LLM代理助力教育，自动完成教学反馈与课程设计。
LLM Agents for Education: Advances and Applications
- 用大模型代理自动化生成作业反馈和设计课程
- 梳理了支持这类应用的数据集、评测基准与算法框架
- 揭示伦理风险与系统集成难题，适合教育科技研究者参考
大型语言模型（LLM）代理正在通过自动化复杂教学任务，推动教育领域的变革，提升教与学的效率。本文系统综述了近年来将LLM代理应用于教育场景的最新进展，重点解决教学反馈生成、课程设计等核心挑战。我们分析了支撑这些代理的技术，包括代表性数据集、评测基准及算法框架。此外，还指出了在教育环境中部署LLM代理所面临的关键挑战，如伦理问题、幻觉现象与过度依赖，以及与现有教育生态系统的整合难题。附录A还提供了面向特定领域教育代理的全面概述，涵盖科学学习、语言学习及职业发展等领域。
原文摘要 · Abstract (English)
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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