This study presents the design and implementation of an AI-supported modeling-based teacher education program integrating Model-Based Learning (MBL) with the 5E learning cycle. The program was implemented with 14 in-service chemistry teachers over 24 lessons and consisted of pre-interviews, three modeling cycles, and post-interviews. During the modeling process, teachers developed dynamic models through dialogic interactions with generative AI (Claude). Metamodeling knowledge levels were explored using seven questions across six domains: nature of models, purpose of models, modeling processes, variability, diversity, and evaluation criteria. Responses were analyzed from objectivist and subjectivist understandings. Teachers identified limitations of the electron sea model in explaining luster ( n = 14), thermal conductivity ( n = 7), malleability and ductility ( n = 6), and electrical conductivity ( n = 3). To address these, teachers developed educational models incorporating the wave nature of electrons, lattice vibrations (phonons), and redistribution of electron clouds. Analysis revealed a shift from predominantly objectivist understandings of models in the pre-assessment toward more subjectivist understandings of models in the post-assessment. Notable improvements were observed in modeling processes, variability, and diversity of models. These changes were observed in the context of modeling activities that involved recognizing model limitations and developing educational models, within which generative AI served as an interactive element supporting dialogic interaction and iterative revision as part of the broader instructional program. Teachers came to understand models not as fixed knowledge but as tentative and revisable inquiry tools. These findings provide direction for teacher education focused on strengthening modeling knowledge in science education.
Park et al. (Thu,) studied this question.