AI Insight
This study examined how acceptance of generative artificial intelligence affects learning engagement among 478 Chinese college students, finding that the relationship is mediated sequentially through learning motivation and hope. Self-directed learning was found to moderate the pathway between learning motivation and hope, strengthening the overall effect. The results suggest that students who accept generative AI tools experience enhanced learning motivation, which leads to increased hope and ultimately greater learning engagement, particularly among those with higher self-directed learning abilities.
Why it matters
These findings provide evidence-based guidance for educators implementing AI tools in higher education, suggesting that fostering learning motivation and hope may be critical for maximizing the benefits of generative AI in educational settings. The study also indicates that interventions supporting self-directed learning skills could amplify the positive effects of AI adoption on student engagement.
Understand the Science
In recent years, generative artificial intelligence (GenAI) has attracted growing attention in higher education; however, the psychological mechanisms through which generative AI acceptance influences students’ learning engagement remain insufficiently understood. This study aimed to examine a moderated sequential mediation model linking generative AI acceptance, learning motivation, hope, learning engagement, and self-directed learning. To this end, the study developed and empirically tested a moderated mediation model via a survey-based research design. Following informed consent procedures, data from 478 Chinese university students were collected using an online questionnaire platform. Data were analyzed using SPSS (v26.0), PROCESS macro (v4.2), and AMOS (v24.0), encompassing descriptive statistics, reliability and validity assessments, confirmatory factor analysis, correlation analysis, and moderated mediation analysis. Results revealed that generative AI acceptance, learning motivation, hope, self-directed learning, and learning engagement were all positively correlated. Moreover, learning motivation and hope served as sequential mediators in the relationship between generative AI acceptance and learning engagement. Furthermore, self-directed learning significantly moderated the path from learning motivation to hope, and the overall moderated mediation effect was statistically significant. These findings provide novel insights into the association between generative AI acceptance and learning engagement by highlighting the roles of learning motivation, hope, and self-directed learning. Specifically, the findings support a theoretically derived sequential pathway in which learning motivation and hope are associated with the relationship between generative AI acceptance and learning engagement, while self-directed learning moderates this pathway. This study contributes to the technology-enhanced learning literature by extending understanding of these relationships and offers practical implications for supporting effective AI-assisted learning in higher education.