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Fostering young learners’ literacy surrounding AI technologies is becoming increasingly important as AI is becoming integrated in many aspects of our lives and is having far-reaching impacts on society. We have developed Knowledge Net and Creature Features, two activity boxes for family groups to engage with in their homes that communicate AI literacy competencies such as understanding knowledge representations, the steps of machine learning, and AI ethics. Our current work is exploring how to transform these activity boxes into museum exhibits for middle-school age learners, focusing on three key considerations: centering learner interests, generating personally meaningful outputs, and incorporating embodiment and collaboration on a larger scale. Our demonstration will feature the existing Knowledge Net and Creature Features activity boxes alongside early-stage prototypes adapting these activities into larger-scale museum exhibits. This paper contributes an exploration into how to design AI literacy learning interventions for varied informal learning contexts. 

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy.