The Adoption of Generative AI in Academic Information Systems: Impacts on User Satisfaction and Learning Efficiency

Abstract

The rapid advancement of Generative Artificial Intelligence (Generative AI) has significantly transformed academic information systems by enhancing learning processes, academic services, and user interaction. This study aims to analyze the adoption of Generative AI in academic information systems and examine its impacts on user satisfaction and learning efficiency in higher education environments. The research employs a quantitative approach using survey data collected from university students and academic users who actively utilize AI-based academic platforms. Structural Equation Modeling (SEM) is used to evaluate the relationships between Generative AI adoption, system usability, user satisfaction, and learning efficiency. The findings indicate that the adoption of Generative AI positively influences user satisfaction through improved accessibility, personalization, and responsiveness of academic information systems. Furthermore, Generative AI significantly enhances learning efficiency by supporting faster information retrieval, adaptive learning experiences, and academic task automation. The study contributes to the growing literature on artificial intelligence adoption in educational information systems and provides practical implications for universities seeking to implement AI-driven digital transformation strategies. The results also highlight the importance of system quality, trust, and user readiness in maximizing the effectiveness of Generative AI technologies in higher education.