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Benchmarking AI design skills: insights from ChatGPT's participation in a prototyping hackathon

Published online by Cambridge University Press:  16 May 2024

Daniel Nygård Ege ,
Henrik H. Øvrebø ,
Vegar Stubberud ,
Martin Francis Berg ,
Martin Steinert  and
Håvard Vestad
Daniel Nygård Ege*
Affiliation:
Norwegian University of Science and Technology, Norway
Henrik H. Øvrebø
Affiliation:
Norwegian University of Science and Technology, Norway
Vegar Stubberud
Affiliation:
Norwegian University of Science and Technology, Norway
Martin Francis Berg
Affiliation:
Norwegian University of Science and Technology, Norway
Martin Steinert
Affiliation:
Norwegian University of Science and Technology, Norway
Håvard Vestad
Affiliation:
Norwegian University of Science and Technology, Norway
*
danieneg@stud.ntnu.no

Abstract

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This study provides insights into the capabilities and performance of generative AI, specifically ChatGPT, in engineering design. ChatGPT participated in a 48-hour hackathon by instructing two participants who acted out its instructions, successfully designing and prototyping a NERF dart launcher that finished second among six teams. The paper highlights the potential and limitations of generative AI as a tool for ideation, decision-making, and optimization in engineering tasks, demonstrating the practical applicability of generating viable design solutions under real-world constraints.

Keywords

prototypingChatGPTartificial intelligence (AI)engineering design
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1999 - 2008
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (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 unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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