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<p>This study developed and evaluated a teacher training model for Artificial Intelligence in Education (AIED) to integrate Artificial Intelligence (AI) into instructional practices. The three-stage model, which encompasses understanding AI, exploring AI pedagogy and ethics, and designing AIED instruction, was systematically designed to incorporate insights from existing literature and global AI education frameworks. Through implementation and evaluation, it was revealed that teachers highly valued hands-on applications, particularly in lesson design and classroom implementation. However, technical elements posed challenges for teachers with limited prior experience in AI. Subject-specific analysis revealed varying AI applications across disciplines, with the natural sciences demonstrating the highest level of engagement. In this regard, the study proposes four key guidelines on hands-on AI applications, differentiated learning pathways, customized training by subject, and extended lesson design support. Future research should investigate the long-term effects of AIED teacher training and interdisciplinary AI applications to further refine AI-integrated education.</p>

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Teacher Training Model for Artificial Intelligence in Education (AIED): Development, Implementation, and Evaluation

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Gahyung Kim,
  • Eunseon Lim,
  • Yuna Kim,
  • Kapsu Kim

摘要

This study developed and evaluated a teacher training model for Artificial Intelligence in Education (AIED) to integrate Artificial Intelligence (AI) into instructional practices. The three-stage model, which encompasses understanding AI, exploring AI pedagogy and ethics, and designing AIED instruction, was systematically designed to incorporate insights from existing literature and global AI education frameworks. Through implementation and evaluation, it was revealed that teachers highly valued hands-on applications, particularly in lesson design and classroom implementation. However, technical elements posed challenges for teachers with limited prior experience in AI. Subject-specific analysis revealed varying AI applications across disciplines, with the natural sciences demonstrating the highest level of engagement. In this regard, the study proposes four key guidelines on hands-on AI applications, differentiated learning pathways, customized training by subject, and extended lesson design support. Future research should investigate the long-term effects of AIED teacher training and interdisciplinary AI applications to further refine AI-integrated education.