A Comprehensive Review of Intelligent AI Tutoring Systems with Personalized Content Recommendation Using Hybrid ML Models.

Abstract— The rapid growth of digital learning platforms has created a strong demand for intelligent systems capable of delivering personalized learning experiences. Traditional e-learning environments often rely on generic content delivery, which fails to adapt to the diverse learning styles, performance levels, and behavioral patterns of students. To address these limitations, this paper presents a comprehensive review of intelligent tutoring systems and proposes a hybrid machine learning–based personalized content recommendation framework. The proposed model integrates collaborative filtering, content-based filtering, and learning-style classification with an ensemble ranking mechanism to generate adaptive learning paths for individual students. The hybrid approach overcomes cold-start issues, enhances recommendation accuracy, and supports continuous learner profiling through real-time feedback analysis. This review highlights the strengths and limitations of existing approaches, identifies key research gaps, and demonstrates how hybrid ML models can significantly improve the effectiveness of AI-driven tutoring systems. The findings suggest promising applications for K–12, higher education, and skill-development platforms, paving the way for next-generation personalized AI tutors.