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LLM Agents for Education: Advances and Applications

Zhendong Chu,Shen Wang,8 Authors,Qingsong Wen

2025 · DOI: 10.48550/arXiv.2503.11733
Conference on Empirical Methods in Natural Language Processing · 68 Citations

TLDR

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, and analyzes the technologies enabling these agents, including representative datasets, benchmarks, and algorithmic frameworks.

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.