We Gave the Humanities to the Internet, and Look What Happened

Over the last two or three decades, something happened to humanistic education that nobody really planned and nobody really noticed until it was already done. Responding to economic pressure and the anxieties of students carrying enormous debt, universities gradually defunded the liberal arts in favor of what we politely called “practical” education: STEM, business, pre-professional programs, these were the future. Majoring in philosophy or literature started to feel like a luxury, the intellectual equivalent of ordering dessert when you can’t afford dinner. The market had spoken, and the market had no patience for Montaigne.

But here is the thing about demand: it doesn’t disappear just because the institution stops supplying it. The hunger for humanistic thought and serious engagement with history, literature, ethics, aesthetics, and the full range of human experience didn’t go away. It migrated. It moved to the internet, where it found new and surprisingly passionate homes: YouTube video essays, Substack newsletters, long-form podcasts, sprawling Twitter threads where scholars and amateurs argued about historiography at midnight. Some of this content is genuinely excellent. You can find more rigorous engagement with certain philosophical questions in a well done three-hour podcast than in many undergraduate seminars. The appetite, it turns out, was never the problem.

The problem is the format.

What a classroom does — even a mediocre one — that the internet structurally cannot do is force productive friction. You cannot scroll past the argument you find uncomfortable in a classroom. You cannot close the tab when the professor asks you to defend your interpretation. You cannot anonymously share your opinions. Someone makes a claim, and you have to find words for what you actually think, not what performs best in front of an audience. That process of articulation under pressure, of being required to sit with difficulty rather than click away from it, is where most of the actual intellectual work of the humanities happens. The insights don’t necessarily come from reading the text. They come from what happens when you try to explain it to someone who disagrees with you.

Online discourse has precisely inverted this. The online incentive structure rewards speed, confidence, blank sarcasm, and tribal signaling. The most engaging response is rarely the most accurate one; it’s the one that makes your side feel validated and the other side look foolish. This isn’t a character flaw inherent to internet users, but, I think, a natural byproduct of an online environment. Twitter and its successors are architecturally optimized for performance, not comprehension.

This matters for something larger than education. The humanities, when taught well, train a specific and rather rare cognitive skill: the ability to genuinely inhabit a viewpoint that is not your own before you evaluate it. Not to pretend to consider it, or perform open-mindedness, but to actually understand it from the inside, to feel the force of the argument, to see why a reasonable person might hold it, to find the strongest version of the position rather than the weakest. This is what close reading is, what historical empathy is, what serious moral philosophy demands. It is not a soft skill. It is a rigorous practice that takes years to develop, and it is precisely the practice that social media most consistently and reliably destroys.

It is perhaps not a coincidence, then, that our retreat from the humanities in formal education and our migration of humanistic discourse to hostile online environments happened in the same decades that produced our current levels of political polarization. I don’t want to oversimplify the causal story because polarization has many parents. But I do think we quietly dismantled the institutional training ground for the cognitive habits that make good-faith disagreement possible, and at the same time replaced it with an environment that punishes those habits and rewards their opposites.

The reassuring part is that AI, among other factors, may be forcing a correction that market logic previously prevented. If the skills that are hardest to automate are precisely the ones the humanities cultivate, such as judgment, moral reasoning, the ability to navigate genuine ambiguity, to communicate across difference, and to understand what motivates other people, then the economic case for a humanistic education may reassert itself in ways that the last few decades foreclosed.

That is, in a sense, what the humanities were always supposed to do. We just forgot, for a while, why that mattered.

The format is not incidental to the outcome; it is the outcome. Or, as Marshall McLuhan said, the medium is the message. Saving the humanities means recovering the institutions and structures — classrooms, seminars, the productive discomfort of being genuinely challenged — that make it possible. Not because the internet lacks good content. It has extraordinary content. (It also has unremarkable slop.) But content without scaffolding is not education. It is entertainment (infotainment?) dressed up in the vocabulary of thought. And maybe we have enough of that. College seminars are not comment sections.

Automated Writing Technology: Generative AI, ChatGPT

In grad school (2015-2020) I focused my dissertation research on what I termed “automated writing technologies.” I was fascinated by such technology because it represented a nexus of the linguistic and the numeric. These computer programs allowed me to explore my fascination with both alphabetic and quantitative languages. Specifically, my project tested the ability of computers to evaluate human-generated text and asked whether automated writing evaluation could, at the time, provide effective formative feedback on the rhetorical dimension of prose writing at the level of metaphor, irony, humor, and analogy rather than the more rote aspects of grammatical correctness. My dissertation findings, while limited, were not promising.

