学习提示：基于自适应LLM的高中辅导提升学生参与度
Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring
AI总结：
提出一种基于14个教学特征的主题感知提示路由模型，通过模拟训练和在线A/B测试，在高中辅导中实现自适应策略切换，提高教学效率并减少交互轮次。
摘要：
LLMs可以个性化教育，尽管当前的静态提示辅导系统难以适应不同的学科。我们开发并测试了一个具有主题感知提示的系统，该系统基于从原始转录中提取的14个教学特征（例如，辅导支架、学生理解）。我们首先在模拟环境中训练一个提示路由模型，然后将其部署到实际高中学生的在线适应中。模拟基准测试显示，路由器的性能优于两个静态基线（$0.694$ vs. $0.647$ 和 $0.64$, $p<0.001$）。A/B测试（$N=656$ 次对话，来自359名学生）显示了从模拟到现实的迁移，其中模型从分析策略切换到支架学习策略。我们的自适应提示选择机制提高了教学效率，保持了教学质量，并减少了约3轮交互（$p=0.007$）。虽然贪婪路由器的练习转化率与基线相当（$19.1\%$ vs. $19.6\%$），但随机采样策略的随机路由器实现了更高的转化率（$28.1\%$）。
英文摘要：
LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop and test a system with subject-aware prompting, based on 14 pedagogical features (e.g., tutor scaffolding, student understanding) extracted from raw transcripts. We first train a prompt routing model in a simulation environment, and then deploy it for online adaptation with actual high-school students. The simulation benchmark shows the router outperforming two static baselines ($0.694$ vs. $0.647$ and $0.64$, $p<0.001$). A/B testing ($N=656$ conversations from 359 students) shows sim-to-real transfer where the model switches from analytical to scaffolding learning strategies. Our adaptive prompt selection mechanism improves instructional efficiency, maintains pedagogical quality and reduces interactions by around 3 turns ($p=0.007$). While a greedy router achieves a comparable exercise conversion rate with the baseline ($19.1\%$ vs. $19.6\%$), a stochastic router that samples strategies leads to a higher conversion rate ($28.1\%$).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
