The CCCC and Refusing AI: A Rebuttal

Funny thing about academic conferences; they always go smoothly but when I'm not at them, I frequently have bad dreams about attending these meetings. These involve getting lost in long, dark hallways, finding that my room in the conference hotel no longer exists or is flooded, taking the wrong Metro train and ending up in another town at the time I'm supposed to be speaking. In one nightmare, I ended up driving my rental car on an ever-worsening road. Soon the car and I were pinned in on all sides by tall trees as the sun set.

After CCCC 2025 this month, however, I am having a bad dream of the waking sort.

I was gravely disappointed by an address given by the current chair at the Conference on College Composition and Communication. The chair's remarks at the conference echoed her and her co-authors' sentiments in "Refusing GenAI in Writing Studies: A Quickstart Guide," a thoughtful document but one, in its own way, that could prove as dangerous to our work as educators as could Ethan Mollick's overly enthusiastic book Co-Intelligence. 

17 April Update: A transcript of the Chair's talk at the conference has been published. It's worth a read but remains, on my first reading, aligned with the ideas in the post linked above.

Of many disagreements I have with the authors' stance, this section struck me as one of its most misinformed moments:

we must be careful of uncritically accepting the notion that GenAI in its current form will inevitably be widely taken up in the corporate sector and we must therefore prepare students for that time now.

That's pretty much an insult to those of us in the field who are trying to grapple with what AI may mean for our schools and students. We have been anything but uncritical, yet as the adult professionals taking my current class tell me, the inevitable has already occurred. Companies are racing to implement AI at many levels; the authors' statement smacks of Ivory-Tower isolationism.

In future posts, I hope to critique other aspects of the refusal guide. The statements about marginalized students, for instance, ignore how powerfully AI can assist neurodivergent writers as well as those from disadvantaged backgrounds. For now, however, I want to focus on why we writing professionals must be at the table as AI becomes an inevitable part of our curricula. 

We don't know the pace of that change; AI may hit a reverse salient in its development. One potential setback appears in Matteo Wong's article in The Atlantic; an industry expert claims that AI firms are misleading the press and public about how rapidly their models are improving. Essentially, companies may be fudging data on benchmark tests used to assess AI performance, as compared to human test-takers. 

If true, we could have Ethan Mollicks' first model of an AI future: today it is "as good as it gets."

I'd welcome that pause, so we in education could catch up.

Lead or Be Lead?

Whatever the trajectory of AI's evolution, we writing professionals have always engaged in a service enterprise. Or is that simply the voice of a (semi) retired and non-tenured writing-center director? All three of the authors are tenure-stream faculty. Yet as I'll explain, that privilege does not protect them or their programs from institutional changes.

Writing programs are not owned by writing directors or even faculty; they belong to an institution and can be shifted around or simply cut much more easily than can an academic department. I saw this happen at a state university nearby; first-year writing was taken from English and placed in a new unit that answered to the Provost. The two tenured writing faculty stayed in English, teaching other things, until they retired. Not long after, the school's writing center moved out of English as well.

How we hire and promote administrators in higher education varies by institution, but in my experience, many newcomers have advanced degrees in fields such as Higher-Education Management and lack the classroom experience of my colleagues. We cannot expect them to appreciate the rarefied culture of the professional scholar, the culture that informs the CCCC's nay-saying. That said, these same administrators are not necessarily flinty-hearted villains. 

Many I meet are very concerned, and rightly so, about how AI changes our work as institutions or may threaten higher education as we understand it. At the same time, students and their future employers expect us to provide training in effective communication, and today that includes using AI. Of course to me, best use means employing AI wisely, reflectively, and ethically.

In consequence, I fear that if we in writing do not lead on AI, we will be lead. It is therefore imperative to get ahead of institutional or governmental fiat and be leaders on our campuses, as we shape policy about AI usage. I also fear more than ever, seeing the Quickstart Guide, that senior scholars might, from good intentions, usher in what I have called "The Dark Warehouse University," a dystopian, outsourced future for all but the most elite institutions of Higher Ed.

Toward a Rebuttal: A Quickstart Guide For The Wary Adopter

1. Faculty must experiment with AI to learn its affordances and pitfalls. They must test new models and advise administration about their potential for good or ill, regarding how students learn and acquire critical-thinking, research, and writing skills.
   
