Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)
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 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.
Cite
@article{arxiv.2505.17238,
title = {Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)},
author = {Clayton Cohn and Surya Rayala and Caitlin Snyder and Joyce Fonteles and Shruti Jain and Naveeduddin Mohammed and Umesh Timalsina and Sarah K. Burriss and Ashwin T S and Namrata Srivastava and Menton Deweese and Angela Eeds and Gautam Biswas},
journal= {arXiv preprint arXiv:2505.17238},
year = {2026}
}
Comments
Peer reviewed; appeared in the International Conference on Artificial Intelligence in Education (AIED25) Workshop on Epistemics and Decision-Making in AI-Supported Education