Online Archive of University of Virginia Scholarship
A More Equitable and Algorithmic Approach to Course Enrollment; Can Bots Decide If I Am Qualified? Machine Learning in Employment Decision Making78 views
Author
Lucio, Matthew, School of Engineering and Applied Science, University of Virginia
Advisors
Morrison, Briana, EN-Comp Science Dept, University of Virginia
Norton, Peter, EN-Engineering and Society, University of Virginia
Abstract
How may artificial intelligence (AI) optimize human processes at minimum social cost?
Every semester thousands of University of Virginia students are waitlisted for classes they need to graduate. An algorithmic approach is proposed that would alleviate course scheduling conflicts, reduce advisor and professor workloads, and improve student satisfaction. The proposed tool would apply machine learning (ML) techniques to determine optimum class schedules given the availability of rooms and professors. Modified enrollment times can offer a more equitable draft-style approach in which every student has an opportunity to enroll in top classes via multiple registration rounds.
In the US, AI companies, HR professionals, employers, employees, unions, shareholders, and others compete to determine how AI may and may not be applied in employment recruitment. The competition primarily pits employers who favor the low-cost, efficient recruitment that AI tools offer against critics who warn that such tools may perpetuate historical inequities and are no substitute for expert human judgment.
Lucio, Matthew. A More Equitable and Algorithmic Approach to Course Enrollment; Can Bots Decide If I Am Qualified? Machine Learning in Employment Decision Making. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2025-12-12, https://doi.org/10.18130/5xtf-7624.
