Fusion-Driven Cognitive AI Model for Personalized Prediction in Multilevel Education Systems

Asma Abdulmana Alhamadi¹^,*

¹Department of Humanities College of Science & Theoretical Studies, Saudi Electronic University Riyadh,
Saudi Arabia

Abstract

Education in the twenty-first century is undergoing a profound transformation fueled by artificial intelligence (AI), the Internet of Things (IoT), and intelligent systems. Traditional and even early digital learning systems often relied on standardized pathways, limiting personalization and reducing learner engagement. To overcome these limitations, this study proposes and evaluates an IoT-enabled adaptive learning framework that integrates educational data analytics with intelligent algorithms to deliver real-time, personalized pathways for learners. Education in the twenty-first century is undergoing a profound transformation fueled by artificial intelligence (AI), the Internet of Things (IoT), and intelligent systems. Traditional and even early digital learning systems often relied on standardized pathways, limiting personalization and reducing learner engagement. To overcome these limitations, this study proposes and evaluates an IoT-enabled fusion-based adaptive learning framework that integrates educational data analytics, ensemble learning, and multi-modal intelligent algorithms to deliver real-time, personalized pathways for learners. The fusion of diverse data sources—ranging from quiz interactions and engagement logs to contextual signals from IoT devices such as smart sensors and wearables—ensures robust, context-aware decision-making. Experimental results using Kaggle datasets demonstrate that Random Forest outperforms XGBoost, with an accuracy rate of 87% and balanced F1-scores. This study shows how AI–IoT fusion can create equitable, eco-friendly, and inclusive learning spaces.

Keywords: Adaptive Learning; Artificial Intelligence; Data Preprocessing; Educational Data Mining; Ensemble Models; Intelligent Systems; Internet of Things; Machine Learning; Personalization; Student Performance Prediction