用专家知识引导LLM，实时提供精准学习反馈。
Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM
- 基于学生思路分析错误类型，生成针对性反馈
- 大规模课程中成绩提升超80%
- 适合需要个性化辅导的在线教育场景
教育干预是提升学生学习效果的有效手段。尽管大型语言模型（LLMs）能够规模化生成自适应反馈，但现有研究在真实教学场景中缺乏明确的即时（Just-in-Time, JiT）反馈方法。本文提出一种框架，通过将LLM与领域专家知识结合，实现自适应反馈。该方法收集学生的书面推理逻辑（策略论文），分析其推理内容中的潜在错误类型，并提供非侵入式反馈以澄清缺失或错误概念。我们在一所大学的大规模课程中部署该框架（N > 1000），结果显示学生表现相比以往学期提升超过80%。最后，通过分析学习轨迹，验证了该框架的教学价值：与LLM的迭代对话有助于学生从误解转向正确认知。
原文摘要 · Abstract (English)
Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback at scale, current studies lack clear methodologies for providing Just-in-Time (JiT) feedback in authentic instructional settings. In this paper, we present a framework that provides adaptive feedback by grounding LLMs with domain-specific expert knowledge. Our approach collects written reasoning logic (strategy essays) from students, analyzes potential error types based on the content of that reasoning, and delivers non-intrusive feedback designed to clarify missing or incorrect concepts. We deploy this framework in a large-scale university course (N > 1000), where it improved student performance by over 80% compared to previous semesters. Lastly, we validate the framework's pedagogical utility by analyzing the learning trajectories; we demonstrate how iterative conversations with LLM facilitate shifting one's misconception to correct understanding.
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