Collaborative research: a compelling continuous structures course for computer science majors

The National Science Foundation Division of Undergraduate Education awarded George Washington University $128,000 on October 15, 2026, under the STEM Education program (CFDA 47.076) to develop curricular materials for an innovative undergraduate course titled Continuous Structures, designed to equip computer science majors with foundational skills in calculus, probability, and statistics needed for advanced work in artificial intelligence, data science, machine learning, simulation, and robotics.

The project, structured as a collaborative effort among George Washington University, Regis University, and Rochester Institute of Technology, will create self-contained course units across multiple application domains that leverage students' prior programming experience to improve learning outcomes and demonstrate the relevance of continuous mathematics in modern computing. The course title deliberately mirrors Discrete Structures, an established mathematics preparation course in computer science curricula, to underscore the equal importance of continuous structures for U.S. competitiveness in the AI-based computing environment. The resulting educational materials are intended to be adoptable by a wide range of U.S. colleges and universities. Performance runs through September 30, 2029, with place of performance in Washington, DC.

Abstract

This project aims to serve the national interest in the age of artificial intelligence and accelerated change by creating materials for an innovative college course titled continuous structures to equip undergraduate U.S. Computer science (CS) students with skills in calculus, probability, and statistics needed to prepare them for advanced coursework in artificial intelligence (AI), data science, machine learning, simulation, and robotics. The course title was intentionally chosen to mirror discrete structures, a long-accepted math-preparation course in CS curricula, to emphasize its equal importance to CS graduates, who need strong foundations in both continuous structures and discrete structures to strengthen U.S. Competitiveness in the new AI- based computing age. This level 1 engaged student learning project directly serves NSF?S education mission to engage undergraduate stem students and improve learning by developing, testing, and using practices and curricular innovations. As the collaborating institutions, george washington university, regis university, and rochester institute of technology, offer computer science programs with different emphases, the resulting educational materials are likely to be usable by a wide range of US colleges and universities. This project plans to investigate whether prior CS students' interest and success in computer programming, when applied to motivational, real-world application domains, can improve learning outcomes in continuous structures. The project will develop coursework that advances students' understanding of symbolic mathematical representations by building on their programming experiences to improve their learning and perceptions of the relevance of continuous structures in modern computing. Course development is structured in three phases: create the materials as multiple self-contained units in each application domain, offer the materials in courses at the collaborating institutions, and revise them as needed to increase their portability and reusability at other institutions. Guiding this course development is a set of computing education research questions that iteratively improve the developed course materials. The NSF iuse: EDU program supports research and development projects to improve the effectiveness of stem education for all students. Through the engaged student learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 not planned for this award.