Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot
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arXiv
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| Format: | Preprint |
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2025
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| author | Hashmi, Syed Furqan Abbas Rebello, N. Sanjay |
| author_facet | Hashmi, Syed Furqan Abbas Rebello, N. Sanjay |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot Hashmi, Syed Furqan Abbas Rebello, N. Sanjay Physics Education 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. |
| title | Analyzing Undergraduate Problem-Solving in Physics Through Interaction With an AI Chatbot |
| topic | Physics Education |
| url | https://arxiv.org/abs/2508.14778 |