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<p>This study examined how pre-service teachers utilize artificial intelligence (AI) tools to integrate computational thinking (CT) in early childhood mathematics education. Through a qualitative case study guided by the TPACK framework, we examined 24 early childhood pre-service teachers enrolled in a mathematics methods course as they developed CT-integrated math lesson plans using AI assistance. Data sources included pre- and post-course open-ended questionnaires, CT exploration assignments, AI interaction documentation, lesson plans, and reflection papers. The findings revealed three patterns in how pre-service teachers leveraged AI as a scaffold: generating initial ideas for CT integration, refining these ideas for specific teaching contexts, and clarifying CT concepts for deeper understanding. Pre-service teachers demonstrated strategic decision-making in their AI use, successfully designing developmentally appropriate CT-integrated activities while maintaining professional judgment in content adaptation. This study highlights the critical need for structured approaches to AI integration in teacher preparation programs that effectively balance technological proficiency with pedagogical integrity. The implications suggest the importance of developing comprehensive frameworks for AI use in early childhood teacher preparation, addressing both theoretical foundations and practical implementation strategies.</p>

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AI-Assisted Integration of Computational Thinking: Pre-service Teachers’ Experiences in Early Childhood Mathematics Education

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Hae Min Yu,
  • Seung H. Kim,
  • Hyeseong Lee

摘要

This study examined how pre-service teachers utilize artificial intelligence (AI) tools to integrate computational thinking (CT) in early childhood mathematics education. Through a qualitative case study guided by the TPACK framework, we examined 24 early childhood pre-service teachers enrolled in a mathematics methods course as they developed CT-integrated math lesson plans using AI assistance. Data sources included pre- and post-course open-ended questionnaires, CT exploration assignments, AI interaction documentation, lesson plans, and reflection papers. The findings revealed three patterns in how pre-service teachers leveraged AI as a scaffold: generating initial ideas for CT integration, refining these ideas for specific teaching contexts, and clarifying CT concepts for deeper understanding. Pre-service teachers demonstrated strategic decision-making in their AI use, successfully designing developmentally appropriate CT-integrated activities while maintaining professional judgment in content adaptation. This study highlights the critical need for structured approaches to AI integration in teacher preparation programs that effectively balance technological proficiency with pedagogical integrity. The implications suggest the importance of developing comprehensive frameworks for AI use in early childhood teacher preparation, addressing both theoretical foundations and practical implementation strategies.