This portfolio presents the architectural framework of an AI-driven Early-Warning Predictive System designed to identify postgraduate and distance-learning students who may be at academic risk. The proposed solution analyzes Learning Management System (LMS) data, predicts risk levels, and supports timely intervention by academic advisors while maintaining fairness, transparency, and accountability.

PROJECT OVERVIEW

Introduction

Higher education institutions increasingly rely on Learning Management Systems to deliver learning experiences. These platforms generate valuable information about student engagement, attendance, assignment submissions, quiz performance, and communication activities. By applying Artificial Intelligence to these data sources, institutions can identify students who may require additional academic support before they experience significant learning difficulties.

PROBLEM STATEMENT

Many postgraduate and distance-learning students struggle silently because instructors often identify academic problems too late. Traditional monitoring methods depend heavily on manual observation and end-of-semester results, limiting opportunities for early intervention.

The proposed AI-driven system addresses this challenge by continuously analyzing LMS activity and generating predictive risk alerts.

PROJECT OBJECTIVES

• Detect academically at-risk students early.
• Improve student retention.
• Support timely academic intervention.
• Reduce dropout rates.
• Enhance institutional decision-making.
• Promote data-driven student support.

TARGET USERS

• Students
• Lecturers
• Academic Advisors
• Faculty Administrators
• ICT Department
• University Management