发表机构
University of Pennsylvania; National Taiwan University（宾夕法尼亚大学; 台湾大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究设计了集成大语言模型聊天机器人与强化学习算法的个性化AI辅导平台，经十所高中的五个月课程验证，其自适应编排练习题可提升学生Python成绩0.15个标准差，且成绩提升源于参与度提高。
AI 中文摘要
生成式AI（GenAI）正通过释放个性化辅导的潜力快速重塑教育领域，但现有新兴平台大多聚焦于GenAI聊天机器人辅导系统，仅能被动回答学生问题。我们假设GenAI聊天机器人辅导系统的效能可通过主动引导学生学习得到大幅提升。为验证该假设，我们设计了一款新型辅导平台，将精心设计的GenAI聊天机器人与用于编排练习题的强化学习算法紧密集成，该算法关键在于利用学生与聊天机器人互动产生的丰富信号，自适应选择难度合适的练习题。我们与台北市政府及美国在台协会合作，将该辅导平台部署至一项为期五个月的课程中，向十所高中的学生教授Python编程，并将学生随机分为固定练习题序列组与我们的自适应序列算法组。研究发现，自适应序列使学生在无人辅助的期末考试中成绩提升了0.15个标准差（据部分估算，相当于6至9个月的在校学习成果）；中介分析显示，成绩提升源于学生参与度的提高。本研究提供了大规模实地证据，表明学生与聊天机器人的互动可为主动优化和个性化学生学习提供有价值的信号。
英文摘要
Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on GenAI chatbot tutors that reactively answer student questions. We hypothesize that the efficacy of GenAI chatbot tutors can be substantially improved by proactively guiding student learning. To test this, we design a novel tutoring platform that tightly integrates a carefully-designed GenAI chatbot with a reinforcement learning algorithm for sequencing practice problems. Critically, this algorithm leverages rich signals from student-chatbot interactions to adaptively select practice problems of an appropriate difficulty level. In partnership with the Taipei City Government and American Institute in Taiwan, we deployed our tutoring platform in conjunction with a five-month course to teach Python to students across ten high schools. We randomized students between a fixed practice problem sequence and our adaptive sequencing algorithm. We find that adaptive sequencing increased unassisted final exam performance by 0.15 standard deviations (equivalent to 6-9 months of schooling by some estimates); mediation analysis suggests that gains were driven by increased engagement. Our work provides large-scale field evidence that student-chatbot interactions provide valuable signals for proactively optimizing and personalizing student learning.
