International Research Journal of Education and Technology

The increasing demand for personalized education has led to the development of AI-driven recommendation systems that assist students in selecting the most suitable courses based on their academic performance and interests. This project presents a Course Recommendation Engine that leverages machine learning and deep learning techniques to provide tailored course suggestions, optimizing student learning pathways. The system integrates collaborative filtering, content-based filtering, and hybrid recommendation approaches to analyze students' academic records, preferences, and career goals.

A key feature of the system is its ability to dynamically adapt to student interests and evolving academic trends by incorporating real-time feedback. The engine employs multi-output classification models, RandomForest algorithms, and deep learning architectures to improve recommendation accuracy. The user-friendly interface ensures seamless interaction, allowing students to explore recommended courses with just a few clicks.

Experimental results demonstrate that the recommendation engine significantly enhances course selection efficiency, reduces decision-making time, and improves student satisfaction. Despite its high accuracy, challenges such as computational resource demands and explainability of deep learning models remain areas for further enhancement. Future improvements will focus on real-time adaptability, explainable AI (XAI) techniques, and computational optimization to make the system more robust and scalable across diverse educational institutions.

KeyWords:

CourseRecommendation Engine,PersonalizedLearning,MachineLearning,Deep Learning,CollaborativeFiltering,Content-BasedFiltering,Multi-Output.Classification,RandomForest AlgorithmExplainable AI (XAI),Academic Performance Analysis