Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval Augmented Generation (RAG)

• URL: http://arxiv.org/abs/2505.17238v2
• Date: Tue, 17 Jun 2025 02:57:43 GMT
• Title: Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval Augmented Generation (RAG)
• Authors: Clayton Cohn, Surya Rayala, Caitlin Snyder, Joyce Fonteles, Shruti Jain, Naveeduddin Mohammed, Umesh Timalsina, Sarah K. Burriss, Ashwin T S, Namrata Srivastava, Menton Deweese, Angela Eeds, Gautam Biswas,
• Abstract summary: Large language models (LLMs) facilitate dynamic pedagogical interactions but undermine confidence, trust, and instructional value.<n>Retrieval-augmented generation (RAG) grounds LLM outputs in curated knowledge but requires a clear semantic link between user input and a knowledge base.<n>We propose log-contextualized RAG (LC-RAG), which enhances RAG retrieval by using environment logs to contextualize collaborative discourse.
• Score: 3.5262811753348715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 allows our collaborative peer agent, Copa, to deliver relevant, personalized guidance that supports students' critical thinking and epistemic decision-making in a collaborative computational modeling environment, C2STEM.

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