NSF Award
2434321
Cornell Tech, in collaboration with Hofstra University and the University of Illinois, seeks to expand its proven Break Through Tech AI Program designed to equip underserved undergraduate students with the skills needed to thrive in the fast-evolving fields of artificial intelligence (AI) and machine learning (ML). The Computing in Undergraduate Education Transformation project specifically targets women, female-identifying students, and other groups underrepresented in technology, addressing the critical need for diversity in these transformative fields. By democratizing access to high-quality AI education through a network of Instructional Hubs, this project aims to double the number of students served annually, ultimately broadening participation for a new generation of diverse AI leaders who will help ensure advances in “responsible AI” that address issues of fairness, ethics, and algorithmic bias and promote US competitiveness in this exploding technical field.<br/><br/>This project focuses on scaling up the "ML Foundations'' component of the Break Through Tech AI program. This nine-week, skills-based training course is delivered by faculty and graduate students from newly established Instructional Hubs at various institutions. The expansion will involve recruiting five new Instructional Hubs, training instructors through a "Train the Trainer" program, and delivering synchronous lab sessions to ensure students gain practical, industry-relevant skills. By the end of the three-year grant period, the program aims to serve 1,500 students annually, significantly enhancing the diversity and readiness of the STEM workforce. This project will contribute to the field by providing a scalable model for inclusive AI/ML education and generating valuable data on the effectiveness of distributed instructional hubs in democratizing access to cutting-edge AI education<br/><br/>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.
