LLM Agents for Education: Advances and Applications

This survey paper comprehensively reviews LLM agents for education, proposing a task-centric taxonomy that categorizes agents based on teaching assistance (classroom simulation, feedback generation, curriculum design) and student support (adaptive learning, knowledge tracing, error correction). The paper analyzes architectures, datasets, benchmarks, and deployment challenges including ethics, hallucination, and overreliance.

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 hig

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