Sustainable AI governance in Islamic higher education: a maqāṣid-based framework aligning global standards with Saudi vision 2030
TL;DR
A maqāṣid-based governance framework that translates core ethical objectives into measurable, sustainability-oriented indicators embedded within lifecycle decision points is developed, structured as a prototype institutional scorecard enabling university administrators and policymakers to evaluate AI adoption within risk management and decision-making workflows.
Abstract
Higher education is undergoing rapid transformation driven by artificial intelligence (AI), creating significant opportunities for sustainable development alongside complex governance and ethical challenges. Existing scholarship on AI in higher education can be broadly categorized into SDG-aligned frameworks, principle-based governance models, and value-based approaches; however, these strands remain insufficiently integrated at the institutional level and lack operational clarity across the AI lifecycle, particularly in procurement and post-deployment audit stages. This study examines how maqāṣid al-sharīʿah can be systematically operationalized as a framework for sustainable AI governance in Islamic higher education, and how such an approach supports socially sustainable and ethically aligned digital transformation under Saudi Vision 2030. The study adopts a systematic mapping approach with integrative synthesis, analyzing 143 peer-reviewed studies retrieved from Scopus, Web of Science, and ERIC (2015–2026). A combined deductive–inductive coding framework identifies structural gaps between ethical principles and institutional governance practices across four AI lifecycle stages: procurement, design, deployment, and audit. The findings reveal a persistent principle–implementation gap most pronounced in procurement and audit processes. In response, the study develops a maqāṣid-based governance framework that translates core ethical objectives into measurable, sustainability-oriented indicators embedded within lifecycle decision points. The framework is structured as a prototype institutional scorecard enabling university administrators and policymakers to evaluate AI adoption within risk management and decision-making workflows. The proposed model advances social sustainability by promoting accountability, equity, and culturally coherent AI integration, offering a structured pathway for aligning institutional AI practices with the strategic priorities of Saudi Vision 2030.