AI-driven global talent prediction: Anticipating international graduate admissions

Abstract

With the help of AI-driven global talent prediction approaches, this research intends to propose a novel method for predicting admissions to international graduate programs. Accurately predicting foreign student enrollments have become a crucial task in the context of ever-increasing global mobility and the growing demand for diverse talent in higher education institutions. Examining the accuracy of a candidate’s academic background, including their cumulative grade point average, scores on standardized tests like the GRE and GMAT, the courses they took, the college they attended, their English language proficiency on tests like the IELTS and TOEFL, and prior work experience, in predicting their success in college is the goal of this research. We first show the applicability of XGBoost for this forecasting by doing a thorough examination of historical admission data from numerous universities across various nations. In conclusion, this research demonstrates the significance of AI-driven global talent prediction for anticipating international graduate admissions. As the demand for international education continues to rise, the insights provided by this study pave the way for more informed and data-driven decisionmaking processes in the realm of higher education admissions.

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