Either the Precarity That Fuels the Banking Model of Education Goes, or We All Do

A few thoughts about plagiarism, precarity, and pedagogy in the era of AI Panic.

You see, as we enter 2023, the academic communities I’m part of are awash in fevered conversation about the Machine Learning text generator known as ChatGPT. ChatGPT is the great-grandchild of GPT-2, a system I tried to call people’s attention to years ago. Back then my colleagues treated my interest in Machine Learning text generation with a sort of bemused concern, uncertain if I was joking or having some sort of anxiety attack. Now they come to me and ask, “have you seen this ChatGPT thing!?!”

I am in no way bitter my previous attempts to spark conversation on this topic went unheeded. In no way bitter.

Anyway, the sudden interest in ChatGPT seems to stem from the fact that it can produce plausible output from prompts that aren’t so different from classroom assignments, like so:

ChatGPT responds to a prompt not unlike an assignment of mine this semester. Prompt at the top of the image, all other content machine-generated.

Note I said plausible not good. ChatGPT writes prose that sounds natural, and which would fool Turnitin, but if often makes some factual mistakes and odd interpretive moves. For example, Veronica Cartwright would like a word with paragraph three above. Paragraph four glosses over the male gender of the creature’s victim in a way that is unsatisfying. Still, these are also mistakes a student might plausibly make. That makes a merely half-assed assignment response difficult to distinguish from a plagiarized one generated by the machine.

Thus, ChatGPT has lead to a veritable panic about the coming wave of machine-generated plagiarism in college classes. The desired responses to this often trend towards the punitive. We need to make a better Turnitin that will detect GPT! We need to make students handwrite everything in class under supervision! We need a tool that will monitor the edit history in a student’s google doc and detect big chunks of pasted in text! We need to assign writing assignments to students arranged in cells around a central observation tower so we can observe them without ourselves being seen and get them to internalize the value of not plagiarizing!

Ok, not that last one, but the other ones I have actually seen proposed.

These punitive measures come from an understandable place of frustration, but they also enshrine what Freire called the banking model of education. In this model, students are passive recipients of Established Knowledge. Writing assignments are designed to ensure the Established Knowledge (either content or writing skills) have been passed on successfully. Students’ reward for demonstrating that they have received the Established Knowledge is a grade and ultimately a credential they can use on the labor market.

Machine Learning text generators threaten this entire learning paradigm by allowing students to fake the receipt of knowledge and thus fraudulently gain credentials they don’t deserve. To prevent this, the thinking goes, punitive measures must be put in place. GPT must be stopped.

Let me now briefly relate an ironic moment of learning from my own life that I think illustrates a different model of the process of education, before going on to explain the social context that makes it almost impossible to get beyond the banking model in the contemporary classroom.

You see, one of my responses to the rise of ChatGPT and its cousins has been to try to understand Machine Learning better. As part of this process, I’ve been working my way through a textbook that teaches Deep Learning concepts using the Python programming language. The book provides a number of sample pieces of Python code that the student is meant to reproduce and run for themselves on their own computer.

One of the code examples from Deep Learning with Python, Second Edition

As I went through the text, I entered the code examples into an interpreter window on my computer and executed them. I re-typed the examples myself, slowly typing out unfamiliar new terms and being careful not to misspell long and confusing variable names. This practice, of copying code examples by hand, is typical of programming pedagogy.

As a writing assignment, this sort of work seems strange. I am literally reproducing the code that’s already been written. I am not asked to “make it my own” (though I did tweak a variable here and there to see what would happen). I am not yet demonstrating knowledge I have acquired, since the code example is in front of me as I type. It’s a practice of mimesis so primitive that, in another context, it would be plagiarism.

And yet, I still did this assignment myself, I did not have it done for me by machine, though it would have been trivial to do so. I have an e-book of my text, I could have simply copied and pasted the code from the book into the interpreter, no AI writing system needed. No one would have caught me, because no one is grading me!

Indeed, I think I chose to write the code by hand in part because no one is grading me. There is nothing for me to gain by “cheating.” I wrote the code, not to gain a credential, but to improve my own understanding. That’s the purpose of an exercise like this, to have the student to read the code slowly and thoughtfully. I often found that I understood my own blind spots better after reproducing the code examples, and quickly started maintaining another interpreter window where I could play around with unfamiliar functions and try to understand them better. At one point, I did matrix multiplication on a sheet of paper to make sure I understood the result I was getting from the machine.

