Students’ engagement with AI-supported learning and its association with academic interest and career intentions in business analytics education

by Yang Cheng, Jaekuk Lee, Florence Martin, William Rand 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.