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<p>As artificial intelligence (AI) increasingly permeates early childhood education, the field faces a critical challenge: the lack of a scientifically grounded framework for AI literacy. While numerous reviews have mapped what tools and pedagogies are currently used, few have examined why the field has developed in its current trajectory. This tertiary review systematically synthesizes 11 existing literature reviews to move beyond descriptive mapping toward a structural diagnosis of early childhood AI literacy. Our synthesis reveals a core consensus on definitions and identifies four major research themes (cognitive understanding, practical skills, ethics, and environmental support). However, a critical analysis uncovers a systemic Tool-Driven bias, where educational goals are often inadvertently defined by the capabilities of commercial hardware rather than by developmental needs. We argue that this bias triggers a ripple effect, leading to superficial assessments and a premature convergence on adult-centric, Western-biased models. To address these structural flaws, this paper proposes a dual-dimensional solution, namely, a “Developmental Translation” dimension to bridge the gap between complex AI mechanics and child psychology, and a “Socio-Cultural Adaptation” dimension to contextualize AI literacy within diverse local values and promote equity. We conclude by advocating for a paradigm shift from technology-centered implementation to child-centered, socially just AI education.</p>

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Framing early childhood AI literacy: What did the literature review tell us?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

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  • Ruiqi Wang,
  • Xiang Li,
  • Davy Tsz Kit Ng

摘要

As artificial intelligence (AI) increasingly permeates early childhood education, the field faces a critical challenge: the lack of a scientifically grounded framework for AI literacy. While numerous reviews have mapped what tools and pedagogies are currently used, few have examined why the field has developed in its current trajectory. This tertiary review systematically synthesizes 11 existing literature reviews to move beyond descriptive mapping toward a structural diagnosis of early childhood AI literacy. Our synthesis reveals a core consensus on definitions and identifies four major research themes (cognitive understanding, practical skills, ethics, and environmental support). However, a critical analysis uncovers a systemic Tool-Driven bias, where educational goals are often inadvertently defined by the capabilities of commercial hardware rather than by developmental needs. We argue that this bias triggers a ripple effect, leading to superficial assessments and a premature convergence on adult-centric, Western-biased models. To address these structural flaws, this paper proposes a dual-dimensional solution, namely, a “Developmental Translation” dimension to bridge the gap between complex AI mechanics and child psychology, and a “Socio-Cultural Adaptation” dimension to contextualize AI literacy within diverse local values and promote equity. We conclude by advocating for a paradigm shift from technology-centered implementation to child-centered, socially just AI education.