The rapid development of educational technology has significantly expanded the range of academic degree programs, associated courses, and corresponding learning resources. This expansion has led to substantial overlaps in course content across programs, making it increasingly difficult for learners to construct coherent and efficient learning paths. Educators must also adapt their materials more effectively to meet the diverse needs of their students. A personalized course and curriculum path recommendation system offers a promising solution by guiding both students and instructors in selecting relevant content and structuring appropriate learning trajectories. Among the techniques available, knowledge graphs (KGs) are particularly effective. By representing information as entities and their interrelationships, KGs provide a structured, semantically meaningful structure for organizing educational data. This conceptual paper introduces a Knowledge-graph driven recommender framework that supports the generation of personalised learning paths aligned with individual learners prior knowledge and goals, while enabling instructors to adapt curriculum design and instructional delivery to identified learner needs. By semantically linking learner’s background knowledge to relevant curricular components by leveraging placement assessments, the framework supports individualized assessments and recommends adaptive learning trajectories. The work outlines its theoretical foundations and explores its potential through relevant literature and graph-based design principles, demonstrating how structured semantic representations support goal-aligned, personalized learning. Through this approach, systems based on the framework have the potential to reduce content redundancy, enhance coherence, and improve the quality of teaching and learning, while fostering a more dynamic and responsive educational environment.