So my re-typing of code becomes a sort of writing assignment that doesn’t verify knowledge it produces knowledge. This assignment isn’t driven by an exterior desire for a credential or grade, but by my own intrinsic desire to learn. In such a situation, plagiarism becomes pointless. No punitive methods are required to stop it.

Lots of people much smarter than me have long advocated for a greater focus on the kind of assignments described above in college classrooms, and a diminished amount of attention to credentials, grades, and the banking model of education. In the wake of ChatGPT, the call for this kind of pedagogy has been renewed. If the banking model can be cheated, all the more reason to pivot to a more engaged, more active, more productive model of learning.

I think this is a great idea, and I intend to do exactly this in my classrooms. However, I think larger social forces are likely to frustrate our attempts at solving this at the classroom level. Namely, our students’ experience of precarity threatens to undermine more engaged learning before it can even begin.

In my experience, the current cohort of college students (especially at teaching-focused Regional Public Universities like mine) are laser-beam focused on credentials, and often respond to attempts to pivot classrooms away from that focus with either cynical disengagement or frustration. I don’t think that’s because they are lazy or intellectually incurious. I think that’s because they are experiencing a world in which they are asked to go into substantial debt to get a college education, and have substantial anxiety about putting effort into learning that is not immediately tied to saleable skills. This is exacerbated by the high stakes of a precarious labor market and shredded system for provisioning public goods that threatens all but the best and most “in-demand” professionals with lack of access to good housing, good health care, a stable retirement, and education for their children.

So, either the precarity goes, or we educators do. The punitive measures that would stop plagiarism in high-stakes classrooms will almost certainly fail. A pivot to learning as a constructive experience will only work with buy-in from students liberated from the constant anxiety of needing to secure job skills to survive.

So, as we enter the Machine Text era this spring, I call on us to engage and organize beyond the classroom and beyond pedagogy. How we build our classes will matter. How we build our society will matter more.

Machine Learning, Thermostat, Wood Stove

As we encounter the far-future quasi-magical technology that is Machine Learning, I wanted to offer up a brief reflection on everyday technology, labor, and meaning that I found interesting. A reflection brought about by trying not to use my thermostat.

At it’s heart, contemporary Machine Learning systems are just fantastically complex versions of that everyday appliance, the thermostat. The thermostat (at least an old-fashioned, “dumb,” thermostat like mine) takes a single data point, the temperature in your house, and uses it to trigger a response: turning on the furnace. When the temperature gets too low, the furnace turns on. When temperature gets high enough, the furnace turns off. This is a dirt simple example of what Norbert Wiener, one of the great-grandparents of current machine learning efforts, called a “feedback loop” in his 1948 classic Cybernetics.

Modern Machine Learning systems are based on the same principle, they just use lots and lots of fancy Linear Algebra to implement a feedback loop that can learn responses to lots and lots of data points all at once. For example, it can learn that all the data points (pixels) in an image should be labelled “cat” by looking at lots of labelled images and understanding how pixels and labels are related. It does this through another feedback loop, illustrated below, a process known as “training” in Machine Learning lingo.

From the excellent “Deep Leaning with Python, Second Edition” by Francios Chollet

Once trained, the Machine Learning system is now able to trigger responses to various input data. That could mean identifying an image, drawing an image to match a label, writing text to respond to a prompt, all sorts of stuff.

Why build a machine that can respond to inputs like this? So we can delegate stuff to it, of course. Just as we delegate keeping the temperature in our homes steady to thermostats, so we’ll be able to delegate all sorts of things to Machine Leaning systems. That’s automation, the delegation of human action to machine action with in the context of various feedback loops (or at least, that’s how you might define automation, if you read Wiener and Latour back to back, like I did once upon a time).

Those arguing in favor of automation often argue that the process can free people from routine “drudgery” and give them more time for “meaningful work.” If a machine can do a form of work, this argument goes, then that form of work didn’t embody the Unique Qualities of Humanity to begin with! Better, and more meaningful for humans to do the Special Unique Human Things and leave the routine alone.

Which brings us back to my thermostat. This winter, we’ve been trying not to use our furnace much, since it’s oil powered and current prices (thanks, Putin!) make it expensive to run. That means we’ve set our thermostat low enough that it only turns the furnace on when we’re in danger of freezing the pipes.

