FW-HTF-R: A New Bridge to the Digital Economy: Integrated AI-Augmented Learning and Collaboration

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

The digital economy remains a powerful locus of opportunity, but most information technology (IT) jobs in this sector are open only to workers with a university degree. Working with Pittsburgh’s largest community college, the project team is designing and deploying new AI-augmented learning technologies to accelerate student learning in community college IT courses. By providing a blueprint that could be used in community college IT degree programs across the country, this project could help address shortages of workers with key technical skills, while creating cost-effective, accessible pathways to living wage digital economy jobs for workers who previously lacked those opportunities. The tools and knowledge created by this project could eventually be applied to other STEM-focused community college degree programs across the nation, potentially impacting the lives of millions. This project is investigating how a theoretical framework developed by learning scientists can be used to align knowledge components in community college IT courses with the most effective AI-driven educational technologies to enhance and accelerate learning. The team is exploring the extent to which intelligent tutoring systems (ITS), computer-supported collaborative learning (CSCL) systems, and example-based learning increase subject mastery, decrease the time needed to achieve it, and enable a wider range of learners to succeed. The project is also exploring how collaborations between community college students in 2-year information technology degree programs and the professional staff of partner firms on real-world (cloud computing) problems may be effected through capstone projects and internships, and if the AI-augmented curricular pathway created by this project moves students into IT jobs relative to students in the standard course pathways. The project team is applying rigorous methods including in-class experiments to assess learning gains, and impact evaluation using propensity-score matching and randomized controlled trials. 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.