S.P.O.T: A Game-Based Application for Fostering Critical Machine Learning Literacy Among Children
Ibrahim Adisa, Ian Thompson, Tolulope Famaye, Deepika Sistla, Cinamon Bailey, Katherine Mulholland, Alison M. Fecher, Caitlin Lancaster, Golnaz Arastoopour Irgens
Abstract
This paper describes S.P.O.T., a game-based application for promoting children's practical understanding of ML concepts and applications. Current tools for teaching ML in K-12 engage students in playful exploration of ML mechanisms and teach ML from a cognitive perspective. However, in S.P.O.T, learners interact with ML within real-life sociopolitical contexts and examine how ML predictions impact their daily lives and communities. Through the immersion of stories that mirror children's lived experiences, S.P.O.T. provides elementary school aged children with opportunities to learn how machine learning applications function and develop children's abilities to critically examine, question, and reimagine the consequences of ML decisions in the real world.