A weblog devoted to the underlying craft of research
“If its object were scientific and philosophical discovery, I do not see why a University should have students.” (John Henry Newman, 1852) In the coming years, I expect, the purpose of higher education will be a recurring topic of discussion among academics. What are students supposed to learn at university? How are they to be … Continue reading Higher Learning, Basic Skills →
“If its object were scientific and philosophical discovery, I do not see why a University should have students.” (John Henry Newman, 1852) In the coming years, I expect, the purpose of higher education will be a recurring topic of discussion among academics. What are students supposed to learn at university? How are they to be improved? What do we want them to become better at? The answer will have to take seriously the fact that we have the authority only to impart and certify knowledge, to arm them only with ideas and “the authority of right reason,” as Scotus Erigena put it. We may only teach and examine our students. Why should students submit to this authority? What do they gain from it? What does society gain from demanding it of them? An alternative, after all, is fast coming into view. Students are learning to use artificial intelligence to “support their learning,” which, unfortunately, is often indistinguishable (sometimes to themselves and sometimes, it seems, even to their teachers) from avoiding the problem of learning altogether. I’ve pointed out before that when Bertrand Russell suggested that a “system of notation” could almost serve as a “live teacher,” and “a perfect notation would be a substitute for thought,” he was probably not imagining the “stochastic parotting” of large language models. But they certainly do seem capable of substituting for a great deal of the thinking we’ve traditionally demanded of our students. We seem to be on the verge of automating the very problem of knowledge. Our students have been given an epistemic workaround. Now, I have served for about two decades as a writing coach at a major European business school and during that time I have developed what I think is a healthy, working epistemology. I don’t claim to have solved all the problems that philosophers have raised in regards to the nature of knowledge, but I have a confident grasp of what we mean, or should mean, when we say that we are “knowledgeable” people and are helping our students to become likewise “able”. As I never tire of telling them, to be knowledge-able is both to be able-to-know things and en-abled by that knowledge to do things they would not otherwise be capable of doing. Knowledge is a competence that is manifest in a performance. In what, then, does that performance consist? What should university students become increasingly better at doing? What does it mean to be able to “know something” for, let us say, “academic purposes”? First, if you are knowledgeable about something you are able to make up your mind about it. Invoking the long tradition of Western epistemology, I usually characterize this competence as the ability to form a justified, true belief. Given a series of experiences or a set of materials, a knowledgeable person is able to reach a determination of what is going on and what it means. They can convert what William James (and, later, Thomas Kuhn) called “the bloomin’, buzzin’ confusion of experience” into sterner stuff: propositional attitudes about stable facts, and theories that arrange concepts into orderly frameworks. Obviously, even very knowledgeable people can be wrong about particular facts and this is why I have increasingly come to emphasize that knowledge isn’t so much being right about things as being able to change your mind about them in an orderly fashion. On the path “from stimulus to science,” as Quine puts it, “we are faced with the problem of error” at every step. Knowledgeable people face this problem, let’s say, squarely. But forming a belief, no matter how true or justified, is not sufficient to be considered knowledgeable in an academic setting. You must not only know what you’re talking about, you must be able to talk about what you know. To know something at university is to be able to hold your own in a discussion about it with other knowledgeable people. For students, this should be understood as an ability to discuss topics raised in class with their fellow students. It is sometimes argued that “academic writing” lacks “relevance” for students because it doesn’t relate to their “lived experience” or “real life”. Against this, I have long tried to argue that the “academic situation” offers an entirely meaningful context for rhetorical engagement. The classroom is a microcosm of the discipline and the students are learning to address their peers and respect their criticism. In that sense, students are apprentice scholars. But nor is that all there is to knowing things “for academic purposes”. Knowledgeable people are not just able to make up their minds and speak their minds; they are able to write it down. The test that I recommend is whether you are able to compose a coherent prose