Technostress, facilitating conditions, and work engagement in AI-integrated language teaching: The mediating role of psychological resilience

Foreign language teachers in resource-constrained universities face unprecedented challenges navigating simultaneous national AI adoption mandates and institutional resource limitations. This cross-sectional study (N = 515) examines how technostress creators and facilitating conditions jointly shape work engagement through psychological resilience among multilingual educators. Drawing on the Job Demands-Resources (JD-R) theoretical framework and Conservation of Resources (COR) theory, structural equation modeling tested seven integrated hypotheses. Results revealed technostress creators negatively predicted psychological resilience (β = -0.233, p < 0.001) and work engagement (β = -0.198, p < 0.001). Facilitating conditions positively predicted resilience (β = 0.329, p < 0.001) and engagement (β = 0.263, p < 0.001). Psychological resilience significantly predicted engagement (β = 0.531, p < 0.001). Bootstrap analysis confirmed resilience partially mediated both technostress-engagement (indirect effect = -0.124, 95% CI [-0.175, -0.079]) and facilitating conditions-engagement relationships (indirect effect = 0.175, 95% CI [0.127, 0.231]). Findings validate integrated JD-R applicability in technology-intensive, resource-constrained educational contexts, providing evidence-based foundations for designing targeted resilience-building interventions and enhanced institutional support systems.