Abstract

In this paper, we are presenting the development of a personalized learning platform to help students stay more engaged and perform better academically. The idea came from seeing how traditional classroom approaches often fail to address individual learning needs. Our platform uses artificial intelligence and data-driven methods to adapt educational content in real-time based on how each student learns. We focused on profiling users, recommending content that fits their learning patterns, and providing immediate feedback.

Our tests showed that students using the system performed better and were more involved compared to traditional learning tools. We believe this platform could scale well and support a more personalized and motivating learning experience for students in diverse environments.

Key contributions of this study include:

• Development of an Adaptive Learning Model: Implementing AI-driven algorithms to personalize educational content based on student behavior.
• Enhanced User Profiling Techniques: Utilizing Data Science techniques to identify strengths, weaknesses, and learning styles.
• Real-Time Feedback Integration: Enabling instant performance analysis and personalized recommendations.
• Comparative Analysis with Traditional Methods: Demonstrating significant improvements in engagement, retention, and academic performance.

Experimental results demonstrate a significant improvement in student performance and engagement compared to conventional learning methods. The proposed platform offers scalability, adaptability, and efficiency, making it a promising solution for modern educational systems. The findings suggest that a personalized learning approach not only enhances learning outcomes but also fosters motivation and self-directed learning among students.