Revolutionizing Math Education: How Advanced Question Generation Streamlines Educator Workflows

EasyChair Preprint 15197

Abstract

Some 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 mathematics. 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 and institutions decide on the ideal way to adopt GenAI into their workflows, so it can be leveraged to create more effective educational experiences for students

Keyphrases: Artificial Intelligence, Generative AI, content creation, hierarchical nature of bloom s taxonomy, mathematics education

@booklet{EasyChair:15197,
  author    = {Yongan Yu and Alexandre Krantz and Nikki G. Lobczowski},
  title     = {Revolutionizing Math Education: How Advanced Question Generation Streamlines Educator Workflows},
  howpublished = {EasyChair Preprint 15197},
  year      = {EasyChair, 2024}}