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Inspiration or indication? Evaluating the qualities of design inspiration boards created using text to image generative AI

Published online by Cambridge University Press:  16 May 2024

Charlie Ranscombe ,
Linus Tan ,
Mark Goudswaard  and
Chris Snider
Charlie Ranscombe*
Affiliation:
Swinburne University of Technology, Australia
Linus Tan
Affiliation:
Swinburne University of Technology, Australia
Mark Goudswaard
Affiliation:
University of Bristol, United Kingdom
Chris Snider
Affiliation:
University of Bristol, United Kingdom
*
cranscombe@swin.edu.au

Abstract

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This study explores the application of image generative AI to support design process by creating inspiration boards. Through an evaluative study, we compare the diversity, quantity, fidelity, and ambiguity of boards generated by image generative AI and traditional methods. The results highlight how generative AI produces a quantity of images, it exhibits limited diversity compared to traditional methods. This suggests a tendency for supporting interpolation rather than extrapolation of ideas, in turn providing insights on best practice and into the optimal stage for its application.

Keywords

inspiration boardsartificial intelligence (AI)design creativityindustrial designvisualisation
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2207 - 2216
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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