Category: Teaching

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Sad Professors

Jay Caspian Kong’s New Yorker article “The Despair of the Professor in the Age of A.I.” is about, well, some professors in “despair” about teaching in the age of AI. This is a late May 2026 entry in Fault Lines, Kong’s “weekly column on politics and the media,” which has lately been a lot about AI and higher education.

Kong says he’s been talking to “academics and instructors at the college and high-school level” whose teaching has been disrupted by AI. “They talk about a sense of loss and of despair, because the one thing that brought them meaning has been erased, or blotted out, by the arrival of A.I.” After introducing his article by discussing a Substack post by J.S. Peters called “Grieving what we’ve Gained (Or, the time I cried in front of my students about AI),” he shares reflections from a dozen faculty, including Peters, about how AI has changed things for them.

If you are teaching college or high school in this new age of AI, I’d recommend reading it.  I don’t agree with a lot of these faculty, but I can certainly relate to what they’re feeling. But lots of things that have caused the despair these professors are describing have nothing to do with AI. A couple of these reflections mention the enrollment cliff and COVID, but I think we need to go back to the mid 2000s for additional causes that have made the work of academics a lot less pleasant: the emergence of the iPhone and social media, both engineered to addict users, especially younger users; the chaos of the first Trump administration, including COVID, George Floyd’s murder, January 6; the increasingly obvious climate crisis our students will inherit; the chaos of the current Trump administration which we’re all experiencing right now; and the runaway costs of attending college in the first place, which seems to have reached a tipping point where a lot more students (and their families) don’t think the return on investment is there anymore.

And then came AI. Plummeting enrollments have hit EMU very hard, and the impact of that– fewer students = fewer opportunities to teach advanced classes, budget cuts everywhere, the beginnings of reorganizing the College of Arts and Sciences, and the not nearly as hypothetical whispers of what if Michigan decided to close some of the many regional universities in the state (including EMU)– has had a much more significant impact on what it means for me to be a professor now than any of this AI stuff. AI is merely the shitty sauce on top of the already shitty sandwich. (I mean shitty here as an adjective, but you can read that differently if you want).

Anyway, the despair these faculty are feeling about AI boils down to the depressing ways students are using AI to cheat and bypass learning. Kong quotes Peters explaining how she had to change her assignments in an effort at making them “AI-proof.” Then this:

[W]hen she presented these new expectations to her students, something unexpected happened. “A wave of sadness washed over me, and I actually got choked up in front of the class.” Peters writes. “‘Before AI,’ I told them, ‘Students used to work hard to come up with their own ideas. I’d help, and they’d struggle, but they’d come to something that was their own. That doesn’t happen anymore and I grieve that.’ ”

I get the sadness. But– and perhaps I’m too cynical after teaching a ton of freshman composition, which is arguably “ground zero” for the never-ending battle against plagiarism–while I am sure that most of Peters’ students embraced the struggle she now feels is lost because of AI, I am also sure that some of her students used to cheat before AI as well.

As I’ve written about many times before, I have never had a lot of “pants on fire” cheating because teaching writing as a process requires students to show their work– the drafts they share in peer reviews, the research they are conducting, etc.– and I never have “one shot deal” paper assignments. Cheating by making fake drafts and the like is more work than just doing the assignment. And, as I have also written about many times before, students cheat when they are desperate and think they are going to fail, and because these students are not usually the sharpest knives in the drawer, it is fairly easy for me to spot.

That said, Peters and the rest of these sad professors are correct that more students are trying to cheat in small and large ways with AI, I think for two reasons: AI is seductive (“you mean all I have to do is push this button and I can be finished with this stupid assignment?”), and the line for what counts or doesn’t count as cheating is fuzzy. Before AI, I had to deal with a few plagiarism issues in each section of freshman comp I was teaching, and most of those were citation mistakes rather than purposeful cheating. I rarely had any cheating issues in the advanced classes. In the last couple of years, I’ve seen two or three AI cheating issues per section of composition (and I failed on the a couple of those students), and there were a couple of AI cheaters in the upper-level class I taught last year that was about AI– and they failed too. If I had a “zero tolerance” AI policy (which is impossible to enforce), then I’m sure I would have a lot more cheating problems.

As a result, I’ve had to change the way I teach writing in two significant ways. First, I talk with my students about AI a lot because it has become one of the major topics of the courses I’ve been teaching lately, and because the absolute best way to encourage students to cheat in small and big ways with AI is for a teacher to never say anything about it. And I think teachers need to be explicit with their students about how using AI to do too much of the work a) defeats the whole point of trying to learn something in a class, and b) generally doesn’t work that well.

Second, I now use software and other enforcement mechanisms to police/detect AI cheating. This is new for me, but it is not at all new for writing teachers to use software– notably Turnitin— to detect plagiarism. I have never used Turnitin because of a host of ethical issues, because I don’t like presuming my students are guilty until proven innocent, and also because I never thought the software worked that well. The way Turnitin detects plagiarism is by comparing a paper with the other papers in its database. That works fine if the student is plagiarizing another student’s paper already in the database, but it does nothing to effectively detect when a student lifts a chunk of text from an article, or if a student gets someone else to write the paper. So Turnitin seemed unnecessary to me.

With AI, I feel like I’ve had to change my mindset to trust but verify, and that’s kind of sad.

As I described in this post, I have required students to use Google Docs in my writing classes for many years now, and long before AI came along. I started doing this because of compatibility issues with word processing programs, because it’s really great for collaboration/peer review activities, and because it helps me grade revisions by examining the document’s version history.

The version history is also useful for detecting signs of plagiarism and/or AI cheating. For example, when a Doc’s version history begins with a blank page, and then, time-stamped just a few second later, all of the text appears all at once and with no mistakes, I can tell the text came from someplace else. There are also a variety of Google extensions tools that can extract A LOT more details out of a Google Doc’s history. Lately, I’ve been using Process Feedback, which is a free extension that generates a report on the writer’s process: when did they make edits, how many words a minute they typed on average, and when they copy-pasted 25 or more characters all at once.

When I have confronted students about what I think is AI cheating that just appears like this, they will often say something like “Yeah, I messed up. I forgot we were supposed to use Google Docs and I wrote the paper in MS Word and then copied it into a Google Doc. Sorry about that.” I’m sure that is sometimes true– much in the way that sometimes dogs do eat homework– but I also know that some students were lying straight to my face. Last semester, I confronted one student who gave me this excuse and asked this person to share with me their Word file; they confessed it was actually AI all along.

That is what is causing these professors despair, and I don’t like it much when students lie to me either. Plus checking through document histories (not to mention clicking on links to research to make sure it’s real, checking quotations to make sure they are real, etc.) takes me more time and work.  But again, this might be a result of having plenty of students over the years who have lied to me about one thing or another. This is not something that is new for me, nor is it something that causes me a lot of despair at this point. It’s just kind of sad.

Several of these reflecting professors expressed very grim outlooks about their specific futures and of all of academia as well. I share those feelings and concerns, but less about AI and more about <gestures broadly> everything else . If I had just turned 40 (rather than 60), I like to think I would be trying to get out of academia and onto a second career of some sort.  But now that I have (likely) fewer than double-digits before I’m able to retire, I’m pretty sure I can ride out whatever happens next.

And one last tangent/point: these reflections of despair and my own frustrations do remind me a bit about an R.E.M. song that is on the not great album Up called “Sad Professor.” That song ends:

Everyone hates a bore
Everybody hates a drunk
Everyone hates a sad professor
I hate where I wound up
I hate where I wound up

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What Happens After AI Destroys Gen Ed?

In all three of the classes I’m teaching this semester (and, like last semester, I’m teaching two sections of first-year writing and Writing, Rhetoric, and AI), I am once again having students read and discuss Hua Hsu’s New Yorker essay from last summer, “What Happens After AI Destroys College Writing?” I blogged about this essay here last semester. As I wrote then, I think Hsu does a good job of capturing the anxieties that both students and teachers have about AI. Ultimately, Hsu’s answer to the question posed by the title (probably written by an editor rather than him) is that AI doesn’t “destroy” college writing, but AI is challenging a lot of assumptions about what college itself is for. Is getting a college degree simply a series of hoops to jump through to earn a degree that assures membership in the upper-middle class, or is it about learning/teaching things?

This time around, I am reading this essay a bit differently. Hsu interviews several students across the country about AI, and the one that stands out the most to my students and me is “Alex,” a pseudonym for a student at New York University, because he is so unusual. Unlike every other student I have caught cheating with AI or anything else, Alex is the unicorn of AI think pieces like this: a criminal mastermind cheater. Hsu quotes Alex as he explains how he uses ChatGPT to write papers and summarize readings for almost everything, and, if he is to be believed, Alex always gets away with it.

I don’t know, maybe Alex told Hsu the truth, and maybe there are students who have cheated like Alex in my classes that I never knew about. But as I’m re-reading and thinking about my own experiences with students talking about how they use AI or the students who try to cheat with it, I’m wondering if Hsu is taking a little “creative license.” Or perhaps Alex is a kind of composite character included because he represents the worst (and mostly exaggerated) fears about AI in education.

