基于日志上下文的检索增强生成在个性化学生代理互动中的应用
Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)
- 1Department of Computer Science, Vanderbilt University, Nashville, USA 2College of Engineering \& Science, University of Detroit Mercy, Detroit, USA 3The School for Science
- Math, Vanderbilt University, Nashville, USA
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出日志上下文化检索增强生成（LC-RAG）方法，通过环境日志增强协作对话的检索能力，使协作同伴代理Copa能提供个性化指导，支持学生在C2STEM环境中的批判性思维和知识决策。
AI中文摘要：
协作对话为学生学习和批判性思维提供了丰富的洞察，对在STEM+C环境中个性化教学代理互动至关重要。尽管大型语言模型（LLMs）能促进动态教学互动，但幻觉会削弱信心、信任和教学价值。检索增强生成（RAG）使LLM输出扎根于精心编纂的知识，但需要用户输入与知识库之间的明确语义联系，这在学生对话中往往较弱。我们提出日志上下文化RAG（LC-RAG），通过使用环境日志来上下文化协作对话，以增强RAG的检索能力。我们的发现表明，LC-RAG在仅基于对话的基线之上提升了检索效果，并使我们的协作同伴代理Copa能够提供相关、个性化的指导，支持学生在协作计算建模环境C2STEM中的批判性思维和知识决策。
英文摘要：
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.
