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Datasets in design research: needs and challenges and the role of AI and GPT in filling the gaps

Published online by Cambridge University Press:  16 May 2024

Mohammad Arjomandi Rad ,
Tina Hajali ,
Julian Martinsson Bonde ,
Massimo Panarotto ,
Kristina Wärmefjord ,
Johan Malmqvist  and
Ola Isaksson
Mohammad Arjomandi Rad*
Affiliation:
Chalmers University of Technology, Sweden
Tina Hajali
Affiliation:
Chalmers University of Technology, Sweden
Julian Martinsson Bonde
Affiliation:
Chalmers University of Technology, Sweden
Massimo Panarotto
Affiliation:
Chalmers University of Technology, Sweden
Kristina Wärmefjord
Affiliation:
Chalmers University of Technology, Sweden
Johan Malmqvist
Affiliation:
Chalmers University of Technology, Sweden
Ola Isaksson
Affiliation:
Chalmers University of Technology, Sweden
*
radmo@chalmers.se

Abstract

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Despite the recognized importance of datasets in data-driven design approaches, their extensive study remains limited. We review the current landscape of design datasets and highlight the ongoing need for larger and more comprehensive datasets. Three categories of challenges in dataset development are identified. Analyses show critical dataset gaps in design process where future studies can be directed. Synthetic and end-to-end datasets are suggested as two less explored avenues. The recent application of Generative Pretrained Transformers (GPT) shows their potential in addressing these needs.

Keywords

data-driven designdataset gapartificial intelligence (AI)design researchgenerative design

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1919 - 1928
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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