Academic Dependency, AI Literacy, and Cognitive Offloading Predict Students’ Cognitive Ability in Generative AI Learning - Andini Noviyanti Fitriani; Rezky Risaldy; Annajmi Rauf; Shera Afidatunisa (Artificial Intelligence in Lifelong and Life-Course Education, 2026)
Purpose – This study examines the cognitive effects of generative artificial intelligence use in higher education by testing whether academic dependency, AI literacy, and cognitive offloading predict students’ cognitive ability.Design/methods/approach – A quantitative cross-sectional survey was conducted with 93 undergraduate students at Universitas Negeri Makassar who actively use generative AI tools for academic purposes. Data were collected through a structured online questionnaire and analyzed using partial least squares structural equation modeling to evaluate measurement reliability and validity and to test structural relationships among academic dependency, AI literacy, cognitive offloading, and student cognitive ability.Findings – The structural model shows that academic dependency, AI literacy, and cognitive offloading positively and significantly predict student cognitive ability. AI literacy is the strongest predictor, indicating that students’ capacity to understand, evaluate, and use AI outputs critically is central to cognitive development. The findings also suggest that adaptive dependency can function as productive scaffolding, while strategic cognitive offloading may support higher-order thinking by reallocating limited cognitive resources.Research implications/limitations – The cross-sectional design limits causal inference, self-reported measures may introduce bias, and a single-institution context limits generalizability.Originality/value – This study provides integrated empirical evidence on the cognitive impact of generative AI use by jointly modeling academic dependency, AI literacy, and cognitive offloading, informing balanced AI literacy interventions and responsible AI governance in higher education.