Instead, we’ve been keeping warm by using a wood-burning stove. The stove has no thermostat, of course, the feedback loop must be manually implemented. If it’s too cold, someone has to build a fire. When it becomes too warm, a handle on the side lets us damp the fire down.

This process involves a fair share of drudgery: emptying the ash pan, placing the kindling, lighting the fire, fetching more wood from the wood pile, keeping the fire fed. It can be tiresome to stay warm this way.

And yet, I often find that building a fire feels like a profoundly meaningful act. I pile the paper and wood together, I light the match, I nurture the flame. Now my family will be warm, instead of cold.

When we think of meaning we tend to think of grand things: gods and heroes, the fate of nations, the unique insight of art. But, I suspect, meaning more often lives here: in the everyday quotidian drudgery of caring for each other.

It’s something worth thinking about, as we learn to automate yet more drudgery away.

Reflecting on Knowledge in the Body in an Era of Prosthetic Dreams

I do not understand how to connect a bicycle pump to a bicycle tire equipped with a Presta valve. Regardless, I routinely use a bicycle pump to refill my bike tires, which are equipped with Presta valves. Perhaps Captain Kirk would be able to use that pair of sentences to trap our nascent AI overlords into a self-destructing loop of logic?

You, a presumably human reader, may also be a bit confused. I’ll explain. When I first purchased a slightly fancy bicycle, I was confronted with the problem of how to connect my bicycle pump to the skinny, fiddly Presta valves on it’s tires. I was used to the wider Schrader valves found on most inexpensive American bicycles, and when I tried to fit the head of my pump over the skinny Presta valve it wobbled to one side and wouldn’t seal in place. Air hissed out as I pumped, rather than entering the tire.

I did as one does to fix a problem these days. I Googled the issue. I watched YouTube videos. Nothing worked, I would attempt to follow the instructions I found, only to end up with the same result.

Then, after many attempts, the pump head sealed to the valve. I don’t know what I did differently that time. I still don’t know. All I know is, I can now attach the bicycle pump to the Presta valve on my bike tires and inflate them. I can do this every time I try. I don’t know how I am doing it, my fingers have simply learned the correct motion. I couldn’t explain that motion if I tried.

We are of course all familiar of with this kind of body knowledge. The way we learn to make a dance move (well, maybe you do, I’m an awful dancer), or pull a saw through a piece of wood, or even balance on a bike. We can’t, and then, with practice, we can. The conscious mind doesn’t really know how. The knowledge is somewhere in some unconscious part of the brain, not really in the limbs, but it might feel closer to the limbs than to consciousness.

For this reason, we perhaps tend to associate this kind of knowledge with manual labor, with the body side of the mind/body split our always too Cartesian society wants to keep a bright line. These reasons almost certainly articulate with our classed, raced, and gendered ideas of what counts as knowledge, as valuable, but that’s not what I want to explore today.

Instead, what I want to do today is make a suggestion in the opposite direction. That many things we think of as “knowledge work” are also closely tied to the same unconscious process that helped me learn to inflate my bike tires. That, as I’m writing this, I don’t consciously think through each word I place on the page. Instead, words often flow from an unconscious place (I sometimes call it my “language engine”) and I make conscious editorial decisions about which of its words I want to write down and which I want to strike and which of the multiple choices it may present me with is best.

The testimony of professional writers suggests to me I’m not the only one like this. William Gibson once said he wrote in “collaboration” with his unconscious mind. Raymond Carver once famously quipped that his most successful pieces happened because “sometimes I sit down and write better than I can.”

That unconscious flow of language may not be part of my conscious mind, but it is part of me. I have trained it through practice. It informs my decisions as I make them, even in a split second. It shapes the thinking that becomes my larger self.

This sort of per-conscious intuitive knowledge is not limited to language and writing. Our mathematical knowledge informs our sense of the numbers we encounter and their relationships. Historical knowledge informs our reaction to current events. Our prejudices and implicit biases are another form of this kind of knowledge, and unlearning those will require practiced engagement with this form.

For this reason, these intuitive senses will remain important for people as decision makers, so as long as we ultimately vest human beings with decision making power, these forms of knowledge in the body will matter. They will inform the sorts of questions we ask, regardless of the tools we have to answer those questions. They will inform the answers that “feel right” regardless of how we get them.

