@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://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.743/",
    doi = "10.18653/v1/2025.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."
}