paragraph about something you know in under half an hour. More specifically, are you in an epistemic position to decide at the end of one day to write a paragraph a start of the next about something you knew was true last week? This is not just a good test of whether you actually know the thing you think is true, it is also a good skill to have in itself. It’s nice to know that you can write half a page about anything you know in half an hour. Doing it regularly should give you a certain confidence, not least that confidence which comes from regularly experiencing that you do, in fact, know things to be true. It is this competence that I hope the university will continue to represent and this confidence (which is not without humility) that I hope we will continue to instill in our students. To that end, I never miss an opportunity to plead for a shift of focus in our assessments, from homework assignments back to on-site, off-line, invigilated written examination. A university student who has taken a course (or is in the middle of a term) should be able to answer a relevant 20-word question given 4 hours and 1000 words (with relevant accommodations for disability) in a way that can be easily graded by their teacher. The ability to write, say, five coherent prose paragraphs is a good proxy for whether the student is able to make up their mind on the subject they are studying, speak their mind, and (of course) write it down. I would certainly argue that the plain in-ability (that the exam conditions of course make, precisely, plain) to write such an essay should call into question their claim to be a “knowledgeable” person on the subject in question. Higher learning is manifest in such basic skills.
Why English Majors Should Take On-Site, Invigilated Written Exams
[I generally don’t like this kind of stunt. But I was able to generate this essay in under 10 minutes using Microsoft Copilot. While it reused some of the language from my prompts, all the paragraphs (and the title of the post) were generated out of whole cloth by the AI. I had a brief … Continue reading Why English Majors Should Take On-Site, Invigilated Written Exams →
[I generally don’t like this kind of stunt. But I was able to generate this essay in under 10 minutes using Microsoft Copilot. While it reused some of the language from my prompts, all the paragraphs (and the title of the post) were generated out of whole cloth by the AI. I had a brief exchange with it about why it is so good at generating prose about Hamlet, and then I asked it to write a five-paragraph essay using this fact to argue for on-site examination. I then simply asked it to add two paragraphs (the fifth and sixth paragraphs in the final version) about how (a) students would be incentivized to write at home and (b) how homework could still be used, giving only indicative grades. I have done no editing of the final text at all. I would say it is 100% in line with my views on the subject. I’m posting it here mainly to document where we’re at today. Copilot’s parting comment was, of course, to offer more help: “Would you like me to tighten this into a formal academic style with references, turn it into a persuasive op-ed, or create a version optimized for a university policy proposal?” I left it there.] In the age of artificial intelligence, the integrity of literary education faces an unprecedented challenge. Large language models (LLMs) have made it remarkably easy for students to produce polished essays on canonical texts without engaging deeply with them. Nowhere is this more evident than with Shakespeare’s Hamlet, a play that has generated centuries of commentary and analysis. Ironically, the very text we most hope students will wrestle with is also the one they can most convincingly fake an understanding of using AI. This reality demands a reconsideration of assessment methods, and on-site, invigilated written exams offer a compelling solution. The problem begins with the nature of LLMs and their training data. These models are built on vast corpora of text, and Hamlet occupies a privileged position in English-language discourse. It appears in countless essays, scholarly articles, and online discussions, giving AI systems a rich interpretive reservoir to draw from. As a result, generating a plausible essay on Hamlet requires no genuine engagement with the text—only a prompt. When students can outsource interpretation so easily, traditional take-home assignments lose their ability to measure understanding. This erosion of authenticity matters because literary study is not merely about producing words; it is about grappling with ambiguity, forming original insights, and developing interpretive judgment. Hamlet is famously complex, inviting questions about action and inaction, mortality, and meaning. These questions cannot be answered by parroting consensus views—they require intellectual struggle. If students bypass that struggle through AI, they miss the very experience that makes literature transformative. Assessment must therefore shift from evaluating polished output to observing the thinking process in real time. On-site, invigilated