Date
Publisher
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.
Study at a glance
- Question: How do undergraduate students use and perceive generative AI in proof-based math courses?
- Setting and data: Three undergraduate proof-based mathematics courses: two abstract algebra courses and one topology course.
- Method: Student surveys and interviews.
Why it matters
With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important.
Key findings
- In three undergraduate proof-based mathematics courses permitting AI use, students engaged directly with generative AI tools.
- Students reported specific perceptions regarding the usefulness and accuracy of generative AI in proof-based coursework.
- Students identified limitations of generative AI tools when using them for proof-based mathematics courses.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
