The Mediating Effect of the Use of AI on the Influence of Work Stress on the Collegiate Work Dynamics of University Teachers

This study examined the mediating effect of artificial intelligence’s (AI’s) use on the relationship between work stress and collegiate work among university professors. Using a cross-sectional quantitative design grounded in the transactional theory of stress and coping, 259 professors from six public and private Peruvian universities in the northern region were surveyed through the Artificial Intelligence Mediation Scale in Work Stress and Collegiate Work (EMIAETC). Partial least squares structural equation modeling (PLS-SEM) was employed to test mediation hypotheses. Results confirmed that work stress significantly increases AI adoption (β = 0.585, p < 0.001), which in turn substantially facilitates collegiate work (β = 0.634, p < 0.001). A complete mediation effect was identified (indirect effect: β = 0.371, p < 0.001), revealing that work stress does not directly influence professional collaboration (β = 0.127, p = 0.143) but operates indirectly through technological adoption. The model explained 49.6% of variance in collegiate work and 34.2% in AI use. The study concludes that AI functions as a compensatory mechanism that transforms stress impact on collaborative dynamics, suggesting that higher education institutions should prioritize strategic digital literacy training and AI infrastructure development to promote effective teacher collaboration in high-demand work contexts.