From cognitive offloading to learning accountability erosion: how generative AI reshapes students’ epistemic trust and educational engagement

Generative artificial intelligence (GAI) is reshaping higher education, yet its significance may lie less in efficiency gains than in how it reconfigures learners’ cognitive, epistemic, and responsibility-related orientations. Drawing on educational psychology, this study examines whether AI use is associated with a sequential process involving cognitive offloading, epistemic trust transfer, and reduced learning accountability. An exploratory sequential mixed-methods design was adopted. Interviews identified four recurring themes: efficiency-oriented learning, redefined epistemic authority, outsourced responsibility, and reflective AI use. These themes informed a structural model tested with student survey data. Findings indicate that greater AI use was associated with stronger cognitive offloading, which was further associated with greater epistemic trust transfer toward AI and lower learning accountability. Reduced learning accountability was also associated with lower learning motivation and greater academic integrity risk. The study integrates cognitive offloading, epistemic trust, and learning accountability within a single explanatory framework, extending AI-supported learning research beyond outcome-based and tool-centered accounts. Theoretically, it positions learning accountability as an ethical dimension of self-regulated learning in AI-mediated contexts. More broadly, GAI may redistribute cognition, authority, and responsibility within learning, raising important questions about sustaining learner agency and accountability in higher education.