# Gen AI in Proof-based Math Courses: A Pilot Study

arXiv: https://arxiv.org/abs/2509.13570 · HTML: https://mlsanity.com/paper/2509.13570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education](https://mlsanity.com/paper/2403.19148/) (2024)
- [Autograding Mathematical Induction Proofs with Natural Language Processing](https://mlsanity.com/paper/2406.10268/) (2024)

## Abstract (arXiv)

With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important. This exploratory study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course, and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools as well as their perceptions of generative AI's usefulness, accuracy and limitations.