We introduce a machine learning-powered course allocation mechanism.
Concretely, we extend the state-of-the-art Course Match mechanism with a
machine learning-based preference elicitation module. In an iterative,
asynchronous manner, this module generates pairwise comparison queries that are
tailored to each individual student. Regarding incentives, our machine
learning-powered course match (MLCM) mechanism retains the attractive
strategyproofness in the large property of Course Match. Regarding welfare, we
perform computational experiments using a simulator that was fitted to
real-world data. Our results show that, compared to Course Match, MLCM
increases average student utility by 4%-9% and minimum student utility by
10%-21%, even with only ten comparison queries. Finally, we highlight the
practicability of MLCM and the ease of piloting it for universities currently
using Course Match