AI-Driven Predictive Models for University Administration

This document is a case study that outlines the implementation of AI-driven predictive models at a leading university to enhance student enrolment, retention, and administrative efficiency. The university faced challenges in increasing student registration and retention rates, prompting the development of three predictive models: the Leads Conversion model, the Student Retention model, and the Class Registration model. Each model was designed to leverage data for improved decision-making. The Leads Conversion model prioritizes potential leads for registration, significantly improving conversion rates. The Student Retention model forecasts student degree completion, enabling targeted support for students at risk of dropping out. The Class Registration model predicts enrolment numbers for each course, facilitating better resource allocation and scheduling. The implementation of these models has resulted in higher course completion rates, reduced labor costs, and enhanced operational efficiency, positioning the university as a pioneer in adopting AI technologies in education.