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 thencame 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 grievethat.’ ”
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
I have three brief and not-so-original thoughts about all this.
First, students don’t all of a sudden “hate” AI; rather, their feelings about AI are complex and contradictory, just like they are for me and pretty much everyone else who has thought about AI for more than 15 minutes. Well, everyone other than tech bro AI evangelists and/or the rich already types who give commencement speeches like this. I have never thought “hardcore use AI to do 90% of the work” cheating is as common as MSM wants us to believe, but students all use AI in ways I have no problems with, though sometimes I think they might drift into the grey areas of cheating. The students in first-year writing tend to be more positive about AI than the juniors, seniors, and MA students I have in upper-level courses, which might also reflect why graduating students might be more inclined to boo. After all, the freshmen in an annoying gen ed composition class might see AI as a great short cut for getting through something they don’t really want to do in the first place, while the students who are graduating and who have been looking at a dismal job market and hearing more and more about how AI is threatening to make their degrees irrelevant have a good reason to boo. But I think a lot of those graduating students have the same attitudes about social media as well: they hate it, but they also can’t quit it, either.
Again, I feel the same way. I don’t want students to use AI to cheat their way through college (generally, and in my classes in particular), but look, I use AI too, and in ways I suspect is pretty common among students as well. I don’t like the data centers springing up around me here in SE Michigan, but I also think the harms are exaggerated, and it hasn’t stopped me from using Claude to brainstorm, to proofread, and to even suggest a sentence or a paragraph or more when I’m stuck on something, particularly some sort of bureaucratic busywork task.
Second, students weren’t booing AI; they were booing the billionaire commencement speakers cheerfully telling young people about to start their adult lives and careers that the future is not them but AI, an emerging technology that is going to make that smiling billionaire even richer, and there’s nothing today’s graduates can do about it. That is definitely the general impression I got from most of my students in the Writing, Rhetoric, and AI course I taught last year. Their attitudes about AI in general were all over the map. Some students remained adamant AI refusers to the end, some students started and remained AI enthusiasts, and most students began and ended somewhere in-between.
My goal for the class wasn’t about convincing students to use or not use AI; rather, my goal was for students to leave the class skeptical about AI, regardless of how they did (or didn’t) use it. I want students (and everyone else) to presume that AI’s output will have to be fact-checked, to not be sucked in by AI’s sycophancy, to never forget AI has no more understanding of the world around it than a toaster, to never trust the puffed up claims of AI’s capabilities, and, above all else, to never forget that all of this is unregulated and unequally enriching a few few people in Silicon Valley, and really REALLY enriching the likes of Elon Musk, Sam Altman, and Donald Trump. I don’t know how successful I was on all these points, but none of my students like any of these technologarchy dudes who are palling around with Trump, and I’ve never had a student disagree with any of the readings or discussions we’ve had about the need for AI regulation.
And third, to quote Barack Obama’s famous line, don’t boo, vote. The only way the government will finally regulate AI (and let’s add social media to that list while we’re at it) is if it becomes a campaign issue. And frankly, I think that these boos and the subsequent viral videos and MSM coverage suggest to me it very well may be a campaign issue in 2026. I’m almost certain it will be in 2028.
I don’t know how likely it is for this to boost turnout or not, since young people still don’t show up at the polls as much as they should, but maybe this is something that will get recent grads and current students out in November. Michelle Goldberg’s Op-Ed “Why College Grads Are Booing Their Commencement Speakers” (she and I are more or less making the same argument) also includes a link to the more complete version of the recent May 2026 Times/Siena National Poll of Registered Voters, and if you dig through that data a bit, there are signs of what might be (hopefully) coming. 62% of 18-29 year olds say that if the election for Congress were held now, they would vote for the generic Democratic candidate. 60% “strongly disapprove” of Donald Trump, and 47% think that AI is “mostly bad,” both of which are larger percentages than any other demographic. 1
Let’s see if anyone remembers these boos in November, but I am hoping that this is helps get more people to pay attention to AI and to recognize how citizens can still do something about it.
Not that the politics of AI split neatly along partisan lines. One of the reasons why Michigan is getting a lot of data centers built in it right now is our Democratic Governor, Gretchen Whitmer, is all for it. And on the right, you’ve got people like Steve Bannon, Tucker Carlson, and Glenn Beck all raising various objections about AI. ↩︎
A couple of weeks ago, I learned that Marcel Cornis-Pop passed away at the age of 79. I had heard a while before this that he had been ill for some time.
Marcel, who often spelled his last name Cornis-Pope, I think because that’s closer to how it was pronounced in Romanian, was a long-time faculty member at Virginia Commonwealth University who came to VCU in 1988, the same year I began work on my MFA in fiction writing. Back in the day, he was quite the influence and mentor.
Our paths actually overlapped before VCU, sort of. Marcel, along with his wife (I believe his children were born in the U.S.), came to America from Romania, first to the University of Northern Iowa in my hometown of Cedar Falls. I have a good friend who took a class or two from him while he was at UNI on Fulbright Scholar appointment. At the time, Romania was a Soviet satellite and one of the most repressive and brutal regimes in the Eastern Bloc, led by Nicolae Ceaușescu and his wife Elena. I don’t know if Marcel was ever imprisoned or threatened per se, but I do remember him talking about how he was involved in the underground publishing and distribution of books published by famous American authors. So I was always under the impression that, really, he had to leave Romania.
Marcel was my introduction to critical theory, I believe in my first semester at VCU. I don’t remember a lot of the details, but there are two things about that seminar that stand out for me still. First, Marcel was not all that interested in “covering” every theory and topic he had on his syllabus if the natural progression of the course took things in a different direction. Someone told me (it might have been my friend at UNI) that they had a class with him where Marcel and his students abandoned most of the planned readings and spent the entire semester analyzing the Henry James short story/novella “The Figure in the Carpet.” Second, the one school of thought/critical theory that he was not at all interested in teaching or entertaining in any serious way (at least way back when) was Marxism, probably for obvious reasons.
As an undergraduate English major at the University of Iowa in the mid-1980s, I had no direct exposure to literary/critical theory in any of my classes. I think that was fairly common then. I knew a couple of different people from Iowa who went off to PhD programs in English after undergrad and then bailed out early when they figured out that at the graduate level, it was no longer about reading and “appreciating” literature. I found the theory all quite fascinating, in no small part because of how Marcel introduced it to his students.
I took an independent study with Marcel, I think in my second year. I remember meeting with him about what this independent study would be about. I suggested a couple of different authors he rejected, and then I mentioned that I had read Thomas Pynchon’s The Crying of Lot 49 as an undergraduate, and I think I had also by that point read V. on my own. That piqued his interest. I said, “I am kind of interested to try to read Gravity’s Rainbow,” but…” and before I could even get out the rest of my sentence, that Gravity’s Rainbow might be way too much of a project to take on, Marcel said, “That, do that. I’ll do an independent study with you about Gravity’s Rainbow.”
That was the most intense self-study experience in close reading that I have ever had. For those unfamiliar: Gravity’s Rainbow is a 760-page novel that is perhaps best compared to books like James Joyce’s Finnegans Wake in that the complexity of it all is intentionally baffling. Sometimes it would take me a couple of days to read five or six pages of it, and without the help of the excellent book by Steven Weisenburger, A Gravity’s Rainbow Companion: Sources and Contexts for Pynchon’s Novel,I’m not sure I would have made it through. So an intense reading experience, and I did finish the book, though I don’t know if I could tell you now anything about what it was “about.” As I recall, I wrote an essay that focused on the trajectory of the V2 rocket; the book begins with the line “A screaming comes across the sky,” and it ends on the last page in a section called “Descent,” where “it was not a star, it was falling, a bright angel of death.”
