The Role of AI Tools in Enhancing Student Satisfaction and Learning Outcomes for Finance Students in the UAE and India
DOI:
https://doi.org/10.47654/v30y2026i1p336-376Keywords:
Artificial Intelligence, Higher Education, Student Satisfaction, Learning Outcomes, Technology Adoption, UAE, IndiaAbstract
Purpose - This study examines the determinants of students’ acceptance and use of artificial intelligence (AI)-based academic support systems in higher education. It focuses on how performance expectancy, information accuracy, and pedagogical fit influence behavioral intention, actual use, and academic performance of the students. Additionally, this study evaluated the mediating roles of behavioral intention and satisfaction in shaping students’ learning experiences in AI-enabled environments across different institutional contexts.
Design/methodology/approach - An extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework is employed using cross-sectional survey data collected from students in higher education institutions in the United Arab Emirates (UAE) and India. Structural Equation Modeling (SEM) was applied to examine the relationships among the constructs. A mediation analysis was conducted to capture indirect effects, and cross-country validation was performed to assess the robustness and generalizability of the model across diverse educational settings.
Findings - The findings revealed that performance expectancy, information accuracy, and pedagogical fit significantly enhanced students’ behavioral intention to adopt AI-based tools (p < 0.01). Behavioral intention positively influences actual usage, which, in turn, improves student satisfaction and academic performance. Satisfaction and behavioral intention were significant mediating mechanisms in the model. Cross-country analysis confirms the structural consistency of the framework, with minor variations reflecting the contextual differences between the UAE and India.
Originality/Value - This study extends the UTAUT framework by integrating AI-specific constructs, including information accuracy and pedagogical fit, into a unified behavioral model. By providing cross-country empirical evidence and incorporating both cognitive and affective mechanisms, this study offers a comprehensive understanding of AI adoption in higher education. Importantly, this study contributes to decision science by explaining how students’ behavioral intentions and satisfaction shape technology adoption decisions and learning outcomes in AI-enabled environments. It provides evidence-based insights for policymakers and educators to support informed decision-making regarding AI integration, resource allocation, and strategic planning in conditions of technological uncertainty.
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Copyright (c) 2026 Mohamed Elsayed Abdelsalam Ghanem, Asma Salman, Muthanna G. Abdul Razzaq, Safaa Sayed Mahmoud (Author); Mohammed Abdul Imran Khan (Corresponding Author)
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