Recent advances in Artificial Intelligence and data visualization have opened new possibilities for rethinking course content design and delivery. This work proposes a knowledge graph–based system that unifies course content management, visualization, and adaptive learning. The system represents course materials, concepts, and learning outcomes as interconnected nodes, enabling intuitive navigation and dynamic updates [1]. By modularizing knowledge into discrete units and linking them through semantic relationships, instructors can maintain relevance and coherence and relevance in rapidly evolving subjects. The integration of large language models (LLMs) enables semantic traversal [2] of the graph for automated organization, assessment, and refinement. This framework aims to reduce instructor workload, improve scalability in large enrollment environments, and create more adaptive, data driven learning experiences.