Hybrid Machine Learning Framework for Personalized Educational Content Recommendation.
Md. Shahid1, Prof. Sarwesh Site 2
1 M.Tech Student, Department of Computer Science and Engineering
All Saints College of Technology, Bhopal, India
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV)
shahidroyen@gmail.com
2 Associate Professor, Department of Computer Science and Engineering
All Saints College of Technology, Bhopal, India
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV)
er.sarwesh@gmail.com
CHAPTER 1 – INTRODUCTION
1.1 Background of the Study
In the last decade, digital learning has rapidly transformed the education landscape. Traditional classroom-based teaching has been supplemented—and in many cases replaced—by online platforms, virtual classrooms, and intelligent learning systems. With the increasing availability of digital devices and the rise of high-speed internet, learners today have access to a vast amount of educational content anytime, anywhere.
However, the biggest challenge in digital learning is personalization. Most learning management systems (LMS) still operate using a one-size-fits-all approach, where every learner receives the same content, difficulty level, and learning path regardless of their unique needs. This results in reduced engagement, uneven learning progress, and difficulty in addressing individual weaknesses.
To overcome these limitations, Intelligent Tutoring Systems (ITS) have emerged. ITS uses artificial intelligence techniques to simulate the role of a human tutor by monitoring student performance, predicting learning needs, and adapting content accordingly. Modern ITS platforms apply Machine Learning (ML), Deep Learning (DL), NLP, and recommendation algorithms to deliver customized content.
The evolution of AI-driven learning has led to the development of AI tutors—systems capable of understanding learner profiles, predicting performance, recommending suitable content, and providing real-time feedback. Although many AI tutors exist, most rely on single-model approaches such as Collaborative Filtering or Content-Based Filtering, which often suffer from cold-start issues, limited personalization depth, and low accuracy.
To address these issues, researchers have begun exploring Hybrid Machine Learning Models that combine the strengths of multiple algorithms. A hybrid approach enhances personalization quality, improves prediction accuracy, and adapts effectively to diverse learning behaviors.
This thesis explores the development of an Intelligent AI Tutor that uses a Hybrid Machine Learning Model to deliver personalized content recommendations and adaptive learning experiences for students.
