Jul 2026· Frontiers in Education· 2 citations· 38 references
TL;DR
This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy and provides a useful lens for interpreting institutional adaptation.
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.
The ability of institutions to leverage opportunities to transform governance in higher education depends on adopting anticipatory governance models that emphasize foresight and stakeholder engagement, as well as adopting changes to the traditional role of both leaders and educators to become data literate, inclusive, collaborative, and forward-thinking.
S. Baroudi· International Journal of Edu...· 1 citation
Artificial intelligence (AI) is revolutionizing higher education through personalized learning and data-driven administration. However, effective governance remains an essential issue for AI adoption, particularly in developing countries such as Morocco, where research is still limited. This study examines the role of governance structures, stakeholder engagement, and institutional preparedness in AI adoption within Moroccan universities through a systematic literature review of 37 peer-reviewed articles from Scopus (2020-2026), using PRISMA guidelines and thematic synthesis. Findings reveal a surge in AI studies since 2024, mostly conceptual and from Global North countries, highlighting multi-level, ethical, participatory, and anticipatory governance models. Stakeholder participation remains mostly top-down, with minimal faculty and student engagement, while readiness comprises infrastructure, literacy, leadership, and policy frameworks. Morocco has centralized control through Law 59.24 and Maroc AI 2030, but lacks context-specific studies. The study offers the Inclusive AI Governance-Readiness Framework, connecting institutional coordination, stakeholder engagement, readiness, and ethics for sustainable AI integration.
Doha Baladi, B. Guennoun· Journal of Interdisciplinary...· 0 citations
The rapid integration of artificial intelligence (AI) into higher education is transforming how universities are governed, managed, and held accountable. While existing scholarship has focused primarily on the pedagogical applications of AI and the ethical implications of algorithmic technologies, less attention has been devoted to how AI reshapes institutional governance and decision-making processes. Addressing this gap, this paper advances the concept of AI managerialism to explain the growing influence of algorithmic systems on university governance and organizational control.The study employs a critical narrative review and conceptual policy analysis, synthesizing scholarship on AI governance, managerialism, and higher education administration. It further examines three purposively selected cases representing key domains of algorithmic governance: the Ofqual algorithm controversy in the United Kingdom, Purdue University’s Course Signals learning analytics system, and the University of Sydney’s response to generative AI. Through cross-case thematic analysis, the study identifies recurring governance issues related to accountability, transparency, participation, and institutional autonomy.Findings suggest that AI-enabled systems can improve administrative efficiency, predictive capacity, and evidence-informed decision-making while simultaneously generating risks associated with opacity, surveillance, stakeholder exclusion, and the centralization of managerial authority. In response, the paper proposes an Ethical AI Governance Framework for Higher Education built on five principles: mission alignment, transparency and explainability, participatory governance, equity auditing, and bounded scope. Extending existing AI ethics frameworks, the model explicitly incorporates institutional mission, shared governance, and organizational accountability into AI oversight processes. The framework provides practical guidance for university leaders and policymakers seeking to balance technological innovation with academic values and democratic governance. The paper concludes that effective AI governance requires institutionally grounded arrangements that ensure AI supports, rather than undermines, the educational mission of higher education.
Tian-Zi Sun, Mark Joseph D. Pastor· American Journal of Educatio...· 0 citations
The framework demonstrates that the sustainable value derived from AI in higher education depends less on the level of the technology adopted than on the ethical bases and consistency of the leadership responsibility for its integration, offering higher education leaders and policymakers a structured path toward responsible AI governance and sustainable institutional transformation.
Asem S. Obied, Ahmed Raja Haj Ali· Frontiers in Education· 0 citations
The study concluded that effective governance requires combining research evidence, sectoral frameworks, and institutional policies, while translating general principles into clear procedures at the university and course levels, and revealed the need for more specialized policies that address privacy, linguistic equity, data protection, and the transparency.
Mohammed Al Mutawtah· Academic Journal of Research...· 0 citations
The review identifies transparency, explainability, human oversight, accountability, proportionality, co-design, and appeal mechanisms as core governance principles in the AIMS AI-Human Framework.
T. Anthuvan, Sunitha Prabhuram, Chirag B. Rathod· World Journal of Entrepreneu...· 0 citations