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<p>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 exploring the influence 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 observed 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 notable barriers, including the lack of institutional AI policies. This study offers actionable implications for MTEs on designing pedagogically grounded training that addresses the practical applications and ethical complexities of GenAI in mathematics classrooms.</p>

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TPACK-based professional development for the AI era: fostering pre-service teachers’ acceptance of generative AI in mathematics classrooms

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Shristi Shrestha,
  • Jiyeong Yi

摘要

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 exploring the influence 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 observed 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 notable barriers, including the lack of institutional AI policies. This study offers actionable implications for MTEs on designing pedagogically grounded training that addresses the practical applications and ethical complexities of GenAI in mathematics classrooms.