Learning Computer Graphics (CG) is a complex process that requires students to master diverse 3D operations within professional software environments. Although recommender systems have been widely studied in education, most applications target domains with abundant structured data, such as mathematics or online courses, and rarely address the unique challenges of CG learning. The absence of domain-specific tools and datasets makes it difficult to provide personalized guidance to students in this field. The present thesis work introduces a novel recommender system embedded directly into Blender, a well-known 3D modeling and animation suite. The system has been designed to diagnose operation-level difficulties and recommend tutorials that address them.