Building AI-Powered Responsible Workforce by Integrating Large Language Models into Computer Science Curriculum

This project aims to serve the national interest by integrating Large Language Models (LLMs) to improve curricula in computer science (CS) education. The field of software development is currently experiencing a profound transformation driven by significant advancements in LLMs. This shift demands that software developers acquire new skills to fully leverage the potential of LLMs. These skills encompass prompt engineering for software development, the management of structural complexity, debugging Artificial Intelligence (AI)-related errors, and adherence to ethical guidelines and principles. This engaged student learning level two project intends to harness the power of LLMs to elevate student engagement, promote active learning, and foster critical thinking within the CS program. This project has the potential to advance CS education, educate students on ethical and responsible AI practices, and promote responsible software development. Specifically, this project will: (a) utilize LLMs as potent educational tools to enhance students' problem-solving and programming proficiency in core programming courses; (b) integrate LLMs as software development tools within the software engineering course sequence; (c) address ethical and legal considerations related to LLMs. To achieve these objectives, the project will develop a comprehensive set of instructional materials to transform teaching methods and assess learning outcomes. Additionally, the project will host faculty development workshops to foster knowledge sharing and collaboration among educators, facilitating the effective adoption and integration of the proposed instructional resources. The project intends to share the results and findings of this project with a broader computer science education community. 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.