The AI Implementation Gap in Higher Education: Navigating the Disconnect Between Technology Adoption, Policy Awareness, and Institutional Governance

A Boon or Bane? Challenges and Opportunities of AI Adoption in Higher Education

Artificial Intelligence (AI) is reshaping higher education by transforming how students learn, how faculty teach, and how institutions manage academic processes. While AI offers opportunities for autonomy, competence, collaboration, and efficiency, it also raises concerns of dependency, quasi-plagiarism, and academic dishonesty. This conceptual paper synthesizes insights from ten institutional cases across global contexts and draws on five theoretical foundations, Diffusion of Innovation, the Technology Acceptance Model, Self-Determination Theory, Social Learning Theory, and Academic Integrity frameworks, to propose a process model of AI adoption and use in higher education. The model explains how faculty engagement, institutional policy, student motivation, peer norms, and integrity enforcement interact to shape learning outcomes, distinguishing authentic use of AI from misuse. By addressing policy gaps, governance challenges, and equity concerns, the study contributes to theory by advancing multi-framework integration and to practice by offering strategies for ethical, responsible, and sustainable AI adoption in higher education.

Anticipatory governance and leadership for AI implementation in higher education: A scoping review

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.

A Boon or a Bane? Challenges and Opportunities of AI Adoption in Higher Education

From Restriction to Empowerment: Responsible AI Governance in Higher Education

Generative AI tools have entered university life faster than institutions have been able to govern them, prompting policy responses that range from outright prohibition to largely unguided adoption. This paper examines the shift from restrictive to empowerment-oriented AI governance in higher education through a convergent parallel mixed-methods study comprising a structured survey (n = 71) and twenty-five semi-structured interviews across eleven academic disciplines at a private university in Bangladesh. The study documents patterns of AI adoption, stakeholder attitudes toward institutional policy, and barriers to responsible use. Participants situated within more empowerment-oriented institutional environments, including those characterised by AI literacy support, clearer ethical guidance, and stronger faculty engagement, reported greater confidence in using AI responsibly and clearer understanding of acceptable practices. By contrast, participants operating under restriction-only policies more often described uncertainty, confusion, and rule evasion. Regression analysis further showed that AI familiarity, frequency of use, and policy awareness were significantly associated with stronger support for empowerment-oriented governance. These findings inform a five-pillar framework for responsible AI integration encompassing AI Literacy Integration, Stage-Based Access, Transparent Use Norms, Assessment Innovation, and Faculty Development. Informed by stakeholder evidence and refined in dialogue with existing literature, the framework offers a practical model for institutions navigating AI governance in resource-constrained contexts. The paper contributes empirical evidence from a developing-country setting that remains underrepresented in current scholarship and highlights how responsible AI governance can support more equitable and context-sensitive higher education.

Navigating Education 5.0: The Interplay of Digital Transformation, AI Ethics, and Evolving Work Competencies

The convergence of artificial intelligence, ubiquitous digital infrastructure, and shifting labor market demands has precipitated a fundamental reimagining of global education systems, widely conceptualized as Education 5.0. This paper critically examines how Education 5.0 is reshaping learning paradigms through accelerated digital transformation while simultaneously confronting the ethical imperatives of AI deployment and the emergent competencies required for an increasingly automated workforce. Drawing on recent policy frameworks, empirical studies, and theoretical analyses, this study argues that the successful navigation of Education 5.0 depends not on technological adoption alone, but on the deliberate integration of ethical governance, human-centered pedagogical design, and adaptive competency frameworks. The analysis reveals that while digital tools offer unprecedented opportunities for personalized and inclusive learning, unresolved tensions between algorithmic efficiency and educational equity, data-driven surveillance and learner autonomy, and skills standardization and creative adaptability threaten to undermine the transformative potential of this paradigm. The article concludes that sustainable progress in Education 5.0 requires policymakers, educators, and technologists to adopt an integrated approach that treats ethical AI governance and evolving competency development as co-constitutive rather than ancillary concerns. Recommendations emphasize the need for robust regulatory frameworks, interdisciplinary curriculum redesign, and sustained investment in digital infrastructure to ensure that Education 5.0 serves human flourishing rather than merely optimizing institutional productivity.

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

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.