Dual mediating effects of learning motivation and hope on the relationship between generative AI acceptance and learning engagement among Chinese college students: moderated mediation effect of self-directed learning

Engineering notes

Furthermore, self-directed learning significantly moderated the path from learning motivation to hope, and the overall moderated mediation effect was statistically significant.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• PDF from original source

Comments