AI-DRIVEN DECISION-MAKING FRAMEWORKS FOR EDUCATIONAL POLICY REFORM: A MULTI-SECTORAL MODEL FOR EVIDENCE-BASED GOVERNANCE

Abstract

The global education sector faces unprecedented challenges, including inequitable access, outdated pedagogical models, teacher shortages, and inefficient policy implementation. Traditional policy making processes, often reliant on retrospective data and bureaucratic inertia, struggle to address these dynamic challenges effectively. This paper introduces a multi-sectoral AI-driven decision making framework (AIDDMF) designed to revolutionize educational governance through real time data integration, predictive analytics, and participatory stakeholder engagement. The framework leverages machine learning (ML), natural language processing (NLP), and ethical AI governance to optimize resource allocation, curriculum design, and teacher professional development. By synthesizing insights from AI applications in education, public administration, and data science, this study presents a scalable, adaptive, and inclusive model for evidence-based policy reform. Empirical case studies from Estonia, Singapore, Rwanda, and Brazil demonstrate the framework’s potential to enhance policy agility, equity, and transparency. The paper also addresses ethical risks, such as algorithmic bias and data privacy, and proposes mitigation strategies to ensure responsible AI deployment. The implications for policymakers, educators, technologists, and civil society are discussed, alongside recommendations for pilot implementation, cross-sector collaboration, and global standardization.