Latent profiles of AI literacy among K-12 students: predictors and links to self-regulated learning
Dan Wang, Qi Wang, Shan Zhang · 2026
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Description
Dan Wang and colleagues published a 2026 survey study mapping the varieties of AI literacy among school students in China. As artificial intelligence enters classrooms, knowing how to use it well and critically has become an educational goal. Drawing on 11,020 students' responses, the team used latent profile analysis, which sorts people by response patterns. They then examined what predicts group membership and how groups differ in self-regulated learning, the ability to steer one's own studying.
Four profiles emerged in progression, from a foundational-limited group through moderate-stable and advanced-developing levels to a high-excellence group. Membership was predicted by gender, only-child status, school stage, family wealth, how often students use AI, parental mediation, and school support. Students in higher literacy profiles showed stronger self-regulated learning. The authors urge schools to teach AI literacy in differentiated ways matched to each profile, not one curriculum for all.
Catalog record
| CREATOR | Dan Wang, Qi Wang, Shan Zhang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |