Online Learning Self-Efficacy, AI Anxiety, and Digital Well-Being in Engineering Students: A Predictive Model - Al-Nasa’h, Mais; Al-Tarawneh, Luae; AlQaralleh, Esam A.; Allabadi, Nizar; Ghaith, Souad; Aldalahmeh, Sami (International Journal of Online and Biomedical Engineering (iJOE), 2026)
This study investigates the relationships among online learning self-efficacy (OLSE), artificial intelligence anxiety (AIA), and digital well-being (DWB) among undergraduate engineering students from six Jordanian universities (N = 428). Using validated self-report measures and a cross-sectional design, descriptive results indicated moderate OLSE (M = 3.24), moderately high AIA (M = 3.97), and moderate-to-low DWB (M = 2.41). A hypothesized mediation model was supported: OLSE was negatively associated with AIA (β = −0.64, p < .001) and positively associated with DWB (β = 0.52, p < .001), while AIA was negatively associated with DWB (β = −0.33, p < .001). The indirect effect of OLSE on DWB through reduced AIA was significant (β = 0.21, 95% CI [0.12, 0.34]), indicating partial mediation. The final model explained 76% of the variance in DWB. These findings highlight the importance of efficacy-building and AI-literacy supports to reduce AI-related anxiety and promote sustainable DWB in AI-enabled engineering education.