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<p>The rapid integration of Artificial Intelligence (AI) into K-12 STEM education presents transformative opportunities alongside significant epistemological and ethical challenges. While AI-powered tools promise personalized learning, they also risk perpetuating algorithmic bias, reconstituting pedagogical practices, and exacerbating educational inequities. This critical conceptual synthesis examines the dual-edged impact of AI, arguing that its benefits are inextricably linked to deeper, often unexamined tensions between efficiency and equity, and between data-driven instruction and the development of human criticality. We move beyond classification to propose a synthesized framework for responsible integration, built on the dialectical reconciliation of Technological Pedagogical Content Knowledge (TPACK) and critical algorithmic literacy. Our framework includes a three-phased implementation roadmap and a tiered professional development model, explicitly designed to navigate these tensions. Key recommendations, such as mandatory bias audits and low-resource adaptation strategies are presented not as procedural fixes but as essential interventions to ensure AI serves as a tool for inclusive, human-centered STEM education that prioritizes pedagogical integrity and student agency.</p>

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Beyond the Algorithm: A Critical Synthesis for Human-Centered AI in K-12 STEM Education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月22日

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  • Shaouna Lodhi,
  • Gillian Roehrig

摘要

The rapid integration of Artificial Intelligence (AI) into K-12 STEM education presents transformative opportunities alongside significant epistemological and ethical challenges. While AI-powered tools promise personalized learning, they also risk perpetuating algorithmic bias, reconstituting pedagogical practices, and exacerbating educational inequities. This critical conceptual synthesis examines the dual-edged impact of AI, arguing that its benefits are inextricably linked to deeper, often unexamined tensions between efficiency and equity, and between data-driven instruction and the development of human criticality. We move beyond classification to propose a synthesized framework for responsible integration, built on the dialectical reconciliation of Technological Pedagogical Content Knowledge (TPACK) and critical algorithmic literacy. Our framework includes a three-phased implementation roadmap and a tiered professional development model, explicitly designed to navigate these tensions. Key recommendations, such as mandatory bias audits and low-resource adaptation strategies are presented not as procedural fixes but as essential interventions to ensure AI serves as a tool for inclusive, human-centered STEM education that prioritizes pedagogical integrity and student agency.