Doctorate thesis defense of Rund MAHAFDAH

Doctorate thesis defense on February 24^th 2025 at 10H00 AM ,in Amphitheater Ibn Khaldoun, SUP'COM 2.

Entitled :Artificial Intelligence of Things (AIoT) Based Model on Academic Learning in the Middle East

Presented by :Rund MAHAFDAH

Committee

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized various industries, including education. This study explores the potential of an AIoT-powered learning model to enhance academic performance in the Middle East by leveraging smart technologies for real-time monitoring, adaptive learning, and personalized feedback. The research develops a framework that integrates IoT-enabled devices to track student engagement and AI-driven algorithms to analyze learning behaviors, predict academic outcomes, and optimize instructional strategies.

The study employs machine learning and deep learning techniques to assess the effectiveness of online learning platforms, identifying key factors that influence student engagement and performance. Additionally, predictive models are deployed to customize educational pathways based on individual learning patterns. Through large-scale data collection from IoT sensors and AI-based analytics, the proposed model enhances e-learning by offering tailored educational experiences, improved adaptability, and dynamic content delivery.

Findings indicate that AIoT can significantly improve the efficiency and accessibility of e-learning systems by enabling real-time feedback, optimizing resource allocation, and fostering interactive and immersive learning environments. The study concludes that blended learning models powered by AI and IoT can bridge educational gaps, promote student-centered learning, and enhance digital education infrastructure across the Middle East. Future research will focus on expanding AIoT frameworks to diverse educational contexts, ensuring scalability, ethical considerations, and data privacy compliance.

Keywords

Machine Learning; Deep Learning; IoT; Education data; Artificial Intelligence, eLearning