written exams restore this integrity by creating conditions where students must rely on their own interpretive resources. Without access to AI, they are compelled to engage directly with the text and articulate their ideas under pressure. This format does not eliminate preparation or external influence, but it ensures that the final performance reflects the student’s own reasoning. In doing so, it preserves the core educational value of literary study: the cultivation of independent thought. Moreover, this kind of exam would incentivize students to practice writing at home. Knowing that their grade will depend on their ability to produce coherent, insightful prose under time constraints, students will naturally rehearse the skills required for success. They will write practice essays, refine their arguments, and internalize the rhythms of literary analysis. In this way, the exam becomes not just an evaluative tool but a motivational one, encouraging sustained engagement with both the text and the craft of writing. Finally, homework can still play a role in this model, provided it is not graded in a way that affects the final mark. Assigning essays for indicative feedback rather than summative assessment allows students to experiment, receive guidance, and improve without the temptation to outsource their work to AI. Teachers can use these assignments diagnostically, identifying areas of weakness and tailoring instruction accordingly. By decoupling homework from high-stakes grading, we preserve its pedagogical value while neutralizing its vulnerability to technological shortcuts. In conclusion, the rise of LLMs has exposed a vulnerability in traditional assessment practices, particularly for canonical works like Hamlet. If we want English majors to truly understand literature rather than merely simulate understanding, we must design exams that cannot be outsourced to machines. On-site, invigilated written exams are not a nostalgic return to the past—they are a necessary adaptation to a future where authenticity is at risk. By embracing this change, we safeguard the intellectual rigor that defines the study of English.
Calculators, Screwdrivers, and Other Tools
On Saturday, I wrote about one objection to my “Prompts and Conditions” post that came up during my faculty development course on “Teaching Writing in the Age of AI.” This post is about another one, which begins with something I say at the end of that post: Surely, we can say what a math student … Continue reading Calculators, Screwdrivers, and Other Tools →
On Saturday, I wrote about one objection to my “Prompts and Conditions” post that came up during my faculty development course on “Teaching Writing in the Age of AI.” This post is about another one, which begins with something I say at the end of that post: Surely, we can say what a math student at any level should be able to do without a calculator, or what a history student should be capable of without the internet. AI generalizes this problem. In each field, we must decide what students should be able to do on their own. A participant reminded me that the calculator analogy has long been discredited as a helpful analogy for understanding AI, especially if our aim is to limit reliance on it. After all, everyone relies on calculators these days, and very few exams ban their use. This hasn’t caused any kind of catastrophe for education. We have simply changed the way we teach and learn math. I think it’s worth looking into this claim in some detail, I should say. After all, it is my impression that many high-stakes exams — like the American SAT — have very specific rules for calculators that enforce limits on the functions that are allowed. I’m pretty sure this has been the case for as long as calculators have been available; their use is in governed by policy. But the general idea that math instruction hasn’t banned them altogether is of course true, nor have we kept teaching the same “old” things is. In any case, talking about “what a math student should be able to do without a calculator,” the participant suggested, was like asking what a carpenter should be able to do without a screwdriver. The whole point of learning the craft is learning how to use the tools. I immediately liked this way of putting it because when he mentioned carpentry I thought he was going to talk about power tools, but the problem, of course, arises already at the level of saws and hammers and screwdrivers. We may as well start there. Would I say, “Surely, we can say what a carpenter’s apprentice should be capable of without a screwdriver”? As it happens, I would answer yes. But I must first emphasize that I have not said that students and apprentices should be examined only without their tools. I have said they should also be examined without their tools and that, in any program of instruction, there must be some set of skills that can be examined this way. It’s not either/or, but both, separately. In the case of the carpenter’s apprentice, I suggested that someone who is able to use the standard toolkit will also be (and should also be) able to talk intelligently about how they would