International Research Journal of Education and Technology

This research unveils a sophisticated Course Recommendation System utilizing FAISS (Facebook AI Similarity Search) vector indexing and cutting-edge embedding techniques to provide personalized course recommendations, meeting the rising demand for tailored learning experiences in modern education. The system meticulously processes an Excel dataset encompassing comprehensive course information, student preferences, academic records, and historical learning patterns. By harnessing state-of-the-art NLP models, it converts both numerical and textual data into high-dimensional vectors, ensuring efficient data representation for similarity searches. FAISS facilitates rapid and accurate similarity searches, enabling real-time retrieval of the most relevant courses based on a student’s distinct background, interests, and learning history. The incorporation of Retrieval-Augmented Generation (RAG) integrates vector-based search with contextual understanding, substantially improving recommendation quality. Engineered for scalability, the system seamlessly adapts to diverse educational platforms, enhancing student engagement and optimizing learning outcomes. Experimental evaluations validate its superior accuracy, efficiency, and adaptability, positioning it as an intelligent solution for contemporary education ecosystems. This platform empowers institutions to deliver data-driven personalization, fostering an enriched learning environment that aligns with individual student needs, ultimately revolutionizing educational content delivery through advanced technology and insightful analytics.

Key Words:  FAISS, Retrieval-Augmented Generation (RAG), personalized course recommendations, state-of-the-art NLP models, high-dimensional vectors.