A Modular Approach to Automated Archery Coaching for Action Quality Assessment and Feedback Generation Using Large Language Models

Archery is a fine-grained skill sport in which small posture deviations can markedly affect performance, motivating the need for reliable automated technique assessment. However, most existing methods still focus on large-amplitude sports and cannot match coach-level nuance. To overcome these limitations, we introduce SEMA (Semantic Evidence-Driven Multimodal Assessment), a large language model (LLM)-based end-to-end system for fine-grained archery action quality assessment. Beyond score prediction and evaluation-text generation, SEMA further supports knowledge-grounded question answering and feedback generation through a hierarchical multi-source knowledge framework that integrates assessment outputs, structured coaching guidance, and general archery knowledge. Experimental results show that SEMA achieves strong performance on the novel AAV dataset, outperforming general-purpose VLMs and adapted prior AQA methods. In addition, we introduce the AAV (Archery Action Video) dataset, the first multimodal, fine-grained action quality assessment (AQA) dataset dedicated to archery, and release it publicly to the community. This dataset addresses a critical gap in current benchmarks for assessing archery action quality and intelligent archery training.