Exploring Machine Teaching with Children
This work addresses improving educational tools for children to understand AI, but it is incremental as it builds on existing machine teaching interfaces.
The study tackled how children can learn machine learning concepts by building and testing image classifiers, finding that 14 children aged 7-13 developed reasoning skills and offering design insights like visible ML metrics and model exchange.
Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).