Latent Profiles of AI Literacy, Self-Directed Learning, and Creative Self-Efficacy among Art Students and Their Associations with Lifelong Learning Competence

Abstract

Generative artificial intelligence (AI) is reshaping the resources art students use for learning and creative production, but technological knowledge, learning regulation, and creative confidence may not develop in parallel. This cross-sectional study used latent profile analysis to examine how self-reported AI literacy (AIL), self-directed learning (SDL), and a brief four-item contextualized measure of creative self-efficacy (CSE) co-occurred among 502 art students from three universities in Shaanxi Province, China. Four profiles were retained as a descriptive representation of the sample: moderate AIL-lower SDL/CSE (15.7%), lower AIL-moderate SDL/CSE (18.7%), higher AIL-moderate SDL/CSE (32.5%), and higher AIL-higher SDL/CSE (33.1%). CSE item loadings ranged from .673 to .724, with alpha = .785, omega = .786, and average variance extracted = .479, indicating adequate internal consistency but limited content coverage and marginal convergent validity. Replacing the four CSE items with their composite mean produced high classification agreement with the primary model (adjusted Rand index [ARI] = .869); removing CSE reduced agreement (ARI = .729) but retained substantial heterogeneity. Neither sensitivity specification uniquely supported four rather than three profiles. Prior AI-course experience distinguished the first profile from the higher AIL-higher SDL/CSE profile, and self-reported lifelong learning competence increased across the four profiles. The findings support flexible course designs, but the profiles remain sample- and specification-bound and should not be used for individual screening.