In contrast, most of Hsu’s interviewees describe using AI in ways that ranged from things everyone, including me, thinks is blatant cheating (for example, AI “writes” the entire paper the student does a little light editing before turning it in) to using it for stuff like brainstorming, for feedback, and other activities that some professors forbid and that I encourage. But within this range of use, most of these students were selective about the courses and assignments where they use AI, especially when it came to blatant cheating: not usually courses in their majors or courses they care about, but the courses they think are irrelevant and just another hoop. I am, of course, talking about General Education.

The role of Gen Ed in undergraduate studies has always been fraught. As a student in the 1980s, I enjoyed and learned a lot from some of the gen ed courses I took. But I also thought a lot of them weren’t worth it, and given the way these courses figured into my undergrad experience, the university seemed to have felt the same way. For starters, a lot of the Gen Ed classes were about subjects I didn’t like and/or I was not good at. Being required to take a science class with a lab felt a lot like being required eat a big bowl of (insert the name of the vegetable you will not eat here) because it’s “good for you.” These were the classes you had to take before you were allowed to have the good stuff.

Plus, the university didn’t seem to think the Gen Ed classes were that important, certainly not as important as the “real” classes in the major. I got credit for about a semester’s worth of gen ed classes in part because of some high school classes I took, but mostly because I did well on the CLEP Tests. The gen ed classes I took were mostly large lecture hall courses with a sage on the stage talking away, a couple of tests and a final. If they were smaller discussion sections, they were almost always taught by graduate students or part-timers– that is, the lowest paid and least empowered instructors on campus.

As an educator, I believe in the theory behind Gen Ed. It provides students with introductions to fundamental skills and subjects they will use in many other courses and throughout life (writing and math classes immediately come to mind), and it helps students be “well-rounded” in terms of diverse experiences, critical thinking, “broader knowledge” of the world beyond their specialization, and so forth. Back when I was a student and it was a lot more common to start college as an “undeclared” major, Gen Ed was also a way of trying out different possibilities.

But that, as they say, was then, and this is now. Attending college has never been cheap, but the cost of attendance at supposedly affordable and public universities (like EMU) has gone through the roof. Taking classes that I thought were a waste of time didn’t cost that much when I was in college; it does now. “Students these days view college as consumers, in ways that never would have occurred to me when I was their age,” Hsu writes. “They’ve grown up at a time when society values high-speed takes, not the slow deliberation of critical thinking.” I think students have always viewed college at least partially as consumers, but higher costs and the increased value society puts on speed have made this worse. Stir in the rest of the shit of the world in (social media, global warming, pandemics, depression, working too many hours to make ends meet, etc.), and the idea of a busy and stressed-out student cutting some corners in classes they think are a waste of time with AI makes a lot of sense.

As I’ve observed here and elsewhere many times before, this is the pattern of AI cheating I’ve seen in my own teaching. I see social media posts from colleagues and I read hair-on -fire MSM pieces and Substack posts about how ALL the students are ALL cheating ALL the time, but that has not been my experience. I don’t get a lot of students cheating with AI– well, unless I’ve had a bunch of Alexes that I never caught– because teaching writing as a process deters plagiarism. Plus I teach small classes (which also deters cheating), and I don’t have any “one-shot deal” paper assignments (as in “write a 5-page paper about ‘x’ and turn it in”) or any multiple choice/short answer tests or whatever, all things that AI can do well.

That said, my students do sometimes cheat, and at least 90% of the AI cheating incidents where I either required the student to redo the assignment (generally the first offense) or failed them (a second offense) have been in first year composition. The more advanced students in the courses I teach in the major or the MA program are all adamant about never cheating on their writing and certainly not with AI, and I believe them.

Like I said before, I skipped a lot of Gen Ed partly because I did well on those CLEP tests but also because I was an English major. That meant I never had to take a math class in college, and I only had to take that one science class I mentioned– astronomy, incidentally, which was the science class most students thought was the easiest option. But if I were a student now and I had to pass a Gen Ed math class along the lines of pre-calculus, I would be screwed.

Maybe I’d do the right thing and seek help from a tutor or someone, or if I got over my math phobias and if I really tried, maybe I could eek out a passing grade on my own. But if AI could help me in various ways that might or might not be allowed by the instructor, that might cross the line from “help” to doing the work for me, and I thought I could get away with it, I would use AI. And if I had to take some kind of class that I thought was a waste of my time, and it turned out that all of the assignments for the class– quizzes, tests, essays, etc.– are ones that anyone could complete with AI, then I would use AI, at least to help cut some corners. Why wouldn’t I?

“Because that wouldn’t be ethical!” you might be thinking. Sure, but if my options are to take the risk to cheat or to fail out of college, I’m going to take the risk. And really, isn’t it unethical (or at least “problematic”) for universities to make students go up to their eyeballs in debt and to spend all that time taking courses that are nothing but a series of busywork and hoop-jumping assignments that some clanker could do just as well?

Now, I don’t really think AI “destroys” Gen Ed (see what I did there?), but it clearly changes how we should teach these classes and also what these classes are for. To me, that mostly means teaching in ways that minimize the usefulness or temptation of cheating with AI: small classes focused more on learning processes rather than making products, and writing assignments that value reflection and research, especially over simple regurgitation of whatever the professor said in the lecture will be important on the test.

We also need to make these classes “real” and as meaningful and important as the classes students get to take in their majors. I can’t begin to imagine what the solution is to all this, but I suppose the first place to start with rethinking Gen Ed is to take a hard look at the courses and their formats. A class that can be successfully completed by someone who was never actually a student in the class– that is, there’s no participation component, there’s no specific context for the subject, there’s no assessment of material that is only delivered/discussed in the class–is a class that could probably be completed with AI. And it is also probably a pretty shitty Gen Ed class.

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Teaching this Fall (TBA): Writing, Rhetoric, and AI

The two big things on my mind right now are finishing this semester (I am well into the major grading portion of the term in all three of my classes) and preparing for the CCCCs road trip that will begin next week. I’m sure I’ll write more on the CCCCs/road trip after I’m back.

But this morning, I thought I’d write a post about a course I’m hoping to teach this fall, “Writing, Rhetoric, and AI.” I’ve set up that page on my site with a brief description of the course– at least as I’m imagining it now. “Topics in” courses like this always begin with just a sketch of a plan, but given the twists and turns and speed of developments in AI, I’ve learned not to commit to a plan too early.

For example: the first time I tried to teach anything about AI was in a course I taught in fall 2022 in a 300-level digital writing course. I came up with an AI assignment based in part on an online presentation by Christine Photinos and Julie Wihelm for the 2023 Computers and Writing Conference, and also on Paul Fyfe’s article “How to Cheat on Your Final Paper: Assigning AI for Student Writing.” My plan at the beginning of that semester was to have students use the same AI tools these writers were talking about, which was OpenAI’s GPT-2. By the time we were starting to work on the AI writing assignment for that class, ChatGPT was released. So plans changed, English teachers started freaking out, etc.

Anyway, the first thing that needs to happen is the class needs to “make”– that is, get enough students to justify it running at all. But right now, I’m cautiously optimistic that it is going to happen. The course will be on Canvas and behind a firewall, but my plan for now is to eventually post assignments and readings lists and the like here. Once I figure out what we’re going to do.

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My Peter Elbow Story

Peter Elbow died earlier this month at the age of 89. The New York Times had an obituary February 27 (a gift article) that did a reasonably good job of capturing his importance in the field of composition and rhetoric. I would not agree with the Times about how Elbow’s signature innovation, “free writing,” is a “touchy-feely” technique, but other than that, I think they get it about right. I can think of plenty of other key scholars and forces in the field, but I can’t think of anyone more important than Elbow.

Elbow was an active scholar and regular presence at the Conference for College Composition and Communication well into the 2000s. I remember seeing him in the halls going from event to event, and I saw him speak several times, including a huge event where he and Wayne Booth presented and then discussed their talks with each other.

A lot of people in the field had one store or another about meeting Peter Elbow; here’s my story (which I shared on Facebook earlier this month when I first learned of his passing):

When I was a junior in high school, in 1982-83 and in Cedar Falls, Iowa, I participated in some kind of state-wide or county-wide writing writing event/contest. This was a long time ago and I don’t remember any of the details about how it worked or what I wrote to participate in it, but I’m pretty sure it was an essay event/contest of some sort– as opposed to a fiction/poetry contest. It was held on the campus of the University of Northern Iowa, which is in Cedar Falls. So because it was local, a bunch of people from my high school and other local schools and beyond show up. My recollection was students participated in a version of a peer review sort of workshop.

This event was also a contest of some sort and there was a banquet everyone went to and where there were “winners” of some sort. I definitely remember I was not one of them. The banquet was a buffet, and I remember going through the line and there was this old guy (well, he would have been not quite 50 at this point) who was perfectly polite and nice and with a wondering eye getting something out of a chaffing dish right next to me. I don’t remember the details, but I think he was asked me about what I thought of this whole peer review thing we did, and I’m sure I told him it was fun because it was.

