Jul 2026· International Journal of Educational Technology in Higher Education· Vol 23· 1 citation· 60 references
TL;DR
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.
Abstract
As the education sector attempts to address rapid changes caused by Artificial Intelligence (AI), it becomes crucial to examine approaches to leadership at the organizational and system levels. This scoping review explores how anticipatory models of governance are conceptualised and operationalised globally within higher education settings in the context of AI-related transformations. Using a scoping review design, academic and grey literature published between 2020 and 2025 was searched across Scopus, EBSCOhost, ERIC, Google Scholar, and ProQuest. Nineteen sources were selected based on clear inclusion and exclusion criteria and analysed thematically. Findings revealed that while AI offers promising opportunities to transform governance in higher education, the ability of institutions to leverage these opportunities 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. However, a gap between theory and implementation remains evident, particularly due to weak policy frameworks and limited digital infrastructure in the Global South, including the Arab world, Sub-Saharan Africa, and Southeast Asia.
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.
Xinyi Jiang, Zuraidah Abdullah· Frontiers in Education· 2 citations
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 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
Artificial intelligence (AI) has rapidly permeated higher education workplaces, yet a significant disconnect exists between employee adoption of AI tools and institutional policy awareness, governance structures, and strategic clarity. This study examines the emergent phenomenon of the "AI implementation gap" in higher education—the disparity between widespread AI tool usage and the institutional frameworks meant to guide such use. Drawing on recent survey data from nearly 2,000 higher education professionals and situating findings within broader theoretical frameworks of technology adoption, organizational change, and higher education governance, this article critically analyzes the current state of AI integration in higher education work environments. Key findings reveal that while 94% of higher education employees report using AI tools for work, only 54% are aware of relevant institutional policies, and more than half have used AI tools not sanctioned by their institutions. The analysis explores the risks, opportunities, and challenges associated with this implementation gap, including concerns about data privacy, misinformation, skill erosion, algorithmic bias, environmental impact, and the largely unmeasured return on investment of AI initiatives. The article also examines the roles of AI vendors, the ethical dimensions of AI adoption, and the implications of voluntary versus mandated technology use. The article concludes with recommendations for institutional leaders, policymakers, and researchers seeking to bridge the gap between AI adoption and governance in higher education contexts.
Jonathan H. Westover· Future of Work: The Journal...· 0 citations
As artificial intelligence (AI) becomes increasingly embedded in higher education, empirical evidence on how institutional governance shapes its equitable and responsible implementation in South African universities remains limited. This study examined how institutional policies and governance practices influence the implementation and equitable use of AI in undergraduate education while contributing to international discourse on responsible AI governance. Guided by an interpretivist research paradigm, the study adopted a qualitative approach and employed a single-case study design within one public university in South Africa. Participants comprised university leaders, academic staff, professional support staff, and undergraduate students involved in or affected by AI governance and implementation. Data were collected through semi-structured interviews, focus group discussions, and document analysis and analysed using thematic analysis supported by inductive coding. The findings indicate that limited policy transparency, context-insensitive governance frameworks, unequal access to AI technologies, and weak institutional accountability can reinforce educational inequalities, particularly among first-generation and under-resourced students. Conversely, participatory governance, transparent decision-making, stakeholder engagement, and enhanced digital literacy promote more equitable and responsible AI implementation. The study proposes a multi-level governance model integrating institutional policy, stakeholder participation, and pedagogical practice to strengthen equitable AI adoption. It concludes that higher education institutions should develop context-sensitive AI governance frameworks, strengthen institutional capacity, and expand equitable access to AI technologies to advance fairness, inclusion, and responsible AI implementation.
R. Lumadi· International Journal of Stu...· 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