A Multidimensional Analysis of AI Literacy Determinants: External Resources, Digital Skills, and Psychological Profile Among University Students.

Tak Sang Chow, Ken To, Bess Yin Hung Lam, Kung Wong Lau

Abstract read

In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tak Sang ChowDepartment of Counselling and Psychology, Hong Kong Shue Yan University, Hong Kong, China.

Ken ToResearch Centre for Digital Education and Blended Intelligence, Hong Kong Shue Yan University, Hong Kong, China.

Bess Yin Hung LamDepartment of Counselling and Psychology, Hong Kong Shue Yan University, Hong Kong, China.ORCID 0000-0002-2778-3469

Kung Wong LauDepartment of Applied Data Science, Hong Kong Shue Yan University, Hong Kong, China.

Funding

Quality Enhancement Support Scheme from Education Bureau 03/QESS/2023

6 · The paper itself

Abstract

External resources, digital competence, and psychological characteristics have each been linked to Artificial Intelligence (AI) literacy, but almost always in isolation, so the unique contribution of any one domain remains unknown. Grounded in social cognitive theory, this study models all three domains jointly. Data were collected from 303 undergraduates at a liberal arts university in Hong Kong and analysed using hierarchical multiple regression. Perceived resources and perceived teacher support explained 29% of the variance in AI literacy; prior digital competence added a further 3%; and personal innovativeness, technology anxiety, and growth mindset in technology added a further 6%, with all three being significant in the final model (total

Indexed as

AI literacyartificial intelligencedigital competencegrowth mindset in technologyhigher educationHong Kongpersonal innovativenessresourcessocial cognitive theoryteacher supporttechnology anxiety

Identifiers

PMID42651621

DOI10.3390/bs16081448

PMCPMC13510224

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.