P120 MAQASID-DRIVEN RESPONSIBLE ARTIFICIAL INTELLIGENCE FRAMEWORK (MRAIF): AN INTEGRATED GOVERNANCE MODEL FOR HIGHER EDUCATION INSTITUTIONS

Abstract

Artificial Intelligence (AI) is rapidly transforming higher education by enhancing teaching, learning, research, and institutional administration. Despite its considerable benefits, the widespread adoption of AI has introduced significant ethical and governance challenges, including concerns related to transparency, accountability, fairness, privacy, academic integrity, and human oversight. While internationally recognised Responsible Artificial Intelligence (Responsible AI) frameworks provide comprehensive governance principles, they are predominantly grounded in secular and regulatory perspectives, offering limited consideration of Islamic ethical values. This conceptual study addresses this gap by proposing the Maqasid-Driven Responsible Artificial Intelligence Framework (MRAIF), an integrated governance framework that aligns the objectives of Maqasid al-Shariah with globally accepted Responsible AI principles for higher education institutions. The study adopts an integrative literature review and conceptual framework development approach, synthesising contemporary literature on Responsible AI, AI governance, higher education, Islamic ethics, and Maqasid al-Shariah. The proposed framework consists of four interrelated components: (1) Ethical Foundation based on Maqasid al-Shariah, (2) Responsible AI Principles, (3) Institutional AI Governance Mechanisms, and (4) Trustworthy and Responsible AI Outcomes. The framework demonstrates how the five objectives of Maqasid al-Shariah can systematically strengthen internationally recognised Responsible AI principles by providing an ethical foundation for institutional policy, academic integrity, AI literacy, data governance, cybersecurity, and responsible decision-making. Unlike existing Responsible AI models, MRAIF establishes a sequential governance logic linking ethical values, governance principles, institutional implementation, and expected outcomes within a unified conceptual model. The study contributes to the Responsible AI literature by extending current governance discourse through the integration of Islamic ethical philosophy with contemporary AI governance, offering a culturally responsive framework that supports trustworthy, human-centred, and sustainable AI adoption in higher education. The framework also provides a theoretical foundation for future empirical validation and institutional policy development.