In the beginning

When ChatGPT first emerged, academia panicked. No surprise there—every major shift in information technology sends academics into a tailspin.

I wasn’t as pessimistic as many of my colleagues. Generative AI was a game-changer, and like it or not, we had to get up to speed fast. There’s no unringing this bell. AI is here to stay, and our students need to know how to use it to remain competitive. For all the dangers and legitimate concerns about generative AI, it also promises fundamentally new and exciting ways to learn.

But for professors, the challenges of this transition are epic. How do we teach essential skills—like writing—when students can just have AI do the work? For that matter, what skills are still essential? Academia has long prided itself on knowing what expertise students need to succeed, but generative AI has pulled that certainty out from under us. The technology is too new, too fast-moving, for us to even guess.

After several weak attempts to make sense of generative AI on my own, I realized I was thinking about it all wrong. AI isn’t just a tool—it’s a dynamic that needs to be understood on its own terms.

Fine, I thought. If AI should be understood on its own terms, then I’d better get to know it through its own voice.

In this blog, I’ll be sharing conversations between generative AI and myself. There will be some light editing here and there, but for the most part, I intend to let them stand on their own. So let’s explore this strange new frontier together. I hope you’ll join the conversation.