Ceci N'est Pas un Drone: Investigating the Impact of Design Representation on Design Decision Making When Using GenAI

TL;DR

This study explores how different design representation methods, such as visual, numerical, or combined, influence designers' decision-making when selecting AI-generated UAV designs, revealing that numerical data alone can enhance optimal choices.

Contribution

It investigates the impact of visual, numerical, and combined design modalities on designer choices in AI-assisted design, highlighting the effectiveness of numerical data alone.

Findings

Numerical performance data alone can lead to better design selection.

Designers prefer visually conventional, axis-symmetric designs.

Design modalities significantly influence designer choices.

Abstract

With generative AI-powered design tools, designers and engineers can efficiently generate large numbers of design ideas. However, efficient exploration of these ideas requires designers to select a smaller group of potential solutions for further development. Therefore, the ability to judge and evaluate designs is critical for the successful use of generative design tools. Different design representation modalities can potentially affect designers' judgments. This work investigates how different design modalities, including visual rendering, numerical performance data, and a combination of both, affect designers' design selections from AI-generated design concepts for Uncrewed Aerial Vehicles. We found that different design modalities do affect designers' choices. Unexpectedly, we found that providing only numerical design performance data can lead to the best ability to select optimal designs. We also found that participants prefer visually conventional designs with axis-symmetry. The findings of this work provide insights into the interaction between human users and generative design systems.