Integration of artificial intelligence applications in the management of higher Education Institutions: A critical review of the literature and emerging practices to enhance institutional efficiency and digital governance - Abdurhman Alhumud, Tahani Abdullah (Research Journal in Advanced Humanities, 2026)
This article examines how artificial intelligence (AI) is being integrated into the management of higher education institutions (HEIs), with particular attention to institutional efficiency, digital governance, strategic decision-making, quality assurance, and student support. The paper addresses a persistent gap in the literature: while AI in higher education is commonly discussed in relation to teaching, learning, and assessment, far less work has synthesized AI as a management and governance phenomenon at the institutional level. The study therefore develops a PRISMA-informed critical review of research published primarily from 2018 to early 2026, drawing on scholarship from higher education studies, learning analytics, educational technology, public management, and digital governance. The review argues that AI in HEI management should not be understood merely as a set of efficiency tools. Rather, AI reconfigures administrative rationalities, redistributes managerial authority, transforms the visibility of institutional processes, and intensifies the role of data infrastructures in governance. Across the literature, the strongest evidence concerns student advising, retention analytics, administrative chatbots, and institutional monitoring systems; however, evidence is much thinner for finance, human resources, and long-term strategic planning. The article proposes a typology of AI-enabled management functions, distinguishes operational efficiency from governance transformation, and advances an integrative model linking AI adoption, institutional efficiency, and digital governance. It concludes that universities need human-centred AI governance, robust data stewardship, transparent accountability structures, and context-sensitive implementation strategies if AI is to support institutional improvement without deepening surveillance, opacity, or inequality.