AI Driven Personalized Course Recommendation System

Abstract

Selecting the appropriate course hasbecome a challenging taskfor studentsowing to the increasing popularity of online learning platforms and the availability of a vast number of learning resources. Studentsfacedifficulties inidentifyingcoursesthatalign withtheirinterests,existingskills,learning background, and future career plans. The conventional approaches for recommending courses lack personalizationandfailtocatert othediverseneeds oflearners, therebyleadingtoinefficientlearning outcomes and low engagement. This paper proposes an AI Driven Personalized Course Recommendation System thathelpsstudentsmake informed learning choices.The proposed system employsartificialintelligenceandmachinelearningalgorithmstoprocessstudentprofiles,interests, skills,andlearning behaviortor ecommendrelevantcourses.Thesystemlearnsfromuserinteractions andfeedbackovertimeandadapt stoprovidemoreac curaterecommendations.Theproposedsystem enablesefficientlearning,improvesstudentengagement,andfacilitatescareer-oriented learning.The proposed approach, in essence, aims to make course selection easier and provide an effective and learner-centric learning experience.

Introduction

The AI-Driven Personalized Course Recommendation System addresses the problem of overwhelming course choices in modern e-learning platforms by using AI and machine learning to deliver personalized, career-oriented learning suggestions. Unlike traditional platforms that rely on popularity-based recommendations, this system tailors suggestions based on user profiles, skills, interests, and career goals.

The system is built on a modular web architecture consisting of user authentication, profile management, course management, a recommendation engine, skill gap analysis, learning roadmap generation, and progress tracking. The recommendation engine matches user profiles with course metadata to rank relevant courses, while the skill gap module identifies missing competencies by comparing user skills with career requirements. A roadmap module then organizes courses into structured learning paths for step-by-step progress.

It follows a content-based recommendation approach, supported by structured datasets and database-driven processing. The system also emphasizes scalability, secure access, and efficient query handling, though it depends heavily on accurate user profiles and internet connectivity.

Evaluation results show low-latency recommendations, instant roadmap generation, and effective skill gap detection, leading to improved learning clarity and reduced confusion in course selection. Overall, the system provides a personalized, explainable, and efficient learning guidance platform, though future improvements could include richer datasets and hybrid recommendation techniques.

Conclusion

This study introduced an AI-Driven Personalized Course Recommendation System that aims to facilitate goal-driven and skill-oriented learning. Throughtheintegrationofuserprofiling,AI-driven recommendation logic, skill gap analysis, and roadmapsystemdesign,thesystemoffersaholistic personalized learning experience. The system’s modularity promotes scalability, ease of maintenance, and adaptability for potential future upgrades.Theexperimentaloutcomeshowsthatthe system is capable of aligning learning content with the users’ career goals, filtering out non-relevant course recommendations, and enhancing overall learningperformance.Futurestudiescaninvestigate thepotentialintegrationofthesystemwithreal-time labor market information and adaptive learning analytics.

References

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Copyright

Copyright © 2026 Dr. S Radha Krishnan, SD Imamsa, R Hemanth, V Komal, S Tirumalesh, R Raghu Sai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.