Mostly though, I remember Marcel for various pieces of advice about academia at the time. I asked him his thoughts on whether or not I should go into a PhD program and what kind of program, something more like literary studies, or something like this new thing I was exposed to at VCU called “composition and rhetoric.” The main thing he advised, something I tell students now when they ask about graduate school, is to go as quickly as possible because there is no point in being a graduate student any longer than necessary. I perhaps took that to an extreme in my PhD (I finished in 3 years), but I still think he was right about that.
Marcel went on to a long and illustrious career at VCU: he was chair of the department in the early 2000s, was one of the founders of a PhD program in Media, Art, and Text, and I believe at one point he was a dean as well. I never thought about it when I knew him way back when (our paths crossed a couple of times after I left Richmond in 1993, at the MLA convention and only briefly), but he too was more or less at the beginning of his academic career in the US when we met.
I think I first read someone bring up the “blue-book solution” for AI cheating shortly after ChatGPT exploded in fall 2022, but as I recall it, it was a joke. “Ha ha, now that AI can write as well as students, we’ll have to make them write by hand and while we’re watching. Ha ha!” My standard comment on social media to posts/articles about going back to handwritten and timed writing in the name of stopping cheating was “why not make them use a stone and chisel?” Ha ha.
Well, here we are three years in, and now blue-books really are a “solution” to AI. According to the Wall Street Journal,sales are up– way up. Earlier in August, Katie Day Good had an op-ed in The Chronicle of Higher Education titled “Bring Back the Blue-Book Exam,” and then at the end of August, Clay Shirky had an op-ed in The New York Times called “Students Hate Them. Universities Need Them. The Only Real Solution to the A.I. Cheating Crisis.” Both of these pieces make (mostly) serious arguments that the only way we can deal with/fight against AI cheating– a “crisis,” apparently– is to go back to the way we used to do these things. Way back.
Jeez.
Katie Day Good teaches at Calvin University in Grand Rapids and is “a media historian and cultural scholar of emerging technologies in education and everyday life.” A lot of her current work seems to be about “cultural movements to disconnect from digital technology and take a ‘digital sabbath,'” so maybe this return to handwriting is kind of in her research/scholarship lane.
But Shirky?!? Here’s a guy who became famous as a new media evangelist, who, in the book Here Comes Everybody, enthusiastically writes about crowd-sourcing everything and the joys of a world where content is both consumed and produced by users. His by-line describes his current job as “a vice provost at N.Y.U.” where he helps “faculty members and students adapt to digital tools.” This is the guy who is suggesting a return to blue-books and oral exams?!?
Jeez again.
Before I get more into the specifics of Good’s and Shirky’s essays, I want to bring up three “bigger picture” problems with blue-books and similar calls to return to the 19th century, problems that don’t come up in either one of these essays. First, blue-books, along with oral exams and other face to face assessments, obviously won’t work for an online class, especially ones that are asynchronous. And roughly speaking, a little over half of all college students take at least one class online, and about a quarter of all college students only take classes online. So what is an online teacher to do, collect blue-books by snail mail?
Second, timed writing like this is bad pedagogy, and people in writing studies have known this forever. No one is an especially good writer when they are being timed and watched, not to mention with no opportunity for things like feedback from peers or revision. I think these exercises are more like filling out a form than writing, and honestly, a better solution is some kind short answer/multiple-choice exam.
Third, and my apologies for offending anyone who thinks that blue-books might be a good idea, this is just fucking lazy. Good and Shirky are suggesting it’s just too much work for a teacher to change the assignment in some way where it is either not effective to use AI or that leans into AI in specific and useful ways. Shirky dismisses doing this work thusly: “We cannot simply redesign our assignments to prevent lazy A.I. use. (We’ve tried.)” It’s just too hard to do anything differently! Instead, Good and Shirky are saying we should travel back in time and just keep pretending that there is no other possible way to change how we do things.
I saw a version of this same logic at the beginning of my career in the early 1990s when word processing and internet technologies were emerging. There were similar efforts then to restrict student access to things like spelling and grammar checkers, or banning students from using online sources. Teachers– especially English teachers, I think– do not like to change how they teach, even when what and how they teach is altered by technology. As a result, teachers often follow the lazier solution, which is to ban the technology. Thus blue-books.
Both Good and Shirky begin the same way all of these AI freak-out essays begin: we can’t trust students at all and every one of them cheats on everything, especially now that it is so easy with AI. Good writes the new capabilities of AI made her rethink the “take-home essays” she used to assign in favor of blue book exams, presumably (in part) because of the possibility of cheating. Shirky begins with a vague story about a philosophy professor he met with who said he simply could not get “several” of his students to stop cheating with AI.
“Take-home essays” (I think she means what I’d call a take-home essay exam) have always required teachers to trust that their students won’t cheat. After all, when the student is working “at home,” there is nothing to stop that student from getting help from others and the internet, or even to get someone else to complete the assignment for them. I don’t know if Good was ever concerned about her students cheating on their take-homes before AI (she doesn’t seem to have been worried), but she started using blue books based merely on the possibility of cheating with AI.
As for Shirky’s philosophy professor colleague: I don’t know what several of them used AI to cheat means (are we talking half the class? three students? what?), but to me, the solution is obvious: fail them. I am going to assume (perhaps wrongly) that this hypothetical professor Shirky cites has a policy that does not allow students to use AI, and I’m also going to assume that the professor explained this policy and the consequences of using AI, which (again, just guessing) was failure. So, what exactly is the problem? If it’s that easy for the professor to catch students cheating, why not just enforce your policy and fail those students?
My own approach has been to be very up-front with students about what I think is and isn’t cheating with AI (and the short version is it is cheating if the writer directly copies/pastes AI output into something that the writer said they wrote). If I think a student is cheating with AI– which, for me, is based on my admittedly not perfect sense of what a particular student’s writing “sounds like,” and the document history of their Google Doc— I talk to them about it. In the last year and a half or so, I have had a lot more students cheating than I did before AI, meaning I’ve had to have a lot more of those uncomfortable conversations with cheating students. I give them another chance to do the assignment right and almost all of them managed to turn things around and pass the class just fine. I had a couple of repeat cheaters last year and I failed them on the spot.
In a post on Substack where she was explaining why she’s using AI detection software, Anna Mills described a confrontation she had with a student who adamantly denied he cheated with AI even though Mills is almost certain he did. After all, students also know AI is difficult to detect. I get it, and it can be hard to prove AI cheating. I’m sure I’ve had students who have managed to get away with some AI that I would have counted as cheating had I known. But every time I have had that “I think you cheated” conversation with a student, be it with AI or old-fashioned plagiarism, that student has confessed, often in tears.
As I’ve said many MANY times before:
Most students do not want to cheat.
Students cheat when they are failing and they are desperate.
Students who cheat are not criminal masterminds and are easily caught.
All that said, it does depend on what exactly counts as cheating, and I don’t think it is cheating if students use AI as part of their process.
Good views this return to handwritten essays as a “balm for my tech-weary soul.” She goes on:
My students’ handwritten essays brim with their humanity. Each page conveys personality, craft, voice, and a “realness” that feels increasingly scarce in our screen-saturated, algorithmically-distorted information environment. As such, handwriting accomplishes something greater now than ever before in education: It restores a sense of trust to the student-teacher relationship that has been shaken by AI.
In the next paragraph, she also brings up some of the other beliefs in handwriting’s “authenticity,” that handwriting helps people make better connections in the brain than typing, that it results in better notes, etc. Well, right before Covid struck, I was researching laptop/cellphone bans in f2f classes and requiring students to take notes by hand. Long story short, the studies I’ve seen about comparing laptop notes with handwritten notes in classrooms– mostly quantitative/experimental methodologies coming out of Education/Psychology– strike me as flawed for all kinds of different reasons. And the claims about handwriting as a tool for judging one’s “authenticity” and identity and the like have been debunked by many researchers– I would recommend in particular the very readable and well-researched book by Tamara Thorton, Handwriting in America: A Cultural History. I also have my own baggage as someone with terrible handwriting, who remembers failing handwriting in the fourth grade, and also as someone who has typed everything I could type since I was in high school.