So then it turns out that this guy was there to give some kind of speech to all of the kids and all of the teachers and other adults that were at this thing. Well, really this was a speech for the teachers and adults and the kids were just there. I don’t remember how many were there, but I’m guessing maybe 100-200 people. I don’t remember anything Elbow talked about and I didn’t think a lot about it afterwards. But then a few years later and when I was first introduced to Elbow’s work in the comp/rhet theory class I took in my MFA program, I somehow figured out that I met that guy once years before and didn’t realize it at the time.

I can’t say I’ve read a ton of his writing, but what I have read I have found both smart and inspirational. It’s hard for me to think of anyone else who has had as much of an influence on shaping the field and the kind of work I do. May his memory be a blessing to his friends and family.

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I’m Still Not Using AI Detection Software; However….

Back in mid-February, Anna Mills wrote a Substack post called “Why I’m using AI detection after all, alongside many other strategies.” Mills, who teaches at Cañada College in Silicon Valley, has written a lot about teaching and AI, and she was a member of the MLA-CCCC Joint Task Force on Writing and AI. That group recommended that teachers use AI detection tools with extreme caution, or to use them not at all.


What changed her mind? Well, it sounds like she had had enough:

I argued against use of AI detection in college classrooms for two years, but my perspective has shifted. I ran into the limits of my current approaches last semester, when a first-year writing student persisted in submitting work that was clearly not his own, presenting document history that showed him typing the work (maybe he. typed it and maybe he used an autotyper). He only admitted to the AI use and apologized for wasting my time when he realized that I was not going to give him credit and that if he initiated an appeals process, the college would run his writing through detection software.
I haven’t had this kind of student encounter over AI cheating, but it’s not hard for me to imagine this scenario. It might be the last straw for me too. And like I think is the case with Mills, I’m getting sick of seeing this kind of dumb AI cheating.

Last November, I wrote here about a “teachable moment” I had when an unusually high number of freshman comp students who dumbly cheated with AI. The short version: for the first short assignment (2 or 3 pages), students are supposed to explain why they are interested in the topic they’ve selected for their research, and to explain what prewriting and brainstorming activities they did to come up with their working thesis. It’s not supposed to be about why they think their thesis is right; it’s supposed to be a reflection on the process they used to come up with a thesis that they know will change with research. It’s a “pass/revise” assignment I’ve given for years, and I always have a few students who misunderstand and end up writing something kind of like a research paper with no research. I make them revise. But last fall, a lot more of my students did the assignment wrong because they blindly trusted what ChatGPT told them. I met with these students, reminded them what the assignment actually was, and to also remember that AI cannot write an essay that explains what you think.

I’m teaching another couple of sections of freshman composition this semester and students just finished that first assignment. I warned them about avoiding the mistakes with AI students made last semester, and I repeated more often that the assignment is about their process and is not a research paper. The result? Well, I had fewer students trying to pass off something written by AI, but I still had a few.

My approach to dealing with AI cheating is the same as it has been ever since ChatGPT appeared: I focus on teaching writing as a process, and I require students to use Google Docs so I can use the version history to see how they put together their essays. I still don’t want to use Turnitin, and to be fair, Mills has not completely gone all-in with AI detection. Far from it. She sees Turnitin as an additional tool to use along with solid process writing pedagogy. Mills also shares some interesting resources about research into AI detection software and the difficulty of accurately spotting AI writing. Totally worth checking her post out.

I do disagree with her about how difficult it is to spot AI writing. Sure, it’s hard to figure out if a chunk of writing came from a human or an AI if there’s no context. But in writing classes like freshman composition, I see A LOT of my students’ writing (not just in final drafts), and because these are classes of 25 or so students, I get to know them as writers and people fairly well. So when a struggling student suddenly produces a piece of writing that is perfect grammatically and that sounds like a robot, I get suspicious and I meet with the student. So far, they have all confessed, more or less, and I’ve given them a second chance. In the fall, I had a student who cheated a second time; I failed them on the spot. If I had a student who persisted like the one Mills describes, I’m not quite sure what I would do.

But like I said, I too am starting to get annoyed that students keep using AI like this.

When ChatGPT first became a thing in late 2022 and everyone was all freaked out about everyone cheating, I wrote about/gave a couple of talks about how plagiarism has been a problem in writing classes literally forever. The vast majority of examples of plagiarism I see are still a result of students not knowing how to cite sources (or just being too lazy to do it), and it’s clear that most students don’t want to cheat and they see the point of needing to do the work themselves so they might learn something.

But it is different. Before ChatGPT, I had to deal with a blatant and intentional case of plagiarism once every couple of years. For the last year or so, I’ve had to deal with some examples of blatant AI plagiarism in pretty much every section of first-year writing I teach. It’s frustrating, especially since I like to think that one of the benefits of teaching students how to use AI is to discourage them from cheating with it.

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Six Things I Learned After a Semester of Lots of AI

Two years ago (plus about a week!), I wrote about how “AI Can Save Writing by Killing ‘The College Essay,'” meaning that if AI can be used to respond to bad writing assignments, maybe teachers will focus more on teaching writing as a process the way that scholars in writing studies have been talking about for over 50 years. That means an emphasis on “showing your work” through a series of scaffolded assignments, peer review activities, opportunities for revision, and so forth.

This past semester, I decided to really lean into AI in my classes. I taught two sections of first-year writing where the general research topic for everyone was “your career goals and AI,” and where I allowed (even encouraged) the use of AI under specific circumstances. I also taught an advanced class for majors called “Digital Writing” where the last two assignments were all about trying to use AI to “create” or “compose” “texts” (the scare quotes are intentional there). I’ve been blogging/substacking about this quite a bit since summer and there are more details I’m not getting to here because it’s likely to be part of a scholarly project in the near future.

But since the fall semester is done and I have popped the (metaphorical) celebratory bottle of bubbly, I thought I’d write a little bit about some of the big-picture lessons about teaching writing with (and against) AI I learned this semester.

Teachers can “refuse” or “resist” or “deny” AI all they want, but they should not ignore it.

As far as I can tell from talking with my students, most of my colleagues did not address AI in their classes at all. A few students reported that they did discuss and use AI in some of their other classes. I had several students in first-year writing who were interior design majors and all taking a course where the instructor introduced them to AI design tools– sounded like an interesting class. I had a couple of students tell me an instructor “forbid” the use of AI but with no explanation of what that meant. Most students told me the teacher never brought up the topic of AI at all.

Look, you can love AI and think it is going to completely transform learning and education, you can hate AI all you want and wish it had never been invented and do all you can to break that AI machine with your Great Enoch sledgehammers. But ignoring it or wishing it away is ridiculous.

For my first-year writing students, most of whom readily admitted they used AI a lot in high school to do things that were probably cheating, I spent some time explaining how they could and could not use AI. I did so in part to teach about how I think AI can be a useful tool as part of the process of writing, but I also did this to establish my credibility. I think a lot of students end up cheating with AI because they think that the teacher is clueless about it– and I think a lot of times, students are right.

You’re gonna need some specific rules and guidelines about AI– especially if you want to “refuse” or “resist” it.

I have always included on my syllabi an explicit policy about plagiarism, and this year I added language that makes it clear that copying and pasting large chunks of text from AI is cheating. I did allow and encourage first-year writing students to use AI as part of their process, and I required my advanced writing students to use AI as part of their “experiments” in that class. But I also asked students to include an “AI Use Statement” with their final drafts, one that explained what AI systems they used (and that included Grammarly), what prompts they used, how they used the AI feedback in their essay, and so forth. Because this was completely new to them (and me too), these AI Use Statements were sometimes a lot less complete and accurate than I would have preferred.

I also insisted that students write with Google Docs for each writing assignment and for all steps in the process, from the very start of the first hint of a first draft until they hand it into me. Students need to share this with me so I can edit it. I take a look at the “version history” of the Google Doc, and if I suddenly see pages of clear prose magically appear in the essay, we have a discussion. That seemed to work well.

Still, sometimes students are still going to cheat with AI, and often without realizing that they’re cheating.

Even with the series of scaffolded assignments and using Google Docs and all of my warnings, I did catch a few students cheating with AI in both intentional and not as intentional ways. Two of these examples were similar to old-school plagiarism. One was from a student from another country who had some cultural and language disconnections about the expectations of American higher education (to put it mildly); I think first-year writing was too advanced and this student should have been advised into an ESL class. Another was a student who was late on a short assignment and handed in an obviously AI-generated text (thanx, Google Docs!). I gave this person a stern warning and another chance, and they definitely didn’t do that again.

As I wrote about in this post about a month ago, I also had a bunch of students who followed the AI more closely than the first assignment, the Topic Proposal. This is a short essay where students write about how they came up with their topic and initial thesis for their research for the semester. Instead, a lot of students asked AI what it “thought” of their topic and thesis, and then they more or less summarized the AI responses, which were inevitably about why the thesis was correct. Imagine a mini research paper but without any research.

The problem was that wasn’t the assignment.  Rather, the assignment asked students to describe how they came up with their thesis idea: why they were interested in the topic in the first place, what kinds of other topics they considered, what sorts of brainstorming techniques they used, what their peers told them, and so forth. In other words, students tried to use the AI to tell them what they thought, and that just didn’t work. It ended up being a good teachable moment.

