Adoption of Artificial Intelligence and Perception of the Quality of Academic Services in University Students

Abstract

The rapid proliferation of artificial intelligence (AI) tools in higher education has transformed the landscape of academic service delivery and student learning experiences. This article examines the relationship between university students' adoption of AI technologies and their perceptions of the quality of academic services, drawing upon an integrative framework that combines the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT2), and the SERVQUAL model for academic service quality assessment. Through a comprehensive review of recent empirical studies, including large-scale surveys across multiple countries (total combined N > 12,000), the analysis identifies perceived usefulness (β = 0.52, p < .001), perceived ease of use (β = 0.41, p < .001), and performance expectancy (β = 0.48, p < .001) as the strongest predictors of AI adoption intention among university students. The findings reveal that AI adoption positively influences students' perceptions of academic service quality, with medium-to-large effect sizes ranging from d = 0.45 to d = 0.72 across studies. Furthermore, AI adoption mediates the relationship between institutional support and student satisfaction, suggesting that universities that effectively integrate AI tools into their academic services achieve higher ratings across all five SERVQUAL dimensions. The study identifies trust in AI, self-efficacy, and social influence as significant moderating factors, while highlighting important regional disparities in adoption rates and quality perceptions. Implications for institutional policy, pedagogical design, and future research are discussed.