From AI Acceptance to Actual Application: Integrating UTAUT, Flow Theory, and TOE to Explain University Lecturers’ AI Application in Teaching

Despite growing interest in artificial intelligence (AI) use in higher education, existing research has focused largely on technology acceptance and behavioral intention. The factors explaining the extent of lecturers’ actual AI application in teaching (AIAT) remain insufficiently understood, particularly in emerging contexts such as Vietnam. To address this gap, this study proposes an integrated model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), Flow Theory (FT), and the Technology–Organization–Environment (TOE) framework. It examines how flow experience (FE), subjective norms (SN), perceived usefulness (PU), environmental innovativeness (EI), team integration (TI), perceived fit (PF), and AI ethics (AIE) are associated with the self-reported extent of university lecturers’ actual AIAT. A quantitative approach was used. Survey data were collected from three hundred university lecturers in Vietnam and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that PU had the largest estimated path coefficient, followed by PF and TI. This finding underscores the importance of technological relevance and collaborative support. AIE and EI also contributed meaningfully to explaining variation in lecturers’ actual application of AI in their teaching. Theoretically, this study integrates UTAUT, FT, and TOE to explain how cognitive, experiential, social, technological, organizational, and ethical factors are associated with lecturers’ actual AI application rather than behavioral intention alone. Practically, the findings provide context-specific evidence from Vietnamese higher education and practical implications for institutional leaders and policymakers seeking to foster responsible and effective AI integration across academic settings.