Transforming Personalized Education through AI-Enhanced Ontology Modelling in Dynamic Adaptive Learning Systems

Abstract

This study explores the impact of integrating advanced AI technologies—AI-enhanced ontology modelling, reinforcement learning (RL), and natural language processing (NLP)—on educational systems. The AI-enhanced ontology modelling demonstrated substantial improvements with manual workload reduction increasing from 20% to 70%, adaptability of learning paths rising from 20% to 70%, and precision and accuracy improving from 20% to 70%. RL algorithms achieved 85% accuracy in predicting optimal learning modules, leading to a 20% improvement in student performance, a 30% increase in task completion rates, and a 15% rise in student engagement. Additionally, NLP techniques resulted in a 50% reduction in quiz creation time and a 95% relevance rate for quizzes, contributing to a 25% improvement in student performance. The dynamic adjustment of quiz difficulty based on real-time performance further enhanced learning outcomes and engagement. These findings highlight the significant benefits of AI technologies in enhancing educational efficiency, personalization, and effectiveness, demonstrating notable gains in content creation, learning experience optimization, and student performance.