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<p>Mathematical problem-posing teaching is a core competency for mathematics teachers. However, the rapid development and integration of artificial intelligence (AI) have transformed its implementation paradigms, imposing higher demands on mathematics teachers’ AI-Technological Pedagogical Content Knowledge (AI-TPACK). To address this, the study developed and validated a mathematics-specific AI-TPACK scale tailored to the context of mathematical problem-posing teaching by integrating AI, TPACK, and problem-posing logic. A questionnaire survey was administered to 158 pre-service mathematics teachers in East China, with SPSS 26.0 used for reliability and validity testing and Mplus 8.3 for Latent Profile Analysis (LPA). The results showed that: (1) The newly developed scale exhibited robust reliability and construct validity, proving suitable for assessing pre-service mathematics teachers’ AI-TPACK in mathematical problem-posing teaching; (2) Pre-service mathematics teachers’ AI-TPACK were categorized into three distinct levels (low, medium, and high), with highly significant differences across all seven dimensions and large effect sizes, and AI-Technological Knowledge (AI-TK) emerged as the key differentiator. These findings underscore the heterogeneity of pre-service teachers’ AI-TPACK development in this specific context, providing empirical evidence for designing differentiated training strategies and optimizing AI-integrated mathematics instruction for problem-posing teaching.</p>

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AI-TPACK Competencies in Mathematical Problem-Posing Teaching: Measurement Instrument Development and Latent Profile Analysis of Pre-Service Teachers

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Hongde Wu,
  • Yimin Ning,
  • Haibin Chang,
  • Min Shi,
  • Weizhong Zhang

摘要

Mathematical problem-posing teaching is a core competency for mathematics teachers. However, the rapid development and integration of artificial intelligence (AI) have transformed its implementation paradigms, imposing higher demands on mathematics teachers’ AI-Technological Pedagogical Content Knowledge (AI-TPACK). To address this, the study developed and validated a mathematics-specific AI-TPACK scale tailored to the context of mathematical problem-posing teaching by integrating AI, TPACK, and problem-posing logic. A questionnaire survey was administered to 158 pre-service mathematics teachers in East China, with SPSS 26.0 used for reliability and validity testing and Mplus 8.3 for Latent Profile Analysis (LPA). The results showed that: (1) The newly developed scale exhibited robust reliability and construct validity, proving suitable for assessing pre-service mathematics teachers’ AI-TPACK in mathematical problem-posing teaching; (2) Pre-service mathematics teachers’ AI-TPACK were categorized into three distinct levels (low, medium, and high), with highly significant differences across all seven dimensions and large effect sizes, and AI-Technological Knowledge (AI-TK) emerged as the key differentiator. These findings underscore the heterogeneity of pre-service teachers’ AI-TPACK development in this specific context, providing empirical evidence for designing differentiated training strategies and optimizing AI-integrated mathematics instruction for problem-posing teaching.