Young learners need opportunities to build critical awareness surrounding AI. To rapidly expand inclusive access to AI education, K-12 educators need activities that (a) are low-cost, (b) do not require specialized technology, (c) can be led with little prior knowledge of AI and used in settings with time constraints and competing curricular needs, and (d) have clear learning objectives and tie-ins across disciplines. ?Unplugged? activities that are playful, hands-on learning experiences that foster learning about AI without programming can address these constraints.

This project explores how high school youth communicate with AI tools and assesses how they learn to use AI tools in context. Though students are using these tools widely, inciting much public discourse, very little is known about how, why, and when they use AI tools, or what they understand about the tools. Through curricular exploration, this study evaluates how historically marginalized high school youth of color interact with AI tools to write papers, write program code, or study for science tests.

Large language models (LLM) represent a new and rapidly changing technological advancement for K12 STEM learning. It is critical at this point in time to investigate and provide pathways for including justice, equity, inclusion, and community cultural capital and wealth in designing LLM-based educational systems. In the context of developing an AI chatbot, this RAPID project will research the ways in which teachers can plan universally designed and culturally relevant and responsive K12 STEM learning activities and environments.

Large language models, especially ChatGPT, have seen exponential growth and have demonstrated early potential to transform teaching and learning. Given the rapidly changing field, there are limited systematic studies on how students and teachers are engaging with these new generative AI tools, leaving schools with little to no data to help them integrate AI in K-12 education. This RAPID project aims to develop a general framework and accompanying computational tools to understand how students and teachers are engaging with generative AI tools.

The integration of artificial intelligence (AI) into advanced manufacturing has promising potential to revolutionize productivity and generate new jobs in smart manufacturing. There is an urgent need to investigate "what to teach" and "how to teach" AI in order to prepare future workforce with the necessary AI skills, as most K-12 educators and schools lack the knowledge and experience to teach students AI skills for smart manufacturing. This project will initiate an age-appropriate career-driven AI educational program for high-school students and evaluate its effectiveness.

AI-powered tools have the potential to transform education, both in formal and informal settings. The immense potential for AI to address challenges in education has created an urgent need to characterize how K-12 education may leverage these powerful tools safely, ethically, and equitably. While the AI in education landscape is changing drastically and rapidly, little is known about K-12 educators' engagement with AI and how newly developed tools and curriculum will be received and integrated into classrooms.

The rapid advances in large language models (LLMs) have presented tremendous opportunities to create interactive, personalized learning resources on a large scale. To fully harness the educational potential of these technologies, it is crucial that teachers - who are at the forefront of daily student interaction and possess indispensable knowledge and expertise - serve as key contributors in the process.

The exponential expansion of artificial intelligence (AI) has created a significant demand on future AI workforce development. Several key challenges exist hindering the widespread incorporation of AI into K-12 curricula, including the lack of teacher development in AI and a comprehensive pedagogical framework for K-12 AI education.

The proliferation of artificial intelligence (AI) and powerful Large Language Models (LLMs) has evoked excitement and confusion among K-12 teachers regarding AI's impact on teaching, assessments, and student work. It is vital for researchers with expertise in human-centered teaching and learning to share empirically grounded proofs-of-concept of teacher-AI teaming to enhance teacher capacities for better learning outcomes for all students.