Evidence map›Paper›PMID 42325350›Full record

ArticleFrontiers in psychology2026

Latent profiles of AI literacy among K-12 students: predictors and links to self-regulated learning.

Dan Wang, Qi Wang, Shan Zhang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Dan WangFaculty of Artificial Intelligence Education, Central China Normal University, Wuhan, Hubei, China.
Qi WangSchool of Physical Education, Hubei University of Education, Wuhan, Hubei, China.
Shan ZhangSchool of Physical Education, Hubei University of Education, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence (AI) literacy has become increasingly critical as AI integrates into K-12 education, yet understanding of the factors influencing students' AI literacy and its relationship with self-regulated learning (SRL) remains limited. Methods: To address this gap, the present study draws on large-scale survey data from 11,020 Chinese K-12 students and employs latent profile analysis (LPA) to identify distinct AI literacy profiles and to systematically examine their predictors and associations with SRL. Results: The results revealed four qualitatively distinct and progressively ordered AI literacy profiles: the foundational-limited profile, the moderate-stable profile, the advanced-developing profile, and the high-excellence profile. Multinomial logistic regression analyses indicated that students' gender, only-child status, educational stage, family socioeconomic status, frequency of AI use, parental active mediation, and school AI support significantly predicted profile membership. Further analyses showed significant differences in SRL across the identified profiles, with students in higher AI literacy profiles demonstrating consistently stronger SRL abilities. Discussion: These findings provide empirical evidence to support the development of inclusive and differentiated AI literacy education in K-12 settings and offer important implications for fostering students' SRL in AI-enhanced learning environments.

Indexed as

artificial intelligence literacyfamily socioeconomic statusK-12 educationlatent profile analysisself-regulated learning

Identifiers

PMID42325350
PMCPMC13280791

What OpenQuestion holds

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LicenceCC BY
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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.