Artificial Intelligence Literacy and the Responsible Use of Generative AI Among University Students in Peru: The Mediating Role of Self-Regulated Learning and the Moderating Effect of Technostress

Abstract

University students’ adoption of generative artificial intelligence has become nearly universal, yet institutional guidance has not kept pace, leaving responsible use dependent on students’ own self-regulation. Drawing on social cognitive theory, this study examined whether self-regulated learning accounts for the association between AI literacy and self-reported responsible use of generative AI, and how technostress relates to that association. A cross-sectional survey of 820 Peruvian undergraduates from public and private universities was analyzed using covariance-based structural equation modeling; a two-stage moderated mediation model with latent interactions was estimated using latent moderated structural equations. Measurement invariance held across gender and university type, and the available diagnostics did not indicate a dominant common-method factor. AI literacy predicted self-regulated learning (β = 0.626) and responsible use (β = 0.243), and self-regulated learning predicted responsible use (β = 0.445). The indirect effect was significant, β = 0.278, 95% CI [0.226, 0.337], accounting for 53.4% of the total effect. Technostress showed no significant moderation of the first stage or the direct path, but the second-stage path was significantly weaker at higher levels of technostress (b2 = −0.211, p < 0.001): the conditional indirect effect was 0.344 at low and 0.111 at high technostress and was not statistically distinguishable from zero above the estimated region-of-significance boundary of +1.40 SD. Technostress was not associated with weaker self-regulation, but with a weaker association between self-regulation and its behavioural expression. Because the design is cross-sectional, all estimates are reported as associations.