So for me, the idea that handwriting is “better” and that it is both possible and reasonable to make judgements about the writer based on their handwriting, that more of one’s humanity is revealed through handwriting– that’s all bullshit.
Shirky doesn’t seem to think that handwriting has the same kind of “Magic” that Good sees in her students’ writing, and he admits that a lot of students and faculty are skeptical of this change. But in the name of rigor and a “more relational model of higher education,” we must return to the way things were done, and he then proceeds to cherry-pick different speech and writing assignments all the way back to the 1300s. In the process, I think he indirectly describes a lot of the pedagogy common to small discussion classes like first year writing: requiring students to meet during office hours, entering into “Socratic dialogue or simple Q&A” with the class, and so forth.
“There is the problem of scale,” with old techniques like oral exams, Shirky admits. “With some lecture classes in the hundreds of students, in-class conversation is a nonstarter.” Well, wait a minute: maybe the past practices we need to return to are smaller classes. Perhaps one of the reasons why I am not that worried about AI cheating is that I feel like I actually do most of these things in the classes I teach now. My students end up doing a lot of writing— discussion posts to readings, scaffolded essays part of the research project, and drafts of work in progress— along with plenty of discussing as well.
So what if every class were no more than 25 students? That wouldn’t be logistically possible, and it wouldn’t be a complete solution to AI cheating either, of course. But it’s a start, and we’ve also known for a very long time that lecturing is also a terrible pedagogy.
I will say this: both end on a vaguely positive note, even if their optimism about the future does not strike me as particularly realistic. Good takes a lot of pleasure in this return to the past, connecting us back to Plato and education as “not a process of pouring knowledge into an empty soul, but as a ‘turning around’ of the soul in the direction of beauty and truth.” She sure seems to think that those blue-books and handwriting can accomplish a lot!
And after spending the rest of his op-ed saying there’s nothing to be done about AI except return to “technology free” classrooms, Shirky ends by predicting higher education will adapt. “Despite frequent pronouncements that college is doomed because students can now get an education from free online courses or TV or radio or the printing press, those revolutions never flattened us. Nor will A.I.” We’ll see. I want to believe Shirky is right, but….
I’m kind of surprised, but I am still coming across essays and Substack posts and such where teachers/professors are freaking out about AI. ChatGPT came out in November 2024, more than two and a half years ago. I would have thought folks would have moved on from these “writing assignments are dead” kinds of pieces by now, but no–throw a brick out a window and you’ll hit one. Here’s a good recent example: “The Death of the Student Essay– and the Future of Cognition” by Brian Klaas. The title is the gist of it– I’ll come back to Klaas’ essay later.
It’s not that these “the death of the assigned paper and now I’m going to make my students chisel everything into stone” eulogies are entirely wrong. As I’ve been saying for a few years now, AI means teachers who used to merely assign writing with no attention to process can’t do that anymore. AI means teachers need to adjust their approach to education. It doesn’t mean that all of a sudden everyone will stop learning.
And before I go any further, I kind of think what I’m writing about here is Captain Obvious wisdom, but here it goes:
Here’s what I mean:
Learning is about gaining knowledge and skills, and humans do this in lots of different ways— play, practice, observation, experiences, trial and error. We learn things from others and the world around us, and while learning is often frustrating, I think learning is pleasurable and fulfilling. All of us start learning right after we’re born— how to get attention, to crawl, to roll, to walk, etc.— through help from our parents of course, but also on our own.
Some things we learn through exposure to the world around us; for example, speech. Of course, parents and others around babies try to help the process along (“say da-da!”), but mostly, babies and toddlers learn how to speak by picking up on how the humans around them are speaking. And as anyone who has parented or spent time around a chatty pre-schooler knows, sometimes it can be challenging to get them to stop talking.
On the other hand, some things we need to be taught how to do by others— not necessarily teachers per se, but other people who know how to do whatever it is we’re trying to learn. Reading and writing are good examples of this, which is one of the ways literacy is different from speech (or, as Walter Ong might have put it, orality). This is one of the reasons why, up until a few hundred years ago, the vast majority of people were illiterate.
Except Tarzan. This is a bit a of tangent, but bear with me:
Edgar Rice Burrough’s famous novel Tarzan of the Apes is an extraordinarily interesting, odd, offensive novel, and most of the adaptations of the book gloss over its over-the-top fantasy and weirdness. At the beginning of the book, Tarzan’s parents are put ashore in Africa after a mutiny on their ship, and his father builds a cabin stocked with the goods Tarzan’s parents were traveling with, including a lot of books. The parents are killed by “apes” (which are somehow different than gorillas, but that’s a different story) and the baby that becomes Tarzan is raised by them.
When he is around 10, Tarzan stumbles across the cabin with its books, and, long story short, he teaches himself to read. He does this by staring at the the marks on the pages of a children’s book, letters that looked like little bugs next to a picture of a strange ape that looked like him, and he figured out those little bugs were b-o-y. ”And so he progressed very, very slowly, for it was a hard and laborious task which he had set himself without knowing it—a task which might seem to you or me impossible—learning to read without having the slightest knowledge of letters or written language, or the faintest idea that such things existed.” Basically, Burroughs is saying “yeah, I know, I know, but just go with it.”
In contrast, education is a technology. To quote from my book, education is the “formal schooling apparatus that enables the delivery of various kinds of evaluations, certificates, and degrees through a recognized, organized, and hierarchical bureaucracy. It’s a technology characterized by specific roles for participants (e.g., students, teachers, professors, principals, deans) and where students are generally divided into groups based on both age and ability.” This is an argument I belabor in some detail— you can read more about it here with the right JSTOR access— but I’m sure anyone reading this has had first-hand experience with what I’m talking about.
Learning and education are a Venn diagram: when schooling goes well, education facilitates learning, and successful learners are rewarded by their educational experiences with degrees and certifications. But sometimes schooling does not go well. For whatever reason, some students, especially in courses like first-year writing, just do not want to be there. That was the case for me in a lot of high school and college classes. Sometimes, it was because of bad teaching, but more often than not, it was my lack of interest in the subject, or the fact that it was a subject I was (and am still) not very good at– anything having to do with math or foreign languages, for example. Whatever the reason though, I knew I had to push through and do the course in order to move on toward finishing the degree.1
Everyone involved in education gets frustrated by the bureaucracies and rules of it, especially when the system that is education gets in the way of learning. For example, even professors in business colleges are annoyed by students who are not there to learn anything but to just get the credential and the job. Students are often annoyed at their professors who don’t seem to know how to help them learn because they are just so bad, and everyone is annoyed with all of the other curricular hoops, paperwork, and constant grading. And that’s because learning is the fun part, and the important part!
But here’s the thing: the occupational, monetary, class, and cultural values of academic credentials– that is, the degree as a commodity– are only possible with the technology of education. It is why students and their families (our “customers”) are willing to pay universities so much money. As I wrote in my book, “Students would probably not enroll in courses or at universities where they didn’t feel they were learning anything, but they certainly would not pay for those courses if there was no credit toward a degree associated with them.”
Educators, and I like to think most students as well, are attracted to the university because they enjoy learning and place a high value on learning for the sake of learning: that is, the humanness of it all. But look, I don’t know anyone who is a teacher or a professor who does this work just for the love of it. This is a job, and if I didn’t get paid, I wouldn’t be doing it. Besides, there is a lot of value in education’s certifications and degrees in all of our day-to-day lives. I find it reassuring that the engineers who designed the car I drive (not to mention the roads and bridges I drive on) have degrees that certify a level of expertise. I am glad my dentist went to dental school, that my doctor went to medical school, and so on.
