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

Abstract

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.