Understanding how children design and what they value in AI interfaces that
allow them to explicitly train their models such as teachable machines, could
help increase such activities' impact and guide the design of future
technologies. In a co-design session using a modified storyboard, a team of 5
children (aged 7-13 years) and adult co-designers, engaged in AI problem
formulation activities where they imagine their own teachable machines. Our
findings, leveraging an established psychological value framework (the Rokeach
Value Survey), illuminate how children conceptualize and embed their values in
AI systems that they themselves devise to support their everyday activities.
Specifically, we find that children's proposed ideas require advanced system
intelligence, e.g. emotion detection and understanding the social relationships
of a user. The underlying models could be trained under multiple modalities and
any errors would be fixed by adding more data or by anticipating negative
examples. Children's ideas showed they cared about family and expected machines
to understand their social context before making decisions.

了解儿童在训练 AI 模型（如可教授机器）的界面设计中的设计方式和价值观，可以增加这类活动的影响力，并指导未来技术的设计。在一个共同设计的会议中，一个团队由 5 个 7-13 岁的儿童和成年共同设计师参与了修改后的故事板上的 AI 问题构思活动，他们设想了自己的可教授机器。我们的研究结果利用已有的心理价值框架（Rokeach 价值调查）揭示出儿童如何概念化并嵌入他们自己设计的 AI 系统中以支持他们的日常活动。具体来说，我们发现儿童的设想需要先进的系统智能，例如情绪检测和理解用户的社会关系。这些基础模型可以利用多种模态进行训练，并通过添加更多数据或预测负面例子来修复任何错误。儿童的想法表明他们关心家庭，并期望机器在做出决策之前了解他们的社会背景。