Comparing human, LLM, and LLM-QFD approaches to technical requirement extraction

This study investigates how large language models (LLMs) support extracting technical requirements from early product pitches. Mechanical engineering students worked under three conditions: manual, LLM-assisted, and LLM combined with a QFD interface. Both AI-assisted conditions improved requirement quality and lowered perceived difficulty. Thematic analysis showed cognitive effort shifted from generating requirements to evaluating and verifying AI outputs, while the LLM-only group reported the most positive attitudes.