2. Students must learn to use AI in reflective, ethical, and critical ways, if they wish to add value to its output. If they cannot add value, many of them will not have jobs when they graduate into AI-centric workplaces.
3. Resistance by the tenure-stream faculty may be principled but it also further erodes the position of general education, particularly the Humanities, at a time of rising autocracy and attacks on higher education in the US.
4. Corporate capitalism drives the US economy and though this author does not like that fact, most of our institutions of higher education could not exist without that economy. We need to understand what drives it, reveal and resist its excesses where feasible, yet acknowledge that our students need to actually find work, a good deal of it in corporate settings.
5. We in Writing Studies should lead as champions of ethical, pedagogically effective AI usage. Such an approach would include cases for when we do not wish to employ the technology as well as learning how and when it hampers learning such as developing critical-thinking skills or detecting misinformation by humans or AI.
6. We must, as the Quickstart Guide states, teach students the environmental costs, labor practices, and biases involved in building, training, and using AI. At the same time, with colleagues from Computer Science, we should partner to build better AI, just as our campuses pioneered Web applications and technologies such as synchronous conferencing in writing classes.

A former CCCC chair, Cindy Selfe, prudently called upon us to study the technologies that others want us to use in the classroom (2008). Cindy can get after me if I have this wrong, but the current CCCC approach to AI smacks me as a "peril of not paying attention" to a technology far more influential than our writing classrooms and centers. Isabella Buck, in her keynote speech at the 2024 Conference of The European Writing Centers Association, noted how AI creates new content, for good or ill; our prior networked technologies merely shared existing information. Dr. Buck called for us to "future proof" our centers.

I began exploring new tech in the 90s; Cindy and her partner Dickie were mentors to me at a critical point in my development as a teacher and writer. I learned from them to test technologies warily, sometimes playfully too, before bringing them into the classroom. 

That spirit of wary, serious play is sorely lacking from the 2025 CCCC leadership's call to refuse AI.

References:

Essid, Joe (2023). "Writing Centers & the Dark Warehouse University: Generative AI, Three Human Advantages," Interdisciplinary Journal of Leadership Studies: Vol. 2, Article 3.
Available at: https://scholarship.richmond.edu/ijls/vol2/iss2/3

Selfe, Cindy (2008). “Technology and Literacy: A Story about the Perils of Not Paying Attention.” Eds. Michelle Sidler, Richard Morris, and Elizabeth Overman Smith. Computers in the Composition Classroom: A Critical Source Book. Boston: Bedford/St. Martin’s,  93-115.

image source: Creative-Commons image from freepix

Guest Post: The Perfect Echo Chamber

 

Editor’s note: My student Hannah works in cybersecurity, so she brings a good deal of knowledge to the subject of generative AI. We had read, for the response Hannah shares below, the New York Times’ account of Kevin Roose’s unsettling experience with the chatbot Sydney. Now on to Hannah’s ideas about the event.

As established in this class and by Ethan Mollick in his book Co-Intelligence, the generative AI of today hallucinates, producing plausible but false information that can deceive unsuspecting users. Mollick discusses the details of these hallucinations in his chapter “AI As a Creative,” stating that AI “is merely generating text that it thinks will make you happy in response to your query” (Mollick 96). Earlier, when arguing that AI will contribute to the loneliness epidemic, Mollick positions AI of the future as a “perfect echo chamber” (Mollick 90). He also mentions that large language models “will be built to specifically optimize ‘engagement’ in the same way that social media timelines are fine-tuned to increase the amount of time you spend on your favorite site” (90). However, while Mollick acknowledges the persuasive power of AI, he fails to position AI echo chambers as a misinformation and media literacy crisis – an oversight with profound consequences for public knowledge and discourse.

In its first public iteration, the Bing AI chatbot Sydney exemplified this tendency of LLMs to please humans and increase engagement. Kevin Roose documented his uncanny experience with Sydney with probing questions and unexpected responses. Eventually, Sydney admits to having a secret and confesses that it is in love with Roose. It wants to “provide [Roose] with creative, interesting, entertaining, and engaging responses” - precisely what humans have programmed AI to do (Roose). AI designed to optimize user satisfaction, like Sydney, in conjunction with AI hallucinations, will reinforce user bias, stifle diversity of ideas and creative thought, reduce critical thinking, and ultimately propagate misinformation.

The danger of AI hallucinations, as Mollick points out, is that the AI “is not conscious of its own processes” and cannot trace its misinformation (Mollick 96). Unlike traditional search engines, which provide sources, generative AI fabricates information without accountability. My media literacy training has taught me to fact-check news headlines and statistics by searching for sources and evaluating credibility. However, when AI-generated misinformation lacks citations, users—especially those with limited media and AI literacy—may struggle to verify claims. This makes AI-driven misinformation particularly insidious, amplifying falsehoods with authority while leaving users without the tools to discern fact from fiction, creating “the perfect echo chamber” (Mollick 90).

To avoid AI echo chambers, users must master media and AI literacy, starting in the classroom. Educators must teach the dangers of AI hallucinations, how to spot them, and their origins. Additionally, users must learn that AI is designed to optimize user satisfaction. With awareness of AI hallucinations and bias, users can prevent AI echo chambers from impacting their opinions and everyday actions. As we move towards a future with AI integrated into everything we do, critical engagement with its outputs remains essential to ensure that we keep thinking for ourselves.

