Understanding student satisfaction with generative AI learning tools in Vietnamese business education: a TAM- and SDT-informed framework

Purpose This study aims to examine the determinants of student satisfaction with their experience of using artificial intelligence (AI) tools for learning-related activities in Vietnamese higher education, addressing a gap in research that has focused primarily on AI adoption and usage, with limited evidence on students’ post-use satisfaction in an emerging-market higher education context. Design/methodology/approach A construct-level framework informed by selected insights from the technology acceptance model (TAM) and self-determination theory (SDT) was used to organise perceived credibility, content quality, perceived utility, cognitive absorption and emotional wellbeing within one explanatory model. Survey data from 380 undergraduate business students were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Findings All five factors were positively associated with satisfaction. The findings indicate that students’ satisfaction depends not only on the functional value of generative AI tools but also on the credibility of their outputs and the cognitive and emotional quality of the learning experience. Originality/value This study shifts attention from AI adoption to satisfaction as a post-use evaluative outcome. Rather than testing TAM or SDT in their entirety, it uses selected insights from both theories to organise complementary technology-related and experiential determinants within a single construct-level framework.