The impact of generative artificial intelligence literacy on higher-order thinking skills in Chinese higher vocational college students: Integrating variable-centered and person-centered approaches

Higher-order thinking skills (HOTS) represent a core competency for cultivating high-quality skilled talent and a pivotal driver of professional competitiveness. Drawing on Cognitive-Affective Theory of Learning with Media and Cognitive Load Theory, this study adopts an integrated approach combining variable-centered and person-centered analyses. This study aims to elucidate the developmental patterns of HOTS among Chinese higher vocational college students and to examine how distinct GAIL profiles are differentially associated with this process. Data were collected from 1345 Chinese higher vocational college students using validated scales measuring GAIL, learning engagement, and HOTS. The findings reveal that: (1) students' GAIL can be categorized into three distinct profiles-Basic Exposure, Skill-Driven, and Balanced Mastery-with profile membership varying significantly by gender, household registration, major, and school background; (2) GAIL is positively associated with HOTS both directly and indirectly through learning engagement; and (3) the three profiles differ markedly in terms of learning engagement and HOTS, with learning engagement mediating the relationship between profile membership and HOTS.