Computer Science and AI for All in Louisiana: Developing a Comprehensive PreK-12 Pathway

About This Grant

Many students in Louisiana, especially in rural areas, are missing out on learning about computer science and artificial intelligence (AI). This is a critical societal issue because AI is becoming a huge part of our world, from how we work to how we live. If our students do not understand AI, they will not be ready for future jobs or to make smart choices in an AI-powered society. This CSforAll reserach-practice partnership between the University of Louisiana Lafayette and three parish school systems across Louisiana aims address the critical issue of limited AI literacy instruction in Louisiana's PreK-12 schools, particularly in rural communities that face significant resource and teacher shortages. The project will support K-12 teachers to teach AI in a way that makes sense for all students, no matter where they live or what resources they have. The objective is to provide every child in Louisiana with access to AI education and insights into exciting technology careers. The central research question explores how a Research-Practice Partnership can effectively develop and implement an AI literacy curriculum across grade levels, ensuring it is relevant to students' experiences and creates pathways into computer science and AI careers. Rather than creating new curriculum, the project's scope involves coordinating existing AI literacy resources and identifying gaps to create a cohesive learning progression based on Bloom's Taxonomy. The investigators will use a mixed-methods approach, beginning with a contextual analysis that includes a landscape analysis of current computer science and AI education in selected rural and urban school systems, and an assessment of student needs and perceptions. This will inform a collaborative workshop where researchers and teachers will map resources and design AI course sequences, followed by implementation, iterative refinement, and dissemination of the framework and professional development materials. 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.