  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).
