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.

翻译：在儿童AI素养活动中强调问题构建至关重要，然而我们缺乏对其结构及支撑条件的实证研究。我们提出，涉及可教学机器的参与式设计能促进问题构建活动。为验证这一假设，我们将问题约减启发式方法融入故事板设计，并邀请由10名儿童（8-13岁）和9名成人组成的大学跨代际设计团队共同设计一台可教学机器。研究发现：儿童在构建AI问题时会借鉴个人经验；他们假设语音与视频功能的存在，探索多样化的机器学习方法，并规划错误处理机制。其设计理念强调人类在AI中的参与性，但部分儿童更倾向自主系统。这些设计优先考虑能力、逻辑、助益性、责任感与服从性等价值观，且倾向于将舒适生活、家庭安全、内心和谐与兴奋感作为终极目标。我们最后探讨了这些发现如何指导未来参与式AI活动的设计。