Technological self-efficacy significantly but modestly predicts faculty AI well-being¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Technological self-efficacy positively predicts AI well-being (β = 0.082, p < 0.05), a comparatively modest effect secondary to AI literacy. → Liu 2026

Evidence¶

Liu 2026¶

Liu, W.; Li, Y.; Yan, Y.; Wang, J.; Wang, J.; Zhang, H. (2026). Exploring University Faculty’s AI Well-Being: A Structural Equation Model of Social Supports, AI Literacy, and Technological Self-Efficacy. Behavioral Sciences. https://doi.org/10.3390/bs16071168

q2 · i? · associational · r2

SEM path estimate from Results §4.2 of the same 502-faculty survey. The article reports a "significant but comparatively modest effect" of technological self-efficacy on AI well-being, with the printed coefficient and significance level.

"Technological self-efficacy also showed a significant but comparatively modest effect (β = 0.082, p < 0.05)"

Discussion¶

Learner Variables¶

• Self-Efficacy — predictor: learners who differ on it differ in outcomes

Related Claims¶

• AI literacy is the strongest predictor of faculty AI well-being in the structural model — related
• Social and organizational support relate to AI well-being indirectly through AI literacy and technological self-efficacy — related
• Organizational support positively predicts both AI literacy and technological self-efficacy among university faculty — related
• Perceived social support positively predicts both AI literacy and technological self-efficacy among university faculty — related
• ANN analysis identifies AI learning self-efficacy and perceived usefulness as the strongest and most stable predictors of AI learning anxiety, with only modest advantage over linear benchmarks — related