The Impact of AI Chatbot–Based Academic Support on Student Loyalty in Higher Education

This study investigates the role of AI chatbots in fostering student loyalty toward educational institutions in the context of academic support and inquiry resolution. Integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), Habit Theory, and Dual-Process Theory, we develop a comprehensive framework examining both the antecedents of AI chatbot engagement and the mechanisms through which engagement translates into institutional loyalty. The theoretical model proposes that habit strength moderates the engagement-satisfaction-loyalty chain, creating two distinct pathways: a deliberate, satisfaction-mediated route (System 2 processing) for low-habit users and an automatic, engagement-driven route (System 1 processing) for high-habit users. Survey data from 223 university students in Ho Chi Minh City, Vietnam, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that conversational quality (β = 0.391), perceived technology capability (β = 0.372), and perceived usefulness (β = 0.161) significantly drive student engagement with AI chatbots, collectively explaining 69% of engagement variance. Engagement demonstrates substantial effects on both satisfaction (β = 0.555) and user loyalty (β = 0.458). Critically, all three moderation hypotheses were supported: habit strength negatively moderates the engagement-satisfaction relationship (β = -0.135) and the satisfaction-loyalty relationship (β = -0.244), while positively moderating the direct engagement-loyalty relationship (β = 0.273). These findings confirm a mechanism substitution model wherein, as habits strengthen, loyalty formation shifts from a satisfaction-mediated deliberate pathway to an engagement-driven automatic pathway. The study contributes to technology adoption literature by demonstrating that habit strength fundamentally alters how loyalty develops, reconciling inconsistent findings in the satisfaction-loyalty literature. Practical implications suggest differentiated retention strategies: satisfaction-focused interventions for novice users and habit reinforcement tactics for experienced users.