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Goldsmiths - University of London

User-Centred Design Actions for Lightweight Evaluation of an Interactive Machine Learning Toolkit

Bernardo, Francisco; Grierson, Mick and Fiebrink, Rebecca. 2018. User-Centred Design Actions for Lightweight Evaluation of an Interactive Machine Learning Toolkit. Journal of Science and Technology of the Arts (CITARJ), ISSN 1646-9798 [Article] (In Press)

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Abstract or Description

Machine learning offers great potential to developers and end users in the creative industries. For example, it can support new sensor-based interactions, procedural content generation and end-user product customisation. However, designing machine learning toolkits for adoption by creative developers is still a nascent effort. This work focuses on the application of user-centred design with creative end-user developers for informing the design of an interactive machine learning toolkit. We introduce a framework for user-centred design actions that we developed within the context of an EU innovation project, RAPID-MIX. We illustrate the application of the framework with two actions for lightweight formative evaluation of our toolkit—the JUCE Machine Learning Hackathon and the RAPID-MIX API workshop at eNTERFACE’17. We describe how we used these actions to uncover conceptual and technical limitations. We also discuss how these actions provided us with a better understanding of users, helped us to refine the scope of the design space, and informed improvements to the toolkit. We conclude with a reflection about the knowledge we obtained from applying user-centred design to creative technology, in the context of an innovation project in the creative industries.

Item Type: Article

Related items in GRO:

TypeURL
Paperhttp://research.gold.ac.uk/19767/

Departments, Centres and Research Units:

Computing
Computing > Embodied AudioVisual Interaction Group (EAVI)

Dates:

DateEvent
12 June 2018Accepted

Item ID:

23667

Date Deposited:

05 Jul 2018 08:17

Last Modified:

11 Jul 2018 16:29

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI: http://research.gold.ac.uk/id/eprint/23667

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