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

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.

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.

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.

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.