A lot of my students do not like AI and don’t use it that much. 

This was especially true in my more advanced writing class– where, as far as I can tell, no one used AI to blatantly cheat. For two of the three major projects of the semester, I required students to experiment with AI and then to write essays where they reflected/debriefed on their experiments while making connections to the assigned readings. Most of these students, all of whom were some flavor of an English major or writing minor, did not use AI for the reflection essays. They either felt that AI was just “wrong” in so many different ways (unethical, gross, unfair, bad for the environment, etc.), or they didn’t think the AI advice on their writing (other than some Grammarly) was all that useful for them.

This was not surprising; after all, students who major or minor in something English-related usually take pride in their writing and they don’t want to turn that over to AI. In the freshman composition classes, I had a few students who never used AI either–judging from what they told me in their AI Use statements. But a lot of students’ approaches to AI evolved as the semester went on, and by the time they were working on the larger research-driven essay where all the parts from the previous assignments come together, they said things like they asked ChatGPT for advice on “x” part of the essay, but it wasn’t useful advice so they ignored it.

But some students used AI in smart and completely undetectable ways.

This was especially true in the first year writing class. Some of the stronger writers articulated in some detail in their AI Use Statements how they used ChatGPT (and other platforms) to brainstorm, to suggest outlines for assignments, to go beyond Grammarly proofreading, to get more critical feedback on their drafts, and so forth. I did not consider this cheating at all because they weren’t getting AI to do the work for them; rather, they were getting some ideas and feedback on their work.

And here’s the thing that’s important: when a student (or anyone else) uses AI effectively and for what it’s really for, there is absolutely no way for the teacher (or any other reader) to possibly know.

The more time I have spent studying and teaching about AI, the more skeptical I have become about it. 

I think my students feel the same way, and this was especially true with the students in my advanced class who were directly studying and experimenting with many different AI platforms and tasks. The last assignment for the course asked students to use AI to do or make something that they could not have possibly done by themselves. For example, one student taught themself to play chess and was fairly successful with that– at least up to a point. Another student tried to get ChatGPT to teach them how to play the card game Euchre, though less successfully because the AI kept “cheating.” Another student asked the AI to code a website, and the AI was pretty good at that. Several students tried to use AI tools to compose music; similar to me I guess, they listen to lots of music and wished they could play an instrument and/or compose songs.

What was interesting to me and I think most of my students was how quickly they typically ran into the AI’s and their own limitations. Sometimes students wanted the AI to do something the AI simply could not do; for example, the problem with playing Euchre with the AI (according to the student) is it didn’t keep track of what cards had already been played– thus the cheating. But the bigger problem was that without any knowledge of how to accomplish the task on their own, the AI was of little use. For example, the student who used AI to code a website still had no idea at all what any of the code meant, nor did they know what to do with it to make it into a real website. Students who knew nothing about music who tried to write/create songs couldn’t get very far. In other words, it was not that difficult for students to discover ways AI fails at a task, which in many ways is far more interesting than discovering what it can accomplish.

I’m also increasingly skeptical of the hype and role of AI in education, mainly because I spent most of the 2010s studying MOOCs. Remember them? They were going to be the delivery method for general education offerings everywhere, and by 2030 or 2040 or so, MOOCs were going to completely replace all but the most prestigious universities all over the world. Well, that obviously didn’t happen. But that didn’t mean the end of MOOCs; in fact, there are more people taking MOOC “courses” now than there were during the height of the MOOC “panic” around 2014. It’s just that nowadays, MOOCs are mostly for training (particularly in STEM fields), certificates, and as “edutainment” along the lines of Master Class.

I think AI is different in all kinds of ways, not the least of which is AI is likely to be significantly more useful than a chat box or to check grammar. I had several first-year students this semester write about AI and their future careers in engineering, logistics, and finance, and they all had interesting evidence about both how AI is being used right now and how it will likely be used in the future. The potential of AI changing the world at least as much as another recent General Purpose Technology, “the internet,” is certainly there.

Does that mean AI is going to have as great of an impact on education as the internet did? Probably, and teachers have had to make all kinds of big and small changes to how they teach things because of the internet, which was also true when writing classes first took up computers and word processing software.  But I think the fundamentals of teaching (rather than merely assigning) writing still work.

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IT’S A WITCH!

Reflecting on Melanie Dusseau’s “Burn It Down: A License for AI Resistance”

I don’t completely disagree with Melanie Dusseau’s advice in her recent Inside Higher Ed column Burn It Down: A License for AI Resistance, but there’s something about her over-the-top enthusiasm for “burning it down” that reminds me of this famous scene from Monty Python and the Holy Grail:

Dusseau, who is a creative writing professor at the University of Findlay, writes “Until writing studies adopted generative artificial intelligence as sound pedagogy, I always felt at home among my fellow word nerds in rhet comp and literary studies.” A bit later, she continues:

If you are tired of the drumbeat of inevitability that insists English faculty adopt AI into our teaching practices, I am here to tell you that you are allowed to object. Using an understanding of human writing as a means to allow for-profit technology companies to dismantle the imaginative practice of human writing is abhorrent and unethical. Writing faculty have both the agency and the academic freedom to examine generative AI’s dishonest training origins and conclude: There is no path to ethically teach AI skills. Not only are we allowed to say no, we ought to think deeply about the why of that no.

Then she catalogs the many many mmmmmaaaaaannnnnnyyyyyy problems of AI in prose I found engaging and intentionally funny in its alarmed tone. Dusseau writes:

Resistance is not anti-progress, and pedagogies that challenge the status quo are often the most experiential, progressive and diverse in a world of increasingly rote, Standard English, oat milk sameness. “Burn it down” is a call to action as much as it is a plea to have some fun. The robot revolution came so quickly on the heels of the pandemic that I think a lot of us forgot that teaching can be a profoundly joyful act.

AI resistance/refusal is catching on. The day after I read this article, I came across (via Facebook) a similar albeit much more academic call for resistance, “Refusing GenAI in Writing Studies: A Quickstart Guide” by Jennifer Sano-Franchini, Megan McIntyre, and Maggie Fernandes. While also calling for the field to “refuse” AI, it’s more of an academic manifesto with a lot of citation, it’s a much more nuanced and complicated, and also still a work in progress. For example, sections that are “coming soon” on their wordpress site include “What Is GenAI Refusal?” and “Practicing Refusal.” Perhaps I’ll write more specifically about this when it is closer to finished, but this post isn’t about that.

Anyway, why does “burning it down” make me think of that Monty Python scene? The peasants bring one of the knights (ChatGPT just told me it was “Sir Bedevere the Wise”— let’s hope that’s right!) a witch (or AI) to be burned at the stake. They’re screaming and enraged, wanting to burn her immediately. The knight asks why they believe she’s a witch, and the evidence the peasants offer up is flimsy. The wise knight walks them through the logic of how to test if the woman truly is a witch: to put her on the scales and see if she weighs as much as a duck and thus floats like wood and thus she too is made of wood and will burn for being a witch. (Stick with me here— the punchline at the end has a twist).

Like the mob, Dusseau has had enough with all these witches/AIs. She wants it gone and for it to have never existed in the first place. But since that’s not possible, Dusseau is calling for like-minded writing teachers to refuse to engage. “To the silent, hopeless AI skeptics and Star Trek fans: resistance is not futile. We simply do not have to participate. Let Melville’s Bartleby provide the brat slogan of our license to resist: ‘I would prefer not to.’”

Now, maybe I’m just not hearing the “drumbeat of inevitability” for embracing AI to teach writing because I’m one of these people teaching a lot with/about AI this semester. But I have no idea what she’s talking about. If anything, it seems like most faculty around here have either ignored AI or banned it. Most of my students this semester have told me that AI has not come up as a topic in their other classes at all.

Before one burns it all down, it probably is a good idea to figure out what “it” is. Maybe Dusseau has already done that. Or maybe she is like a lot of my fellow academic AI resisters who don’t know much about AI and think that it is only for brute-force cheating. Maybe she knows better and is making an informed decision about resisting AI; it’s hard for me to tell.

I think her arguments for why we should refuse AI boil down to two. First, AI requires giant data centers and it takes A LOT of electricity and water to run those sites. That is completely true, and that doesn’t even get into the labor exploitation that went into training LLMs and monitoring content, the monopolistic and unregulated giant corporations that control all this, etc. All true, but look: these data centers also power EVERYTHING we do online and they have been an environmental problem for decades. So it’s not that she’s wrong, but I suspect that Dusseau isn’t thinking about refusing Facebook or Google searches anytime soon.

The second argument is that it ruins writing. Like almost every other person I’ve read making this argument, Dusseau references Ted Chiang’s New Yorker article “Why A.I. Isn’t Going to Make Art” in passing. What she doesn’t mention is Chiang’s definition of art is really fiction writing, and he sets the bar extremely high as to what counts as “art.” I prefer Matteo Wong’s response in The Atlantic, “Ted Chiang Is Wrong About AI Art,” but I’ll leave that debate for another time.

