PathReco: AI-Powered Domain-Specific Course Recommendation System Using Sentence-BERT and Knowledge Graphs

Kunal S. Kavathekar, Tanvi R. Mane and Waman R. Parulekar

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 971-977

Abstract: Finding educational resources, particularly in technology-related subjects, has gotten easier with the rapid expansion of online learning platforms. However, students are frequently finding it challenging to choose courses that fit both their present skill level and their area of interest due to the wide range of choices available. A significant number of recommendation systems currently in use rely on user ratings, popularity rankings, or basic keyword matching, which often produce redundant or unrelated suggestions. Additionally, these systems struggle to fully understand skill descriptions in natural language and rarely examine the required links across learning topics. In order to provide organized and customized learning recommendations, this paper provides a domain-specific course recommendation system that first takes into account the user's chosen domain and then evaluates their current skill set. In order to find suitable courses, the system analyses user-provided skills with course descriptions. It also models the links between abilities and topics to make sure that recommendations follow to the proper required sequence. The system enhances the quality and relevance of suggestions by fusing structured knowledge representation with semantic analysis of textual material. For assessment, a dataset of technology-focused courses was created. The findings show that the system is capable of producing insightful course recommendations that correspond with the user's current level of knowledge as well as their area of interest.

Keywords: Course Recommendation System; Domain-Specific Recommendation; Knowledge Graph; Personalised Learning; Sentence-BERT (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1548

DOI: 10.32628/IJSRST26133101

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