EDU4All: An Intelligent platform Transforming Learning with LMS Powered by Machine Learning

Abstract

This study investigates how Artificial Intelligence (AI) is integrated into Learning Management Systems (LMS) and aims to propose a new platform called (Edu4ALL), which will enhance traditional education while ensuring that the virtual school's resources are available to all students during crisis periods. The proposed Edu4ALL is a collaborative platform taking the form of an LMS with a new feature-based Smart Education System (SES), having the capacity to deliver live-scheduled online sessions covering the considered area, assess schools, teachers, and students using collected data. The collected data is securely aggregated in a data warehouse, facilitating efficient processing, integration, and analysis for predictive modeling. The data used in this study were sourced from the Iraqi Ministry of Education, based on results from various schools during the 2022-2023 academic year. The learning management system incorporates four AI prediction models: school graduation prediction model, government prediction model, school performance prediction model, and teacher performance prediction model. These models utilize different machine learning methods such as the XGBoost and Random Forest Regressor algorithms to forecast outcomes accurately. The predictive models achieved high accuracy rates: model 1 (99.88%) identifies student performance gaps, supporting targeted interventions to improve instructional content; model 2 (99.5%) highlights top-performing teachers, emphasizing resource alignment and professional development; model 3 (99%) predicts top-performing schools, underscoring the role of institutional strategies and resource optimization; and model 4 (97%) evaluates government performance, ensuring equitable resource allocation and policy improvements. From the results obtained, it is concluded that Edu4ALL serves as an innovative system to improve Iraq's education delivery and resource management.