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¶

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? Digital competence predicted critical evaluation, creative application, and ethical competence but not technical proficiency or communication proficiency; perceived resources predicted only communication proficiency (β = 0.22, p < 0.001) and creative application (β = 0.17, p < 0.05). → 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 · r2

Subdimensional regression models (all five significant, explained variance 0.26–0.32) on the 303-student sample. The article states these findings "challenge any straightforward reading of digital competence as a general technical foundation", suggesting its contribution operates through judgement and application rather than operational facility.

"Digital competence predicted critical evaluation, creative application, and ethical competence, but not technical proficiency or communication proficiency. Perceived resources predicted only communication proficiency (β = 0.22, p < 0.001) and creative application (β = 0.17, p < 0.05)."

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
• Technology anxiety's positive association with AI literacy is confined to critical evaluation and ethical competence and absent from the three performance-oriented subdimensions — related
• Iterative coding of 27 AIL sources yields seven dimensions, with technical, application and critical-thinking dimensions most frequent and legal/regulatory knowledge rarest — related
• Prior digital competence significantly predicts AI literacy after external resources are controlled, adding 3% of variance — related
• 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
• Technology anxiety is a positive predictor of AI literacy once other determinants are controlled, despite a negative zero-order correlation — related
• The eight-predictor model explains 27.3% of variance in AI dependency, and multidimensional AI literacy adds explanatory value over a single aggregate score — related