Designing Embodied Metaphors and Analogies for AI Literacy: An Iterative Study with Experts, Teachers, and Children

Teaching complex AI concepts to children presents significant challenges, as educators often struggle to both grasp nuanced mechanisms and translate them into engaging learning experiences. Drawing on embodied cognition and analogical reasoning theories, this work aims to create embodied metaphors and analogies that simplify the mechanisms and ethical issues of AI recommendation systems for middle-school students. We employed a multi-phase design pipeline grounded in the Design-Based Research (DBR) methodology to integrate feedback from key stakeholders, including domain experts, students, and teachers, for linking abstract AI concepts to concrete and familiar experiences. This process began with collaborative brainstorming with seven AI experts to generate diverse embodied representations. The resulting designs underwent mixed-method evaluations with both middle-school students and teachers, which revealed positive shifts in student attitudes and provided insights for further iteration. The design cycles ultimately uncovered key challenges, such as balancing feature fidelity with cognitive overload, and yielded design implications for developing effective embodied and analogical representations for children’s AI literacy.