Playful Probing: Towards Understanding the Interaction with Machine Learning in the Design of Maintenance Planning Tools

In the context of understanding interaction with artificial intelligence algorithms in a decision support system, this study addresses the use of a playful probe as a potential speculative design approach. We describe the process of researching a new machine learning (ML)-based planning tool for mai...

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Bibliographic Details
Main Authors: Jorge Ribeiro, Licínio Roque
Format: Article
Language:English
Published: MDPI AG 2022-11-01
Series:Aerospace
Subjects:
Online Access:https://www.mdpi.com/2226-4310/9/12/754
Description
Summary:In the context of understanding interaction with artificial intelligence algorithms in a decision support system, this study addresses the use of a playful probe as a potential speculative design approach. We describe the process of researching a new machine learning (ML)-based planning tool for maintenance based on aircraft conditions and the challenge of investigating how playful probes can enable end-user participation during the process of design. Using a design science research approach, we designed a playful probe protocol and materials and evaluated results by running a participatory design workshop. With this approach, participants facilitated speculative design insights into understandable interactions, especially with ML interaction. The article contributes with a design of a playful probe exercise to collaboratively study the adjustment of practices for CBM and a set of concrete insights on understandable interactions with CBM.
ISSN:2226-4310