How Human–AI Interaction Impacts Sustainable Learning Resilience: Evidence from Western China’s Underdeveloped Higher Education

Despite the promise of human–AI interaction in enhancing learning outcomes, its contribution to fostering sustainable learning resilience, particularly in underdeveloped regions, remains insufficiently examined. Prior research has inadequately investigated the psychological processes underlying the relationship between human–AI interaction and the development of resilience. To address these gaps, this study adopts the Cognition–Affect–Conation (CAC) framework to explore how task–technology fit and system quality collectively shape the dynamics of sustainable learning resilience, mediated by perceived value and trust. Survey responses were collected from 617 students across 34 universities in Western China, using both online and offline methods. The findings indicate that task–technology fit and system quality substantially influence students’ perceptions of value and trust in human–AI interactions, which in turn strengthen their sustainable learning resilience. Additionally, these mechanisms exert a significant positive influence on different academic disciplines. This research advances the understanding of how human–AI interactions facilitate sustainable learning resilience and provides actionable insights for implementing equitable technology solutions in higher education, particularly in resource-constrained environments.