Perceived satisfaction, perceived usefulness, and interactive learning environments as predictors of university students’ self-regulation in the context of GenAI-assisted learning: an empirical study in mainland China

Abstract

Given the potential risks of learners’ misuse of generative artificial intelligence (GenAI), including over-reliance, privacy concerns, and exposure to biased outputs, it is essential to investigate university students’ self-regulation in GenAI-assisted learning. Self-regulated learning enables university students to set goals, monitor their learning progress, and adjust strategies, thereby enhancing the effectiveness of GenAI-assisted learning. Guided by the three-tier model of self-regulation, which encompasses individual characteristics, cognitive and emotional factors, and behavioral intention, this study employed a mixed-method approach. Structural equation modeling (SEM) was used to quantitatively examine the relationships among key variables, while interviews provided qualitative insights, enabling a comprehensive exploration of factors influencing self-regulation in GenAI-assisted learning. Using a sample of 607 university students (e.g., prospective mathematics teachers) from Mainland China, this study found that compared to perceived self-efficacy and interactive learning environments, information system quality showed a stronger influence on learners perceived usefulness and satisfaction in GenAI-assisted learning. In predicting learner perceived self-regulation, perceived usefulness was a stronger predictor than the interactive learning environment and perceived satisfaction. Similarly, perceived usefulness was a stronger predictor of behavioral intention than perceived satisfaction and self-regulation. This study further investigated the partial mediating effects of perceived usefulness, perceived satisfaction, and perceived self-regulation among other variables. This study proposes a conceptual model to explore the interconnectedness of these factors in GenAI-assisted learning. It highlights the importance of information system quality for educators and recommends that researchers further investigate the dynamic factors influencing self-regulation in GenAI-assisted learning environments.