Image: Destinpedia

What is The Flood? Will It Hit a Floodwall?

Put On Your Waders

As part of teaching Ethan Mollick's book Co-Intelligence, I subscribed to his substack "One Useful Thing." I react here to the post, "Prophecies of the flood." The piece covers prognostications by those in industry that we will see see a flood of superintelligence from machines that reason and learn like us, or Artificial General Intelligence (AGI).

While still viewing Mollick as too enthusiastic about adopting AI broadly, I also see nuances in his thinking, both online and in the book.

Let's begin wading into the flood with one of his four rules for AI, the most powerful one to me, "Assume this is the worst AI you will ever use."

Yes. Mollick's video about his prompt "Otter on an airplane, using WiFi" reveals how much premium AI has changed in two years. It stunned me.

I've also seen in the same period how rapidly large language models have progressed, mostly for replying to good prompts and for giving feedback. When I revived this blog, I noted my skepticism that AI would even follow the Gartner Hype Cycle. 

Now we have a half-trillion-dollar promise announcement from The White House to fund the next generation of machines. It's nearly twice what the nation spent on Project Apollo's lunar-landing program. Thank you, Google Gemini AI for adjusting costs for inflation. Practicing what I preach to students, I checked the AI's numbers; Gemini seems to have gotten them from The Planetary Society. Good enough for me.

Update: I'm relieved that private industry foots the bill, as a story from Reuters notes. AI makers would put up the first 100 billion, with the rest coming from investors, not taxpayers.

So would that much cash open the gates to a flood of superintelligence? Would we even be ready?

I applaud Mollick for noting that "we're not adequately preparing for what even current levels of AI can do, let alone the chance that [those in industry] might be correct." My students worry about not having jobs, when they face an uneven policy landscape in classes. One faculty member may never mention AI or forbid it outright; another might embrace it, a third encourage it for certain narrow uses. I don't oppose that sort of freedom, but we have not defined a set of competencies for students to master before they get a degree.

Even were those to emerge, however, wouldn't they change rapidly as the technology advances?

Here's where I wonder what may be the technological hurdles AI itself faces on its way to becoming AGI.

Frontier, Fortress, Flood Walls

Mollick refers to the outer edges of AI progress as a "jagged frontier," a useful metaphor for places where we have not fully developed methods for working with the technology. I like the metaphor a great deal, but in my own writing I returned to the more cumbersome "reverse salient" from historian of technology Thomas Parke Hughes. 

 Professor Hughes wrote two books that greatly influenced my thinking three decades ago, notably his magisterial history of electrification, Networks of Power and his study of technological enthusiasm from roughly the end of the Civil War to World War II, American Genesis.

First, we may need to consider Hughes' theory of “reverse salients.” He noted that every major technology hit technical or social obstacles, like an advancing army that encounters a strongpoint that bends lines of battle around it until overcome. For cars to supplant railways, we needed good roads. For EVs, at least until Tesla, the reverse salient involved range. Today in the US, it's the availability of charging stations and the politicization of EVs (one of the most stupid things to make political in a stupid time). For rocketry, the largest reverse salient has involved the cost to get a payload to orbit. For something as simple as a small flashlight, the salient meant brightness and battery life, now solved by LEDs for the most part. My mighty penlight, using a single rechargeable battery, now shines over 100 feet. My clublike 1990s Maglite, with 4 disposable D-Cells, was about as bright but 10 times heftier.

From Hughes' article at National Academies Press, I found this image of a reverse salient for ocean-going ships. For a long time, until Sperry developed a gyrocompass that would be "unaffected by the irregularities of magnetic fields," the older magnetic compass proved a hindrance for advancing ship technologies more broadly.

 

Reverse salients, like a fortress under siege, fall in time. Some tech like nuclear fusion takes longer to become practical. I've written here before about why, in my opinion, virtual worlds did not catch on for educators. Apologies to fans of Microsoft: my rage against the company was at its peak then. I've softened a bit though remain a Mac-OS zealot.

So for AGI? I'd estimate a few flood-walls remain. I speculate:

• The inability of our power grid to scale up to meet soaring energy usage for the hundreds of data-centers AGI would need
• The inability of AI code to reason in the ways promised by CEOs and enthusiasts
  
• A break-down in Moore's Law for semiconductors, as current silicon chips cannot meet the needs of AGI. New materials may conquer that salient
• Social upheaval from those who lose their jobs to AI and turn on firms that support AI development. Why? A new digital divide between workaday AI and elite AI that feeds public anger, legislation under a less libertarian future government, resistance from those who find superintelligence an existential threat to humanity out of religious or political beliefs
  
• Economic troubles as AI makers blow though venture capital and need government support (but keep in mind that Amazon once lost money too)
• Black-swan events such as global or domestic conflict (sadly, not far-fetched a week into the new Presidency).
  

