用对话日志增强检索生成，让教学代理更懂学生思考。
Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)
- 结合对话日志与检索生成，提升教学对话的上下文相关性。
- 在C2STEM环境中显著改善检索准确率，支持学生批判性思维。
- 适合教育AI、智能辅导系统开发者参考。
协作对话为理解学生学习过程和批判性思维提供了丰富线索，对个性化STEM+C场景中的教学代理互动至关重要。尽管大语言模型（LLMs）促进了动态教学交互，但幻觉问题削弱了可信度与教学价值。检索增强生成（RAG）通过整合精选知识来约束输出，但其效果依赖于用户输入与知识库之间的语义关联，而学生对话中该关联往往较弱。为此，我们提出日志上下文增强的RAG（LC-RAG），利用环境日志对协作对话进行上下文建模，以增强RAG的检索能力。实验表明，相较于仅基于对话的基线，LC-RAG显著提升了检索性能，并使我们的协作同伴代理Copa能够提供更相关、个性化的指导，有效支持学生在协作计算建模环境C2STEM中的批判性思维与认知决策。
原文摘要 · Abstract (English)
Collaborative dialogue offers rich insights into students' learning and critical thinking, which is essential for personalizing pedagogical agent interactions in STEM+C settings. While large language models (LLMs) facilitate dynamic pedagogical interactions, hallucinations undermine confidence, trust, and instructional value. Retrieval-augmented generation (RAG) grounds LLM outputs in curated knowledge, but requires a clear semantic link between user input and a knowledge base, which is often weak in student dialogue. We propose log-contextualized RAG (LC-RAG), which enhances RAG retrieval by using environment logs to contextualize collaborative discourse. Our findings show that LC-RAG improves retrieval over a discourse-only baseline and enables our collaborative peer agent, Copa, to deliver relevant, personalized guidance that supports students' critical thinking and epistemic decision-making in the collaborative computational modeling environment C2STEM.
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