  Emphasizing problem formulation in AI literacy activities with children is
vital, yet we lack empirical studies on their structure and affordances. We
propose that participatory design involving teachable machines facilitates
problem formulation activities. To test this, we integrated problem reduction
heuristics into storyboarding and invited a university-based intergenerational
design team of 10 children (ages 8-13) and 9 adults to co-design a teachable
machine. We find that children draw from personal experiences when formulating
AI problems; they assume voice and video capabilities, explore diverse machine
learning approaches, and plan for error handling. Their ideas promote human
involvement in AI, though some are drawn to more autonomous systems. Their
designs prioritize values like capability, logic, helpfulness, responsibility,
and obedience, and a preference for a comfortable life, family security, inner
harmony, and excitement as end-states. We conclude by discussing how these
results can inform the design of future participatory AI activities.
