AskNatureGPT: an LLM-driven concept generation method based on bio-inspired design knowledge
Author(s)
Type
Journal Article
Abstract
Concept generation is the early stage in the engineering design process to produce initial design concepts. By applying bio-inspired design (BID) knowledge, designers can employ biological analogies for solution-driven BID concepts. Solution-driven BID starts with knowledge of a specific biological system for technical design. Despite the proven benefits of solution-driven BID, the gap between biological solutions and engineering problems hinders its effective application, with designers frequently encountering misaligned problem-solution pairs and facing multidisciplinary knowledge gaps in concept generation. Therefore, this research proposes a large language model (LLM) based concept generation method – AskNatureGPT – to automatically search for problems, transfer biological analogy, and generate solution-driven BID concepts in the form of natural language. A concept generator and two evaluators are identified and fine-tuned based on the LLM. The method is evaluated by an ablation study, machine-based quantitative assessments, subjective human evaluations, and a case study. The results show our method can generate solution-driven BID concepts with high quality.
Date Issued
2026-01-01
Date Acceptance
2025-03-10
Citation
Journal of engineering design, 2026, 37 (1), pp.238-272
ISSN
0954-4828
Publisher
Taylor and Francis Group
Start Page
238
End Page
272
Journal / Book Title
Journal of engineering design
Volume
37
Issue
1
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-NonCommercial-NoDerivatives License(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 properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has beenpublished allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Subjects
bio-inspired design
Concept generation
conceptual design
data-driven design
Engineering
Engineering, Multidisciplinary
large language model
Science & Technology
Technology
Publication Status
Published
Date Publish Online
2025-04-02