Intelligent Content Generation Mechanisms for College Basketball Training Driven by Generative AI

Abstract

We propose an explainable ensemble framework for intelligent content generation in college basketball training, addressing the
critical need for both high-quality outputs and interpretable decision-making in generative AI systems. The framework integrates heterogeneous multi-modal models with dedicated interpretability mechanisms, combining a Vision Transformer for spatial analysis of game
footage and a GPT-4 architecture for textual content generation. A novel attention-based feature attribution module quantifies component
contributions, while a rule-guided rationale synthesizer incorporates basketball knowledge for human-understandable explanations. Parallel computation streams for generation and explanation enable iterative refinement via user feedback. Our approach uniquely optimizes
generation performance and interpretability through architectural innovations in model interaction and interpretability propagation. Experiments validate the frameworks ability to generate actionable training content with transparent rationales, bridging generative AI and
coaching applications. This work advances explainable AI by offering a scalable solution for domains requiring creative and accountable
automated content generation.