Direct Effects of Key UTAUT Drivers on AI Intention to Use in Teaching and Learning - Norshima Humaidi; Nurul Atiqah Zamri (Journal of Educational Science and Technology (EST), 2026)
In today's academic environment, where academic staff face increasingly heavy workloads, the integration of Artificial Intelligence (AI) applications holds promise for enhancing productivity. This study investigates academic staff perceptions regarding the adoption of AI technologies to improve their work efficiency, focusing on key factors influencing their adoption intentions in teaching and learning. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the research examines four dimensions: performance expectancy, effort expectancy, social influence, and facilitating conditions. Using a quantitative cross-sectional approach, data was collected via online questionnaire from 151 academic staff at Universiti Teknologi MARA (UiTM) Selangor. The findings highlight that both effort and performance expectancy significantly influence the intention to adopt AI, whereas social influence and facilitating conditions do not exhibit significant effects. These insights underscore the importance of positive perceptions and usability in fostering AI adoption within academic settings. The study concludes with actionable recommendations for organizations to enhance productivity and societal well-being through effective AI integration. Finally, implications for further research and practical strategies are discussed.