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Exploring the potential of ChatGPT for finding engineering biomimetic solutions: A theoretical framework and practical insights
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Type
Article
Citation
Yeter, I. H., & Le Ferrand, H. (2024). Exploring the potential of ChatGPT for finding engineering biomimetic solutions: A theoretical framework and practical insights. Journal of Bionic Engineering, 21, 2730–2744. https://doi.org/10.1007/s42235-024-00606-3
Abstract
Biomimicry is an interdisciplinary field that aims to provide sustainable solutions to technical issues. However, learners often encounter challenges in the application of biomimicry due to the multidisciplinary requisites and abstract thinking skills required. Although multiple hands-on activities and teaching strategies have been explored, significant obstacles remain. Recently, generative artificial intelligent tools have become readily accessible to the general public, among which is ChatGPT. ChatGPT is known for generating detailed responses to user inquiries and has demonstrated efficacy in enhancing learning, although its specific application to biomimicry education has yet to be explored. To bridge this knowledge gap, this study seeks to evaluate the capabilities of ChatGPT in helping its users identify biomimetic solutions. It is found that the effectiveness of ChatGPT in biomimicry education significantly depends on the user’s ability to formulate knowledgeable and effective prompts. Although, a novice user can use ChatGPT to get a fundamental overview of the technical challenge and explore potential sources of bioinspiration. The study proposes a theoretical framework to guide users in the effective use of ChatGPT for biomimicry education and application. In addition, users are cautioned against ChatGPT responses and advised to employ it as a tool to complement their own knowledge gaps. The results from this study can offer insights for teachers and self-directed learners on the effective use of prompts in ChatGPT for biomimicry education. Future investigations will seek to validate this framework by evaluating users’ experiences and feedback on its application in creating prototypes.
Date Issued
2024
Publisher
Springer
Journal
Journal of Bionic Engineering
DOI
10.1007/s42235-024-00606-3