A Generative AI-Driven Industrial Design Framework for Human–GenAI Co-Creation

Generative AI (GenAI) is accelerating design space exploration and multimodal prototyping in industrial design (ID), bringing new efficiencies and possibilities to early-stage ideation and cross-media expression. Yet many studies do not clearly define stage-wise human–GenAI roles, preserve constraints as traceable cross-stage artifacts, or provide verifiable stage-wise evaluation, undermining traceability in both concept convergence and concept-to-engineering handover. To address these issues, this paper proposes GID-HGCC, a GenAI-driven human–GenAI co-creation ID framework that links four core stages: requirements confirmation, concept generation, concept evaluation, and 3D modeling. First, it specifies stage-wise responsibilities and defines the corresponding inputs and outputs. Second, it establishes a traceable cross-stage artifact flow—“structured prompts–candidate concepts–evaluation outputs–3D engineering issue list”—to support continuous constraint transmission and explicit documentation. Third, it integrates a multi-dimensional evaluation criteria system with IVIFNs–CRITIC–TOPSIS for concept ranking, and further strengthens convergence reliability via preference–consistency diagnostics. The framework is validated through a case study on a portable passive cervical spine rehabilitation training device. Expert preferences over stage-wise co-creation artifacts exhibit an overall medium-to-high level of consistency, and the Top-5 overlap between each expert and the group ranking ranges from 0.80 to 1.00. These results demonstrate that GID-HGCC offers an operational reference for constraint-guided human–GenAI co-creation in ID, improving traceability and handover reliability from requirements confirmation to engineering refinement.