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.

翻译：大型语言模型（LLM）智能体正通过自动化复杂教学任务、优化教学过程与学习体验，推动教育领域的变革。本文系统综述了近期应用LLM智能体应对教育场景关键挑战（如反馈评语生成、课程设计等）的研究进展。我们分析了支撑这些智能体的关键技术，包括代表性数据集、评估基准及算法框架。同时，重点探讨了LLM智能体在教育场景部署中的核心挑战，涉及伦理问题、幻觉与过度依赖现象，以及与现有教育生态的整合问题。除核心技术聚焦外，附录A还提供了领域专用教育智能体的全面概览，涵盖科学教育、语言学习、职业发展等方向。