go about a particular task, without holding any of the tools in their hands. Also, an apprentice woodworker can be sent into the woodshed to pick out some boards that would be ideally suited to making a particular piece of furniture. This requires no tools, only a good grasp of the materials themselves (a “feel” for them, if you will). It might also be worth seeing if they can “eyeball” rough dimensions, i.e., whether they have realistic intuitions about size and space. (I am sometimes told horror stories by teachers of quantitative methods about students who do not immediately recognize that a calculation they have let a spreadsheet carry out is off by three orders of magnitude and even in the wrong direction: positive when they should be negative, negative when they should be positive. It is worth having students estimate calculations, without a calculator, simply to make sure they have a realistic sense of the thing they are calculating.) I think it is true that we must accept AI into writing instruction just as we have accepted calculators into math instruction. We can’t burry our heads in the sand (or, perhaps more precisely, require our students to tie their hands behind their backs). But, just as we can require an apprentice to be able to tell us how they plan to go about a project before they pick up a tool and show us what they’re capable of, we can just as reasonably at least require university students to tell us how they would use AI to solve a writing problem. But we can go further. My participant suggested that I was ignoring what we know about “embodied cognition” (and we might add “extended mind”). But I am absolutely on board with those sorts of views. We exist in an environment of tools and machines, which not only help us to get around, but shape our very being. I will insist, however, that our environment also includes other people and the language we use to communicate with them. Our words, as Heidegger pointed out, are part of the “equipmental contexture” of our existence, our being-with-others. Teaching students how to write good prose by themselves is very much a way of helping them embody their knowledge. I want to stress that my point is that AI “generalizes” the issue. (Indeed, Silicon Valley keeps promising us something the call AGI: “artificial general intelligence.”) With minimal prompting, AI is increasingly able to simulate almost any academic competence at least “passably” (deserving a C, let’s say, or what we call a 7 in Denmark.) If universities are to maintain their assessment integrity, we need to find a way to make sure that the actual bodies of the students are capable of something in particular, something that reflects 3 to 5 years of study. And that means we have to come up with some things we can test whether their bodies can do.
I’m running a faculty development course this month called “Teaching Writing in the Age of Generative AI” and it has led to some interesting discussions. I asked the participants to read and reflect on my post, “Prompts and Conditions,” which I wrote a couple of years ago, when the challenge that AI might pose for … Continue reading On Ruining the Weekend →
I’m running a faculty development course this month called “Teaching Writing in the Age of Generative AI” and it has led to some interesting discussions. I asked the participants to read and reflect on my post, “Prompts and Conditions,” which I wrote a couple of years ago, when the challenge that AI might pose for higher education was just coming into view. I think the basic idea of the post was agreeable to most of the participants, but a number of them had some interesting reactions to what I would call the “rhetoric” of the post. I want to address two of them in particular in a post each. One of the participants noted a parenthesis at the end of what I took to be a very practical remark about setting deadlines. Here’s what I had written: If we imagine classes are held on Mondays, students can be given the take-home prompts at the end of the class and submit their essays on Friday (there’s no need to ruin their weekend). “Doesn’t this suggest that our students don’t want to learn? Why would we presume that studying on the weekend ruins it?” asked the participant. He recalled that when he was a student he was happy to spend his evenings and weekends studying and, indeed, that he felt that he was expected to do so by his teachers. Is this something that we have suddenly abandoned? (And, we might add, does AI force us to do so?) Now, I must say that I had not expected anyone to take this remark as seriously as that. The idea of “ruining” a weekend by doing school work was only intended as a lighthearted gesture at the priorities of young people. But perhaps playing to these priorities is ill-advised; and perhaps it comes off as condescending, even to the students who have them. It’s always worth thinking about the rhetorical effects of our pedagogical strategies. In defending my choice of words at the time, I did point out that he was taking a rather hard line against another kind of concern that teachers often express for their students: not everyone has