Multi-Level Barriers to Generative AI Adoption Across Disciplines and Professional Roles in Higher Education
arXiv:2603.27052v3 Announce Type: replace-cross 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
The rapid development and public release of GenAI models have forced higher education institutions to confront adoption challenges, making this a critical period for understanding and addressing barriers.
Understanding structural barriers to GenAI adoption in higher education is crucial for effective integration, policy development, and ensuring equitable access to and utilization of advanced AI tools across institutions and disciplines.
The focus shifts from individual-level factors to systemic and structural issues as primary impediments to GenAI adoption in academia, demanding institutional rather than just individual solutions.
- · Academic researchers studying AI adoption
- · Educational technology providers developing integration solutions
- · Universities that proactively address structural barriers
- · Higher education institutions slow to adapt
- · Traditional teaching methodologies
- · Individual faculty struggling with unsupported GenAI integration
Higher education institutions will likely invest more in understanding and dismantling structural barriers to GenAI adoption, such as infrastructure, training, and policy.
This shift may lead to new funding models for educational technology and AI integration, and the creation of specialized roles within universities focused on AI strategy.
The successful integration of GenAI across higher education curricula and operations could significantly alter pedagogical methods, research output, and the administrative efficiency of universities globally.
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Read at arXiv cs.AI