The buzz was that it would radically change the academic landscape.
The buzz was that it had made cheating for students so much easier.
The buzz was that, in a few years, it would replace my job as a writing teacher.
So why, for heaven’s sake, did I think it was a good idea to teach a course on generative AI?
One answer, in truth, was that some of the anxiety in the wake of the release of ChatGPT in the fall of 2022 had gotten to me. I don’t know if I had fully bought into the idea that generative AI would replace me in a matter of years, but a comment made by my endlessly supportive colleague John Behrens struck a chord:
“It’s not AI that will take your job; it’s people who know how to use AI who will take your job.”
And then I started hearing more about student concerns, about their own anxieties, whether regarding their own future employment, or regarding what was happening here on campus—its impacts on their learning, its impacts on their relationships with teachers, and its impacts on their relationships with one another.
At that point, it seemed well past time to meet the AI monster head on. It was time to teach a class on generative AI in order to better understand how I and my colleagues in the University Writing Program—not to mention anyone teaching a writing-intensive course—might engage this technology in a meaningful and productive way. At least I’d be on my home turf.
Which is not to say I approached the work of building that course with a tremendous amount of confidence, or with a great deal of certainty. I was even carrying with me a considerable amount of ambivalence about these generative AI tools, concerned as I was that I might actually be doing a disservice to my students by having them potentially circumnavigate the challenging but necessary work of writing and learning and critical thinking that is core to my pedagogical principles.
But that’s where the team at the Office of Digital Learning, through their Digital Learning Sprints program, were such invaluable partners, providing the space, guidance, and resources to help me clarify for myself how I might deploy this course in a way that was responsible both to me and my students.
Meeting on a biweekly basis over the course of several months, learning designers Kuangchen Hsu and Yi Lu—alongside project manager Salonee Seecharan, and under the galvanizing and resolute direction of Sonia Howell—helped me think and work through, in beautifully methodical fashion, various features of the course. We started with learning goals, and from there worked through assignment design, course structure, and active learning strategies, to name only a few.
Perhaps most valuable to me were the strategies we discussed around responsive pedagogy as a consequence of AI’s “emergent” nature, one in which the latest updates seem to come every few weeks and new tools pop up even more rapidly than that. Being clear-sighted about the course objectives, while employing assessment practices that were capacious and flexible in capturing student learning, provided a scaffold for me that allowed me to begin day one of the course with a greater sense of ease and confidence, both of which carried through the whole semester.
Put another way, the ODL team members were the expert thought-partners that, frankly, ChatGPT could never be.
It came as no surprise that the class—titled Writing in the Age of AI, the first iteration of which ran in the fall of 2024 as an Advanced Writing and Rhetoric course—ultimately provided its own surprises and discoveries.
To name only one example, I wasn’t fully expecting the rollercoaster of attitudes toward generative AI held by my students—or, more specifically, that those attitudes would change from day to day as rapidly as they did. Any given student would one day be awash in awe and disbelief at what AI could do, only to find themselves the next day disappointed by what it couldn’t do, and sometimes even sullen by what its impact has been on our sense of humanity and culture.
Nor was I expecting to learn that what might be most necessary for everyone partaking in a course like Writing in the Age of AI—student and teacher alike—was a sense of resourcefulness. We must possess an almost scrappy willingness to face the mutable and uncertain world of an emergent technology, such that we might discover and better understand the ways to write with, through, and against it.
It became clear to me during the span of the course that, if I’m going to ask my students to adopt this kind of resourcefulness, it was all the more important that the very foundations of the course provide a safety net of sorts to them. Its organizational structure, its assignment design, its assessment practices, and, maybe most importantly, my own pedagogical demeanor had to help them feel comfortable taking risks and experimenting in order to become agents of their own learning.
Ultimately, if I was able to do that for my students that fall semester, it was only because of the safety net with which I had myself been provided while creating the course, thanks to the generous efforts of Yi, Kuangchen, Salonee, and Sonia. They gave that net to me, it turns out, so that I, in turn, could give one to my students.
Nathaniel Myers is an associate teaching professor in the University Writing Program. He received funding through the 2024 Digital Learning Sprints.
Interested in more on AI? You can watch videos from Nathaniel and others in our AI for Teaching and Learning Video Series.