The relationship between hope and learning engagement among Chinese university students: the serial mediating roles of generative AI acceptance and self-directed learning

Abstract

The rapid development of generative artificial intelligence (GenAI) has transformed higher education and created new opportunities for student learning. However, the psychological and behavioral factors associated with students’ learning engagement in AI-supported learning environments remain insufficiently understood. Drawing upon Hope Theory, the Technology Acceptance Model (TAM), and Self-directed Learning Theory, this study proposed a serial mediation model to examine the serial mediating roles of generative AI acceptance and self-directed learning in the association between hope and learning engagement. Data were collected from 478 Chinese university students via an online questionnaire. The data were analyzed using SPSS (Version 26.0) and AMOS (Version 24.0), and structural equation modeling (SEM) was employed to evaluate the proposed model. The results showed that hope was positively associated with learning engagement. Moreover, generative AI acceptance and self-directed learning each demonstrated significant indirect associations in the relationship between hope and learning engagement. A significant serial indirect association through generative AI acceptance and self-directed learning was also observed. These findings are consistent with a psychological–technological–behavioral framework linking hope, generative AI acceptance, self-directed learning, and learning engagement in AI-supported learning environments. The study contributes to the application of Hope Theory in the context of generative AI-supported education, advances the growing literature on generative AI acceptance in higher education, and provides practical implications for fostering students’ learning engagement through the effective integration of GenAI and self-directed learning.