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<p>The rapid entry of generative artificial intelligence (GAI) into classrooms presents opportunities and constraints for teacher education, yet the processes through which preservice mathematics teachers (PMTs) develop integration competencies remain poorly understood. This study investigates the relationship between PMTs’ technological pedagogical content knowledge (TPACK) and their acceptance of GAI technologies following a structured course. Employing a multi-method research design, 36 PMTs participated in a 14-hour course designed to enhance both TPACK competencies and GAI acceptance. Quantitative data were collected through validated TPACK and GAI acceptance scales administered pre- and post-intervention, while qualitative insights were obtained through semi-structured interviews with 10 purposively selected participants. Paired-samples t-tests revealed statistically significant improvements in both TPACK and GAIA scores, with large effect sizes across all subdimensions. Regression analysis demonstrated that TPACK significantly predicted GAI acceptance, explaining 61% of the variance. Qualitative findings revealed three distinct developmental phases: initial skepticism and limited awareness, discovery of strategic prompt engineering skills, and recognition of GAI’s pedagogical affordances alongside concerns about professional identity. Theoretically, these findings extend the TPACK framework by demonstrating its predictive validity for GAI acceptance and supporting the emergence of AI-TPACK as a distinct construct requiring specialized competencies. For practice, the results suggest that teacher education programs should embed GAI training within existing TPACK frameworks, provide explicit instruction in prompt engineering, and create opportunities for critical dialogue about AI’s role in teaching. These findings underscore the critical role of TPACK as a foundational competency for GAI integration in mathematics education.</p>

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Examining the relationship between pre-service teachers’ technological pedagogical content knowledge and generative artificial intelligence acceptance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Ümit Kul,
  • Metin Besalti,
  • Sedef Çelik Demirci,
  • Samet Korkmaz

摘要

The rapid entry of generative artificial intelligence (GAI) into classrooms presents opportunities and constraints for teacher education, yet the processes through which preservice mathematics teachers (PMTs) develop integration competencies remain poorly understood. This study investigates the relationship between PMTs’ technological pedagogical content knowledge (TPACK) and their acceptance of GAI technologies following a structured course. Employing a multi-method research design, 36 PMTs participated in a 14-hour course designed to enhance both TPACK competencies and GAI acceptance. Quantitative data were collected through validated TPACK and GAI acceptance scales administered pre- and post-intervention, while qualitative insights were obtained through semi-structured interviews with 10 purposively selected participants. Paired-samples t-tests revealed statistically significant improvements in both TPACK and GAIA scores, with large effect sizes across all subdimensions. Regression analysis demonstrated that TPACK significantly predicted GAI acceptance, explaining 61% of the variance. Qualitative findings revealed three distinct developmental phases: initial skepticism and limited awareness, discovery of strategic prompt engineering skills, and recognition of GAI’s pedagogical affordances alongside concerns about professional identity. Theoretically, these findings extend the TPACK framework by demonstrating its predictive validity for GAI acceptance and supporting the emergence of AI-TPACK as a distinct construct requiring specialized competencies. For practice, the results suggest that teacher education programs should embed GAI training within existing TPACK frameworks, provide explicit instruction in prompt engineering, and create opportunities for critical dialogue about AI’s role in teaching. These findings underscore the critical role of TPACK as a foundational competency for GAI integration in mathematics education.