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There are many initiatives that teach Artificial Intelligence (AI) literacy to K-12 students. Most downsize college-level instructional materials to grade-level appropriate formats, overlooking students' unique perspectives in the design of curricula. To investigate the use of educational games as a vehicle for uncovering youth's understanding of AI instruction, we co-designed games with 39 Black, Hispanic, and Asian high school girls and non-binary youth to create engaging learning materials for their peers. We conducted qualitative analyses on the designed game artifacts, student discourse, and their feedback on the efficacy of learning activities. This study highlights the benefits of co-design and learning games to uncover students' understanding and ability to apply AI concepts in game-based learning, their emergent perspectives of AI, and the prior knowledge that informs their game design choices. Our research uncovers students' AI misconceptions and informs the design of educational games and grade-level appropriate AI instruction. 

Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox. We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s solve for threshold strategy to also solve for weights. 

Middle School students in the United States are exposed to an unprecedented number of AI-driven consumer products. This exposure demands that educators help students develop their personal understandings of these technologies to engage with them responsibly. Designing age-appropriate AI curricula for middle school students calls for collaboration and partnership between computer and learning scientists, as well as middle school teachers. Over a 3-year period, we co-designed and successfully implemented an AI education curriculum across 9 geographically and economically diverse schools, offering it to a total of 1551 students. Drawing from our analyses of the curriculum and teacher and student experiences, we propose an effective format for teaching, assessing, and implementing fundamental AI education for middle school settings in the United States. Our research also highlights the value of empowering teachers through co-design; enriching their professional development and improving students’ AI literacy. 

Over the past year, our AI4GA team of university faculty and middle school teachers have co-designed a middle school AI curriculum. In this poster we share how we used co-design both as a tool for collaboratively developing engaging AI activities and as a mechanism for mutual professional development. We explain our co-design process, give examples of curriculum materials provided to teachers, and showcase several teacher-created activities. We believe this approach to curriculum development centers the lived experiences of teachers and leverages the knowledge and expertise of university researchers to create high quality and engaging AI learning experiences for K-12 students. 

The AI4GA project is developing a nine-week elective course called Living and Working with Artificial Intelligence and piloting it in several Georgia middle schools. Since we aspire to educate all students about AI, the course addresses a wide range of student abilities, levels of academic preparedness, and prior computing experience, and leaves room for teachers to adapt the material to their own students' needs and interests. The course content is primarily focused on unplugged activities and online demonstration programs. We also provide small programming projects using AI tools as an option for teachers to incorporate. In this poster we describe lessons learned from initial pilot offerings by five teachers who taught 12 sections of the course totaling 299 students. We present evidence that middle school students can successfully engage with substantive technical content about Artificial Intelligence. 

Children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonolog- ical, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appreciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could potentially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine translation, or question answering.