Human-AI co-ideation via combinational generative model
File(s)
Author(s)
Type
Journal Article
Abstract
Ideation is a critical step in the engineering design process, enabling designers to develop creative and innovative concepts and prototypes. Currently, the ideation workflow requires designers to generate new designs based on product requirements, heavily relying on their personal expertise and experience. To advance human-AI collaboration design and assist designers in the idea-generation process, this paper proposes an Object Combination Generative Adversarial Network (OC-GAN) for combinational creativity. The proposed method includes an image encoder module and a cross-domain object combination generator module. The image encoder module captures and encodes image structure information into latent space, while the cross-domain object combination generator module leverages GANs to combine object images based on user preferences, producing new design images. A design case study is used to evaluate the new ideation approach and reveal not only strong cross-domain concept combination capabilities but also improvement in designers' workflow and provision of novelty to the design case.
Highlights
An AI approach to improve the efficiency of idea generation in the design process.
A case study evaluates its support for idea generation and design creativity.
The OC-GAN is used for multi-domain object image combining tasks.
Exemplifies the feasibility of human-AI collaboration design for enhancing creativity.
Highlights
An AI approach to improve the efficiency of idea generation in the design process.
A case study evaluates its support for idea generation and design creativity.
The OC-GAN is used for multi-domain object image combining tasks.
Exemplifies the feasibility of human-AI collaboration design for enhancing creativity.
Date Issued
2026-02-01
Date Acceptance
2025-05-07
Citation
Journal of engineering design, 2026, 37 (2), pp.458-494
ISSN
0954-4828
Publisher
Taylor and Francis Group
Start Page
458
End Page
494
Journal / Book Title
Journal of engineering design
Volume
37
Issue
2
Copyright Statement
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
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Subjects
artificial intelligence
combinational creativity
CREATIVITY
Engineering
Engineering, Multidisciplinary
generative adversarial networks
Ideation
Science & Technology
Technology
TECHNOLOGY
visual stimuli
Publication Status
Published
Date Publish Online
2025-06-01