So, to circle back to how this connects with AI in general and with Brian Klaas’ essay in particular: I think the vast majority of the “AI and the end of student writing” essays I have read (including this one) are incorrect in at least two ways. The first way, which I have been writing about for a while now and which I mentioned at the beginning of this post, is about the distinction between assigning writing as a product and teaching writing as a process. Like most teachers, Klaas does not seem to have a series of assignments, peer reviews, opportunities to revise, etc.; he’s assigning a term paper and hoping students write something that demonstrates they understood the content of the class. Klaas writes “Previously,” meaning before AI, “there was a tight coupling between essay quality and underlying knowledge assembled with careful intelligence. The end goal (the final draft) was a good proxy for the actual point of the exercise (evaluating critical thinking). That’s no longer true.” By quality, I think Klaas means grammatical correctness, and I don’t think that has ever been the primary indicator of a student’s critical thinking. Yes, the students who write the best essays also tend to write in grammatically correct prose, but that’s a pretty low bar. And don’t even get me started on the complexities scholars in my field could unpack in Klaas’ claim about the “coupling” between “quality” and “intelligence.”
Klaas also doesn’t seem that interested in doing the extra work of teaching writing either. He writes:
More than once, a student quite clearly used ChatGPT, but to try to cover their tracks, they peppered citations for course readings—completely at random—throughout the text. For example, after a claim about an event in 2024 in Bangladesh, there was a citation for a book written ten years earlier—about the Arab Spring. “Rather impressive time machine they must have had,” I commented.
After a career working to develop expertise, countless hours teaching, and my best attempts to instill a love of learning in young minds, I had been reduced to the citation police.
I’m sure Klaas is correct and this student was cheating, but I’ve got some bad news for him: if you want students to use proper citation style, you have to teach it. And, as I’ve written about before, teaching citation is even more important with AI for a variety of reasons, including the fact that AI makes up citations like this all the time.
But again, Klaas doesn’t want to teach writing anyway; “Next year, my courses will be assessed with in-person exams.” Well, if Klaas was assigning writing so students could write essays that are like answers to questions in an exam, maybe he should have just given an exam in the first place.
This leads me back to my Captain Obvious Observation: learning and education are not the same thing. Yes, any of us can use AI as a crutch to skip our innate needs and desires to learn, but AI’s real impact is how it disrupts the technologies and apparatuses of education. Klaas says as much, ultimately. He points out that AI probably means “universities will need to find ways to certify that grades are the byproduct of carefully designed systems to ensure that assessments were produced by students.” And in passing, he writes “We must not fall into the trap of mistaking the outputs of writing (which are increasingly substitutable through technology) from the value of the cognitive process of writing (which hones mental development and cannot be substituted by a machine).”
Exactly. And I think we know how to do that.
First, we have to teach students about AI, and that’s especially true if we don’t want them to use it. For example, had Klaas explained to his students that AI makes up citations all the time, they might not have tried to cheat like that in the first place. It’s not enough to just say “don’t use it.”
Second, we need to lean more into learning, and we need to be more obvious in explaining to our students why this is important. Teachers need to do a better job of explaining to students and ourselves why we ask students to do things like write essays in the first place. It’s not just so teachers have something to assess as evidence of what grade that student deserves. That’s education. Rather, we have students write essays (or write code, do math problems, conduct mock experiments, etc.) because we’re hoping they might learn something.
Third, we need to change how we teach in ways that discourage relying too much on AI and encourage students to do the learning themselves. Unfortunately, this is a lot of work, and I think this is actually what Klaas and others lamenting the “death” of student writing are really complaining about. The “write a paper about such and such” assignments faculty have been relying on forever won’t work anymore. Though maybe that assignment you thought worked well before AI actually wasn’t that effective either?
“Moving on” did not necessarily mean finishing the course– I dropped several as an undergraduate to avoid a D or an F. Also, I was lucky and unlucky as an undergraduate when it came to my two weakest school subjects. For my degree in English back in the 1980s, I did not have to take any math courses at all. However, I was required to have four semesters of a foreign language. If I had had to take the math class that my EMU English majors have to take as part of general education, I’m not sure I would have made it. On the other hand, EMU students do not have to take a foreign language. I studied German, and I was terrible at it, which is why it took me about seven tries (including a summer school class) to pass the four semesters I needed. ↩︎
I am home from the 2025 Conference for College Composition and Communication, after leaving directly after my 9:30 am one man show panel and an uneventful drive home. I actually had a good time, but it will still probably be the last CCCCs for me. Probably.
The first part of the original title, “Echoes of the Past,” was just my lame effort at having something to do with the conference theme, so disregard that entirely. This has nothing to do with sound. The first part of my talk is the part after the colon, “Considering Current Artificial Intelligence Writing Pedagogies with Insights from the Era of Computer-Aided Instruction,” and that is something I will get to in a moment, and that does connect to the second title,
“The Importance of Paying Attention To, Rather Than Resisting, AI.” It isn’t exactly what I had proposed to talk about, but I hope it’ll make sense.
So, the first part: I have always been interested in the history of emerging technologies, especially technologies that were once new and disruptive but became naturalized and are now seen not as technology at all but just as standard practice. There are lots of reasons why I think this is interesting, one of which is what these once-new and disruptive technologies can tell us now about emerging writing technologies. History doesn’t repeat, but it does rhyme, and history prepares the future for whatever is coming next.
For example, I published an essay a long time ago about the impact of chalkboards in 19th-century education, and I’ve presented at the CCCCs about how changes in pens were disruptive and changed teaching practices. I wrote a book about MOOCs where I argued they were not new but a continuation of the long history of distance education. As a part of that project, I wrote about the history of correspondence courses in higher education, which emerged in the late 19th century. Correspondence courses led to radio and television courses, which led to the first generation of online courses, MOOCs, and online courses as we know them now and post-Covid. Though sometimes emerging and disruptive technologies are not adopted. Experiments in teaching by radio and television didn’t continue, and while there are still a lot of MOOCs, they don’t have much to do with higher education anymore.
The same dynamic happened with the emergence of computer technology in the teaching of writing beginning in the late ’70s and early ’80s, and that even included a discussion of Artificial Intelligence– sort of. In the course of poking around and doing some lazy database searches, I stumbled across the first article in the first issue– a newsletter at the time– of what would become the journal Computers and Composition, a short piece by Hugh Burns called “A Note on Composition and Artificial Intelligence.”
Incidentally, this is what it looks like. I have not seen the actual physical print version of this article, but the PDF looks like it might have been typed and photocopied. Anyway, this was published in 1983, a time when AI researchers were interested in the development of “expert systems,” which worked with various programming rules and logic to simulate the way humans tend to think, at least in a rudimentary way.
Incidentally and just in case we don’t all know this, AI is not remotely new, with a lot of enthusiasm and progress in the late 1950s through the 1970s, and then with a resurgence in the 1980s with expert systems.
In this article, Burns, who wrote one of the first dissertations about the use of computers to teach writing, discusses the relevance of the research in the field of artificial intelligence and natural language processing in the development of Computer Aided Instruction, or CAI, which is an example of the kind of “expert system” applications of the time. “I, for one,” Burns wrote, “believe composition teachers can use the emerging research in artificial intelligence to define the best features of a writer’s consciousness and to design quality computer-assisted instruction – and other writing instruction – accordingly” (4).
If folks nowadays remember anything at all about CAI, it’s probably “drill and kill” programs for practicing things like sentence combining, grammar skills, spelling, quizzes, and so forth. But what Burns was talking about was a program called Topi, which walked users through a series of invention questions based on Tagmemic and Aristotelian rhetoric.
There were several similar prompting, editing, and revision tools at the time. One was Writer’s Workbench, which was an editing program developed by Bell Labs and initially meant as a tool for technical writers at the company. It was adopted for writing instruction at a few colleges and universities, and
John T. Day wrote about St. Olaf College’s use of Writer’s Workbench in Computers and Composition in 1988 in his article “Writer’s Workbench: A Useful Aid, but not a Cure-All.” As the title of Day’s article suggests, the reviews to Writer’s Workbench were mixed. But I don’t want to get into all the details Day discusses here. Instead, what I wanted to share is Day’s faux epigraph.
