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As artificial intelligence (AI) profoundly reshapes our personal and professional lives, there are growing calls to support pre-college aged youth as they develop capacity to engage critically and productively with AI. While efforts to introduce AI concepts to pre-college aged youth have largely focused on older teens, there is growing recognition of the importance of developing AI literacy among younger children. Today’s youth already encounter and use AI regularly, but they might not yet be aware of its role, limitations, risks, or purpose in a particular encounter, and may not be positioned to question whether it should be doing what it’s doing. In response to this critical moment to develop AI learning experiences that can support children at this age, researchers and learning designers at the University of California’s Lawrence Hall of Science, in collaboration with AI developers at the University of Southern California’s Institute for Creative Technologies, have been iteratively developing and studying a series of interactive learning experiences for public science centers and similar out-of-school settings. The project is funded through a grant by the National Science Foundation and the resulting exhibit, The Virtually Human Experience (VHX), represents one of the first interactive museum exhibits in the United States designed explicitly to support young children and their families in developing understanding of AI. The coordinated experiences in VHX include both digital (computer-based) and non-digital (“unplugged”) activities designed to engage children (ages 7-12) and their families in learning about AI. In this paper, we describe emerging insights from a series of case studies that track small groups of museum visitors (e.g. a parent and two children) as they interact with the exhibit. The case studies reveal opportunities and challenges associated with designing AI learning experiences for young children in a free-choice environment like a public science center. In particular, we focus on three themes emerging from our analyses of case data: 1) relationships between design elements and collaborative discourse within intergenerational groups (i.e., families and other adult-child pairings); 2) relationships between design elements and impromptu visitor experimentation within the exhibit space; and 3) challenges in designing activities with a low threshold for initial engagement such that even the youngest visitors can engage meaningfully with the activity. Findings from this study are directly relevant to support researchers and learning designers engaged in rapidly expanding efforts to develop AI learning opportunities for youth, and are likely to be of interest to a broad range of researchers, designers, and practitioners as society encounters this transformative technology and its applications become increasingly integral to how we live and work. 

Facility with foundational practices in computer science (CS) is increasingly recognized as critical for the 21st century workforce. Developing this capacity and broadening participation in CS disciplines will require learning experiences that can engage a larger and more diverse student population (Margolis et al., 2008). One promising approach involves including CS concepts and practices in required subjects like science. Yet, research on the scalability of educational innovations consistently demonstrates that their successful uptake in formal classrooms depends on teachers’ perceived alignment of the innovations with their goals and expectations for student learning, as well as with the specific needs of their school context and culture (Blumenfeld et al., 2000; Penuel et al., 2007; Bernstein et al., 2016). Research is nascent, however, about how exactly to achieve this alignment and thereby position integrated instructional models for uptake at scale. To contribute to this understanding, we are developing and studying two units for core middle school science classrooms, known as Coding Science Internships. The units are designed to support broader participation in CS, with a particular emphasis on females, by expanding students’ perception of the nature and value of coding. CS and science learning are integrated through a simulated internship model, in which students, as interns, apply science knowledge and use computer programming as a tool to address real-world problems. In one unit, students gain first-hand experience with sequences, loops, and conditionals as they program and debug an interactive scientific model of a coral reef ecosystem under threat. The second unit engages students in learning concepts related to data analysis and visualization, abstraction, and modularity as they code data visualizations using real EPA air quality data. A core goal for both units is to provide students experience with some of the increasingly prevalent ways that computer science is integrated into the work of scientists.