Integrating data science and AI-aware learning into introductory chemistry and biology pathways at a two-year college

The National Science Foundation Division of Undergraduate Education awarded Seattle Central Community College $499,997 on October 1, 2026, under the STEM Education program (CFDA 47.076) to design and implement interdisciplinary learning modules integrating data science and artificial intelligence awareness into introductory chemistry and biology courses.

The project will develop, test, and study a set of curriculum materials guided by the Interdisciplinary Data Investigation Framework that immerse two-year college students in data analysis, visualization, and interpretation using real scientific and societal contexts. The modules will serve both STEM and non-STEM students at the college, providing early exposure to data, computing, and AI-related skills through examples connecting to everyday life. Partnerships with regional research institutions will supply authentic datasets and inform the modules with current workforce applications.

The project pursues two primary goals: establishing a strategic and sustainable approach to embedding data- and AI-aware learning across early STEM pathways that strengthens faculty practice and builds institutional capacity, and cultivating student understanding of the data-driven and AI-enabled nature of contemporary science. The work will use mixed-methods research—surveys, assessments, interviews, and classroom observations—to document the impact of these experiences on student interest in STEM, their understanding of data and AI in scientific work, and their perspectives on academic and workforce pathways.

Performance takes place in Seattle, Washington, with a period of performance from October 1, 2026, through September 30, 2029.

Abstract

This project aims to serve the national interest by providing two-year college students with early exposure to data, computing, and AI-related skills. Many stem fields depend on data and artificial intelligence (AI), yet many students may not get early chances to build these skills. This track 1 ityc project will design materials for chemistry and biology classrooms that help stem and non-stem students engage with analysis and interpretation of real data that connects to everyday life. The project will also document the impact of these experiences on students? Interest in stem, their understanding of the role of data and AI in scientific work, and their perspectives on future academic and workforce pathways. Partnerships with regional research institutions will provide authentic data sets and provide input on current workforce applications. The project will design, test and study a set of interdisciplinary learning modules for introductory chemistry and biology courses at a two-year college. These modules will be guided by the interdisciplinary data investigation framework and immerse students in data analysis, visualization and interpretation using real scientific and societal contexts. The project has two main goals: 1) to develop a strategic and sustainable approach for embedding data- and AI-aware learning across early stem pathways, creating a model that strengthens faculty practice, supports institutional capacity building, and advances data-rich undergraduate stem education, and 2) to cultivate students' understanding and curiosity about the data-driven and AI enabled nature of contemporary science, fostering awareness of how discovery, innovation, and interdisciplinary collaboration define today?s stem enterprise. This research will use a mixed-methods approach, utilizing surveys, assessments, interviews and classroom observations to study student learning, engagement and course outcomes. Partnerships with research and health organizations will provide real data sets and relevant applications. The projects will produce practical strategies and a scalable model that other institutions can use to bring data-driven learning into stem education. The NSF iuse: innovation in two-year college stem education (ityc) program seeks to accelerate and advance knowledge about the impact of emerging and evidence-based practices in undergraduate stem education at two-year colleges. 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.- subawards are planned for this award.