Knowledge Graphs (KGs) play a crucial role in academic orientation by structuring and organizing information to help students navigate their educational choices. However, static graphs face limitations when adapting to the evolving aspirations of students and the continuous introduction of new academic programs. To address these challenges, we present and compare three approaches for dynamizing KGs: rule-based updates, incremental updates, and dynamic embeddings. Our methodology involves transitioning from a static KG to a dynamic KG by integrating these techniques, enabling continuous updates and improved adaptability to student profile changes. The results demonstrate that dynamic embeddings offer greater flexibility, incremental updates ensure higher precision, and rule-based updates guarantee data consistency. Together, these approaches significantly enhance the performance and relevance of KGs for academic orientation, ensuring that recommendations remain accurate and up to date.