Generative AI and Inspirational Material: Investigating Qualities of GenAI Inspiration Boards and Design Implications

This study investigates how text-to-image Generative AI (GenAI) influences the qualities of inspiration boards produced during early-stage industrial design projects. To do so, we conducted a comparative analysis of inspiration boards created using traditional methods and text-to-image GenAI created by a sample of industrial designers. The resulting boards were assessed through visual content analysis, examining manifest qualities (image quantity and diversity) and attributes influential in inspiration seeking and creativity (ambiguity, fidelity, and analogical distance). Findings reveal that GenAI-supported boards are more extensive but narrower in scope, supporting interpolation over extrapolation of ideas. In contrast, traditional boards support greater analogising through more abstract and varied content. These contrasting patterns highlight important differences between traditional and GenAI-supported design activities that suggest when, during the design process, they may be best used. As such, the study contributes to emerging discourse on AI-assisted creativity by identifying how GenAI may be better aligned with refinement tasks than with open-ended inspiration seeking.

keywords: text-to-image generative artificial intelligence; inspiration boards; design creativity; early stage industrial design

• with: Charlie Ranscombe (lead), Chris Snider, and Mark Goudswaard
• year: December 2025 — ongoing