Date
Publisher
arXiv
Generative Artificial Intelligence (GenAI) is rapidly reshaping higher education, yet barriers to its adoption across different disciplines and institutional roles remain underexplored. Existing literature frequently attributes adoption barriers to individual-level factors such as perceived usefulness and ease of use. This study instead investigates whether such barriers are structurally produced. Drawing on a multi-method survey analysis of 272 academic and professional services (PSs) staff at a Russell Group university, we examine how disciplinary contexts and institutional roles shape perceived barriers. By integrating multinomial logistic regression (MLR), structural equation modelling (SEM), and semantic clustering of open-ended responses, we move beyond descriptive accounts to provide a multi-level explanation of GenAI adoption. Our findings reveal clear, systematic differences: non-STEM academics primarily report ethical and cultural barriers related to academic integrity, whereas STEM and PSs staff disproportionately emphasize institutional, governance, and infrastructure constraints. We conclude that GenAI adoption barriers are deeply embedded in organizational ecosystems and epistemic norms, suggesting that universities must move beyond generalized training to develop role-specific governance and support frameworks.
Study at a glance
- Question: How do perceived barriers to generative AI adoption vary across academic disciplines and professional roles?
- Setting and data: A Russell Group university in the UK, mid-June to late July 2025, survey of 272 academic and professional service staff.
- Method: Mixed methods survey analysis combining multinomial logistic regression, structural equation modelling, and semantic clustering.
Why it matters
Understanding whether generative AI barriers stem from individual skills or organizational structures determines whether universities should provide training interventions or design role-specific governance frameworks.
Key findings
- Professional services staff had significantly higher odds of reporting institutional barriers relative to cultural barriers compared to academic staff.
- Non-STEM academics reported the highest share of ethical barriers concerning academic integrity, while STEM staff more frequently reported individual and institutional constraints.
- Structural equation modelling revealed that institutional role directly predicted guidance, attitudes, literacy, and ethical and institutional barriers, while discipline directly predicted attitudes and cultural barriers.
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