Changes in Pre-Service Physics Teachers’ TPACK and Collaborative Problem Solving Associated with an AI-Supported CTD-PBL Module: A Quasi-Experimental Study

Generative artificial intelligence (AI) is increasingly entering teacher education, yet evidence remains limited on its responsible integration into discipline-specific pedagogical preparation. This study examined whether an AI-supported Collaborative TPACK Competency Development module based on problem-based learning (CTD-PBL) was associated with greater pre–post gains in pre-service physics teachers’ self-reported technological pedagogical content knowledge (TPACK) and perceived collaborative problem-solving (CPS) processes. Informed by ADDIE, the 8-week module used DeepSeek as a bounded scaffold for collaborative lesson design, feedback, verification, and reflective revision while preserving teacher judgment. An intact-class quasi-experimental pre-test/post-test design involved 130 third-year pre-service physics teachers at a public university in western China. Two existing classes were randomly allocated at the class level to CTD-PBL or conventional instruction. Compared with the conventional group, the CTD-PBL group reported higher post-test TPACK scores (M = 4.04 vs. M = 3.40, p < 0.001, d = 1.02) and higher perceived CPS process scores (M = 3.62 vs. M = 3.05, p < 0.001, d = 0.88), with stronger pre–post gains in both outcomes. The findings provide a bounded curriculum design case showing how generative AI can be embedded in physics teacher education through problem-based tasks, collaborative scaffolding, and human verification procedures.