I think this kind of sums up a lot of the profession’s feelings about the writing technologies that started appearing in classrooms– both K-12 and in higher education– as a result of the introduction of personal computers in the early 1980s. CAI tools never really caught on, but plenty of other software did, most notably word processing, and then networked computers, this new thing “the internet,” and then the World Wide Web. All of these technologies were surprisingly polarizing among English teachers at the time. And as an English major in the mid-1980s who also became interested in personal computers and then the internet and then the web, I was “an enthusiast.”
From around the late 1970s and continuing well into the mid-1990s, there were hundreds of articles and presentations in major publications in composition and English studies like Burns’ and Day’s pieces, about the enthusiasms and skepticisms of using computers for teaching and practicing writing. Because it was all so new and most folks in English studies knew even less about computers than they do now, a lot of that scholarship strikes me now as simplistic. Much of what appeared in Computers and Composition in its first few years was teaching anecdotes, as in “I had students use word processing in my class and this is what happened.” Many articles were trying to compare writing with and without computers, writing with a word processor or by hand, how students of different types (elementary/secondary, basic writers, writers with physical disabilities, skilled writers, etc.) were harmed or helped with computers, and so forth.
But along with this kind of “should you/shouldn’t you write with computers” theme, a lot of the scholarship in this era raised questions that have continued with every other emerging and contentious technology associated with writing, including, of course, AI: questions about authorship, the costs (because personal computers were expensive), the difficulty of learning and also teaching the software, cheating, originality, “humanness” and so on. This scholarship was happening at a time when using computers to practice or teach writing was still perceived as a choice– that is, it was possible to refuse and reject computers. I am assuming that the comparison I’m making here to this scholarship and the discussions now about AI are obvious.
So I think it’s worth re-examining some of this work where writers were expressing enthusiasms, skepticisms, and concerns about word processing software and personal computers and comparing it to the moment we are in with AI in the form of ChatGPT, Gemini, Claude, and so forth. What will scholars 30 years from now think about the scholarship and discourse around Artificial Intelligence that is in the air currently?
Anyway, that was going to be the whole talk from me and with a lot more detail, but that project for me is on hold, at least for now. Instead, I want to pivot to the second part of my talk, “The Importance of Paying Attention To, Rather Than Resisting, AI.”
I say “Rather Than Resisting” or Refusing AI in reference to Jennifer Sano-Franchini, Megan McIntyre, and Maggie Fernandes website “Refusing Generative AI in Writing Studies,” but also in reference to articles such as Melanie Dusseau’s “Burn It Down: A License for AI Resistance,” which was a column in Inside Higher Ed in November 2024, and other calls to refuse/resist using AI. “The Importance of Paying Attention To,” is my reference to Cynthia Selfe’s “Technology and Literacy: A Story about the Perils of Not Paying Attention,” which was first presented as her CCCC chair’s address in 1998 (published in 1999) and which was also expanded as a book called Technology and Literacy in the Twenty-first Century.
If Hugh Burns’ 1983 commentary in the first issue of Computers and Composition serves for me as the beginning of this not-so-long-ago history, when personal computers were not something everyone had or used and when they were still contentious and emerging tools for writing instruction and practice, then Selfe’s CCCCs address/article/book represents the point where computers (along with all things internet) were no longer optional for writing instruction and practice. And it was time for English teachers to wake up and pay attention to that.
And before I get too far, I agree with eight out of the ten points on the “Refusing Generative AI in Writing Studies” website, broadly speaking. I think these are points that most people in the field nowadays would agree with, actually.
But here’s where I disagree. I don’t want to go into this today, but the environmental impact of the proliferation of data centers is not limited to AI. And when it comes to this last bullet point, no, I don’t think “refusal” or resistance are principled or pragmatic responses to AI. Instead, I think our field needs to engage with and pay attention to AI.
Now, some might argue that I’m taking the call to refuse/resist AI too literally and that the kind of engagement I’m advocating is not at odds with refusal.
I disagree. Word choices and their definitions matter. Refusing means being unwilling to do something. Paying attention means to listen to and to think about something. Much for the same reasons Selfe spoke about 27 years ago, there are perils to not paying attention to technology in writing classrooms. I believe our field needs to pay attention to AI by researching it, teaching with it, using it in our own writing, goofing around with it, and encouraging our students to do the same. And to be clear: studying AI is not the same as endorsing AI.
Selfe’s opening paragraph is a kidding/not kidding assessment of the CCCCs community’s feelings about technology and the community’s refusal to engage with it. She says many members of the CCCCs over the years have shared some of the best ideas we have from any discipline about teaching writing, but it’s a community that has also been largely uninterested in the focus of Selfe’s work, the use of computers to teach composition. She said she knew bringing up the topic in a keynote at the CCCCs was “guaranteed to inspire glazed eyes and complete indifference in that portion of the CCCC membership which does not immediately sink into snooze mode.” She said people in the CCCCs community saw a disconnect between their humanitarian concerns and a distraction from the real work of teaching literacy.
It was still possible in a lot of English teacher’s minds to separate computers from the teaching of writing– at least in the sense that most CCCCs members did not think about the implications of computers in their classrooms. Selfe says “I think [this belief] informs our actions within our home departments, where we generally continue to allocate the responsibility of technology decisions … to a single faculty or staff member who doesn’t mind wrestling with computers or the thorny, unpleasant issues that can be associated with their use.”
Let me stop for a moment to note that in 1998, I was there. I attended and presented at that CCCCs in Chicago, and while I can’t recall if I saw Selfe’s address in person (I think I did), I definitely remember the times.
After finishing my PhD in 1996, I was hired by Southern Oregon University as their English department’s first “computers and writing” specialist. At the 1998 convention, I met up with my future colleagues at EMU because I had recently accepted the position I currently have, where I was once again hired as a computer and writing specialist. At both SOU and EMU, I had colleagues– you will not be surprised to learn these tended to be senior colleagues– who questioned why there was any need to add someone like me to the faculty. In some ways, it was similar to the complaints I’ve seen on social media about faculty searches involving AI specialists in writing studies and related fields.
Anyway, Selfe argues that in hiring specialists, English departments outsourced responsibility to the rest of the faculty to have anything to do with computer technology. It enabled a continued belief that computers are simply “tool[s] that individual faculty members can use or ignore in their classrooms as they choose, but also one that the profession, as a collective whole–and with just a few notable exceptions–need not address too systematically.” Instead, she argued that what people in our profession needed to do was to pay attention to these issues, even if we really would rather refuse to do so: “I believe composition studies faculty have a much larger and more complicated obligation to fulfill–that of trying to understand and make sense of, to pay attention to, how technology is now inextricably linked to literacy and literacy education in this country. As a part of this obligation, I suggest that we have some rather unpleasant facts to face about our own professional behavior and involvement.” She goes on a couple of paragraphs later to say in all italics “As composition teachers, deciding whether or not to use technology in our classes is simply not the point–we have to pay attention to technology.”
Again, I’m guessing the connection to Selfe’s call then to pay attention to computer technology and my call now to pay attention to AI is pretty obvious.
The specific case example Selfe discusses in detail in her address is a Clinton-Gore era report called Getting America’s Children Ready for the Twenty-First Century, which was about that administration’s efforts to promote technological literacy in education, particularly in K-12 schools. The initiative spent millions on computer equipment, an amount of money that dwarfed the spending on literacy programs. As I recall those times, the main problem with this initiative was there was lots of money spent to put personal computers into schools, but very little money was spent on how to use the computers in classrooms. Self said, “Moreover, in a curious way, neither the CCCC, nor the NCTE, the MLA, nor the IRA–as far as I can tell–have ever published a single word about our own professional stance on this particular nationwide technology project: not one statement about how we think such literacy monies should be spent in English composition programs; not one statement about what kinds of literacy and technology efforts should be funded in connection with this project or how excellence should be gauged in these efforts; not one statement about the serious need for professional development and support for teachers that must be addressed within context of this particular national literacy project.”
