The lack of actionable guidance is a fundamental limitation in Action Quality Assessment (AQA), as traditional methods provide overall scores without offering specific insights for improvement. Moreover, existing interpretable approaches often rely on expensive supervised spatial annotations or yield noisy, unsigned saliency maps. To address these challenges, we propose Learning Interpretability Based Supervoxels (LIBS), a novel framework for generating instructional feedback. Distinguishing itself from fully supervised methods, LIBS employs an unsupervised soft-clustering mechanism to segment videos into coherent supervoxels without requiring pixel-level mask annotations. This allows for scalable, fine-grained spatio-temporal analysis while preserving action continuity. Furthermore, we introduce a sensitivity propensity analysis to quantify the contribution of each supervoxel. Unlike traditional attribution methods, this mechanism explicitly decomposes the quality score into positive (strengths) and negative (flaws) components, enabling the system to decode abstract scores into concrete, actionable instructions. Experimental validation across multiple datasets demonstrates that LIBS achieves superior interpretability and efficiency compared to state-of-the-art baselines, marking an improvement from diagnostic to instructional AQA applications.