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Several reports in education have called for transforming physics learningenvironments by promoting sensemaking of real-world scenarios in light ofcurricular ideas. Recent advancements in Generative-Artificial Intelligence hasgarnered increasing traction in educators' community by virtue of its potentialin transforming STEM learning. In this exploratory study, we adopt amixed-methods approach in comparatively examining student- and AI-generatedresponses to two different formats of a physics problem through the cognitivelenses of sensemaking and mechanistic reasoning. The student data is derivedfrom think-aloud interviews of introductory students and the AI data comes fromChatGPT's solutions collected using Zero shot approach. The results highlightAI responses to evidence most features of the two processes throughwell-structured solutions and student responses to effectively leveragerepresentations in their solutions through iterative refinement of arguments.In other words, while AI responses reflect how physics is talked about, thestudent responses reflect how physics is practiced. Implications of theseresults in light of development and deployment of AI systems in physicspedagogy are discussed.

Student and AI responses to physics problems examined through the lenses of sensemaking and mechanistic reasoning