jing
%A Yu, Philip S.
%A Wen, Qingsong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F chu-etal-2025-llm
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.743/
%P 13782-13810
Markdown (Informal)
[LLM Agents for Education: Advances and Applications](https://aclanthology.org/2025.findings-emnlp.743/) (Chu et al., Findings 2025)
ACL
- Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, and Qingsong Wen. 2025. LLM Agents for Education: Advances and Applications. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 13782–13810, Suzhou, China. Association for Computational Linguistics.