Motivational and appraisal factors shaping generative AI use and intention in Austrian higher education students and teachers

This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine factors influencing generative AI (genAI) use among Austrian higher education students ( n = 3,094) and teachers ( n = 1,767). We applied confirmatory structural equation modeling (SEM) to replicate prior evidence on performance expectancy, effort expectancy, and social influence, and introduced partial least squares SEM (PLS-SEM) to examine challenge and threat appraisals as additional predictors. Behavioral intention strongly predicted genAI use (β = 0.75, p < 0.001 for students; β = 0.48, p < 0.001 for teachers), with performance expectancy, effort expectancy, and social influence as key positive predictors. Effort expectancy was particularly salient for teachers, reflecting time constraints. Gender differences emerged primarily among students: females reported lower subjective competence, intrinsic motivation, and challenge appraisals, but higher threat appraisals; differences were weaker in teachers. Linear regression analyses showed that challenge appraisals—predicted by intrinsic motivation, trust in genAI, and genAI-related subjective competence—positively influenced behavioral intention, whereas threat appraisals had a small negative impact (β ≈ −0.03). The extended model explained substantial variance in behavioral intention ( R 2 ≈ 0.8) and genAI use (students R 2 = 0.34; teachers R 2 = 0.18). These findings highlight the importance of aligning AI integration with user needs, motivation, and affective responses to support meaningful and ethical genAI adoption in higher education. Future research should consider individual differences, institutional culture, and evolving AI landscapes to optimize adaptive AI use across diverse educational stakeholders.