Background: Ensuring the quality of user stories is vital to Agile Software Development. Rule-based tools like AQUSA, based on the Quality User Story (QUS) framework, offer reliable structural checks but struggle with context-sensitive or pragmatic issues. Large Language Models (LLMs) have emerged as potential alternatives, yet prior studies often rely on small datasets, older models, or lack direct comparison with rule-based baselines. Objective: This study aims to assess the effectiveness of modern LLMs relative to a rule-based tool (AQUSA) for detecting defects in user stories, considering both structural and contextual dimensions. Method: We conduct a large-scale comparative evaluation involving AQUSA and three GPT-family LLMs (GPT-5, GPT-5-mini, and GPT-4), using 182 user stories drawn from three industrial datasets. We apply both quantitative metrics (precision, recall, F1-score) and qualitative analysis of feedback clarity and defect relevance. Results: GPT-5-mini achieved the highest recall (0.81) and overall F1-score (0.62), while AQUSA attained the highest precision (0.61) with significantly fewer false positives. GPT-5 showed high hallucination rates and instability; GPT-4 was overly conservative, leading to under-detection of defects. Conclusion: Neither rule-based nor GPT-family LLM-based approaches suffice in isolation. Rule-based tools enforce structural rigor, while LLMs capture nuanced linguistic and pragmatic flaws. We advocate a hybrid “Dual-gate” strategy—using AQUSA for structural validation followed by lightweight LLMs for contextual refinement—to improve the reliability and scalability of user story quality assessment in agile environments.