Fostering AI Literacy through Embodiment and Creativity across Informal Learning Spaces

Artificial intelligence (AI) is in many of our everyday activities—from unlocking phones to running Internet searches to parking cars. Yet, most instruction on how AI works is only in computer science courses. The unique role that AI plays in making decisions that affect human lives heightens the need for education approaches that promote public AI literacy. Little research has been done to understand how we can best teach AI in informal learning spaces. This project will engage middle school age youth in learning abouts AI through interaction with museum exhibits in science and technology centers. The exhibits employ embodied interactions and creative making activities that involve textiles, music making, and interactive media. The research will build on three exhibit prototypes that teach about concepts including bias in data in machine learning, AI decision-making processes, and how AI represents knowledge. Female-identifying and Title 1 youth will be recruited as participants during the exhibit design iterations and testing. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments

Researchers will explore two key research questions: 1) How can the design of interactive museum exhibits encourage interest development in and learning about AI among learners without a Computer Science background by using embodiment and creative making? and 2) How do embodied interaction and creative making mediate learning about AI in informal learning environments? The project will take a design-based research approach, iteratively building on existing exhibit prototypes and testing them in-situ with learners. Data sources and modes of analysis will include retrospective surveys to assess interest, content knowledge gain, creativity, learning talk analysis of audio recordings, and coding of embodied movements in video recordings. Learning talk analysis will identify instances of joint sensemaking during naturalistic interactions with our exhibit to reveal connections between sensemaking talk; learners' behaviors and embodied actions during real-time collaborative knowledge building; and outcomes in knowledge, interest, and creativity measures as elicited in retrospective surveys. The final set of exhibits will be rigorously evaluated with over 500 museum visitors. The key contributions of this work will include a set of rigorously tested exhibits, publicly available exhibit designs, a set of design guidelines for developing AI literacy museum exhibits, and an improved understanding of the relationship between AI-related learning and interest development, embodiment, and creativity.