LLM Agents for Education: Advances and Applications
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866914305186725888 |
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| author | Chu, Zhendong Wang, Shen Xie, Jian Zhu, Tinghui Yan, Yibo Ye, Jinheng Zhong, Aoxiao Hu, Xuming Liang, Jing Yu, Philip S. Wen, Qingsong |
| author_facet | Chu, Zhendong Wang, Shen Xie, Jian Zhu, Tinghui Yan, Yibo Ye, Jinheng Zhong, Aoxiao Hu, Xuming Liang, Jing Yu, Philip S. Wen, Qingsong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM Agents for Education: Advances and Applications Chu, Zhendong Wang, Shen Xie, Jian Zhu, Tinghui Yan, Yibo Ye, Jinheng Zhong, Aoxiao Hu, Xuming Liang, Jing Yu, Philip S. Wen, Qingsong Computers and Society Artificial Intelligence Computation and Language Human-Computer Interaction 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. |
| title | LLM Agents for Education: Advances and Applications |
| topic | Computers and Society Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.11733 |