ArticleBMC psychology2026
Psychological and technological predictors of AI literacy profiles: a latent profile analysis among Chinese college students.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- How does artificial intelligence literacy affect university students' innovative behavior? A serial mediation analysis based on latent profiles.Frontiers in psychology · 2026Article
- Latent profiles of AI literacy among K-12 students: predictors and links to self-regulated learning.Frontiers in psychology · 2026Article
- Artificial intelligence literacy among nursing students and its association with learning engagement.Frontiers in public health · 2026Article
- Latent profiles of college students' AI literacy and their association with AI acceptance: the sequential indirect role of perceived affordances and growth mindset.Frontiers in psychology · 2026Article
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Abstract
backgroundAI literacy is increasingly important in college students' academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students' AI literacy, especially how individual psychological characteristics and features of AI technology contribute to this variation. This oversight limits the formulation of tailored strategies to meet the students' various demands in an era shaped by rapid AI advancement.
objectivesThis study aims to adopt an individual-centered approach to identify distinct AI literacy profiles among college students. In addition, it investigates, based on affordance theory, how positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism predict assignment to different profiles.
methodsA total of 808 Chinese college students participated in this survey. Latent profile analysis (LPA) was employed to classify students into distinct AI literacy profiles. Multinomial logistic regression was conducted to examine how psychological and technological factors predict profile classification.
findingsThis study identified four distinct AI literacy profiles among college students: preliminary contact type, ethical orientation type, balanced development type, and behavioral conservatism type. These profiles showed significant differences in positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism, highlighting diverse psychological and technological characteristics inherent to each group.
conclusionsThis study underscores the heterogeneity of AI literacy within the college student population and detects four distinct AI literacy profiles with unique psychological and technological traits. The findings indicate that students' AI literacy is profoundly affected by emotional tendencies, motivational drives, and technological variables, highlighting the need for tailored educational strategies that address the distinct psychological and technological drivers of each literacy profile.
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