Ethical Governance Models for Artificial Intelligence Deployment in K–12 Education: Balancing Algorithmic Personalization, Accountability and Child Protection Policy

Abstract

The integration of Artificial Intelligence (AI) in K–12 education offers unprecedented opportunities for personalized learning, adaptive instruction, and data-driven insights. However, the deployment of AI systems in child-centered contexts raises critical ethical, regulatory, and operational challenges, including algorithmic bias, data privacy risks, and inequities in access. This review synthesizes contemporary literature on AI governance frameworks, emphasizing the tension between innovation, accountability, and child protection. Key dimensions explored include algorithmic transparency, normative ethical principles, institutional and regulatory models, and multi-stakeholder governance architectures. The paper critically examines mechanisms for oversight, impact assessment, and redress, highlighting best practices to safeguard student welfare while leveraging AI’s pedagogical potential. By consolidating evidence from recent empirical studies, policy analyses, and ethical frameworks, this review provides actionable guidance for policymakers, educators, and developers aiming to implement responsible AI in schools. The findings underscore the necessity of balancing personalization with robust child protection policies, fostering equitable educational outcomes, and embedding ethical accountability at every stage of AI deployment. Ultimately, this study contributes to a foundational understanding of ethical governance strategies that can ensure AI technologies enhance learning while protecting the rights, wellbeing, and privacy of young learners.