Datasets in design research: needs and challenges and the role of AI and GPT in filling the gaps
Paper in proceeding, 2024

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.

artificial intelligence (AI)

generative design

data-driven design

design research

dataset gap

Author

Mohammad Arjomandi Rad

Chalmers, Industrial and Materials Science, Product Development

Tina Hajali

Chalmers, Industrial and Materials Science, Product Development

Julian Martinsson Bonde

Chalmers, Industrial and Materials Science, Product Development

Massimo Panarotto

Chalmers, Industrial and Materials Science, Product Development

Kristina Wärmefjord

Chalmers, Industrial and Materials Science, Product Development

Johan Malmqvist

Chalmers, Industrial and Materials Science, Product Development

Ola Isaksson

Chalmers, Industrial and Materials Science, Product Development

Proceedings of the Design Society

2732527X (eISSN)

Vol. 4 1919-1928

2024 International Design Society Conference, Design 2024
Cavtat, Dubrovnik, Croatia,

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Design

DOI

10.1017/pds.2024.194

More information

Latest update

8/6/2024 1