PlushPal: Storytelling with Interactive Plush Toys and Machine Learning

Abstract

This paper presents PlushPal, a web-based design tool for children to make plush toys interactive with machine learning (ML). With PlushPal, children attach micro:bit hardware to stuffed animals, design custom gestures for their toy, and build gesture-recognition ML models to trigger their own sounds. We describe how, in the context of storytelling, PlushPal introduces core concepts in ML including data sampling and model evaluation. We conducted online workshops and in-person play sessions with 11 children between 8-14 years old building interactive stuffed animals with PlushPal. In these play sessions, we observed how children imagined bringing their toys to life using ML, as well as how children's data literacy changed as a result of experimenting with sensors, data sampling, and building their own ML models. Our work contributes a novel design space for children to express their ideas using gesture, as well as a description of observed debugging practices, building on efforts to support children using ML to enhance creative play.