Selfe closes with a call for action and a need for our field and profession to recognize technology as important work we all do around literacy. I’ve cherry-picked a couple of quotes here to share at the end. Again, by “technology”, Selfe more or less meant PCs, networked computers, and the web, all tools we all take for granted. But also again, every single one of these calls applies to AI as well.
Now, I think the CCCCs community and the discipline as a whole have moved in the direction Selfe was urging in her CCCCs address. Unlike the way things were in the 1990s, I think there is widespread interest in the CCCC community in studying the connections between technologies and literacy. Unlike then, both MLA and CCCCs (and presumably other parts of NCTE) have been engaged and paying attention. There is a joint CCCC-MLA task force that has issued statements and guidance on AI literacy, along with a series of working papers, all things Selfe was calling for back then. Judging from this year’s program and the few presentations I have been able to attend, it seems like a lot more of us are interested in engaging and paying attention to AI rather than refusing it.
At the same time, there is an echo–okay, one sound reference– of the scholarship in the early era of personal computers. A lot of the scholarship about AI now is based on teachers’ experiences of experimenting with it in their own classes. And we’re still revisiting a lot of the same questions regarding the extent to which we should be teaching students how to use AI, the issues of authenticity and humanness, of cheating, and so forth. History doesn’t repeat, but it does rhyme.
Let me close by saying I have no idea where we’re going to end up with AI. This fall, I’m planning on teaching a special topics course called Writing, Rhetoric, and AI, and while I have some ideas about what we’re going to do, I’m hesitant about committing too much to a plan now since all of this could be entirely different in a few months. There’s still the possibility of generative AI becoming artificial general intelligence and that might have a dramatic impact on all of our careers and beyond. Trump and shadow president Elon Musk would like nothing better than to replace most people who work for the federal government with this sort of AI. And of course, there is also the existential albeit science fiction-esque possibility of an AI more intelligent than humans enslaving us.
But at least I think that we’re doing a much better job of paying attention to technology nowadays.
The first time I attended and presented at the CCCCs was in 1995. It was in Washington, D.C., and I gave a talk that was about my dissertation proposal. I don’t remember all the details, but I probably drove with other grad students from Bowling Green and split a hotel room, maybe with Bill Hart-Davidson or Mick Doherty or someone like that. I remember going to the big publisher party sponsored by Bedford-St. Martin’s (or whatever they were called then) which was held that year at the National Press Club, where they filled us with free cocktails and enough heavy hors d’oeuvres to serve as a meal.
For me, the event has been going downhill for a while. The last time I went to the CCCCs in person was in 2019– pre-Covid, of course– in Pittsburgh. I was on a panel of three scheduled for 8:30 am Friday morning. One of the people on the panel was a no-show, and the other panelist was Alex Reid; one person showed up to see what we had to say– though at least that one person was John Gallagher. Alex and I went out to breakfast, and I kind of wandered around the conference after that, uninterested in anything on the program. I was bored and bummed out. I had driven, so I packed up and left Friday night, a day earlier than I planned.
And don’t even get me started on how badly the CCCCs did at holding online versions of the conference during Covid.
So I was feeling pretty “done” with the whole thing. But I decided to put in an individual proposal this year because I was hoping it would be the beginning of another project to justify a sabbatical next year, and I thought going to one more CCCCs 30 years after my first one rounded things out well. Plus it was a chance to visit Baltimore and to take a solo road trip.
This year, the CCCCs/NCTE leadership changed the format for individual proposals, something I didn’t figure out until after I was accepted. Instead of creating panels made up of three or four individual proposals, which is what the CCCCs had always done before– which is whatevery other academic conference I have ever attended does with individual proposals— they decided that individuals would get a 30-minute solo session. To make matters even worse, my time slot was 9:30 am on Saturday, which is the day most people are traveling back home.
Oh, also: my sabbatical/research release time proposal got turned down, meaning my motivations for doing this work at all has dropped off considerably. I thought about bailing out right up to the morning I left. But I decided to go through with it because I was also going to Richmond to visit my friend Dennis, I still wanted to see Baltimore, and I still liked the idea of going one more time and 30 years later.
Remarkably, I had a very good time.
It wasn’t like what I think of as “the good old days,” of course. I guess there were some publisher parties, but I missed out on those. I did run into people who I know and had some nice chats in the hallways of the enormous Baltimore convention center, but I mostly kept to myself, which was actually kind of nice. My “conference day” was Friday and I saw a couple of okay to pretty good panels about AI things– everything seemed to be about AI this year. I got a chance to look around the Inner Harbor on a cold and rainy day, and I got in half-price to the National Aquarium. And amazingly, I actually had a pretty decent-sized crowd (for me) at my Saturday morning talk. Honestly, I haven’t had as good of a CCCCs experience in years.
But now I’m done– probably.
I’m still annoyed with (IMO) the many many failings of the organization, and while I did have a good solo presenting experience, I still would have preferred being on a panel with others. But honestly, the main reason I’m done with the CCCCs (and other conferences) is not because of the conference but because of me. This conference made it very clear: essentially, I’ve aged out.
When I was a grad student/early career professor, conferences were a big deal. I learned a lot, I was able to do a lot of professional/social networking, and I got my start as a scholar. But at this point, where I am as promoted and as tenured as I’m ever going to be and where I’m not nearly as interested in furthering my career as I am retiring from it, I don’t get much out of all that anymore. And all of the people I used to meet up with and/or room with 10 or so years ago have quit going to the CCCCs because they became administrators, because they retired or died, or because they too just decided it was no longer necessary or worth it.
So that’s it. Probably. I have been saying for a while now that I want to shift from writing/reading/thinking about academic things to other non-academic things. I started my academic career as a fiction writer in an MFA program, and I’ve thought for a while now about returning to that. I’ve had a bit of luck publishing commentaries, and of course, I’ll keep blogging.
Then again, I feel like I got a good response to my presentation, so maybe I will stay with that project and try to apply for a sabbatical again. And after all, the CCCCs is going to be in Cleveland next year and Milwaukee the year after that….
The New York Times ran an editorial a couple of weekends ago called “The Authoritarian Endgame on Higher Education,” where the first sentence was “When a political leader wants to move a democracy toward a more authoritarian form of government, he often sets out to undermine independent sources of information and accountability.” The editorial goes on to describe the hundreds of millions of dollars of cuts in grants, and while the cuts are especially large and newsworthy at Johns Hopkins ($800 million) and Columbia ($400 million), they’re happening in lots of smaller amounts at lots of research universities. Full disclosure: my son is a post-doc at Yale, and while his lab has not been severely impacted by these cuts (yet), it is and continues to be a looming problem for him and his colleagues.
The NYT’s editorial board is correct: Trump is following the playbook of other modern authoritarian leaders (Putin, Orban in Hungary, Modi in India, Erdogan in Turkey, etc.) and is trying to weaken universities. Trump and shadow president Musk are cutting off the funding from the National Institute of Health (and other similar federal agencies) to research universities not so much because of waste and fraud and wanting to end DEI initiatives, and they’re destroying the rest of the federal government not because they want to save money. They’re doing it to consolidate power. They are trying to revamp the U.S. into an authoritarian system run by big tech and billionaires. I wish MSM would remind people more often that this is what is going on right now.
Then last week, Princeton President David A. Graham wrote a piece published in The Atlantic in which he insisted that now was the time for universities like Columbia to stand up to the Trump administration in the name of academic freedom. He quotes Joan Scott, the leader of the American Association of University Professors, who said “Even during the McCarthy period in the United States, this was not done.” The day after The Atlantic ran Graham’s column, Columbia more or less caved in and appeared to be ready to give Trump what he wanted.
