Demystifying generative AI adoption in higher education: an integrated GRAI–AKM framework
Li Xiao, Hanoof Bandar Alkasham, Norah Sultan Alsalamah, Donia KhalfallahPurpose
Generative Artificial Intelligence (Gen AI) is rapidly transforming higher education, yet successful institutional adoption requires effective knowledge governance, organizational readiness and coordinated decision-making. Existing studies have predominantly relied on individual-level technology acceptance models, providing limited insight into organizational and knowledge management capabilities for sustainable implementation.
Design/methodology/approach
A qualitative research design was employed using open-ended written responses from 20 academic professionals recruited through Prolific. The qualitative responses were analyzed using thematic analysis, and the resulting themes were mapped onto the proposed graphs with results and actions interconnected (GRAI) and active knowledge models (AKM) framework (GRAI–AKM).
Findings
Six themes were identified: (1) professional training and AI literacy, (2) ethical governance and academic integrity, (3) structural capital flexibility and knowledge processes, (4) trust, transparency and relational capital, (5) platform maturity and technological infrastructure and (6) human–AI complementarity. Professional training and AI literacy emerged as the most-discussed themes. The findings suggest that Gen AI adoption depends on the interaction of human, structural, technological, and knowledge resources rather than technology acceptance alone.
Research limitations/implications
Although based on a qualitative sample and self-reported written responses, the study advances knowledge management research by integrating Intellectual Capital (IC) and the Knowledge-Based View (KBV) into a comprehensive framework.
Practical implications
The findings highlight the importance of AI literacy, ethical governance, organizational learning and human–AI collaboration for sustainable adoption of Gen AI in higher education.
Originality/value
The study provides qualitative empirical support for the GRAI–AKM framework, offering a multidimensional perspective on knowledge management for sustainable adoption of Gen AI.