Large Language Models (LLM) were around during this time, but quite rudimentary. They were about as good as the predictive texting on our smartphones, limited to accurately predicting one or two words at a time. In 2017, however, researchers from Google published Attention Is All You Need, a revolutionary paper about the Transformer architecture that serves as the foundation of generative AI and which represents the “T” in GPT.

By the time I finished grad school in 2020, OpenAI’s second generation LLM, GPT-2, was available to the public. Now, less than three years later, we have OpenAI’s GPT-3 and ChatGPT, as well as other proprietary AI text generators from Google, IBM, Facebook, and more. This area of technology has advanced rapidly, and I am fortunate to have graduated when I did to ride this wave.

While I am not a computer scientist, and don’t pretend to be one despite my attempts to read and understand some of the primary papers from this field, I bring a background in rhetoric and writing assessment to the study of generative AI generally and Large Language Models specifically. My experience in writing pedagogy/assessment and the history of rhetoric prepares me to contribute to the scholastic conversations about these breakthrough technologies in productive ways.

I urge other humanities and liberal arts scholars to join this conversation. I think many of us in the liberal arts have much to contribute to decisions about how to incorporate generative AI into educational and professional institutions, which we need to start thinking about sooner than later. I also think we should make an effort to try to learn some of the more technical aspects of LLMs in order to be conversant with the programmers and companies designing them.

To that end, I will be updating this site mostly about future research regarding LLMs, GPT, and generative AI more broadly, considering both theoretical questions (does AI use rhetoric in the same way we do?) and practical applications (how can we use this technology to better teach writing and thinking?)

I recently gave a talk to my university about what LLM technology is and how we might work with rather than against it in our classrooms. I also published an op-ed on Thursday 23 February 2023 in the Dallas Morning News about the same topic, how generative AI represents an opportunity to re-think and re-define how we teach writing. Finally, I gave an interview to The Texas Standard, a Texas public radio station, on why I’m not as anxious about generative AI’s impact on higher education as some others in my field. The segment aired around 10.30am Wednesday morning on 22 February 2023.

Stay tuned for more updates.

“I can’t teach writing in only one semester”

One complaint I’ve consistently heard (and myself made) during all my years teaching college writing is that one or two semesters is not enough time to teach writing. One finding I’ve repeatedly come across in all my years poring over educational research is that the largest source of variance in student writing performance is the interaction between student and task. In other words, the fewer the number of tasks assigned in your course (assignments, papers, tests, etc.), the higher the variation in student performance, which means the less reliably (consistently) your class measures whatever it is we conceive of as “writing ability.” This seems intuitive to me, and it is consistently found in the literature. On average two different assignments, in the same course and even for the same student, are basically nonpredictive of performance in one by performance in another. A student can write an amazing editorial and absolutely bomb a research paper. Does that make your course an unreliable measurement of writing ability? Maybe.

The logical response to this is simple: just assign many more and wildly different tasks in your course to better cover the vast domain of writing ability and thus reduce performance variance. But, alas, that’s where the time constraint comes in. It’s virtually impossible to assign more than four major assignments a semester. Are four writing assignments enough to reliably capture writing ability? No way. Not given the infinite amount of genres and writing tasks out there and our evolving definition of writing ability.

So then, what if we increased the number of assignments, but made them smaller and spent less time on each? 10 small writing assignments a semester, instead of four major ones? Would 10 assignments more reliably capture writing ability and minimize our measurement error? Statistically, yes. Intuitively, I think yes, too. But I understand the resistance to this idea. There is value, I think, in longer, more in-depth writing assignments. I bet most freshman college students haven’t written a paper longer than 10 pages, and at some point they absolutely should write one. (I think multiple.)

But I wonder if that value can be realized in a freshman writing class. What is the realistic purpose of a one semester, freshman college writing class after all? If our time, and thus our measurement instrument, is narrowed to one semester, maybe we should break up the cognitive trait we intend to measure into smaller chunks, since the whole construct can never be reliably captured in a semester with four major assignments. It’s like we’re trying to measure several miles with four yardsticks. If we’re only going to get one (or two at most) semesters(s), maybe we should adjust the use of our narrowed instrument accordingly, by using it on more, smaller, varied tasks. Then instead of measuring miles with yardsticks we’ll at least be measuring yards with rulers.