[2603.27052] Multi-Level Barriers to Generative AI Adoption Across Disciplines and Professional Roles in Higher Education

Computer Science > Computers and Society arXiv:2603.27052 (cs) [Submitted on 27 Mar 2026] Title:Multi-Level Barriers to Generative AI Adoption Across Disciplines and Professional Roles in Higher Education Authors:Jianhua Yang, Kerem Öge, Adrian von Mühlenen, Abdullah Bilal Akbulut, Tanya Suzanne Carey, Chidi Okorro View a PDF of the paper titled Multi-Level Barriers to Generative AI Adoption Across Disciplines and Professional Roles in Higher Education, by Jianhua Yang and 5 other authors View PDF Abstract: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 i...