This research explores the feasibility of leveraging a knowledge-based approach to address these limitations. A Design Science Research approach guided the research, resulting in the artefact of a novel Knowledge-Based Recommender System (KBRS) that links the course’s learning objectives with students’ career goals in the domain of information systems through competencies to recommend courses.

The study comprises a literature review, identifying limitations in existing RS and proposing a KBRS to overcome these limitations. The research derived specific requirements for the KBRS tailored to the context of the study programme Master of Science in Business Information Systems (MSc BIS) at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW). Moreover, the work suggests using external sources and ChatGPT 3.5 to facilitate the creation of the knowledge base and overcome the knowledge engineering bottleneck as the main weakness inherent to KBRS. Subsequent phases implemented and evaluated the KBRS by involving students and lecturers, demonstrating its effectiveness for course recommendations in higher education. However, challenges arose in accurately and efficiently matching learning objectives to competencies, highlighting areas for future research.

In essence, this work confirms the viability of KBRS in aligning course recommendations with students’ career aspirations, emphasising the need for further investigation to refine this approach for enhanced effectiveness in educational settings.