All Quiet on the AI Front: The Future of Undergraduate Math Research, Viewed from Duluth
Abstract
In this time of uncertainty and anxiety about artificial intelligence (AI) in mathematics, it is more important than ever to reflect on how we can prepare the next generation of professional research mathematicians for success. In this article, we report, from the front lines, on how AI is affecting the long-running summer math research program at the University of Minnesota Duluth. We do not have answers to the big questions about how AI will shape the future of professional mathematics. What we can offer instead is a collection of practical observations about the role of AI in the 2026 Duluth program: how it was useful and where it presented potential pitfalls; what surprised us and how we adapted; and what went well and what we wished we had done differently.
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BibTeX
@misc{defant2026quiet,
title = {{All Quiet on the AI Front: The Future of Undergraduate Math Research, Viewed from Duluth}},
author = {Colin Defant and Eliot Hodges and Noah Kravitz},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.28615},
eprint = {2609.28615},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28615},
}
RIS
TY - UNPB
TI - All Quiet on the AI Front: The Future of Undergraduate Math Research, Viewed from Duluth
AU - Colin Defant
AU - Eliot Hodges
AU - Noah Kravitz
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.28615
UR - https://arxiv.org/abs/2609.28615
AB - In this time of uncertainty and anxiety about artificial intelligence (AI) in mathematics, it is more important than ever to reflect on how we can prepare the next generation of professional research mathematicians for success. In this article, we report, from the front lines, on how AI is affecting the long-running summer math research program at the University of Minnesota Duluth. We do not have answers to the big questions about how AI will shape the future of professional mathematics. What we can offer instead is a collection of practical observations about the role of AI in the 2026 Duluth program: how it was useful and where it presented potential pitfalls; what surprised us and how we adapted; and what went well and what we wished we had done differently.
ER -