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<p>Rapid advances in artificial intelligence (AI) have transformed instruction and learning. This study developed and evaluated a nationwide teacher training model for enhancing the AI knowledge, AI pedagogical knowledge, AI content knowledge, and AI pedagogical and content knowledge (AIPACK) of inservice and preservice elementary teachers (ISETs and PSETs, respectively) with non-STEM backgrounds. The study applied a quasi-experimental design and included 31 ISETs and 28 PSETs who participated in an 18-hr training program covering basic knowledge about AI, AI-driven technologies, AI tools, AI platforms, and strategies for integrating AI into subject-specific teaching. The participants’ AIPACK was assessed before and after training by using pretests and posttests of AIPACK questionnaire. The findings validated the effectiveness of the AIPACK model. The results revealed no significant difference in the pretest and posttest AIPACK scores between the ISETs and PSETs but significant improvements in all dimensions of AIPACK in both groups after training. This study also discussed the implications of the AIPACK training model for professional development, teacher education, and potential directions for future research in the era of AI.</p>

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A model for developing AI pedagogical and content knowledge in inservice and preservice non-STEM elementary teachers

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Ya-Ching Fan,
  • Bor-Chen Kuo,
  • Pei-Chen Wu,
  • Chen-Huei Liao

摘要

Rapid advances in artificial intelligence (AI) have transformed instruction and learning. This study developed and evaluated a nationwide teacher training model for enhancing the AI knowledge, AI pedagogical knowledge, AI content knowledge, and AI pedagogical and content knowledge (AIPACK) of inservice and preservice elementary teachers (ISETs and PSETs, respectively) with non-STEM backgrounds. The study applied a quasi-experimental design and included 31 ISETs and 28 PSETs who participated in an 18-hr training program covering basic knowledge about AI, AI-driven technologies, AI tools, AI platforms, and strategies for integrating AI into subject-specific teaching. The participants’ AIPACK was assessed before and after training by using pretests and posttests of AIPACK questionnaire. The findings validated the effectiveness of the AIPACK model. The results revealed no significant difference in the pretest and posttest AIPACK scores between the ISETs and PSETs but significant improvements in all dimensions of AIPACK in both groups after training. This study also discussed the implications of the AIPACK training model for professional development, teacher education, and potential directions for future research in the era of AI.