Collaborative Research: Scaling Up AI Education for Rural Upper Elementary Students with Immersive Problem-Based Learning

About This Grant

As artificial intelligence (AI) continues to advance and transform society, it is essential that researchers work in direct partnership with teachers to prepare students to understand the world in which they are growing up. Advancing this goal across K-12 education requires a clear understanding of how to introduce AI concepts to elementary school students and how to effectively support teachers in doing so. The PrimaryAI scale-up project advances foundational knowledge in K-12 AI education that leverages immersive problem-based learning pedagogies for upper elementary learners in grades 3 to 5. The project will reach over 5,000 upper elementary students and more than 60 teachers while expanding the research and implementations across multiple states. The project team will partner with teachers from rural communities in Alabama, Indiana, and North Carolina to engage their students in authentic AI-infused problem solving. This approach aims to foster students' interest in science, technology, engineering, and mathematics (STEM) and equip them with fundamental AI knowledge they will need to thrive in the future. The project will investigate key factors that influence successful scaling of an AI education curriculum across multiple state contexts. It will examine the interplay among teacher professional development, localized classroom adaptation, collaborative design methods, and student learning and interest. These elements are central to understanding the conditions for implementation and mechanisms that sustain and expand the use of AI curricula on a large scale in rural upper elementary classrooms. The project will address three primary research questions: (1) What AI concepts serve as entry points for rural teachers to integrate AI into instruction, considering local contexts and individual pathways? (2) What are the impacts on student outcomes for learning, engagement, and STEM interest across rural contexts? and (3) How do local factors in each state's rural context influence the reception, implementation, and outcomes of PrimaryAI? Research questions will be addressed using multiple data sources as part of Design-Based Implementation Research (DBIR) (Fishman & Penuel, 2018). Pre-and post-tests will be used to assess impacts on student learning and interest. The research team has developed assessments for AI concepts, AI planning, computer vision, and machine learning (Chakraburty et al.,2023). To address the first question, the team will collaborate with teachers from rural communities in Alabama, Indiana, and North Carolina. The team will document ongoing collaborative discussions, professional learning processes, teacher designs, and plans for implementation. For the second question, the project will conduct comprehensive analyses of student outcomes using pre-post assessments of AI knowledge and skills, student engagement, STEM interests, observations of student interactions, and student interviews. Additionally, a cross-case analyses to explore commonalities and differences across various rural contexts and implementations will be conducted. To address the third question, a detailed case studies within each rural community to understand local factors such as pedagogical goals, student interests, community priorities, and educational policies is planned. Outcomes will include locally-contextualized versions of the PrimaryAI curriculum, comprehensive teacher professional development guides, case studies that detail successful strategies and challenges, and recommendations for scalability. Ultimately, the project will advance understanding of effective practices and approaches for integrating AI education into rural elementary classrooms. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts, and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.