I think what Dusseau means by “writing” is writing that is personal, expressive, and “creative,” poetry and fiction and the like. Of course, AI is not the right tool for that. It’s not for writing a heartfelt fan letter from a child to an Olympic athlete, and Google found that out with the backlash to their “Dear Sydney” ad campaign this summer. (If you don’t know what I’m talking about check out the great post Annette Vee wrote about this called “Why ‘just right’ is wrong: What the Gemini ad ‘Dear Sydney; says about writing that people choose to do.”) Everyone I follow/read about AI agrees with this.

But most writing tasks are not personal, expressive, or creative, and that is particularly true for many writing tasks we all have to do sometimes, often reluctantly, for school or for work: routine reports, memos, forms, the kind of things we call “paperwork.” A lot of students are required to write when they would “prefer not to,” which is why students sometimes use AI to sometimes cheat on writing assignments. So yes, like Dusseau, I don’t want AI writing my journal entries, personal emails, or anything else that’s writing I choose to do, and I don’t want students to cheat. But there’s a role for AI with some of these not-chosen writing tasks that is perhaps useful and not cheating.

The other problem is that Dusseau’s own resistance is not going to stop any of her students or her colleagues from using AI. I don’t know if AI-based writing tools are going to inevitably be a part of writing pedagogy or not, but I do know that AI is going to continue to be a tool that people are going to continue to use. I have students in all of my classes (though more of them in the class of English majors) who are AI refusers, and I think that’s really important to note here: not all students are on board with this AI stuff either. But for my students who seem to know how to use AI effectively and as something akin to a brainstorming/proofreading/tutoring tool, it seems to work pretty well. And that’s the kind of AI use that is impossible for a teacher to detect.

So to me, the council of the knight is best. Before we burn this AI witch, why don’t we see what we’re up against? Why don’t we research this a bit more? Why don’t we not burn it own but instead (to very generally reference Cynthia Selfe’s Technology and Literacy in the 21st Century) pay attention to it and on alert?

But here’s the thing: in that Monty Python scene, it turns out she is a witch.

The punchline in that scene goes by so quick it took me a few viewings to realize it, but the woman does weigh the same as the duck, thus is made out wood, and thus is a witch. The peasants were right! SHE’S A WITCH!

Because like I said at the beginning of this, I don’t completely disagree with Dusseau. I mean, I still don’t think “burn it down” is a good strategy— we gotta pay attention. But I’m also not saying that she’s wrong about her reasons for resisting AI.

My semester isn’t quite over, and I have to say I am not sure of the benefits of the up-front “here is how to use AI responsibly” approach I’ve taken this semester, particularly in freshman comp. But I do know an impassioned and spirited declaration to students about why they too should burn it all down is not going to work. If writing teachers don’t want their students to use AI in their courses, they cannot merely wish AI away. They need to learn enough to understand the basics of it, they need to explain to students why it’s a bad idea to use it (or they need to figure out when using AI might be okay), and they’re going to have to change their writing assignments to make them more AI proof.

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AI Cheating as a Teachable Moment

My students and I have reached the part of the semester where they are mostly working on finishing the assignments, and where I’m mostly working on reading/commenting/evaluating those assignments. So busy busy busy. Anyway, as kind of an occasional break from that work, I wrote this post in bits and pieces over the last week or two about how a particular example of AI “cheating” became a “teachable moment.”

I think there’s AI CHEATING and there’s AI “cheating,” much in the same way that there is PLAGIARISM and then there’s “plagiarism.” By PLAGIARISM, I mean the version where a student hands in a piece of writing they did not compose at all. The most obvious example is when a student pays someone else to do it, perhaps from an online paper mill. I know this happens, but I don’t think I’ve ever seen it— unless it was that good I didn’t notice.

More typically, students do this cheating themselves by copying, pasting, and slightly tweaking chunks of text from websites to piece together something kind of like the paper. This is usually easy to spot and for two reasons. First, the same Google searches students use to find stuff to cheat with also works for me to find the websites and articles they used to cheat. Second and perhaps more importantly, students only plagiarize like this when they know they’re failing and desperate, so it’s easy to spot.

The much more common kind of “plagiarism” I see is basically accidental. A lot of students— especially first year students— do not understand what needs to be cited and what does not. This is because citation is both confusing and a pain in the ass, so students sometimes do not realize they had to have a citation at all, or they just skip it and figure no one will notice. Fortunately, it’s easy to spot when students drop in a quote from an article without citation because of the writing shift: the text goes from a college freshman grappling with their prose suddenly to a polished and professional writer, often with specialized word choices and jargon. And as often as not, students do cite some of the article they’re accidentally plagiarizing, so it’s pretty easy to check.

This is a “teachable moment:” that is, one of those things that happens in a class or an assignment where it’s an opportunity to reinforce something that has already been taught. This is where I remind the student about what we already talked about: how unintentional plagiarism is still plagiarism, that this is specifically an example of why it’s important to cite your sources correctly, and so forth. This tends to click.

Similarly, there’s AI CHEATING and then there’s AI “cheating,” and I have seen examples of both in my first year writing classes this semester. The big example of extreme AI CHEATING I’ve seen so far this semester is not that interesting because it was so textbook: desperate failing student clumsily and obviously uses AI, I called the student out about it, student confesses, I gave the student the choice to fail or withdraw rather than going through the rig-a-ma-roll of getting that student expelled (oh yes, that is something I could have done). Slight tangent: if catching AI cheaters is as easy and as obvious as it seems to be, what’s the problem? Conversely, if students are using AI effectively as a tool to help their process (brainstorming, study guides, summarizing complicated texts, proofreading, etc.) and if that use of AI isn’t detectable by the teacher, well, what’s the problem with that?

The AI “cheating” example from this semester was a more interesting and teachable moment. Here’s what happened:

The first assignment in my freshman comp classes is a 2-3 page essay where students explain their initial working thesis and how they came up with it. It’s a low-stakes getting started kind of assignment I grade “complete/incomplete.” As I explain and remind students repeatedly, this is not an essay where they are trying to convince the reader to believe their thesis. Rather, this is an essay about the process of coming up with the working thesis in the first place. What I want students to write about is why they’re interested in their topic, what sorts of brainstorming activities they tried to come up with their topic, what sorts of conversations they had about this project with me and with classmates, and so forth.

This semester, the topic of research in my first year writing classes is “your career goals and AI.” I’ve also spent a lot of class time explaining why I think AI is not that useful for cheating because it just can’t do these assignments very well. But I also explained how AI might be useful as part of the process as well. For example, a lot of these students really struggle with coming up with a good and researchable topic idea/thesis, and even though most of AI’s ideas for a thesis about career goals and AI aren’t great, it does help them get beyond staring at a blank page.

I’ve given a version of this assignment for a long time, and in previous semesters and pre-AI, two or three students (out of 25) messed it up. It’s usually because the students didn’t understand the assignment, or they weren’t paying attention to/didn’t do any of the prewriting exercises we discussed in class. So they try to fake it by writing what ends up being a really short research paper without any research. I gave these students a do-over, and that usually was enough to get them back on track.

This semester, I had closer to half of the students in my two sections mess this up. I’m sure some of these students just didn’t get the assignment/didn’t do the prewriting activities, but what I think happened more is a lot of students got a little lazy and hypnotized by the smooth, mansplaining prose of AI. So instead of remembering what the assignment was about, they just took what the AI was feeding it about their working thesis ideas and tweaked that a bit.

The teachable moment? I met with the students who messed this up, reminded them what the assignment was actually supposed to be, and I pointed out that this was exactly the kind of thing that AI cannot do: it can’t help you write about what you think. At least not yet.

This was a couple weeks ago, and for most of my students, I think it clicked. I still have a number of students who are struggling and unlikely to pass for all kinds of reasons, but that’s typical for freshman comp. Some students (particularly the ones on the way to failing) are still trying to use AI for cheating, but for the most part, I think students have learned the lesson.

I ask students to include an “AI Use Statement” where they describe how they used AI, or to say explicitly that they didn’t use any AI. This is a brand-new thing for both them as students and me as a teacher, so they sometimes forget or they don’t explain their AI use as clearly as I wanted. And I am sure some students are fibbing a little about how much AI they used. But for the most part, what students are telling me is they aren’t using AI to write at all, or they’re using Grammarly for proofreading (which I think counts as AI), they are using an AI for some ideas about a particular paragraph, and/or getting started or some other brainstorming kind of suggestion.

Which makes this all a teachable moment for me as well: I think the lesson I’ve learned (or re-learned) from this is that the best way to prevent/discourage students from using AI to cheat is to get out in front of the issue. I’m not saying that all writing teachers ought to allow their students to use AI; in fact, as we’re approaching the end of the semester, I’m not sure if it is a good idea to encourage and sanction the use of AI in classes like first year writing. But I am sure that is is a very good idea for writing (and other kinds of ) teachers to be up-front about AI. I think when teachers do spend some time talking about what does or doesn’t work with AI, students are less likely to use it to cheat in that class— if they use it at all.

