Prior digital competence significantly predicts AI literacy after external resources are controlled, adding 3% of variance¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Digital competence significantly predicted AI literacy after perceived resources and perceived teacher support were controlled (H2 supported). → Chow Tak Sang 2026

Evidence¶

Chow Tak Sang 2026¶

Chow Tak Sang, To Ken, Lam Bess Yin Hung, Lau Kung Wong. (2026). A Multidimensional Analysis of AI Literacy Determinants: External Resources, Digital Skills, and Psychological Profile Among University Students. Behavioral Sciences. https://doi.org/10.3390/bs16081448

q2 · i? · associational · r1

Step 3 of the hierarchical regression on the same 303-student survey added digital competence (SDiCoS scale). The article reports "The R-square value of model 3 is 0.32 in contrast with 0.29 in model 2", and notes the perceived-resources coefficient fell from 0.39 to 0.24 while remaining significant.

"The model shows significant improvement over the second model ∆F (1, 297) = 15.10, p < 0.001. The R-square value of model 3 is 0.32 in contrast with 0.29 in model 2"

Discussion¶

Learner Variables¶

• Digital Literacy — predictor: learners who differ on it differ in outcomes

Related Claims¶

• Digital skills gaps span demographic groups and shape e-learning, job performance, and earnings — the general claim this finding bears on, with the studies behind it
• Personal innovativeness, technology anxiety, and growth mindset in technology each significantly predict AI literacy after external resources and digital competence are controlled, adding 6% of variance — related
• Predictors operate selectively across AI literacy subdimensions: digital competence fails to predict technical proficiency or communication proficiency, and perceived resources predict only communication proficiency and creative application — related