Exploring Integration of Generative Artificial Intelligence in Computer Science Education: A Senior Design Pilot

This project aims to serve the national interest by improving undergraduate computing education through structured integration of generative artificial intelligence (GenAI) in courses, enhancing student preparation for AI-driven careers. It addresses the growing challenge of students relying on GenAI tools without guidance, which may limit critical thinking and team-based problem-solving. By embedding scaffolded GenAI practices into a Senior Design course at University of Nevada Las Vegas (UNLV), a large public research university, the project aligns student learning with modern software development workflows. A distinctive element is the use of a platform that analyzes thousands of real-world GenAI-assisted software patches to inform curriculum design and provide authentic case studies. Broader impacts include creating scalable, open teaching resources and replication guides to support adoption across institutions, strengthening the pipeline for a competitive software engineering workforce. This Level 1 Engaged Student Learning project seeks to advance understanding of how structured GenAI integration can improve student adaptability and contributions in collaborative development contexts. The project plans to pilot scaffolded GenAI workflows in UNLV's Senior Design course, emphasizing prompt design, debugging practices, peer review, and transparent documentation aligned with industry standards. A quasi-experimental design with treatment and control groups will allow rigorous comparison of structured GenAI instruction against independent exploration. The evaluation will use a convergent mixed-methods approach, combining repository analyses, surveys, reflections, and rubrics informed by professional software workflows to assess impacts on teamwork, adaptability, and documentation quality. Through longitudinal tracking and broad dissemination, including peer-reviewed publications, conference presentations, and shared tools, the project will generate insights into conditions under which structured GenAI use enhances learning. 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.