Developing the ability to think critically about AI and interpret its outputs requires an understanding of AI bias, a key skill for both AI users and future developers. While some initiatives have introduced teens to algorithmic bias, few have engaged them in actively identifying and quantifying bias in real-world generative AI systems. This paper presents BiasViz, an interactive tool that leverages project-based and narrative-centered learning to help middle school students (11-14 year old) analyze AI bias in large language models. We conducted a study of 28 students’ interactions with BiasViz to evaluate its efficacy in fostering critical thinking about AI bias. Our findings suggest that BiasViz successfully introduced most students to AI bias, and some used the tool to explore personally relevant biases. We identify opportunities for the tool’s iteration and associated curriculum to promote learning and share insights for designing learning environments that foster youth’s critical thinking about AI.

Museums play a critical role in promoting public understanding of emerging technologies like artificial intelligence (AI), but it is unclear what design features lead to learning about AI in museums. We contribute a design research exploration of how embodiment and creativity foster AI literacy in museum exhibits. We present design prototypes of three museum exhibits—DataBites, Knowledge Net, and LuminAIx—that aim to teach middle schoolers about AI. We present results from a qualitative analysis of an in-museum study in which we examined participants’ understanding of and interest in AI through interviews and video recordings. Our findings illuminate how creativity fosters interest in AI and how different forms of embodiment contribute to learning about AI. We recommend that AI museum exhibits utilize creative and personally relevant activities to engage middle schoolers, support hybrid conceptualizations of AI, and leverage tangible interaction to make AI concepts approachable.

This doctoral research proposes AI DoodleLab, a web-based learning tool designed to foster AI literacy through project-based creative drawing.AI DoodleLab engages middle school students in teaching AI by drawing multiple examples of an imaginary creature, allowing the AI to extract features, generalize patterns, and generate a refined version.Through this process, children develop an intuitive understanding of core AI concepts, including supervised learning, feature extraction, generalization, and model limitations.This research will explore how creative, hands-on interactions enhance children's AI literacy by designing AI DoodleLab.Future studies will focus on evaluation methods, such as qualitative analysis, interviews, and custom assessment instruments, to investigate the tool's educational impact.Additionally, we aim to examine the interdisciplinary potential of AI DoodleLab in art classrooms and its expansion beyond drawing to storytelling and animation.This research contributes to designing effective AI education tools, positioning AI as a co-creative learning partner for young learners.

It is essential to increase children’s understanding of artificial intelligence and machine learning as they encounter it through their daily activities. We have developed DataBites, a museum exhibit aimed at fostering middle-school-age children’s understanding of supervised machine learning. DataBites engages visitors in learning about the steps and practices of supervised machine learning, using three guiding design principles: embodied interaction, creativity, and collaboration. Our design allows learners to use tangible pieces to collaboratively create their own labeled examples of pizzas and sandwiches to include in a training dataset for an image-based machine-learning pizza/sandwich classification algorithm. The algorithm can classify sandwiches and pizzas by learning patterns from people’s examples. Learners can view the results and self-evaluate how well their dataset did at enabling the algorithm to distinguish between the two items. This poster paper contributes a novel design and approach to engaging children in learning about AI in museum settings.