A Simple Example

Back to my “regular programming” with a post/update/stack/whatever these things are calls that is more on brand….

My students and I have reached the part of the semester where they are mostly working on finishing the assignments, and where I’m mostly working on reading/commenting/evaluating those assignments. So busy busy busy. Anyway, as kind of an occasional break from that work, I wrote this post in bits and pieces over the last week or two about how a particular example of AI “cheating” became a “teachable moment.”

I think there’s AI CHEATING and there’s AI “cheating,” much in the same way that there is PLAGIARISM and then there’s “plagiarism.” By PLAGIARISM, I mean the version where a student hands in a piece of writing they did not compose at all. The most obvious example is when a student pays someone else to do it, perhaps from an online paper mill. I know this happens, but I don’t think I’ve ever seen it— unless it was that good I didn’t notice.

More typically, students do this cheating themselves by copying, pasting, and slightly tweaking chunks of text from websites to piece together something kind of like the paper. This is usually easy to spot and for two reasons. First, the same Google searches students use to find stuff to cheat with also works for me to find the websites and articles they used to cheat. Second and perhaps more importantly, students only plagiarize like this when they know they’re failing and desperate, so it’s easy to spot.

The much more common kind of “plagiarism” I see is basically accidental. A lot of students— especially first year students— do not understand what needs to be cited and what does not. This is because citation is both confusing and a pain in the ass, so students sometimes do not realize they had to have a citation at all, or they just skip it and figure no one will notice. Fortunately, it’s easy to spot when students drop in a quote from an article without citation because of the writing shift: the text goes from a college freshman grappling with their prose suddenly to a polished and professional writer, often with specialized word choices and jargon. And as often as not, students do cite some of the article they’re accidentally plagiarizing, so it’s pretty easy to check.

This is a “teachable moment:” that is, one of those things that happens in a class or an assignment where it’s an opportunity to reinforce something that has already been taught. This is where I remind the student about what we already talked about: how unintentional plagiarism is still plagiarism, that this is specifically an example of why it’s important to cite your sources correctly, and so forth. This tends to click.

Similarly, there’s AI CHEATING and then there’s AI “cheating,” and I have seen examples of both in my first year writing classes this semester. The big example of extreme AI CHEATING I’ve seen so far this semester is not that interesting because it was so textbook: desperate failing student clumsily and obviously uses AI, I called the student out about it, student confesses, I gave the student the choice to fail or withdraw rather than going through the rig-a-ma-roll of getting that student expelled (oh yes, that is something I could have done). Slight tangent: if catching AI cheaters is as easy and as obvious as it seems to be, what’s the problem? Conversely, if students are using AI effectively as a tool to help their process (brainstorming, study guides, summarizing complicated texts, proofreading, etc.) and if that use of AI isn’t detectable by the teacher, well, what’s the problem with that?

The AI “cheating” example from this semester was a more interesting and teachable moment. Here’s what happened:

The first assignment in my freshman comp classes is a 2-3 page essay where students explain their initial working thesis and how they came up with it. It’s a low-stakes getting started kind of assignment I grade “complete/incomplete.” As I explain and remind students repeatedly, this is not an essay where they are trying to convince the reader to believe their thesis. Rather, this is an essay about the process of coming up with the working thesis in the first place. What I want students to write about is why they’re interested in their topic, what sorts of brainstorming activities they tried to come up with their topic, what sorts of conversations they had about this project with me and with classmates, and so forth.

This semester, the topic of research in my first year writing classes is “your career goals and AI.” I’ve also spent a lot of class time explaining why I think AI is not that useful for cheating because it just can’t do these assignments very well. But I also explained how AI might be useful as part of the process as well. For example, a lot of these students really struggle with coming up with a good and researchable topic idea/thesis, and even though most of AI’s ideas for a thesis about career goals and AI aren’t great, it does help them get beyond staring at a blank page.

I’ve given a version of this assignment for a long time, and in previous semesters and pre-AI, two or three students (out of 25) messed it up. It’s usually because the students didn’t understand the assignment, or they weren’t paying attention to/didn’t do any of the prewriting exercises we discussed in class. So they try to fake it by writing what ends up being a really short research paper without any research. I gave these students a do-over, and that usually was enough to get them back on track.

This semester, I had closer to half of the students in my two sections mess this up. I’m sure some of these students just didn’t get the assignment/didn’t do the prewriting activities, but what I think happened more is a lot of students got a little lazy and hypnotized by the smooth, mansplaining prose of AI. So instead of remembering what the assignment was about, they just took what the AI was feeding it about their working thesis ideas and tweaked that a bit.

The teachable moment? I met with the students who messed this up, reminded them what the assignment was actually supposed to be, and I pointed out that this was exactly the kind of thing that AI cannot do: it can’t help you write about what you think. At least not yet.

This was a couple weeks ago, and for most of my students, I think it clicked. I still have a number of students who are struggling and unlikely to pass for all kinds of reasons, but that’s typical for freshman comp. Some students (particularly the ones on the way to failing) are still trying to use AI for cheating, but for the most part, I think students have learned the lesson.

I ask students to include an “AI Use Statement” where they describe how they used AI, or to say explicitly that they didn’t use any AI. This is a brand-new thing for both them as students and me as a teacher, so they sometimes forget or they don’t explain their AI use as clearly as I wanted. And I am sure some students are fibbing a little about how much AI they used. But for the most part, what students are telling me is they aren’t using AI to write at all, or they’re using Grammarly for proofreading (which I think counts as AI), they are using an AI for some ideas about a particular paragraph, and/or getting started or some other brainstorming kind of suggestion.

Which makes this all a teachable moment for me as well: I think the lesson I’ve learned (or re-learned) from this is that the best way to prevent/discourage students from using AI to cheat is to get out in front of the issue. I’m not saying that all writing teachers ought to allow their students to use AI; in fact, as we’re approaching the end of the semester, I’m not sure if it is a good idea to encourage and sanction the use of AI in classes like first year writing. But I am sure that is is a very good idea for writing (and other kinds of ) teachers to be up-front about AI. I think when teachers do spend some time talking about what does or doesn’t work with AI, students are less likely to use it to cheat in that class— if they use it at all.

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Four Rules For Discouraging Cheating with AI in Writing Classes

An indirect but positive review of Mollick’s “Co-Intelligence”

This semester, I’m teaching two sections of first year writing (aka freshman comp) and an advanced writing course called Digital Writing, and both have AI elements and themes. In first year writing, the research theme is “Your Career and AI.” In the Digital Writing course, the last two writing projects are going to be waist-deep in writing with AI. Maybe one day I will better understand/make use of Substack’s newsletter function to chronicle these classes in more detail, but that’s later.

For Digital Writing, we’re reading and discussing Ethan Mollick’s Co-Intelligence: Living and Working with AI. If you’re reading posts like this because you too are trying to make sense out of the what AI is about, there’s a good chance you’ve already heard of Mollick’s book and his Substack, One Useful Thing. If you haven’t heard of Mollick and you want to know more about AI but you’re overwhelmed with the firehose of news and information, then his book is for you. Co-Intelligence is a well-written, accessible, and a thoroughly researched 30,000 foot overview in less than 250 pages printed in a big font. It’s enough to get the “AI curious” up to speed on the current state of things (it was published in April 2024), while also pointing readers to ideas for further reading and research.

Mollick is a business professor at the Wharton School at the University of Pennsylvania, so he is primarily interested in how AI will impact productivity and innovation. I think we conceptualize teaching a bit differently, and like everything I’ve read about AI, Mollick is making some claims I doubt. But we’re mostly on the same page.

One of the most cited/mentioned chapters in Mollick’s book is “Four Rules for Co-Intelligence.” In brief, those rules are:

  • Always invite AI to the table, meaning you have to experiment and try to use AI (or really, different platforms, so AIs) for lots and lots of different things in order to discover what it/they can and can’t do.

  • Be the human in the loop: it’s a bad idea to completely turn over a task to AI, both because AI makes a lot of mistakes (aka hallucinations) and humans ought to be in charge rather than the other way around.

  • Treat AI like a person (but tell it what kind of person it is). AI doesn’t behave same way as other computer applications, so Mollick says we need to be conversational with it as if it were a human. Mostly he’s talking about creating context and scenarios in AI prompts, as in “You are an experienced teacher speaking to skeptical students about the value of group work. What advice would you give those students?”

  • Assume this is the worst AI you will ever use, which is perhaps the most accurate of these AI rules.

So, in that spirit, here are four more rules about teaching writing and AI— specifically, what teachers can do to discourage students from using AI to cheat.

Of course, I’m far from the first person to come up with four more rules for AI— I’m not even the first person to come up with four more rules for AI and writing! For example, there’s this fine post from Jane Rosenzweig at her site Writing Hacks “Four Rules for Writing in the Age of AI,” and also this guest post at John Warner’s Substack site by high school teacher and writer Brett Vogelsinger, “Artificial Intelligence and Writing: Four Things I Learned Listening to my High School Students.” Both great posts and great thoughts.

