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

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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.