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.

Earlier this week, I wrote about the history of progressive math education, the culture wars it has inspired over the past hundred years, and the controversy over the California Math Framework. Today, I want to start with a much broader question: What do we really know about how to teach math to children? The answer is not all that much--and what little we do know is highly contested. An American math education usually proceeds in a linear fashion, with the idea that one subject prepares you for the next. Take, for example, the typical path through mathematics for a relatively advanced student.

Vincent Chen is a student at Stanford University studying Computer Science. He is also a Research Assistant at the Stanford AI Lab. It's not entirely clear what level of mathematics is necessary to get started in machine learning, especially for those who didn't study math or statistics in school. In this piece, my goal is to suggest the mathematical background necessary to build products or conduct academic research in machine learning. These suggestions are derived from conversations with machine learning engineers, researchers, and educators, as well as my own experiences in both machine learning research and industry roles.

Matthew Peterson is a pretty inspirational guy. As a dyslexic child he found math class difficult, so as an adult he resolved to totally change the way math is taught. After completing his studies in biology, electrical engineering, and Chinese language and literature at the University of California, Irvine, Peterson co-founded the nonprofit MIND Research Institute and set about developing "Spatial Temporal (ST) Math," a computer game-based method of teaching that doesn't rely on language as a medium. Instead it uses spatial-temporal reasoning--the ability to move stuff around in your mind and work out how it fits together. Proponents point to recent findings in neuroscience and education research--showing that early music training can enhance spatial-temporal reasoning, for example--as justification for this shift.

In his two seasons with the Ravens, John Urschel has started at three positions -- left guard, center, and right guard. Now, he's back in school trying to earn a third degree. As a collegiate standout at Penn State, Urschel earned his bachelor's degree and master's degree in math there, and was working on a second master's in math education when he was a fifth-year senior. That meant teaching classes on top of his football workload. But the balance was never an issue for Urschel, one he said in his first minicamp with the Ravens was one he enjoyed.