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As AI becomes increasingly integrated into everyday life, supporting children’s AI literacy is essential. While prior work in Child-Robot-Interaction has primarily used robots as programmable artefacts or learning companions for introducing AI concepts, the role of a robot as an embodied AI student remains underexplored. We investigate social robot teaching as a pathway to help children intuitively understand supervised learning. We designed a prototype in which children teach a robot using biased and unbiased training data and iteratively observe its performance. A pilot study with three children preliminarily examines: 1) whether and how this interaction fosters intuitive understanding of AI training and bias, and 2) initial design considerations for future prototype interactions. Our findings offer early evidence of the potential of social robot teaching for AI literacy.
Roland Siegwart, Nicola Tomatis