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<p>This mixed-methods study investigates the impact of generative artificial intelligence (AI) on the development of Technological Pedagogical Content Knowledge (TPACK) among eight pre-service mathematics teachers. Over the course of an academic semester, participants engaged with generative AI tools, such as ChatGPT, as virtual peer mentors to support lesson planning and technology integration. Quantitative data were collected through TPACK self-assessment surveys administered before, during, and after the intervention, while qualitative insights were gathered via semi-structured interviews. The results revealed improvements in participants’ technological, pedagogical, and content knowledge, with the most noticeable growth observed in pedagogical domains. Qualitative findings highlighted that generative AI facilitated the creation of personalized learning materials, enhanced technological confidence, and supported interactive teaching strategies, although challenges remained in interpreting AI-generated responses and ensuring the quality of AI-assisted resources. The study suggests the potential of generative AI to foster TPACK development in teacher education, while also identifying areas for further support and research.</p>

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Exploring the impact of generative AI on pre-service mathematics teacher TPACK: a mixed-method study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Mailizar Mailizar,
  • Mukhlis Hidayat,
  • Muhammad Syukri,
  • Dyana Wijayanti

摘要

This mixed-methods study investigates the impact of generative artificial intelligence (AI) on the development of Technological Pedagogical Content Knowledge (TPACK) among eight pre-service mathematics teachers. Over the course of an academic semester, participants engaged with generative AI tools, such as ChatGPT, as virtual peer mentors to support lesson planning and technology integration. Quantitative data were collected through TPACK self-assessment surveys administered before, during, and after the intervention, while qualitative insights were gathered via semi-structured interviews. The results revealed improvements in participants’ technological, pedagogical, and content knowledge, with the most noticeable growth observed in pedagogical domains. Qualitative findings highlighted that generative AI facilitated the creation of personalized learning materials, enhanced technological confidence, and supported interactive teaching strategies, although challenges remained in interpreting AI-generated responses and ensuring the quality of AI-assisted resources. The study suggests the potential of generative AI to foster TPACK development in teacher education, while also identifying areas for further support and research.