Transforming Introductory Computer Science Instruction with an AI-Driven Classroom Assistant

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

This project aims to serve the national interest by using artificial intelligence (AI) technology to transform introductory programming classrooms into active, engaging, and supportive learning environments for a diverse range of students. Advances in AI are rapidly transforming every aspect of human life, including education. The collaborative project team plans to develop INSIGHT, an AI-driven classroom assistant for introductory computer science that provides real-time support to both students and instructors. The INSIGHT classroom assistant has the potential to improve introductory computer science education by supporting students with adaptive feedback and supporting instructors with aggregate student coding analytics. The proposed project has the potential to help instructors to dynamically adapt their instruction to students' conceptual understanding of computer science concepts. This project has three main objectives. First is to design, develop, and iteratively refine the INSIGHT AI-driven classroom assistant. Second is to develop a deep understanding of how students learn computer science with AI-driven classroom assistants. Third is to design a set of effective instructional support principles for coding-enriched classroom interactions. The project team will collaborate with instructors in a wide range of introductory computer science courses at large and small public universities, and Historically Black Colleges and Universities. The team plans to evaluate the impacts of INSIGHT on improving students’ learning and engagement. Results from the project will be shared with practitioners and researchers through workshops and presentations at conferences on computer science and engineering education. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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.