LLM Agents for Education: Advances and Applications

作者：Zhendong Chu, Shen Wang, Jianhe Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen · 年份：2025 · DOI：10.18653/v1/2025.findings-emnlp.743 · 被引用次数：62 · 研究领域：Multi-Agent Systems and Negotiation、Intelligent Tutoring Systems and Adaptive Learning、Online Learning and Analytics

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.