And of course, Trump signed an executive order to close down the Department of Education– which is not something that Trump can do without Congress, but never mind the details of the law.
This is all very bad for all kinds of reasons that go well beyond the impact on these institutions. This is grant money from agencies like the National Institutes of Health to fund research, typically the kind of basic research that the private sector doesn’t do– but of course, research that the private sector profits from greatly. Just about every medical breakthrough you can think of over the last 75 years has been a result of this partnership between the feds and research universities, but to use one example close to my own heart (and the rest of my body) right now: take Zepbound. One of the origins of these current weight loss drugs was basic research the NIH and other federal government agencies did back in the 80s and 90s about the venom of Gila monsters, the kind of research MSM and politicians frequently mock– “why are we spending so much money to research lizards?” Because that’s where discoveries are made that eventually lead us to all sorts of surprising benefits.
But there is one detail about the way this story is being reported that bothers me. MSM puts all universities into the same bucket when the reality is much more complicated than that. The universities most impacted by Trump’s actions are very different kinds of institutions than the ones where I’ve spent my career.
In my book about MOOCs (More Than A Moment), I wrote a bit about the disparity between different tiers of universities, and how MOOCs (potentially) made the distance between higher ed’s haves and have-nots even greater. I frequently referenced the book A Perfect Mess: The Unlikely Ascendancy of American Higher Education by David F. Labaree. If you too are interested in the history of higher education (and who isn’t?), I’d highly recommend it. Among other things, Labaree describes the unofficial but well-understood hierarchy of different institutions. At the bottom fourth tier of this pyramid are community colleges, and I would also add proprietary schools and largely online universities. Roughly speaking, there are about 1,000 schools in this category. Labaree says that the third tier consists of universities that mostly began as “normal schools” in the 19th century, though I would add into that tier lots of small/private/often religious/not elite colleges, along with most other regional institutions. There are probably close to 1500 institutions in this category, and I think it’s fair to say most four-year colleges and universities in the US are in this group. EMU, which began as the Michigan State Normal School, is smack-dab in the middle of this tier.
The second tier and top tier are probably easiest for most non-academic types to understand because these are the only kinds of places that MSM routinely reports on as being “higher education.” Roughly speaking, these two tiers are comprised of about the top 150 or so national universities on the US News and World Report Rankings of Universities, with the top fifty or so in those rankings being the tippy-top 1 tier. By the way, EMU is “tied” as the 377th school on the list.
Now, those universities at the tippy-top that receive a lot of NIH and other federal grants– Columbia, Johns Hopkins, Michigan, Yale, etc.– have a serious problem because those grants are a major revenue stream. But for the rest of us in higher ed, especially on the third tier? Well, I was in a meeting just the other day where one of my colleagues asked an administrator when EMU could expect to see a cut in federal funding. This administrator, who seemed a little surprised at the question, pointed out that about 25% of our funding comes from state appropriations, and the rest of it comes from tuition. The amount of direct federal funding we receive is negligible.
And herein lies the Trump administration’s challenge at taking over education in this country, thankfully. Unlike most other countries in the world where schooling is more centralized, public education in the United States is quite decentralized and is mostly controlled by states and localities. As this piece from Inside Higher Ed reminds us, the main role of the federal government in higher education (besides collecting data about higher education nationwide, working with accreditors, and overseeing students’ civil rights) is to run the student loan and Pell Grant programs. The Trump administration has repeatedly said they want these programs to continue even if they are successful at eliminating the Department of Education. Not that I completely believe that– Trump/Musk might want to cut Pell grants, and they are trying to roll back Biden’s moves on loan forgiveness. But given how many students (and their parents) depend on these programs, including MAGA voters, I don’t see these programs going away.
In other words, now is a good time to be at a third-tier university.
Now, that New York Times editorial does have one paragraph where they acknowledge this difference between the haves and have-nots:
We understand why many Americans don’t trust higher education and feel they have little stake in it. Elite universities can come off as privileged playgrounds for young people seeking advantages only for themselves. Less elite schools, including community colleges, often have high dropout rates, leaving their students with the onerous combination of debt and no degree. Throughout higher education, faculty members can seem out of touch, with political views that skew far to the left.
I don’t know how much Americans do or don’t “trust” higher education, but the main reason why EMU and similar universities have a much higher dropout rate is we admit students more selective universities don’t. I don’t remember the details, but I heard this story years ago about this administrator in charge of admissions at EMU. When he was asked why our graduation rate is around 50% while the University of Michigan’s rate is more like 93%, he responded “Why isn’t U of M’s graduation rate 100%? They only admit students they know will graduate.” In contrast, EMU (and most other universities in the third tier) takes a lot of chances and admits almost everyone who applies.
I’m biased of course, but I think a more accurate way to frame the role of third-tier/regional universities is as institutions of opportunity. We give folks a chance at a college degree who otherwise would have few options. We aren’t a school that helps upper-middle-class kids stay that way. We’re a school that helps working class/working poor students improve their lives, to be one of the first (if not the first) people in their families to graduate from college. Sure, a lot of the students we admit don’t make it for all kinds of different reasons. But I think the benefits we provide to the ones who succeed in graduating outweigh the problems of admitting students who are just not prepared to go to college. Though I’ll admit it’s a close call.
Anyway, I don’t know what those of us working on the lower levels of the pyramid can do to help those at the top, if there’s anything we can do. That’s the frustration of everyone against Trump right now, right? What can we do?
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.
As I wrote about earlier in December, I am “Back to Blogging Again” after experimenting with shifting everything to Substack. I switched back to blogging because I still get a lot more traffic on this site than on Substack, and because my blogging habits are too eclectic and random to be what I think of as a Newsletter. I realize this isn’t true for lots of Substackers, but to me, a Newsletter should be about a more specific “topic” than a blog, and it should be published on a more regular schedule.
So that’s my goal with “Paying Attention to AI.” We’ll see how it works out. Because I still want to post those Substack things here– because this is a platform I control, unlike any of the other ones owned by tech oligarchs or whatever, and because while I do like Substack, there is still the “Nazi problem” they are trying to work out. Besides, while Substack could be bought out and turned into a dumpster fire (lookin’ at you, X), no one is going to buy stevendkrause.com, and that’s even if I was selling.
Anyway, here’s the first post on that new Substack space.
Welcome to (working title) Paying Attention to AI
More Notes on Late 20th Century Composition, CAI, Word Processing, the Internet, and AI
My goal for this Substack site/newsletter/etc. is to write (mostly to myself) about what will probably be the last big research/scholarly project of my academic career, but I still don’t have a good title. I’m currently thinking “Paying Attention to AI,” a reference to Cynthia Selfe’s “Technology and Literacy: A Story about the Perils of Not Paying Attention,” which was her chair’s address at the 1997 Conference for College Composition and Communication before it was republished in the journal for the CCCs in 1999 and also expanded into the book Technology and Literacy in the Twenty-First Century.
But I also thought something mentioning AI, Composition, and “More Notes” would be good. That’s a reference to “A Note on Composition and Artificial Intelligence,” a brief 1983 article by Hugh Burns in the first newsletter issue of what would become the journal Computers and Composition. AI meant something quite different in the late 1970s/early 1980s, of course. Burns was writing then about how research in natural language processing and AI could help improve Computer Assisted Instruction (CAI) programs, which were then seen as one of the primary uses of computer technology in the teaching of writing— along with the new and increasingly popular word processing programs that run on these newly emerging personal computers.
Maybe I’ll figure out a way to combine the two into one title…
This project is based on a proposal that’s been accepted for the 2025 CCCCs in Baltimore, and also a proposal I have submitted at EMU for a research leave or a sabbatical for the 2025-26 school year. 1 I’m interested in looking back at the (relatively) recent history of the beginnings of the widespread use of “computers” (CAI, personal computers, word processors and spell/grammar checkers, local area networks, and the beginnings of “the internet”).
