标题： Agent4Edu：通过生成代理生成学习者响应数据以用于智能教育系统 显示英文标题

标题： Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems

摘要： 个性化学习是智能教育系统中一种有前景的教育策略，旨在提高学习者的实践效率。 然而，离线指标与在线表现之间的差异显著阻碍了其进展。 为解决这一挑战，我们引入了Agent4Edu，这是一种新颖的个性化学习模拟器，利用大型语言模型（LLMs）在人类智能方面的最新进展。 Agent4Edu具有由LLM驱动的生成代理，这些代理配备了针对个性化学习算法的学习者档案、记忆和行动模块。 学习者档案使用真实世界的响应数据进行初始化，捕捉练习风格和认知因素。 受人类心理学理论启发，记忆模块记录练习事实和高级摘要，并整合反思机制。 行动模块支持各种行为，包括练习理解、分析和响应生成。 每个代理都可以与个性化学习算法交互，例如计算机自适应测试，从而对定制服务进行多方面的评估和增强。 通过全面评估，我们探讨了Agent4Edu的优势和劣势，强调了代理与人类学习者之间响应的一致性和差异性。 代码、数据和附录可在https://github.com/bigdata-ustc/Agent4Edu公开获取。 显示英文摘要

摘要： 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.