The integration of artificial intelligence (AI) in higher education is reshaping pedagogical practices across all disciplines; however, its role in tourism education remains underexplored. To bridgethe gap, therefore, this study examines the factors influencing tourism students’ acceptance of AIenhanced learning tools by drawing on an extended unified theory of acceptance and use of technology (UTAUT) framework. This research incorporates perceived trust, perceived risk, and attitude asadditional constructs to conduct a quantitative survey of graduate, postgraduate students, researchscholars, and tourism educators from multiple institutions and regions across India. The study’s findings established that performance expectancy, effort expectancy, and social influence positively predicted students’ attitudes. Facilitating conditions, perceived trust, and attitude positively predictedbehavioral intentions; perceived risk did not. Gender significantly moderated the links from performance expectancy → attitude, effort expectancy → attitude, and perceived risk → intention, but notthe other paths. The study’s findings extend UTAUT by incorporating context-specific constructs.Furthermore, it offers practical insights for implementing AI-driven pedagogy in tourism education.
Sufi et al. (Thu,) studied this question.