Students’ engagement with AI-supported learning and its association with academic interest and career intentions in business analytics education - Yang Cheng; Jaekuk Lee; Florence Martin; William Rand (PLoS ONE, 2026)
Artificial intelligence (AI) tools are increasingly embedded in higher education, yet limited research has examined how sustained AI usage intentions in AI-supported learning environments are associated with learning motivation and longer-term educational development. Treating AI use as a course-embedded learning experience rather than a discrete adoption decision, this study investigates how students’ perceptions of AI-supported learning are associated with continued usage intentions and how such intentions subsequently relate to academic interest and career-related intentions. Grounded in post-adoption technology continuance research, motivation theory, and Social Cognitive Career Theory, we develop and test a structural model linking perceived AI enhancement, interactivity, fun, and coolness to continued AI usage intentions, academic interest, and career-choice intentions. Survey data were collected from undergraduate students enrolled in business analytics courses and analyzed using structural equation modeling. The results show that perceived enhancement, interaction, fun, and coolness are each significantly associated with continued AI usage intentions in coursework. Continued AI usage intentions, in turn, are positively related to academic interest in business analytics, and academic interest statistically mediates the relationship between continued AI usage intentions and career-choice intentions. However, indirect effects from these antecedent variables to academic interest through continued AI usage intentions were not statistically significant. By conceptualizing continued AI usage intentions as an ongoing learning process that is linked to how students engage with disciplinary knowledge over time, this study advances understanding of the developmental role of AI-supported instruction in higher education. The findings contribute to research on technology, knowledge, and learning, and offer practical implications for designing AI-supported learning environments that foster sustained usage intentions, interest development, and future-oriented educational pathways.