Students' Validation Criteria for AI-Generated Physics Solutions

Abstract

A physics solution can be clear without being valid. This distinction becomes important when students read AI-generated solutions that appear coherent but still require physical and mathematical checking. We examined how pre-service physics teachers in a calculus-based introductory electromagnetism course evaluated AI-generated solutions to a charged-rod electric-field problem after attempting the problem themselves. Although the AI-generated solutions analyzed were correct, no student accepted a response as valid after a first reading. Students who had solved the problem fully or in part could examine the responses using physical and mathematical criteria, while others withheld acceptance because they could not follow the mathematical development. These results suggest that, in physics instruction, AI-generated solutions should be treated less as answers to be accepted or rejected and more as worked-out responses that students learn to reconstruct, question, and test.