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<p>This study addresses a pressing need in early childhood education: a comprehensive pedagogical framework for early AI learning. Educators currently lack an instructionally actionable framework that specifies both what to teach and how to teach it for preliterate learners. Grounded in constructivist, constructionist, sociocultural, and embodied learning theories, the study proposes a 3C-3H + DS framework—cognitively appropriate, culturally responsive, and computational-thinking-focused learning that engages children’s heads, hearts, and hands and is supported by dialogic teacher scaffolding. Using a pedagogical framework-building approach, the study conducted a systematic synthesis of relevant learning theories, extant AI literacy frameworks, and empirical studies on early AI learning, yielding three integrated pillars—AI concepts, AI practices, and AI perspectives—and an incremental progression from concrete play toward early symbolic reasoning. For policy and practice, the framework provides a usable roadmap for curriculum design, teacher professional development, and tool selection, while outlining priorities for empirical validation and scalable implementation in future research.</p>

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Building early AI literacy: developing a pedagogical framework for early childhood education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月7日

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  • Yufeng Qian

摘要

This study addresses a pressing need in early childhood education: a comprehensive pedagogical framework for early AI learning. Educators currently lack an instructionally actionable framework that specifies both what to teach and how to teach it for preliterate learners. Grounded in constructivist, constructionist, sociocultural, and embodied learning theories, the study proposes a 3C-3H + DS framework—cognitively appropriate, culturally responsive, and computational-thinking-focused learning that engages children’s heads, hearts, and hands and is supported by dialogic teacher scaffolding. Using a pedagogical framework-building approach, the study conducted a systematic synthesis of relevant learning theories, extant AI literacy frameworks, and empirical studies on early AI learning, yielding three integrated pillars—AI concepts, AI practices, and AI perspectives—and an incremental progression from concrete play toward early symbolic reasoning. For policy and practice, the framework provides a usable roadmap for curriculum design, teacher professional development, and tool selection, while outlining priorities for empirical validation and scalable implementation in future research.