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Data-inspired co-design for museum and gallery visitor experiences

Darzentas, Dimitrios; Cameron, Harriet; Wagner, Hanne; Craigon, Peter; Bodiaj, Edgar; Spence, Jocelyn; Tennent, Paul; Benford, Steve

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Authors

Dimitrios Darzentas

Harriet Cameron

Hanne Wagner

Edgar Bodiaj

Jocelyn Spence

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STEVE BENFORD steve.benford@nottingham.ac.uk
Dunford Chair in Computer Science



Abstract

The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.

Citation

Darzentas, D., Cameron, H., Wagner, H., Craigon, P., Bodiaj, E., Spence, J., …Benford, S. (2022). Data-inspired co-design for museum and gallery visitor experiences. AI EDAM, 36, Article e3. https://doi.org/10.1017/S0890060421000317

Journal Article Type Article
Acceptance Date Oct 5, 2021
Online Publication Date Feb 9, 2022
Publication Date Feb 9, 2022
Deposit Date Dec 17, 2021
Publicly Available Date Mar 29, 2024
Journal Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Print ISSN 0890-0604
Electronic ISSN 1469-1760
Publisher Cambridge University Press (CUP)
Peer Reviewed Peer Reviewed
Volume 36
Article Number e3
DOI https://doi.org/10.1017/S0890060421000317
Keywords Artificial Intelligence; Industrial and Manufacturing Engineering
Public URL https://nottingham-repository.worktribe.com/output/7022259
Publisher URL https://www.cambridge.org/core/journals/ai-edam/article/datainspired-codesign-for-museum-and-gallery-visitor-experiences/F56D93C79E7875EB3A2E5828C40E6E4D
Related Public URLs https://www.cambridge.org/core/journals/ai-edam
Additional Information Copyright: Copyright © The Author(s), 2022. Published by Cambridge University Press; License: This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.; Free to read: This content has been made available to all.

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