Artificial Intelligence Self-Efficacy, Technostress, and Critical Thinking: Evidence from Peruvian University Students

The rapid integration of artificial intelligence (AI) into higher education has renewed concern that technology-related strain may erode students’ higher-order thinking. Drawing on a social-cognitive framework, this study tested whether AI self-efficacy and technostress predict critical thinking in 340 Peruvian undergraduates, who completed three validated scales (Yoon’s Critical Thinking Disposition Inventory; the brief General Self-Efficacy Scale for AI, GSE-6AI; and the RED-Technostress scale). Data were modeled with PLS-SEM, with significance from 5,000 bootstrap resamples and an out-of-sample predictive assessment (PLSpredict, CVPAT). AI self-efficacy was a robust positive predictor of critical thinking (β = 0.42, p < .001), whereas technostress had no direct effect (β = −0.06, p = .50); AI self-efficacy was associated with higher technostress (β = 0.24, p < .001), but that path did not carry over to critical thinking (no mediation). A hypothesized moderation (AI self-efficacy mattering more at higher technostress) was not confirmed: it reached significance under the two-stage method but not under a stricter bootstrap, and is therefore treated as preliminary. The model explained a modest share of variance in critical thinking (R² = .17) and was invariant across public and private universities. These findings reposition technostress as, at most, a distal correlate and identify confident, literate engagement with AI, rather than the absence of strain, as the more promising lever for cultivating critical thinking.