AI self-efficacy as a predictor of satisfaction with studies: the mediating role of research motivation among Peruvian University students
Artificial intelligence (AI) is progressively transforming higher education by influencing students’ learning experiences, motivation, and satisfaction with studies. However, limited evidence exists regarding the psychological mechanisms through which AI-related competencies contribute to students’ academic well-being, particularly in Latin American university contexts. This study examined the mediating role of research motivation in the relationship between AI self-efficacy and satisfaction with studies among Peruvian university students. A quantitative, cross-sectional, and explanatory design was employed with a sample of 559 Peruvian university students. Participants completed validated instruments assessing AI self-efficacy, research motivation, and satisfaction with studies. Data were analyzed using structural equation modeling (SEM) with robust estimators. The findings revealed positive and statistically significant relationships among all study variables. AI self-efficacy significantly predicted research motivation ( β = .313, p < .001) and satisfaction with studies ( β = .100, p = .039). In addition, research motivation significantly predicted satisfaction with studies ( β = .448, p < .001) and exerted a partial mediating effect, accounting for 58.3% of the total effect of AI self-efficacy on satisfaction with studies. The results suggest that students’ perceived competence in the use of AI-based technologies contributes to satisfaction with studies both directly and indirectly through the strengthening of research motivation. These findings highlight the importance of promoting digital literacy, AI self-efficacy, and research-oriented pedagogical strategies to foster student engagement and well-being in higher education.