AI-enabled governance in higher education: a systematic review of applications, outcomes, and emerging implications

Abstract

This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy. Although AI has been widely examined in teaching and learning contexts, its role in institutional governance, strategic decision-making, resource allocation, quality assurance, risk management, and organizational learning remains less systematically understood. Following PRISMA guidelines, 27 peer-reviewed studies published between 2010 and 2025 were selected from Web of Science and Scopus and analyzed through descriptive mapping and thematic synthesis. AI adoption was concentrated in strategic, administrative, and risk-related governance domains, where predictive analytics and AI-integrated decision-support systems supported institutional coordination and data-informed decision-making. Operational efficiency and predictive accuracy were the most frequently emphasized outcomes, whereas transparency, accountability, trust, equity, workload redistribution, and governance reconfiguration remained comparatively underexamined. Three governance mechanisms were identified: anticipatory modelling, data-driven coordination, and accountability-oriented sense-making. AI-enabled governance operates not only as a technical tool but also as an institutional infrastructure that reshapes feedback, information flows, and decision routines. Organizational learning provides a useful lens for interpreting institutional adaptation, although further empirical research is required on collaborative professional learning and university competitiveness. Future studies should strengthen theoretical foundations, use longitudinal and multi-institutional designs, examine AI integration in decision-making, and address ethical and policy challenges. https://osf.io/ues47, identifier: 10.17605/OSF.IO/UES47.