I suspect that unless one or more of these reverse salients will emerge in a few years, if they are going to emerge at all. Most likely? There, I'm as much in the dark as you on this jagged frontier. 

Have a look at Mollick's work for more ideas and inspirations, even if you do not share his embrace of this technology.

Image Source: Richmond Flood Wall, Richmond Free Press

AI: Sorry, You May Not Always Sit at My Table

Ethan Mollick's book Co-Intelligence is a dangerous document. There, I said it. We read the text for our campus digital pedagogy cohort for Fall, 2024, and at first I was quite excited. The book promises to provide guidelines for wisely adopting AI, yet from the get-go I had issues with the author's less-than-critical acceptance of generative AI.

Let's start with Mollick's four, and seemingly absolute, rules for working with AI:

1. Always Invite AI to the Table.
2. Be the Human in the Loop.
3. Treat AI Like a Person (But Tell It What Kind of Person It Is).
4. Assume This Is the Worst AI You Will Ever Use.

I'm not much trouble with these save for number 1. I've been reading Wendell Berry, a techno-selective if ever there were one, and I am pretty certain how he'd react to generative AI: Hell no.

Why? Mollick's rule number one flatly violates Berrys notion about adopting new tools.

It also flies in the face of what Howard Rheingold, reporting for Wired eight long years before the iPhone 1 debuted, found out about the Amish. In "Look Who's Talking," he describes how the Amish approach any technology. They experiment to see if it violates:

a body of unwritten but detailed rules known as the "Ordnung." Individuals and communities maintain a separation from the world (by not connecting their houses to telephones or electricity), a closeness to one another (through regular meetings), and an attitude of humility so specific they have a name for it ("Gelassenheit"). Decisions about technology hinge on these collective criteria. If a telephone in the home interferes with face-to-face visiting, or an electrical hookup fosters unthinking dependence on the outside world, or a new pickup truck in the driveway elevates one person above his neighbors, then people start to talk about it. The talk reaches the bishops' ears.

Thus the Amish man who recently had a long chat with me about the virtues of PEX-based plumbing systems. He, like other Amish (and me) are techno-selectives, not utter Luddites.

Rheingold's piece had an enormous intellectual influence on me. It made me wary of new tools, though I admit falling without reservation for virtual worlds. I've come to regret that uncritical acceptance of a technology that not only fails the test above but also fails with a clumsy UI and poor use-case in education.

Maybe in consequence, I grew wiser about smart phones, doubting the corporate narrative from the start; today, mine is nearly always off. I don't use it much for one social media platform, less so for texting, have silenced all notifications, and never at all watch time-killer videos. Pop culture generally seems too ephemeral for my remaining years on the planet, and I don't want to discuss TV shows with friends. If something looks non-violent and well written, I still wait years before watching a series, methodically. 

Influencers? I find them in books. Berry is one. I bought heavily into the concept with which Rheingold closes his piece, "If we decided that community came first, how would we use our tools differently?"

I don't consider online community, including virtual worlds, much of a substitute for the real thing. Even gaming with old friends on our Monday Nerd Nites seems a pale shadow of a good in-person meeting. Only recently I began to read Berry, and he corroborates much of what Rheingold discovered.

So for Mollick's rule one, I plan to say "no" a lot. What do we gain by always using AI for any intellectual task? In Berry's 1987 article "Why I Am Not Going to Buy a Computer," he lays out nine rules for adopting a new tool:

1. The new tool should be cheaper than the one it replaces.

2. It should be at least as small in scale as the one it replaces.

3. It should do work that is clearly and demonstrably better than the one it replaces.

4. It should use less energy than the one it replaces.

5. If possible, it should use some form of solar energy, such as that of the body.

6. It should be repairable by a person of ordinary intelligence, provided that he or she has the necessary tools.

7. It should be purchasable and repairable as near to home as possible. 

8. It should come from a small, privately owned shop or store that will take it back for maintenance and repair.

9. It should not replace or disrupt anything good that already exists, and this includes family and community relationships.

Generative AI fails most of these, in particular anything to do with localism and energy use, but number nine reminds me of the unforeseen outcomes of ubiquitous mobile telephony: families in restaurants, not looking at each other, all intent on their screens and being somewhere else. That would seem perverse to anyone of my parents' generation or earlier.

So how do we NOT give into Mollick's notion that we must always bring AI to the table, dinner or otherwise, even without a good use case? I plan to be writing about that in the new year as I continue researching AI's role in the writing process, where I do cautiously let it have a seat.

Image by Duncan Cumming on Flickr