Enhancing designer creativity through human-AI co-ideation: a co-creation framework for design ideation with custom GPT
Author(s)
Type
Journal Article
Abstract
The emergence of large language models (LLMs) provides an opportunity for AI to operate as a co-ideation partner during the creative processes. However, designers currently lack a comprehensive methodology for engaging in co-ideation with LLMs, and there is a limited framework that describes the process of co-ideation between a designer and ChatGPT. This research thus aimed to explore how LLMs can act as codesigners and influence creative ideation processes of industrial designers and whether the ideation performance of a designer could be improved by employing the proposed framework for co-ideation with custom GPT. A survey was first conducted to detect how LLMs influenced the creative ideation processes of industrial designers and to understand the problems that designers face when using ChatGPT to ideate. Then, a framework which based on mapping content to guide the co-ideation between humans and custom GPT (named as Co-Ideator) was promoted. Finally, a design case study followed by a survey and an interview was conducted to evaluate the ideation performance of the custom GPT and framework compared with traditional ideation methods. Also, the effect of custom GPT on co-ideation was compared with a non-artificial intelligence (AI)-used condition. The findings indicated that if users employed co-ideation with custom GPT, the novelty and quality of ideation outperformed by using traditional ideation.
Date Issued
2025-09-09
Date Acceptance
2025-07-29
Citation
Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 2025, 39
ISSN
0890-0604
Publisher
Cambridge University Press
Journal / Book Title
Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume
39
Copyright Statement
© The Author(s), 2025. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:// creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
License URL
Subjects
co-creation
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
custom GPT
Engineering
Engineering, Manufacturing
Engineering, Multidisciplinary
generative AI
human-AI co-ideation
industrial design
large language model
Science & Technology
Technology
Publication Status
Published
Article Number
e22
Date Publish Online
2025-09-09