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arXiv · 2608.12533

Probing AI-generated physics solutions and preparing students to critique them

Nikhil Sanjay Borse·Amir Bralin·Sean Savage·N. Sanjay Rebello
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Abstract

This study examines Artificial Intelligence (AI)-generated physics solutions from two connected perspectives: how prompt design shapes these solutions and how students can be prepared to critique them. Using a rotational-mechanics problem, we adapted a problem-classification framework to examine prompt variations, evaluating OpenAI's o4-mini responses with the Minnesota Assessment of Problem Solving (MAPS) rubric. Well-specified prompts improved solution completeness; underspecified and multimodal prompts exposed weaknesses in physics reasoning and correctness. In the student-evaluation phase, 24 introductory physics lab groups evaluated an o4-mini solution to this problem after either independently solving a related problem or critiquing its AI-generated solution with MAPS-based reflection questions. Problem-solving-only groups exhibited uncritical or misconception-based critiques; MAPS-guided groups identified more expert-aligned issues, including skipped numerical procedures and undefined notation. Together, our findings contribute to physics education research by showing how AI-generated solutions can ground both model-reasoning benchmarks and improved student critique of that reasoning through MAPS-based reflection.

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Nikhil Sanjay Borse, Amir Bralin, Sean Savage, N. Sanjay Rebello. 2026-08-12. Probing AI-generated physics solutions and preparing students to critique them. https://arxiv.org/abs/2608.12533

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