Preparing Student Teachers for Professional Development: Mentoring Generative Artificial Intelligence (AI) Learners in Mathematical Problem Solving

Rapid technological advancements are reshaping pedagogical expertise development, offering novel pathways to equip educators with 21st-century professional competencies. This study proposes an innovative AI-driven professional development approach and investigates its impact on student teachers' competence development. Twenty-eight third-year student teachers participated in tasks to mentor artificial intelligence (AI) learners, applying mentor-acquired knowledge and skills. Task performance and task processes were used to delineate teacher knowledge and teaching practices, respectively, while data from professional development surveys were thoroughly analyzed to gain in-depth insights into teacher perspectives. Findings reveal that AI teaching practice significantly enhanced participants' knowledge acquisition. Notably, high-performance groups (HPG) demonstrated complex mentoring patterns emphasizing procedural mentoring. Conversely, the low-performance group (LPG) preferred a more directive and factual approach, whose behavioral patterns appeared less significant. Furthermore, AI teaching practice also had a positive effect on student teachers' perspectives toward professional knowledge and AI literacy. The findings of this study contribute to the theoretical and practical understanding of integrating AI-based learning activities into teacher education.