the luxury of devoting their entire lives to school while they are attending university. Many have jobs on the side; some even have families to tend to. “Ruining the weekend” may be more existential for some students than merely skipping a night on the town. This seemed to elicit some nods in the room, including from my critic. In any case, it’s important to remember that deadlines are always somewhat arbitrary and are likely to occasion both procrastination at first and consternation at last among some students. So I spend a lot of time teaching (and coaching) students (and faculty) to plan their work in orderly, half-hour “moments” of composition so that they can comfortably meet their deadlines without having to miraculate a text at the eleventh hour. For the same reason, whenever it is up to me, I like to place that eleventh hour before noon on a Friday, rather than midnight on a Sunday. It’s just a good way to signal that you may as well get the work done during the working week, as part of your regular day-to-day program of study. It keeps the task of doing an assignment in proportion. Like I say, I don’t want to dismiss the concern about the rhetorical force of talk about “ruining” our students weekend by asking them to study. But perhaps it is precisely the question of whether what they do during their “free” time is chosen or assigned. A student that wanted to read a book you have suggested or do some writing of their own can still have that hope “ruined” by poor planning and ostensibly “generous” deadlines. I don’t claim to have a definitive take on how to talk about school-life balance with students and how important we should presume (whether in our thoughts or in our speech) learning is to them. As an occasion to give it some thought, my participants remark is well taken. I’m happy to hear more thoughts in the comments below.
When I talk about academic writing with students at the Danish Design School, I begin by showing them a short clip of Thomas Leslie, Professor of Architecture at the University of Illinois, talking about Pier Luigi Nervi’s approach to designing structures. Before I unpack it, I encourage you to take five minutes to watch it … Continue reading The Human Scale →
When I talk about academic writing with students at the Danish Design School, I begin by showing them a short clip of Thomas Leslie, Professor of Architecture at the University of Illinois, talking about Pier Luigi Nervi’s approach to designing structures. Before I unpack it, I encourage you to take five minutes to watch it yourself (from 27:25 to 32:12) and see if you can discern the lessons for writers that I extract from it. First of all, it’s useful to think of your writing problem as in some way related or analogous to the problems of your academic discipline. (When I talk to students in innovation management, for example, I tell them that “Good writing is the creative destruction of bad ideas”; when I talk to project management students, I try to get them to see to that a collaborative writing project is one of the most difficult projects they may ever manage.) Getting students think of writing a paper as a “design problem” is especially apt, not least on Leslie’s definition of a “designer”: “someone who thinks things through.” Another great point that he makes early on is that Nervi’s greatness lay in not “throwing up his arms” at “impossible constraints” but, rather, going ahead and working within them, to “hack together” a solution under the material conditions he had been given. Indeed, for Nervi, the aim was to build something “out of almost nothing” and this, of course, is the essential and difficult problem of the writer: to use the very limited materials provided by words to express what we know. Nervi, as any writer must, was also able to adapt his thinking to the time constraints he was given. He had a deadline and he made sure that his design could be realized within it. But what I really love about Leslie’s presentation here is the way he relates the handmade components (the ferrocemento components of the roof) to the larger project. The “rigorous, algorithmic process” of producing and assembling the pieces ends up producing an impressive “architectural space”. The “structural form” is intimately related to “the pattern that comes from the human scale of the process”. Nervi’s design assumed that he would be “working with human beings” and this sensibility is then felt even in the enormous exhibition hall that results. Something important emerges from the fact that, as Leslie puts it, “people are actually making all of the things.” This is something I latch onto, especially, as you can imagine, in this so-called “age of AI”. A larger text, like a research paper or thesis, will necessarily be a complex structure with many working parts. But it is important that the reader experience this overarching (!) “architectural form,” not as something that was “generated” by a monstrously intelligent machine on the basis of some “large” stochastic “model” but, rather, as something that was crafted by human hands, one paragraph at a time, one deliberate moment after another.
