Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)
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arXiv
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| Format: | Preprint |
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2025
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| author | Cohn, Clayton Rayala, Surya Snyder, Caitlin Fonteles, Joyce Jain, Shruti Mohammed, Naveeduddin Timalsina, Umesh Burriss, Sarah K. S, Ashwin T Srivastava, Namrata Deweese, Menton Eeds, Angela Biswas, Gautam |
| author_facet | Cohn, Clayton Rayala, Surya Snyder, Caitlin Fonteles, Joyce Jain, Shruti Mohammed, Naveeduddin Timalsina, Umesh Burriss, Sarah K. S, Ashwin T Srivastava, Namrata Deweese, Menton Eeds, Angela Biswas, Gautam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG) Cohn, Clayton Rayala, Surya Snyder, Caitlin Fonteles, Joyce Jain, Shruti Mohammed, Naveeduddin Timalsina, Umesh Burriss, Sarah K. S, Ashwin T Srivastava, Namrata Deweese, Menton Eeds, Angela Biswas, Gautam Computation and Language 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. |
| title | Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG) |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.17238 |