Jul 2026· Journal of Higher Education Theory and Practice· 0 citations
TL;DR
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.
Abstract
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.
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.
Nayyer Naseem, Maureen Leary, Johnson C. Smith University· 1 citation
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
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
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
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
Although blended learning (BL) represents a transformative educational model, it is unclear how digital transformation (DT) shapes the dynamics of BL, particularly through the integration of artificial intelligence (AI), in higher education. This study examines how artificial intelligence–supported self-regulated learning (AI-SRL), informed by cognitive load theory and AI-driven pedagogy, can enhance BL models to promote equitable, inclusive education and advance sustainable development within the context of ongoing DT. Three hundred and ten higher-education students in the United Arab Emirates (UAE) who participated in BL courses completed an online survey. In addition, 11 faculty members and academic leaders were interviewed. The findings show that the use of AI mediates the relationship between DT and students’ behavioural and academic outcomes – namely, procrastination, heuristic processing, and academic achievement. DT increases the adoption of AI to improve academic performance while simultaneously increasing procrastination and reliance on heuristic (surface-level) processing. AI-SRL mitigates these negative outcomes, reducing procrastination and heuristic processing. Furthermore, AI-integrated pedagogy empowers students to harness the benefits of AI-enhanced DT to improve academic performance while minimising adverse cognitive and behavioural effects. This study provides a nuanced understanding of BL in higher education, showing that the use of AI helps shape student experiences and outcomes.
N. Shaya, Rawan Abukhait, Muhammad Nisar Khattak et al.· Journal of Applied Learning...· 0 citations