Why I Refuse to Read the Paper David Gunkel “Co-Authored” with ChatGPT
The journal Human-Machine Communication recently published a paper by David Gunkel called “Prompted by me. Generated by ChatGPT,” which he promoted on X under the slogan “Death of the Author.” I had a look and quickly made up my mind not to read it. After a little more thought, I realized that my personal decision … Continue reading Why I Refuse to Read the Paper David Gunkel “Co-Authored” with ChatGPT →
The journal Human-Machine Communication recently published a paper by David Gunkel called “Prompted by me. Generated by ChatGPT,” which he promoted on X under the slogan “Death of the Author.” I had a look and quickly made up my mind not to read it. After a little more thought, I realized that my personal decision should also be a maxim for others. “I think it’s important for academics to refuse to read this kind of paper,” I said, reposting his paper. Over the past few days, David and I have been going back and forth about it, and he finally suggested that I should make an argument for my refusal. This post is a (too long) first draft of that argument. TL;DR: David’s “process note” abrogates responsibility for the text by pre-emptively deferring an unspecified amount of its intention to a stochastic process. The note is not a declaration but a disclaimer. This renders the text uninterpretable. Let me begin with what I hope is an elementary logical point that I was surprised to have to make repeatedly in the threads on X. If there are good reasons not to read a text, they are not to be found in that text itself. If that were the case, one would have to read it in order to defend one’s refusal to read it, a catch 22. Indeed, on X, David has implied that I am rejecting an argument that I am refusing to read, but this isn’t a fair criticism of my stance. I am refusing to even hear an argument on the basis of how it has been presented. What, then, are my reasons and on what basis have I formed them? I did, of course, have to read some of the paper before refusing to go on. I read the title, the byline, the abstract, and the process note. I have also consulted the journal’s policies, especially those related to AI. And I have had a look at the editors and editorial board of the journal; David serves on the latter. In the “Process Note on Composition and Attribution” — which, to be precise, has been placed in a box and is signed, enigmatically, “The Authors,” although the paper formally has only one author, namely, David J. Gunkel — the paper presents itself as a “human-machine collaboration”. Part of the note is procedural and part of it is philosophical. The procedure is stated in the middle of the note: The text that is presented here emerged through an iterative process involving prompts, generated responses, revisions, and theoretical provocations that circulated between David J. Gunkel and ChatGPT 4o. At times, the human component of the team guided or corrected; at other times, the model generated formulations that challenged and even recontextualized the human’s own assumptions or direction. I want to stress that I have no objections to this procedure. This is no doubt already a common way to compose text “in the age of AI” and it’s altogether likely that most scholars and students in the future will compose their texts in this way to a greater or lesser extent. To anticipate my philosophical objections (or my objections to the philosophical part of the note) I might take issue with the presentation of David as “the human component of the team,” which does indeed sound like it was generated by the machine, not the man, but the iterative process that is being described here is a perfectly legitimate use of technology in the production of texts and, indeed, or perhaps better, in the development of one’s ideas. Prompting a language model to probe the prose of the world strikes me as a reasonable thing to do in some cases, even using it to “raise the temperature” of (i.e., introduce some randomness into) one’s thinking is also legitimate if you like that sort of thing. In any case, it is not what David did with the machine, but the significance he accords it for the authorship of his text, that motivates my refusal to read it. Before we get to that, however, we have to answer the question of why, if the process note can be attributed to “the Authors” (in the plural), the published paper has only one author, namely, like I say, David — indeed, David J. Gunkel, whom I suspect the David I know and love to goad on X might not fully identify with. Here the answer is simple and is to be found in the publication ethics statement of the journal, Human-Machine Communication: No tool, program, or other forms of AI (such as a large language model, i.e., LLM) will receive a byline (recognition as an author) on an article published in Human-Machine Communication. Currently, machines do not have the ability to accept responsibility or discipline for work created and therefore are prohibited from authorship. Authors using any machine-generated content, similar to content created by an LLM, are required to document this in both the acknowledgments and methods