Optimizing Decision-Making in Higher Education Institutions through AI-Driven Business Intelligence in the Digital Era
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Maxsi Ary
Rian Andriani
Willma Fauzzia
Digital transformation in higher education requires institutions to adopt intelligent technologies that support more accurate and data-driven decision-making. This study investigates the integration of Artificial Intelligence (AI) into Business Intelligence (BI) systems within Indonesian universities using the Technology Acceptance Model (TAM) as the theoretical framework. A mixed-methods approach was employed, involving surveys (n = 75) and interviews with academic leaders and information technology (IT) staff. The results show that AI-enhanced BI systems significantly improve decision-making effectiveness, particularly in academic planning and administrative efficiency. Regression analysis revealed that perceived usefulness and perceived ease of use explained 58% of the variance in decision effectiveness, while all variables combined (including management support and digital literacy) accounted for 69%. These findings validate the TAM in the context of AI-based decision systems in education. This study contributes both theoretically and practically by offering evidence-based recommendations for strengthening data-driven culture and institutional readiness for adopting intelligent information systems.
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