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

Abstract 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.