Academic Self-Efficacy and Dependence on Artificial Intelligence in Peruvian Higher Education: The Mediating Effect of Performance Expectancy

Abstract

This study aimed to examine how academic self-efficacy influenced dependence on generative artificial intelligence among Peruvian university students, while testing the mediating role of performance expectancy. A quantitative, non-experimental, cross-sectional explanatory design was implemented with 430 students from public universities in northern Peru (Piura, Tumbes, Lambayeque, La Libertad–Trujillo, and Cajamarca). Data were collected in November–December 2025 using a structured 5-point Likert questionnaire measuring academic self-efficacy, AI-related performance expectancy, and AI dependence. The model was estimated through PLS-SEM in SmartPLS and supported strong measurement quality, with high indicator loadings and robust internal consistency, convergent validity, and discriminant validity. Structural results showed that academic self-efficacy exerted a strong negative direct effect on AI dependence (β = −0.707; p < 0.001) and a positive effect on performance expectancy (β = 0.55; p < 0.001). Performance expectancy, in turn, significantly increased AI dependence (β = 0.707; p < 0.001) and mediated the relationship between self-efficacy and dependence (indirect β = 0.389; p < 0.001), with substantial explained variance for dependence (R2 = 0.45). Overall, self-efficacy functioned as a direct protective factor, yet a concurrent indirect pathway increased dependence via heightened expectations of academic gains from AI. The study recommended strengthening student autonomy and metacognitive regulation, implementing critical AI literacy, and redesigning assessment tasks and guidance to encourage responsible, complementary use rather than cognitive substitution.