The most common concern about AI I read on Facebook (though not so much on Substack) from other professors and teachers is students using it to cheat on writing assignments. So this post isn’t about how to use AI to teach writing— maybe I’ll write more about that when I have a better sense of the answer. This is about how teachers can create an environment that discourages students from cheating with AI. It’s not foolproof. Sometimes, usually when they are desperate to try anything to pass the class, students cheat.

Teach writing as a process; don’t assign writing as a product.

I kicked off my writing about AI in this blog post from December 2022 “AI Can Save Writing by Killing ‘The College Essay.’” It’s the most frequently read post on the old blog. I wrote it in response to two different articles published in The Atlantic at the time arguing that the new ChatGPT had made writing assignments impossible and irrelevant.

Teaching writing as a process has been the mantra in composition and rhetoric since the late 1970s. Scholars debate the details about what this means, but in a nutshell, teaching writing as a process means setting up a series of assignments that begin with pre-writing invention exercises (freewriting and other brainstorming techniques, for example), activities that lead to rough drafts which are shared with other students through peer review. When students hand this work in, the instructor’s feedback is geared toward revision and (hopefully) improvement on future projects. My first year writing course is typical in that it is about research and students complete a research essay project. But long before we get to that assignment, students complete a series of smaller scaffolded assignments that build up to the larger essay. Again, none of this is new and it is how I was taught to teach writing back in the late 1980s when I started as a graduate teaching assistant.

I teach writing this way because there is good evidence that it works better than merely assigning writing. I also think teaching writing as a process deters plagiarism and other forms of cheating (including with AI). I require students to build their research writing projects through a series of smaller and specialized assignments, and to share their work in progress with other students in peer review. It’s awfully hard to fake this. Also, as I wrote back in July, I now make the process more visible by requiring students to complete their essays from beginning through final revisions on a Google Doc they share with me so I can view the document history and see what it is they did to put their writing together.

In contrast, assigned writing projects have always been much easier to cheat on. Before AI, students cheated with the internet, paper mills, by getting others doing the writing, or (at least according to my father who went to college in the early 1960s) with the library of papers that fraternities kept on hand.

There’s also the issue of the purpose of writing assignments in the first place. Teaching writing as a process is especially important in a course where the subject itself is writing and there is a lot of attention to how students craft their sentences and paragraphs. I realize that’s different from a class where the subject is literature or political science or business administration. But besides the fact that we should teach (not just assign) writing across the curriculum, writing assignments should ask students what they think about something. In research-based courses like freshman comp, students write about the research they did to persuade and inform both me and their classmates about something. It’s one of the reasons why I like teaching this class: my students are always teaching me new things. In my classes that are not as research-based (like Digital Writing), students write and reflect on the assigned readings and other projects of the class in order to share with readers what they think.

Assigned writing tasks tend to seek specific answers based on the content of the course— write about the theme of madness in Hamlet, about the balance of power between the three parts of the federal government, about they key causes of the great recession, etc. In evaluating assigned writing, teachers are less interested in what students think and are more in seeing if students correctly repeated the content of the course the teacher delivered through lectures, activities, and readings. In other words, assigned writing is an assessment tool, like an exam— and in most cases, it probably would be a more effective to use an exam.

Now, teaching writing as a process is A LOT more work for everyone because it means more reading, more teacher commenting, and more checking in with students’ writing as they progress through these assignments. This is why at the vast majority of colleges in the U.S., first year writing courses have 25 or fewer students. Some colleagues who teach lecture courses with 100 or so students who also assign papers have asked me how they’re supposed to teach writing as a process in these courses. My answer is I wouldn’t. Instead, I’d rely on short written responses to readings, quizzes, and exams.

Any course assignment that could be completed without being present in that course is a bad assignment.

A lot of the hype around AI is about how great it is at passing tests— LSAT, GRE, SAT, etc. etc.— and how that is supposed to mean something. But besides the issue of whether AI can pass these tests because it “knows” or because the test questions were part of the content used to create the AI, I think we all know this is not how school works. I mean, if on the first day of a course I introduced all the writing assignments, and then a student showed up on the second day and said “I finished everything— can I get my A now?” the answer, obviously, is no.

Which brings me to this second rule: if a teacher gives students an exam or an assignment that could be successfully completed without ever being in the class, then that’s a bad assignment. This is something I never thought about before AI. In the old old days, I don’t think it made much difference. When I went to college in the mid 1980s, if someone could pass an intro to chemistry exam or a history 101 exam without ever attending the class, what’s the problem? They already had enough mastery of the subject to pass the class anyway. That started to end with students doing Google searches to pass exams, and now that AI can answer all those questions in that history 101 class final in real time, it’s completely over.

AI isn’t attending classes with our students (at least not yet), and so it is not as useful to cheat on exams or assignments that have specific connections to the course. That’s easy enough to do in the kinds of courses I teach, though I have to assume this is more complicated in a subject like calculus where the concepts and methods transcend classroom boundaries. But perhaps an even easier way to address this problem is for the teacher to make participation count as part of the grade. As I discussed in this post, my classes have a participation grade component that counts for about 30% of the grade.

AI detection software doesn’t work and it never will.

A lot of teachers want to skip these first two rules and instead just rely on some kind of app that can detect what parts of a student’s paper were written by an AI. Essentially, they want something like the plagiarism detection software Turnitin many of these teachers have used for years. Though as a quick glance at the Turnitin website reveals, they are shifting from plagiarism detection along with AI detection as well.

Plagiarism detection software has been a divisive topic in writing studies for years. While I know lots of teachers routinely require their students to run their papers through Turnitin for a plagiarism check, I never have done this because I don’t think it’s necessary and I don’t think Turnitin is as good of a tool as many users seem to think. This is especially true with AI detection. According to Turnitin, the false-positive rate for “fully human-written text” is less than 1%, but up to 20% for AI writing. And that is just for the very common and very dumb way people use to cheat with AI: writing a simple prompt and copying and pasting the answer with few changes. I have to assume the ineffectiveness in detecting AI goes down if the human using the AI effectively: for brainstorming, proofreading/editing, chatting with it about revision ideas, and so forth.

It’s a futile effort, especially as the AIs improve and as all of us (including our students) learn more about how to use them for not just cheating. Which leads me to my last point:

Teachers at all levels need to learn more about AI.

Colleges and universities are certainly trying. The two talk things I did last year about AI were both faculty development events, and the attendance at both was pretty good. I know folks here at EMU have held similar events, and I get the impression this is pretty common at most colleges and universities. And faculty have heard of AI at this point, of course.

The problem is I’m not sure any of the faculty development or the oodles of news stories about AI has resulted in any differences in teaching. This is mostly just based on my own sense of things, but I did informally poll my current students (I have about 70 this semester) the other day about AI in other classes they were taking. A few students mentioned classes where they are using AI for various assignments. A few other students mentioned instructors who expressly forbid the use of AI. I asked these students if they thought the instructor had any way of enforcing that; “no.” But the majority of my students said that the topic has not come up at all. That’s a problem.

I’m not saying every teacher now needs to embrace AI and incorporate it into their teaching. Not at all. Besides experimenting with AI in my teaching, I’ve been doing a lot of writing and reading about AI that is (hopefully) going to turn into a research project. I think my teaching with AI experiments are going well, but I honestly don’t know if this is something I’ll continue to do in the future. I feel the same way about AI generally: it probably is going to “change everything,” but it also might end up being another one of those things (like MOOCs, which was the subject of my last major research project) that never lives up to the hype.

What I am saying though is AI is here now and it looks like it’s going to be (probably) a big deal for some time to come. It is not just going to “go away” and it cannot be ignored. A professor or teacher can continue to refuse to engage with AI for valid ethical or personal reasons, but that is not going to stop everyone else from using it. That includes some of our students who are using AI simplistically to cheat, perhaps by feeding the teacher’s writing assignment into ChatGPT and copying/pasting whatever the AI comes up with. Fortunately, it’s pretty easy to spot that sort of AI use. But what teachers cannot easily recognize or stop is a student who uses AI more in the way that it is really meant to be used: as a tool to help/improve what humans do, not replace it.

So start learning about AI, even if you hate it. Mollick’s book is a good place to start.

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Classroom Cell Phone Bans, Before and After Covid

It’s a good idea, though I didn’t used to think so.

When cell phones first showed up 25-20 years ago, I didn’t think much about a policy in my classes because there wasn’t much you could do with those flip phones besides talk to someone, and students knew they couldn’t do that. The main issue back then was students would forget to silence their phone and they’d go off in the middle of class. I’m not proud to admit this, but if a student’s cell phone rang during a discussion, I would make them sing a song for the group. I had heard this was the common practice in Norway and Sweden; the goal was to have a slightly embarrassing but also funny way of reminding students to turn off their phones. There’s no way I would do anything like this nowadays, of course— though it did work pretty well back then.

When smartphones came along, I continued my kind of non-policy policy: don’t let it be a distraction, and if you need to use it for taking notes or looking something up, feel free. A lot college instructors instituted strict bans on both phones and laptops, but these policies always struck me as reactionary and unnecessary. I didn’t want to be that sage on the stage who insists on complete attention from every student for every minute of class. And I didn’t want to be a hypocrite either. In faculty meetings, especially the larger ones, most of my colleagues have their laptops open and they are clearly multitasking. That’s what I do. Anyway, I always had a few students who could not resist the distraction and fondled their devices constantly, but I usually let it go, figuring that these students were mostly hurting themselves and that the overall benefits of these devices outweighed the harms.

