Collaborative Research: EAGER: AI-Assisted Just-in-Time Scaffolding Framework for Exploring Modern Computer Design

About This Grant

There is a need to train skilled computer architects to design innovative computer hardware. Software-based simulation is the backbone of computer system design and development. Such tools are also widely used for teaching computer architecture concepts. Currently, the simulators used for educational purposes have a steep learning curve, are not interesting for beginners, and are error-prone. Therefore, these simulators are mostly used by experienced researchers. This project introduces a novel framework and technology called Scaffolded AI-driven Learning Simulation (SAILS). SAILS enables an interactive and supportive computer architecture learning platform and offers design exercises covering different learning modes and difficulty levels. In the development phase, SAILS will be used by instructors at the University of Kansas and Florida International University to teach introductory and advanced computer architecture courses to about 400 undergraduate and graduate computer science and engineering students every year. Once SAILS is fully developed, it will serve as a framework to teach computer architecture in several US institutions. SAILS implements a novel AI-driven paradigm for reducing the learning curve of computer architecture simulators in educational settings. SAILS implements a front end that reduces the complexity of simulating simple to advanced systems for students with various backgrounds. SAILS back-end seamlessly connects to a state-of-the-art computer architecture simulator and provides just-in-time personalized assistance to the users. The assistance is provided by a centralized AI model trained by individual users’ and team data and global users’ experience with the framework. SAILS integrates the faded scaffolding approach to provide appropriate levels of support to individual learners and teams to maximize their learning. SAILS’s easy-to-use graphical user interface, engaging learning activities, and personalized scaffolding support a broad and diverse student population, including female and underrepresented minority students, in the computer architecture and design field. 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.