From Intention to Reflection: Understanding Self-Directed Learning in the Use of Generative AI in Vietnam

Abstract

This study extends the Theory of Planned Behavior (TPB) to explore how students’ behavioral intentions toward using generative artificial intelligence (GenAI) are associated with their reflective engagement and self-directed learning (SDL) in higher education. As GenAI tools such as ChatGPT increasingly mediate learning, understanding how learners’ intentions are linked to autonomous and reflective learning behaviors becomes essential. Data were collected from 149 first-year university students (predominantly female) in Vietnam who had prior experience with GenAI for academic purposes. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examined relationships among attitudes, subjective norms, perceived behavioral control, behavioral intention, actual use, reflection, and two dimensions of SDL, including intentional learning and self-management. The results reveal that attitudes and perceived behavioral control significantly predict students’ intentions and actual use of GenAI, whereas subjective norms have no significant effect. Behavioral engagement is positively associated with reflection and both dimensions of SDL, while reflection is positively related to intentional learning and self-management, confirming its mediating role within the proposed model linking motivation-related constructs with autonomous learning outcomes. These findings highlight reflection as a metacognitive mechanism that links students’ behavioral engagement with GenAI and their SDL-related outcomes. Theoretically, the study advances TPB by positioning reflection and SDL as outcome constructs within the proposed model, rather than fixed learner traits. Practically, it suggests that educators and institutions working with first-year university students or similar learner populations should integrate reflective activities and AI literacy into curricula to promote critical, ethical, and autonomous engagement with GenAI. Designing learning environments that position AI as a reflective partner, rather than merely a content generator, supports learners’ self-regulation and reflective engagement. Overall, this research contributes to understanding how intentional and reflective interaction with GenAI is associated with deeper and more autonomous learning of students among first-year university students in a GenAI-supported learning context.