Governance Models for Responsible AI in Education: A Framework for Ethical Implementation

Abstract

Artificial Intelligence (AI) in education offers transformative potential for personalized learning and administrative efficiency but raises ethical concerns about fairness, privacy, and accountability. This study proposes a Responsible AI Governance Framework (RAIGF) that integrates ethical oversight, stakeholder engagement, and transparent auditing for AI systems in education. Using a mixed-methods approach, we evaluate the RAIGF in three case studies: personalized learning platforms (Canada), automated grading systems (Australia), and student data analytics (UK). Results show a 45–60% reduction in ethical risk scores, 40% improvement in stakeholder trust, and 50% enhancement in compliance with educational regulations. The framework promotes equitable and transparent AI use in education. This research bridges education technology, ethics, and policy, offering a scalable model for responsible AI governance.