Using AI-Based Predictive Analytics to Quickly Find At-Risk Students in Higher Education

Abstract

This study aims to develop and implement an intelligent academic monitoring system, the Academic Intelligence Centre. The primary objective is to monitor student grades and attendance in real time. This methodology employs a statistical risk model that integrates grades and absences to provide a 'risk index.' The platform was developed using the Streamlit framework and the Plotly library for visual analysis. This configuration facilitates fundamental, predictive, and advisory data tasks. The technology recognized students at danger of failure prior to finals and offered automatic assistance, including advice programs. The solution provides a comprehensive overview of student data. It assists educators in transitioning from basic reporting to intelligent, AI-driven judgments. The results indicate that the utilization of AI in academic advising accelerates assistance and enhances student retention in higher education.