BROWARD EDUCATION FOUNDATION, INC

NSF Awards · FY 2026 · 2026-06

Districts Helping Districts: Expanding and Amplifying Network Support for K-12 AI Integration is a 16-month project that extends Broward County Public Schools’ existing K-12 computational thinking pathway to support the coherent use of generative artificial intelligence (AI) tools. As AI systems are rapidly introduced into classrooms, districts face the challenge of moving beyond basic tool use to ensure that students understand the computational ideas that underpin these technologies. This project builds on an ongoing collaboration between Digital Promise and Broward County Public Schools to align AI integration to four core computational competencies already embedded across grades K-12. The project will develop and test AI-enhanced lessons, revise district curriculum maps, and prepare teachers to help students understand not only how to use AI tools, but how those tools function. By grounding AI adoption in an established computational thinking pathway, the project aims to support more coherent and sustainable approaches to district-wide AI literacy that can be integrated systematically across grade levels and subject areas. Accordingly, this project contributes to the goals stated in the Dear Colleague Letter NSF 25-035 regarding advancing AI education for the American youth. This project will extend Broward County Public Schools’ existing K-12 computational thinking pathway to design and study a district-wide model for integrating generative AI in conceptually grounded ways. Guided by three research questions, the project will align AI integration to four core computational competencies (pattern recognition, computational modeling, algorithmic thinking, data practices) while developing and piloting grade-banded, AI-enhanced lessons and revising the district’s curriculum maps to reflect cumulative learning across grade levels. The work will unfold over three stages: 1) collaborative curriculum design and pathway annotation, 2) classroom piloting with a cohort of teachers, and 3) iterative refinement leading to a revised K-12 integrated computational thinking + AI pathway. Using Design-Based Implementation Research, the project will collect and analyze survey, interview, observation, and student artifact data to examine teacher learning, developmental progression, and consistency of implementation. The project will contribute a scalable model for aligning AI literacy with established computational thinking pathways at the district level. 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.

NSF Awards · FY 2025 · 2025-10

Data science is a vital link in bridging computational thinking with artificial intelligence and machine learning. Therefore, it is essential that all students have an entry level comprehension of data science for their future careers and civic life. This CSforAll Pathways project is a research-practice partnership between Digital Promise, in collaboration with Looking Glass Ventures, Data Science 4 Everyone, and four school districts across four different states: Broward County (FL), Indian Prairie (IL), Iowa City (IA), and Talladega County (AL) that aims to support school districts in developing practical and scalable pathways for teaching data science from early elementary through high school. The project helps districts design and implement instructional progressions that integrate data science with computational thinking and core academic subjects. Key to the work is adapting each district’s specific existing computational pathways and considering specific context needs, while also holding to a common trajectory and key data science concepts. The project contributes to new knowledge about how districts can design and scale data science pathways. Grounded in a research practice partnership model, the project examines how district leaders adapt and extend existing computational thinking pathways to incorporate data science and machine learning. The research focuses on understanding the conditions that support integration, including district-level planning structures, instructional tools, and cross-district collaboration. Using a mixed-methods approach, the study analyzes implementation artifacts, district leader and teacher feedback, and course data to identify scalable practices and assess their impact on instructional coherence and district-level capacity. By comparing implementation approaches across four districts, the project advances models for sustainable, peer-driven expansion of integrated computing and data science education, contributing to the growing evidence base on how to support innovation at the systems level. 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.