In fact, as I blogged about back in June and September 2019, I was on a research release in the fall 2019 semester to work on a project officially called “Investigating Classroom Technology Bans Through the Lens of Writing Studies,” but which I more informally called the “Classroom Tech Bans Are Bullshit” project. I was studying the quantitative/experimental research that had been done about students using laptops and devices in classrooms, most of which was done by folks in education and/or psychology, to try to connect it to the practices and qualitative/observational sorts of research on this in writing studies. I gave a talk about starting this project at the Corridors 2019 conference at Oakland University (and I repurposed this for the online version of the CCCCs in 2020) called “Laptop/Cell Phone Bans are Bullshit (or Maybe Not).” The very short version for now is a lot of the scholarship argued it was better for students to take notes by hand rather than with a device, and that (IMO) was and is bullshit. But the “Maybe Not” part of the talk was about the problems of multitasking, how devices themselves are distracting to others, and the ways in which social media applications are designed to be as addictive as slot machines.

The next semester was the start of Covid. I and almost all my EMU colleagues taught online from the middle of that Winter 2020 semester through Winter 2022, and most high schools in Michigan were all online for those two years as well. That time online changed everyone in higher ed, but especially the students. I blogged about this in more detail here at the end of the 2022-23 school year and after being back to teaching f2f. In brief, two years of online courses was enough for a lot of students to forget they couldn’t behave the same way in person and in a classroom as they did when they were online and alone at home and often still in bed. After all, if you’re a student in a Zoom class with the camera off or in an asynchronous online class, no one cares if you’re texting or watching cat videos as the same time as you’re doing online class stuff. The freshmen had more problems with distraction then the juniors and seniors, but even some of the better students in the upper-division classes could not stop staring at their phone right in the middle of discussions.

In other words, I went from a few students not paying attention to most of them not paying attention. This was obviously bad for students, but it was also bad for me. Like I said, I don’t need to be the at the center when I’m teaching. But when a lot of students are ignoring everything and everyone around them, including me, it’s hard to not take that personally. And at the end of the day, my students’ behavior was just rude.

So in Fall 2023, I started doing something I never thought I’d do: I began class by asking students to place their cell phones on a table in the front of the room. Their phones are in sight, but out of reach. I’ve had a few students resist this by giving some reason (kids, ill relatives, etc.) why they must be in contact at all times. I tell them to leave their phones on, and if it rings, take the call in the hall. (No one has had to take a call). Also worth mentioning: I explain why I do all this by sharing a version of what I just wrote here, including the reality that learning how to participate in a f2f conversation with other humans without staring at your cell phone is a good adulting skill to have.

The class discussions improved immediately. Sure, some students grumbled about it, but no one complained on the end of the semester course evaluations where students tend to complain about all sorts of things. Now, I only do this for the first three weeks or so of the semester. After they get the idea, I tell them they can keep their phones— as long as they remain face down on the desk or otherwise stowed away. There is often a backsliding moment where I once again collect their phones, but that too is a teachable moment.

This semester when I first asked students to put their phones on the table in the front of the room, none of them hesitated or seemed surprised. I suspect I was not the first teacher they have had since Covid with a similar policy. The other day was the first class meeting in my more advanced class where I told students they could keep their phones, and one student went ahead and put their phone on the table I had previously set up anyway. “I think this helps,” she said.

For what its worth, I think this bad behavior with cell phones is fading, at least at the college level. A lot of my students have heard about and thought about cell phone addiction and the problems of various social media platforms, so I do think that there is a lot more awareness of the problem of staring at the phone too much. I think we’re soon returning to before Covid times with cell phones— and lots of other things, too.

I used to be against these bans, but not anymore

Cell phone bans in K-12 schools have been in the news at the start of this school year. Several states have instituted measures to ban cell phones in elementary and secondary schools, and bans are happening in prestigious private schools as well. The research suggests these policies help students to pay attention in class and also to socialize and interact with their peers in real time. Interestingly enough, a lot of the objections to these policies are coming from parents who are used to being in contact with their children at all times.

It’s a good idea, though I didn’t used to think so.

When cell phones first showed up 25-20 years ago, I didn’t think much about a policy in my classes because there wasn’t much you could do with those flip phones besides talk to someone, and students knew they couldn’t do that. The main issue back then was students would forget to silence their phone and they’d go off in the middle of class. I’m not proud to admit this, but if a student’s cell phone rang during a discussion, I would make them sing a song for the group. I had heard this was the common practice in Norway and Sweden; the goal was to have a slightly embarrassing but also funny way of reminding students to turn off their phones. There’s no way I would do anything like this nowadays, of course— though it did work pretty well back then.

When smartphones came along, I continued my kind of non-policy policy: don’t let it be a distraction, and if you need to use it for taking notes or looking something up, feel free. A lot college instructors instituted strict bans on both phones and laptops, but these policies always struck me as reactionary and unnecessary. I didn’t want to be that sage on the stage who insists on complete attention from every student for every minute of class. And I didn’t want to be a hypocrite either. In faculty meetings, especially the larger ones, most of my colleagues have their laptops open and they are clearly multitasking. That’s what I do. Anyway, I always had a few students who could not resist the distraction and fondled their devices constantly, but I usually let it go, figuring that these students were mostly hurting themselves and that the overall benefits of these devices outweighed the harms.

In fact, as I blogged about back in June and September 2019, I was on a research release in the fall 2019 semester to work on a project officially called “Investigating Classroom Technology Bans Through the Lens of Writing Studies,” but which I more informally called the “Classroom Tech Bans Are Bullshit” project. I was studying the quantitative/experimental research that had been done about students using laptops and devices in classrooms, most of which was done by folks in education and/or psychology, to try to connect it to the practices and qualitative/observational sorts of research on this in writing studies. I gave a talk about starting this project at the Corridors 2019 conference at Oakland University (and I repurposed this for the online version of the CCCCs in 2020) called “Laptop/Cell Phone Bans are Bullshit (or Maybe Not).” The very short version for now is a lot of the scholarship argued it was better for students to take notes by hand rather than with a device, and that (IMO) was and is bullshit. But the “Maybe Not” part of the talk was about the problems of multitasking, how devices themselves are distracting to others, and the ways in which social media applications are designed to be as addictive as slot machines.

The next semester was the start of Covid. I and almost all my EMU colleagues taught online from the middle of that Winter 2020 semester through Winter 2022, and most high schools in Michigan were all online for those two years as well. That time online changed everyone in higher ed, but especially the students. I blogged about this in more detail here at the end of the 2022-23 school year and after being back to teaching f2f. In brief, two years of online courses was enough for a lot of students to forget they couldn’t behave the same way in person and in a classroom as they did when they were online and alone at home and often still in bed. After all, if you’re a student in a Zoom class with the camera off or in an asynchronous online class, no one cares if you’re texting or watching cat videos as the same time as you’re doing online class stuff. The freshmen had more problems with distraction then the juniors and seniors, but even some of the better students in the upper-division classes could not stop staring at their phone right in the middle of discussions.

In other words, I went from a few students not paying attention to most of them not paying attention. This was obviously bad for students, but it was also bad for me. Like I said, I don’t need to be the at the center when I’m teaching. But when a lot of students are ignoring everything and everyone around them, including me, it’s hard to not take that personally. And at the end of the day, my students’ behavior was just rude.

So in Fall 2023, I started doing something I never thought I’d do: I began class by asking students to place their cell phones on a table in the front of the room. Their phones are in sight, but out of reach. I’ve had a few students resist this by giving some reason (kids, ill relatives, etc.) why they must be in contact at all times. I tell them to leave their phones on, and if it rings, take the call in the hall. (No one has had to take a call). Also worth mentioning: I explain why I do all this by sharing a version of what I just wrote here, including the reality that learning how to participate in a f2f conversation with other humans without staring at your cell phone is a good adulting skill to have.

The class discussions improved immediately. Sure, some students grumbled about it, but no one complained on the end of the semester course evaluations where students tend to complain about all sorts of things. Now, I only do this for the first three weeks or so of the semester. After they get the idea, I tell them they can keep their phones— as long as they remain face down on the desk or otherwise stowed away. There is often a backsliding moment where I once again collect their phones, but that too is a teachable moment.

This semester when I first asked students to put their phones on the table in the front of the room, none of them hesitated or seemed surprised. I suspect I was not the first teacher they have had since Covid with a similar policy. The other day was the first class meeting in my more advanced class where I told students they could keep their phones, and one student went ahead and put their phone on the table I had previously set up anyway. “I think this helps,” she said.

For what its worth, I think this bad behavior with cell phones is fading, at least at the college level. A lot of my students have heard about and thought about cell phone addiction and the problems of various social media platforms, so I do think that there is a lot more awareness of the problem of staring at the phone too much. I think we’re soon returning to before Covid times with cell phones— and lots of other things, too.