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arXiv · 2508.14778

Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot

Syed Furqan Abbas Hashmi·N. Sanjay Rebello
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Abstract

Providing individualized scaffolding for physics problem solving at scale remains an instructional challenge. We investigate (1) students' perceptions of a Socratic Artificial Intelligence (AI) chatbot's impact on problem-solving skills and confidence and (2) how the specificity of students' questions during tutoring relates to performance. We deployed a custom Socratic AI chatbot in a large-enrollment introductory mechanics course at a Midwestern public university, logging full dialogue transcripts from 150 first-year STEM majors. Post-interaction surveys revealed median ratings of 4.0/5 for knowledge-based skills and 3.4/5 for overall effectiveness. Transcript analysis showed question specificity rose from approximately 10-15% in the first turn to 100% by the final turn, and specificity correlated positively with self reported expected course grade (Pearson r = 0.43). These findings demonstrate that AI-driven Socratic dialogue not only fosters expert-like reasoning but also generates fine-grained analytics for physics education research, establishing a scalable dual-purpose tool for instruction and learning analytics.

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Syed Furqan Abbas Hashmi, N. Sanjay Rebello. 2025-08-20. Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot. https://doi.org/10.1119/perc.2025.pr.hashmi

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