Beyond First Impressions: Unveiling the Drivers of AI Continuance Intention in Academia

Abstract

This study investigates the factors influencing the continued use of artificial intelligence (AI) tools in higher education, focusing on students in Indonesia. While AI presents substantial opportunities for enhancing academic performancesuch as supporting assignments, references, and analytical thinking-its sustained adoption faces barriers. Drawing on the Technology Acceptance Model (TAM) and related theories, the research examines the impact of perceived ease of use, perceived usefulness, information quality, user interface, and problemsolving capabilities on user satisfaction and continuance intention. A survey of 250 university students who have used AI tools was conducted using a purposive sampling method. The data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that perceived ease of use significantly influences both perceived usefulness and satisfaction, but not continuance intention. Perceived usefulness, in contrast, directly enhances satisfaction and continuance intention. Furthermore, perceived information quality, intuitive user interfaces, and strong problem-solving support positively affect user satisfaction. Ultimately, satisfaction emerged as a key determinant of continuance intention, emphasizing its mediating role. The model demonstrates good reliability, validity, and predictive relevance, with high $\mathbf{R}^{\mathbf{2}}$ and GoF values. These findings underscore the importance of designing AI technologies that align with user expectations, offer meaningful educational value, and provide an accessible, user-centered experience. The study offers practical implications for developers and educational institutions aiming to increase AI adoption and long-term engagement in academic contexts.

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