Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913170182897664 |
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| author | Gao, Weibo Liu, Qi Yue, Linan Yao, Fangzhou Lv, Rui Zhang, Zheng Wang, Hao Huang, Zhenya |
| author_facet | Gao, Weibo Liu, Qi Yue, Linan Yao, Fangzhou Lv, Rui Zhang, Zheng Wang, Hao Huang, Zhenya |
| contents | Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the discrepancy between offline metrics and online performance significantly impedes their progress. To address this challenge, we introduce Agent4Edu, a novel personalized learning simulator leveraging recent advancements in human intelligence through large language models (LLMs). Agent4Edu features LLM-powered generative agents equipped with learner profile, memory, and action modules tailored to personalized learning algorithms. The learner profiles are initialized using real-world response data, capturing practice styles and cognitive factors. Inspired by human psychology theory, the memory module records practice facts and high-level summaries, integrating reflection mechanisms. The action module supports various behaviors, including exercise understanding, analysis, and response generation. Each agent can interact with personalized learning algorithms, such as computerized adaptive testing, enabling a multifaceted evaluation and enhancement of customized services. Through a comprehensive assessment, we explore the strengths and weaknesses of Agent4Edu, emphasizing the consistency and discrepancies in responses between agents and human learners. The code, data, and appendix are publicly available at https://github.com/bigdata-ustc/Agent4Edu. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems Gao, Weibo Liu, Qi Yue, Linan Yao, Fangzhou Lv, Rui Zhang, Zheng Wang, Hao Huang, Zhenya Computers and Society Artificial Intelligence Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the discrepancy between offline metrics and online performance significantly impedes their progress. To address this challenge, we introduce Agent4Edu, a novel personalized learning simulator leveraging recent advancements in human intelligence through large language models (LLMs). Agent4Edu features LLM-powered generative agents equipped with learner profile, memory, and action modules tailored to personalized learning algorithms. The learner profiles are initialized using real-world response data, capturing practice styles and cognitive factors. Inspired by human psychology theory, the memory module records practice facts and high-level summaries, integrating reflection mechanisms. The action module supports various behaviors, including exercise understanding, analysis, and response generation. Each agent can interact with personalized learning algorithms, such as computerized adaptive testing, enabling a multifaceted evaluation and enhancement of customized services. Through a comprehensive assessment, we explore the strengths and weaknesses of Agent4Edu, emphasizing the consistency and discrepancies in responses between agents and human learners. The code, data, and appendix are publicly available at https://github.com/bigdata-ustc/Agent4Edu. |
| title | Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10332 |