AI-Driven Business Intelligence for Predictive Student Admission Governance
Abstract
This study examines how an existing student admission dashboard can be transformed into an AI-driven Business Intelligence model for predictive student admission governance in higher education. The study is motivated by the limitation of descriptive dashboards, which visualize admission and registration performance but do not provide forecasting, risk classification, early warning, or recommendation support for strategic decision-making. Using a mixed-methods case study design, this study analyzed dashboard indicators, institutional data practices, user needs, and decision-making requirements through dashboard analysis, interviews, focus group discussion, user survey, expert review, and predictive feature simulation. The existing dashboard was analyzed as a descriptive Business Intelligence layer, while the proposed extension was designed to integrate forecasting, risk scoring, early warning, and recommendation support. The findings show that the existing dashboard supports institutional visibility but remains limited to descriptive monitoring and target comparison. The proposed AI-driven extension addresses this limitation by enabling program-level risk identification, target achievement prediction, and evidence-based intervention planning. This study concludes that AI-driven Business Intelligence can function as a predictive governance instrument that supports more anticipatory, integrated, and data-driven student admission decision-making in higher education.
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