Policy-Aware Computing Models for Digital Education Governance

Abstract

Digital education governance -- the institutional and regulatory frameworks through which educational data use, AI system deployment, and platform operations are controlled, monitored, and accountable -- is increasingly challenged by the technical complexity of modern learning systems that span multiple jurisdictions, data controllers, and regulatory regimes simultaneously. Policy-aware computing models embed governance rules, regulatory requirements, and institutional policies directly into the computational systems that operate on educational data -- enabling automated policy enforcement, real-time compliance monitoring, and audit trail generation that manual governance processes cannot achieve at the speed and scale of modern digital education platforms. This paper proposes the Policy-Aware Education Computing Framework (PAECF), a systematic design and evaluation of five policy-aware computing architectures -- XACML-based access control, ODRL policy language integration, smart contract governance, policy-as-code automation, and AI-assisted policy compliance monitoring -- applied to four digital education governance domains: learner data access control, AI system compliance monitoring, cross-institutional data sharing, and credential verification governance. PAECF is evaluated across 12 institutional governance deployments over 18 months, introducing the Governance Effectiveness Score (GES) integrating policy enforcement accuracy, compliance monitoring coverage, and governance overhead. Key results: policy-as-code automation achieves the highest GES (0.912), reducing policy violation incidents by 84.2% and governance overhead by 48.4%; smart contract governance enables cryptographically verifiable cross-institutional credential exchange; AI-assisted compliance monitoring achieves 94.2% policy violation detection rate. The framework provides a governance architecture selection guide for educational institutions and regulators.