Abstract
Abstract With the deepening integration of artificial intelligence and education, digital intelligence technologies are driving fundamental transformations in students’ learning modalities. However, their impact on college students’ online learning behavior remains controversial. This research focuses on investigating mechanism through which digital intelligence technology influences college students’ online learning behavior. Grounded in technology-embodiment theory and incorporating analytical elements from information ecosystem theory, we constructed a theoretical framework that conceptualizes the impact of digital intelligence technologies on online learning behavior. The model identifies four primary antecedent conditions: system quality, information service, technology perception, and technology self-efficacy. A mixed-methods approach integrating structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) was implemented for empirical investigation, with comprehensive analysis conducted on data obtained from 852 validated student questionnaires. Furthermore, the analysis revealed two distinct activation patterns in the development of college students’ online learning behavior: technology-driven modality and cognition-driven modality. Notably, we identified the canonical configuration path for digital intelligence technology influencing online learning behavior as follows: information service • technology perception • (system quality + technology self-efficacy). This study elucidates the impact mechanisms of digital intelligence technologies on college students’ online learning behavior from a techno-existential relationship perspective, providing empirical evidence to inform the optimization of online learning behavior among university students.