Teaching Thursday: I Learned it By Watching You

This year I’ve been enjoying the handwringing about the use of generative AI by students. It’s been exciting to see how people hardened into camps and how fierce the “debates” have become. I’ve even come to enjoy the sometimes cloying moralizing that characterizes the “Never AI” camp and the techno-utopian imaginings of the pro-AI camp. 

Of course, part of the reason why generative AI is marketed to students looking to avoid having to write papers, literature reviews, or other kinds of assignments is because these assignments are both highly formulaic and very common, it is easy for generative AI to mimic the structure, tone, and even content of these essays. This reflects the formulaic character both of academic writing and also of academic thinking. There are exceptions of course; scholars who can turn a literature review into a nuanced intellectual history. But we should be honest that most literature reviews, for example, are not the most valuable parts of the articles that we read. More than that, as scholars we often lean on things like critical book reviews and historiographic essays to help us unpack the relationships between works of scholarship and streamline our understanding of disciplinary practice. We can argue that book reviews and historiographic essays are still human generated, but their tendency toward formulaic expression and standardized organization make them a very constrained and, as a result, banal form of writing (in most cases). The line between this kind of writing and that gloop produced by generative AI tends to be fairly thin. As a result, when our students read our work and the work generated by large-language models, they often fail to discern the distinction between human-made and machine-made interventions. This is as much our fault as writers and thinkers and our students’ fault as readers and learners.

As pressure to publish or perish becomes ever more pointed, the tendency toward formulaic work becomes more pronounced and even necessary to keep the scholarship machine (and the scholarly publishing machine) humming. In short, we’ve created the perfect storm for generative AI, and the increase in articles written by robots demonstrates that scholars are not immune from the temptation to take shortcuts. This, in turn, feeds the proliferation of journals, ranking system, and impact factors. 

When we think about how our students engage with AI, it perhaps would behoove us to start with our own behaviors as scholars. Students pick up on our priorities. If we treat writing as a transactional activity designed to satisfy the requirements of funding organization, to fortify our impact factor, or to maintain our contraction obligations, our writing (research and thinking) habits will show this not only in what we write, but how we write (and teach writing).

This semester, I’m facilitating a faculty reading group on Tricia Bertram Gallant and David A. Rettinger, The Opposite of Cheating: Teaching for Integrity in the Age of AI. (2025).  I’ve blogged on the book here. 

So far, attendance at the reading group has been highly uneven (to be polite). Moreover, most in attendance haven’t completed the rather short reading. To be clear, these reading groups are voluntary. Presumably faculty and staff signed up for these groups because they anticipated some benefit. They had the option of several different meeting times and the meetings were scheduled weeks in advance. Still, faculty struggled to turn up, struggled to complete the readings, and struggled to participate.

Just as pressures on faculty to publish or perish have contributed to student use of AI by reducing the complexity and creativity of academic writing, faculty and students share innumerable competing pressures when it comes to attending class, doing the reading, and participating in discussion. I’ve written about the challenges of attendance on this blog a number of times (here, here, here, and here) and how it isn’t a sign that students (or for that matter, my colleagues) don’t care, but rather a sign that our expectations are increasingly incompatible with current realities. At a minimum, our own struggles with attendance should make us more able to empathize with our students.

At best, it should offer a kind of insight into why things like generative AI offer such an appealing short cut. When faculty struggle to find time to do all that they want to do, this creates conditions where the shortcuts promised by AI can thrive. Instead of meeting at a set time to discuss a book, we discuss its contents with an AI bot. Instead of doing the entire reading, we ask AI to summarize the text.  These conditions extend to our students as well (and to our administrators, our friends, and to our lives outside the university).