From Automation to Engagement: Teaching Academics' Future Plans for AI in Higher Education

Abstract

Generative Artificial Intelligence (GenAI) is increasingly being used within higher education to enhance curriculum design, automate academic tasks, and personalise learning. Despite this, little is known about teaching academics’ attitudes, intentions and behaviours and in turn, how they use and plan to use GenAI in practice. This qualitative study explored teaching academic staff attitudes, intentions and behaviours regarding their use and future use of GenAI. Data was collected via five open ended questions focusing on current teaching practices. The Theory of Planned Behaviour informed the analytical process. Academic staff were drawn from 20 different disciplines across 24 countries. Six main themes were identified: Efficiency and Automation, Ethics and Authenticity, Quality, Customised Learning, Institutional Policy-Practice and Sociocultural Implications. Overall, there was a dichotomy in responses. Participants noted that while GenAI could streamline teaching and enhance resource quality, it might also increase short-term workload and reduce the role of human judgement, risking unreliable outputs. They also highlighted limited institutional policies, strategies, and training. These attitudes have implications for updating policies, practical strategies, and meaningful training in higher education institutions.