Enhancing designer creativity through human–AI co-ideation: a co-creation framework for design ideation with custom GPT

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.

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Publication Details

Journal
Artificial intelligence for engineering design analysis and manufacturing
Published
2025-01-01
DOI
https://doi.org/10.1017/s0890060425100127
Citations
2
Primary Topic
Design Education and Practice
Type
article
Field-Weighted Citation Impact
1.43
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article

Enhancing designer creativity through human–AI co-ideation: a co-creation framework for design ideation with custom GPT

Yuan Yin, Pan Wang, Peter Childs, Yihua Wang et al.
2 citations
Artificial intelligence for engineering design analysis and manufacturing
Design Education and Practice
1.43
article

Enhancing designer creativity through human–AI co-ideation: a co-creation framework for design ideation with custom GPT

Yuan Yin, Pan Wang, Peter Childs, Yihua Wang, Zhibin Zhou, Yash Khinvasara, Geesje Josine Creijghton, Tessa Scholing
article en
2 citations

Abstract

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.

Artificial intelligence for engineering design analysis and manufacturingVol. 39
Hong Kong Polytechnic University (HK), Imperial College London (GB), Delft University of Technology (NL)
Openalex Percentile: Top 17%
Design Education and Practice
1.43
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