Access the full transcript for this episode

“It struck me that academic integrity is a serious issue, but one whose treatment I felt was overly punitive. I don’t want us to have to act as police for our students. Students very much want to do the work, but they often are just ignorant, for whatever reason, of what academic standards at the university level are. And so I wanted to instill this kind of restorative justice framework to make moments where students do falter and they do make mistakes, I wanted to turn those into teachable moments where they could learn, and turn what is a bad situation into perhaps a positive one.” —Taiyo Inoue

Today, we speak with Sarah Senk and Taiyo Inoue, co-hosts of My Robot Teacher, which is a podcast affiliated with the California Learning Lab. Sarah and Taiyo discuss how they both bring their respective lenses of comparative literature and mathematics to examine the question and implementation of AI in education, sharing concrete classroom and academic policy uses for LLMs. They touch on academic integrity through a restorative-justice lens, the idea of AI as an opaque cultural archive, and examining higher education as a “slow disaster.” Finally, they end with valuable advice for faculty listening in, giving tips on how to approach AI.

To hear more about Sarah and Taiyo’s thoughts about all things AI and education, listen to their podcast, My Robot Teacher!

“When we talk about cultural memory, we’re thinking about things that no one individual or social group could hold in their minds. It’s the stuff that is recorded in archives, libraries, cultural practices, arts, etc., and so all of that stuff trained large language models. And so I think you can think about large language models as a kind of archive, but a pretty opaque one.”—Sarah Senk

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit datascienceeducation.substack.com