Join us for another semester of Dibner Discussions! These discussions are informal and are meant to offer you a space to explore the topic of the month with others. Free pizza will be provided! Please take a look at our discussion guidelines and our running resource document to check out previous discussion topics and resources.

This semester we'll meet on September 22, October 20, & November 17. Below you'll find more info about topics for each discussion (usually updated about two - three weeks before each discussion). 

September 22 - Generative AI & College 

Since OpenAI's release of ChatGPT in late 2022, colleges and K-12 educators have had to grapple with what it means to teach in the world of readily accessible generative artificial intelligence. Students and everyday consumers of generative AI have had to decide how (if at all) tools like ChatGPT will factor into their lives. Almost three years later, it seems like generative AI is here to stay. This discussion will focus on what it means to have access to generative AI tools, how these tools have impacted you (yes, I want to hear from you!), what it means to be a university student now, and more.  

October 20 - AI & The Tyranny of Convenience 

In his 2018 opinion piece for the New York Times, legal scholar Tim Wu grapples with the idea that the convenience technology affords us comes at a cost. In this month’s discussion we’ll explore this idea – from algorithms on TikTok and Instagram designed to capture and hold your attention to generative AI that can offer answers to almost any question, write emails, and enable vibe coding – and examine what it means to use and rely on these platforms or tools. 

November 17 - Data, Privacy, & AI

Data, data, everywhere but not a drop to drink (sorry – not sorry). In a 2024 article published in Nature, author Nicola Jones cites research that estimates generative AI developers will run out of human-created data to train their models by 2028. Companies like Google and Meta have turned to using user-created data to train their models. What does privacy look like in an age of so-called data scarcity and should we care? This discussion will explore these topics and provide space to think about privacy more broadly.