Synthesizing the Use of Generative Artificial Intelligence for Teaching and Learning in Undergraduate Computer Science

This project aims to serve the national interest by building an understanding of how generative artificial intelligence (GenAI) can be effectively utilized in undergraduate computer science education. GenAI technologies hold promise for providing personalized computer science learning at scale. However, the proliferation and the rapid advances of these technologies make it difficult to determine how to most effectively design GenAI-based instruction in undergraduate computer science courses. This Level I Engaged Student Learning project will use systematic review methods to explore, document, and synthesize how GenAI technologies have been used in undergraduate computer science contexts. The significance of this work includes advancing understanding of when and in what contexts GenAI facilitates student learning, enabling evidence-based decision-making regarding when and how to utilize these technologies. Project goals include (a) critically examining how faculty are designing GenAI-based pedagogy and the resulting impacts on student learning, and (b) quantifying the effects of GenAI-based instruction as compared to other methods and documenting the factors that moderate these effects. A primary contribution of this project will be the development of a novel evidence-based design framework with concrete and actionable steps for integrating GenAI in undergraduate computer science education. Outcomes of this research can help answer questions such as, to what extent generative artificial intelligence can improve learning and what specific skills or pedagogical approaches can benefit the most from these technologies. 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.