So, as we confront our new Machine Learning equipped reality, I’m not sure we should be too eager to abandon the idea that students ought to be able to show that they have incorporated knowledge at a bodily level. Historically, that’s one thing the essay has done. Show me you know this thing well enough to engage with and adapt it. Show me you have it in your body. A student who avoids doing such an assignment by outsourcing it to an AI cannot themselves be transformed by it, and that’s a problem, even if they will always have an AI in their pocket in the future!

We should be thoughtful about what we ask students to learn in this way, but I don’t think we should stop asking it.

Dry Dreaming with Machine Learning

We don’t yet know what the full social and cultural impacts of Machine Learning text and image generators will be. How could we, the damn things are only a few months old, the important impacts haven’t even started to happen yet. What we do know is this: Machine Learning image and text generators are a lot of fun to play with.

It’s that pleasure I want to briefly reflect on today. Playing with something like DALL-E, or Stable Diffusion, or ChatGPT is for me reminiscent of the kind of slot-machine loop a game like Minecraft sets up. In Minecraft, you spend an hour whacking away at cartoon rocks with your cartoon pick for the reward of occasionally coming across a cartoon diamond. When you’re playing with Stable Diffusion you spend an hour plugging in various prompts, trying different settings, using different random seeds, for the reward of occasionally generating something that strikes you as pleasing.

Stable Diffusion Imagines “A piece by Peter Blake entitled ‘An afternoon with William Gibson'”

What’s pleasing to me about the images I come across in this way is, often, how they capture an idea that I could imagine but not realize (as a visual artist of very little talent). In this sense that they translate ideas into artistic work without the intervening phase of mastering an artistic medium of expression, image generators call to mind the idea of “Dry Dreaming” from William Gibson’s short story “The Winter Market.”

In this short story, which prefigures in many ways Gibson’s later Sprawl novels, Gibson imagines a technology that basically reads the minds of artists (with the mind-machine interface of a net of electrodes familiar to many cyberpunk stories) and outputs artistic vision directly to a machine recording that can then be edited and experienced by an audience. At one point, the main character of the story muses about how this technology allows artistic creation by those lacking traditional artistic skill:

you wonder how many thousands, maybe millions, of phenomenal artists have died mute, down the centuries, people who could never have been poets or painters or saxophone players, but who had this stuff inside, these psychic waveforms waiting for the circuitry required to tap in....

On the surface, DALL-E and Stable Diffusion (and text generators like GPT-3, though my own personal experience of this is different since I’m a bit better with text) seem to do just this. Let us create direct from ideas, jumping over all the fiddly body-learning of composition and construction.

But of course, there is a crucial difference between the imagined and actual technology. The “dry dreaming” Gibson imagined was basically imaging a short cut around the semiotic divide between signifier and signified: it exported meaning directly from a person’s brain to a recording. Let’s leave aside for a moment if such a thing would ever be possible, I think we can perhaps still relate to the desire behind the dream. If we’ve ever struggled to put an idea down in words, we understand the fantasy here. Just take the idea out of my head and give it to someone else directly!

Almost but not quite, ChatGPT

But DALL-E and Stable Diffusion very much do not take ideas directly from the user’s head. They take a textual set of signs from the user, and give back a visual set of signs based on what they have learned by statistically correlating all the sets of textual and visual signs they could find. What they do is, in fact, almost the opposite of what Gibson imagined with dry dreaming. Instead of direct transfer of signified with no distorting signifier in the way, they are dealing with the pure play of signifiers, without the weight of meaning to slow them down.

Of course, the signified re-enters the picture in the moment that I, the user, select an image and think “oh yes, that’s what I meant!” or even “oh wow, that’s what that could mean!” But of course, those reactions happen in the presence of the sign already drawn for us, the re-imagined imagination of the vast set of signs that were the training data for the machine.

That moment is a lot of fun, but the change it heralds for meaning itself is at least as profound as those brought about by recording technology and documented by Kittler in “Gramophone, Film, Typewriter.” If recording allowed for the remembering of signs without the intervention of human meaning-making, then machine learning generators may allow for the creation of signs without the intervention of human meaning-making.

What that does, I don’t think any of us know yet. But it does something.