Date
Publisher
arXiv
Educators have started to turn to Generative AI (GenAI) to help create new course content, but little is known about how they should do so. In this project, we investigated the first steps for optimizing content creation for advanced math. In particular, we looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content. We conducted two studies to: (1) explore the capabilities of current versions of publicly available GenAI and (2) develop an improved framework to address the limitations we found. Our results showed that GenAI can create math problems at various levels of quality with minimal support, but that providing examples and relevant content results in better quality outputs. This research can help educators decide the ideal way to adopt GenAI in their workflows, to create more effective educational experiences for students.
Study at a glance
- Question: How can generative AI be used to create high-quality practice problems for advanced math?
- Setting and data: Not stated in the abstract
- Method: Two studies testing generative AI's ability to generate practice problems and developing an improved framework
Why it matters
Educators have started to turn to Generative AI to help create new course content, but little is known about how they should do so to produce high-quality, relevant practice problems for advanced math.
Key findings
- In advanced math, generative AI produced higher-quality practice problems when provided with examples and relevant course content.
- Generative AI generated math practice problems with minimal support, but the questions varied across different levels of quality.
- An improved framework addressed the limitations found in current publicly available generative AI tools for math content creation.
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
