TPACK-based Professional Development for the AI Era: Fostering Pre-service Teachers' Acceptance of Generative AI in Mathematics Classrooms

Abstract As Generative AI (GenAI) becomes more prevalent, the need to prepare pre-service teachers (PSTs) for its use is a critical challenge for mathematics teacher educators (MTEs). Yet, little is known about how to best foster PSTs’ adoption and critical use of GenAI in mathematics classrooms. This study addresses this gap by evaluating the impact of a 90-minute professional development workshop, grounded in the Technological Pedagogical Content Knowledge (TPACK) framework, on PSTs’ technology acceptance in mathematics education. A mixed-methods design was employed, using pre- and post-surveys based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model for quantitative data and semi-structured interviews and workshop discussions for qualitative data. Quantitative analysis revealed statistically significant positive shifts in many aspects of technology acceptance, except for PSTs’ perceived risks of the technology. Qualitative analysis identified key facilitators to adoption, such as GenAI's utility for instructional efficiency, alongside significant barriers, including the lack of clear institutional guidance. The findings demonstrate that TPACK-based professional development opportunities can enhance PSTs’ responsible adoption of GenAI in mathematics education. This study provides actionable implications for MTEs on designing pedagogically grounded training that addresses GenAI's practical applications and ethical complexities in mathematics classrooms.