AI is ready, but are the students. From answer-making to sense-making

Students in introductory physics often struggle with interpreting kinematics graphs, especially when connecting graphical features to physical meaning. We investigated AI tool use in kinematic graphing analysis as an intervention to support active sensemaking. This study examines GPT-5 performance on the Test of Understanding Graphs in Kinematics (TUG-K) and the way students interacted with GPT-5 during introductory physics activities involving graphical analysis. For the first part of the study, GPT-5 responses were evaluated across repeated TUG-K trials and compared to previously reported GPT-4 performance. Following that analysis, student-GPT-5 dialogue episodes were collected and analyzed using an inductive coding scheme. GPT-5 performance results show markedly improved accuracy. Student-GPT-5 dialogue episodes evolved from mostly procedural answer-making requests early in the term, to more reflective sensemaking interactions at the end of the term, suggesting that instructional framing is needed for AI tools to function as meaningful peer-like learning supports.