Burns’ and Selfe’s articles make nice bookends for this era for me because between the late 1970s until about the mid 1990s, there were hundreds of presentations and articles in major publications in writing studies and English about the role of personal computers and (later) the internet and the teaching of writing. Burns was enthusiastic about the potential of AI research and writing instruction, calling for teachers to use emerging CAI and other tools. It was still largely a theory though since in 1983, fewer 8% of households had one personal computer. By the time Selfe was speaking and then writing 13 or so years later, over 36% of households had at least one computer, and the internet and “World Wide Web” was rapidly taking its place as a general purpose technology altering the ways we do nearly everything, including how we teach and practice writing.
These are also good bookends for my own history as a student, a teacher, and a scholar, not mention as a writer who dabbled a lot with computers for a long time. I first wrote with computers in the early 1980s while in high school. I started college in 1984 with a typewriter and I got a Macintosh 512KE by about 1986. I was introduced to the idea of teaching writing in a lab of terminals— not PCs— connected to a mainframe unix computer when I started my MFA program at Virginia Commonwealth University in fiction writing in 1988. (I never taught in that lab, fwiw). In the mid-90s and while in my PhD program at Bowling Green State University, the internet and “the web” came along, first as text (remember Gopher? Lynx?) and then as GUI interfaces like Netscape. By the time Selfe was urging the English teachers attending the CCCCs attendees to, well, pay attention to technology, I had starte my first tenure-track job.
A lot of the things I read about AI right now (mostly on social media and MSM, but also in more scholarly work) dhas a tinge of the exuberant enthusiasm and/or the moral panic about the encroachment of computer technology back then, and that interests me a great deal. But at the same time, this is a different moment in lots of small and large ways. For one thing, while CAI applications never really caught on for teaching writing (at least beyond middle school), AI shows some real promise in making similar tutoring tools actually work. Of course, there were also a lot of other technologies and tools way back when that had their moments but then faded away. Remember MOOs/MUDs? Listservs? Blogs? And more recently, MOOCs?
So we’ll see where this goes.
1 FWIW: in an effort to make it kinda/sorta fit the conference theme, this presentation is awkwardly titled ““Echoes of the Past: Considering Current Artificial Intelligence Writing Pedagogies with Insights from the Era of Computer-Aided Instruction.” This will almost certainly be the last time I attend the CCCCs, my field’s annual flagship conference, because, as I am sure I will write about eventually, I think it has become a shit show. And whether or not this project continues much past the April 2025 conference will depend heavily on the research release time from EMU. Fingers crossed on that.
And the challenges of an AI world where everyone is above average
I’ve been an Apple fanboy since the early 1980s. I owned one Windoze computer years ago that was mostly for games my kid wanted to play. Otherwise, I’ve been all Apple for around 40 years. But what the heck is the deal with these ads for Apple Intelligence?
In this ad (the most annoying of the group, IMO), we see a schlub of a guy, Warren, emailing his boss in idiotic/bro-based prose. He pushes the Apple Intelligence feature and boom, his email is transformed into appropriate office prose. The boss reads the prose, is obviously impressed, and the tagline at the end is “write smarter.” Ugh.
Then there’s this one:
This guy, Lance, is in a board meeting and he’s selected to present about “the Prospectus,” which he obviously has not read. He slowly wheels his office chair and his laptop into the hallway, asks Apple’s AI to summarize the key points in this long thing he didn’t read. Then he slowly wheels back into the conference room and delivers a successful presentation. The tagline on this one? “Catch up quick.” Ugh again.
But in a way, these ads might not be too far from wrong. These probably are the kind of “less than average” office workers who could benefit the most from AI— well, up to a point, in theory.
Among many other things, my advanced writing students and I read Ethan Mollick’s Co-Intelligence, and in several different places in that book, he argues that in experiments when knowledge workers (consultants, people completing a writing task, programmers) use AI to complete tasks, they are much more productive. Further, while AI does not make already excellent workers that much better, it does help less than excellent workers improve. There’s S. Noy and W. Zhang’s Science paper “Experimental evidence on the productivity effects of generative artificial intelligence;” here’s a quote from the editor’s summary:
Will generative artificial intelligence (AI) tools such as ChatGPT disrupt the labor market by making educated professionals obsolete, or will these tools complement their skills and enhance productivity? Noy and Zhang examined this issue in an experiment that recruited college-educated professionals to complete incentivized writing tasks. Participants assigned to use ChatGPT were more productive, efficient, and enjoyed the tasks more. Participants with weaker skills benefited the most from ChatGPT, which carries policy implications for efforts to reduce productivity inequality through AI.
Now, Mollick is looking at AI as a business professor, so he sees this as a good thing because it improves the quality of the workforce, and maybe it’ll enable employers to hire fewer people to complete the same tasks. More productivity with less labor equals more money, capitalism for the win. But my English major students and I all see ourselves (accurately or not) as well-above-average writers, and we all take pride in that. We like the fact we’re better at writing than most other people. Many of my students are aspiring novelists, poets, English teachers, or some other career where they make money from their abilities to write and read, and they all know that publishing writing that other people read is not something that everyone can do. So the last thing any of us who are good at something want is a technology that diminishes the value of that expertise.
This is part of what is behind various declarations of late for refusing or resisting AI, of course. Part of what is motivating someone like Ted Chiang to write about how AI can’t make art is making art is what he is good at. The last thing he wants is a world where any schmuck (like those dudes in the Apple AI ads) can click a button and be as good as he is at making art. I completely understand this reason for fearing and resisting AI, and I too hope that AI doesn’t someday in the future become humanity’s default story teller.
Fortunately for writers like Chiang and me and my students, the AI hype does not square with reality. I haven’t played around with Apple AI yet, but the reviews I’ve seen are underwhelming. I stumbled across a YouTube review by Marques Brownlee about the new AI that is quite thorough. I don’t know much about Brownlee, but he has over 19 million subscribers so he probably knows what he is talking about. If you’re curious, he talks about the writing feature in the first few minutes of this video, but the short version is he says that as a professional writer, he finds it useless.
The other issue I think my students and I are noticing is that the jagged frontier Mollick and his colleagues talk about— that is, the line/divide between tasks the AI can accomplish reasonably well and what it can’t— is actually quite large. In describing the study Mollick and his colleagues did which included a specifically difficult/can’t do with AI jagged frontier problem, I think he implies that this frontier is small. But Mollick and his colleagues— and the same is true with these other studies he quotes on this— are not studying AI in real settings. These are controlled experiments, and the researchers are trying to do all they can to eliminate other variables.
But in the more real world with lots of variables, there are jagged frontiers everywhere. The last assignment I gave in the advanced writing class asked students to attempt to “compose” or “make” something with the help of AI (a poem, a play, a song, a movie, a website, etc. etc.) that they could not do on their own. The reflection essays are not due until the last week of class, but we have had some “show and tell” exchanges about these projects. Some students were reasonably successful with making or doing something thanks to AI— and as a slight tangent: some students are better than others at prompting the AI and making it work for them. It’s not just a matter of clicking a button. But they all ran into that frontier, and for a lot of students, that was essentially how their experiment ended. For example, one student was successful at getting AI to generate the code for a website; but this student didn’t know what to do with the code the AI made to make it actually into a website. A couple of students tried to use AI to write music, but since they didn’t know much about music, their results were limited. One student tried to get AI to teach them how to play the card game Euchre, but the AI kept on doing things like playing cards in the student’s hand.
This brings me back to these Apple ads: I wish they both went on just another minute or so. Right after Warren and Lance confidently look directly at the camera with smug look that says to viewers “Do you see what I just got away with there,” they have to follow through with what they supposedly have accomplished, and I have a feeling that would go poorly. Right after Warren’s boss talks with him about that email and right after Lance starts his summary, I am pretty sure they’re gonna get busted. Sort of like what has happened when I have suspected correctly that a student used too much AI and that student can’t answer basic questions about what it is they (supposedly) wrote.
