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.
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.
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
It is concluded that in order for undergraduate education to continue to be relevant in a society where AI is pervasive, governance must change toward process-oriented evaluation and relational originality.
Joe Mutebi, Brian Mugisha, Ibrahim Adabara et al.· F1000Research· 0 citations
First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.
Sharifuzzaman, M. Rahman· Asian Journal of Contemporar...· 0 citations
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.
Felix Tersoo Gbaeren· INTERNATIONAL JOURNAL OF SOC...· 0 citations
The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design, and offers a replicable model for fashion programs and other disciplines seeking responsible AI integration.
D. Shen· PUPIL International Journal...· 0 citations