AI Powered Couse Recommendation System for Private Education

Abstract

In the context of private education, students face challenges in selecting suitable courses from an everexpanding curriculum, often leading to confusion and suboptimal academic decisions. This study introduces an AI-powered course recommendation system specifically designed to assist students in private educational institutions in Sri Lanka. By leveraging machine learning algorithms, particularly content-based and collaborative filtering techniques, along with data prediction algorithms such as decision trees and regression models, the system processes student data including academic performance, interests, and current course enrollments to generate personalized course recommendations. The methodology involves comprehensive data collection, preprocessing, and the development of models that are trained and validated against real-world educational data. The system's performance has shown a marked improvement in aligning course selections with student preferences, resulting in enhanced satisfaction and academic outcomes. The study also discusses the implications of integrating AI and predictive analytics in educational decision-making, emphasizing the potential to improve student guidance and success rates. Future work will focus on the development of an accessible user interface and the exploration of the system's adaptability across different educational contexts. The proposed system aims to support educators and students by streamlining the course selection process, ultimately transforming the educational experience in private institutions.