  Most attention in K-12 artificial intelligence and machine learning (AI/ML)
education has been given to having youths train models, with much less
attention to the equally important testing of models when creating machine
learning applications. Testing ML applications allows for the evaluation of
models against predictions and can help creators of applications identify and
address failure and edge cases that could negatively impact user experiences.
We investigate how testing each other's projects supported youths to take
perspective about functionality, performance, and potential issues in their own
projects. We analyzed testing worksheets, audio and video recordings collected
during a two week workshop in which 11 high school youths created physical
computing projects that included (audio, pose, and image) ML classifiers. We
found that through peer-testing youths reflected on the size of their training
datasets, the diversity of their training data, the design of their classes and
the contexts in which they produced training data. We discuss future directions
for research on peer-testing in AI/ML education and current limitations for
these kinds of activities.
