Multi-Level Barriers to Generative AI Adoption Across Disciplines and Professional Roles in Higher Education
As Generative Artificial Intelligence (GenAI) continues to evolve, its influence on higher education is becoming increasingly pronounced. However, barriers to the adoption of this technology across various disciplines and professional roles are yet to be thoroughly investigated. Existing research often points to individual-level factors such as perceived usefulness and ease of use as the primary obstacles to adoption. This article explores whether these barriers are actually constructed by the structural dynamics within educational institutions.
The study is based on a comprehensive multi-method survey analysis involving 272 academic and professional services (PSs) staff at a prominent Russell Group university. It delves into how specific disciplinary contexts and institutional roles contribute to the perceived barriers surrounding GenAI. By employing advanced statistical techniques such as multinomial logistic regression (MLR), structural equation modeling (SEM), and semantic clustering of qualitative responses, the research offers a nuanced, multi-level perspective on the challenges of adopting GenAI in higher education.
Key Findings
The results of the study indicate pronounced differences in the barriers faced by different groups within the academic landscape:
- Non-STEM Academics: This group predominantly identifies ethical and cultural barriers. Concerns surrounding academic integrity and the potential misuse of AI-generated content are particularly prevalent. These barriers reflect a broader hesitation to embrace GenAI due to fears about maintaining scholarly standards.
- STEM Staff and Professional Services: In contrast, staff from STEM disciplines and professional services reported a distinct set of challenges. These individuals are more likely to highlight institutional, governance, and infrastructure constraints as significant barriers. Issues such as inadequate technological support, unclear policies, and lack of institutional commitment to integrating GenAI into existing frameworks were cited as major obstacles.
Implications for Higher Education Institutions
The study concludes that the barriers to GenAI adoption are deeply entrenched in the organizational ecosystems of universities and shaped by epistemic norms unique to each discipline. This finding underscores the need for higher education institutions to move beyond one-size-fits-all training programs. Instead, there is an urgent call for the development of role-specific governance and support frameworks that take into account the diverse needs and challenges faced by different academic and professional groups.
By acknowledging and addressing these multi-level barriers, universities can better facilitate the integration of GenAI into their educational practices. This not only enhances the learning experience but also prepares students and staff to navigate the complexities of an increasingly AI-driven world.
In summary, the adoption of Generative AI in higher education is not merely a matter of individual readiness; it is a complex interplay of disciplinary contexts, institutional structures, and cultural attitudes. As the landscape of higher education continues to evolve, understanding these dynamics will be crucial for successful integration.