section of a submission. Furthermore, HMC may place an LLM-badge on the article as an acknowledgment to readers about the practice. Now, I do in fact have an issue (to be taken up in another post) with the requirement to “document” machine-generated content and the practice of labelling it with badges. (I note, however, that HMC has not placed an LLM-badge on David’s article for some reason). But I completely agree that AIs should never be given a byline. In a moment, we’ll get to my core objection, which is that the process note explicitly recognizes ChatGPT as an author and therefore seems to prima facie violate HMC’s own policy here but, before we get to that, I want to note that, “HMC supports the ICMJE definitions of authorship,” which state that: “Authors should not list AI and AI-assisted technologies as an author or co-author, nor cite AI as an author.” I will agree with anyone who suggests that David’s paper follows the letter of these rules, but I will insist that it violates their spirit. Indeed, the purpose of the paper, as stated in the abstract, is to “disrupt” those rules or, as David also sometimes likes to say, “think otherwise” about them. That is what I refuse to do and therefore refuse to read the paper. Taking the procedural part as read, then, the process note makes two assertions of a more philosophical nature that I reject. The first is a general reflection on collaborative writing: As with any collaborative work, it can be difficult — if not impossible — to draw clear lines demarcating where the contribution of one partner ends and the other begins. This is especially the case when one of the partners is a large language model. The first sentence here is unobjectionable. But is there any meaningful sense in which the contribution of a large language model is “especially” difficult to distinguish from that of a human co-author? On the face it, since it is possible to document every step of the process, it would seem much easier to demarcate the contributions made by the language model. We just need to consider the ongoing, unrecorded conversation between two peers, over months and years, that results in a conventionally co-authored paper. But, more importantly, this inability to itemize the contributions of each author is precisely why co-authors are conventionally both held responsible for the entire paper that their names are signed to. That is, the consequence of the lack of clear lines is simply a shared, un-demarcated responsibility for the text. Since the journal’s author guidelines implicitly rule out this taking of responsibility by ChatGPT, David’s attempt to “attribute” the contribution of some of the content, content that is not demarcated from his own, is a de facto abrogation of responsibility for the meaning of an (unspecified) portion of the text. After the procedural description that I already quoted, the note ends as follows: In this case, the language model was not used as an instrument by a human agent but functioned like a co-author and interlocutor — producing text, proposing structures, and participating in the generation of new insights. The result of this effort is not the product of one or the other, but of this entangled (and difficult to disentangle) interchange. I have emphasized “like a coauthor” because I believe this is a violation of (the spirit of) the journal’s rules against giving language models “recognition as an author” (recall the ICMJE statement that we should “not list AI and AI-assisted technologies as an author or co-author, nor cite AI as an author.“) HMC’s guidelines are clear: “Currently, machines do not have the ability to accept responsibility or discipline for work created and therefore are prohibited from authorship.” That is, David may be right that he can’t himself disentangle his own contribution from ChatGPT — though I would say that scholars should normally pride themselves on being able to disentangle their own contribution from their sources — but before you publish a work you must accept responsibility for it. My objection to David’s note is that he does not do this. He obscures the “authorship” of the whole paper behind a screen of stochastic processes that he claims he cannot “disentangle” from his own intentions. Now, having not read it, the only access I have to the “intention” of “the Authors” is the abstract of the paper, which is the basis on which we often decide whether or not a paper is worth our time to read. Here it is in full: This essay—which is not only about human-machine collaboration but is a performance in human-machine collaboration—interrogates the shifting terrain of authorship and creativity in the age of generative artificial intelligence (GAI). Challenging both the instrumentalist view of technology and the romantic myth of the singular genius, it argues for a reconceptualization of creative production as distributed, dialogical, and co-constituted. Drawing on both theoretical innovations in poststructuralism and the practices of pre- and post-modern content creators, the essay repositions the algorithm not as a mere tool but as an active participant in the generation of meaning. In doing so, it exposes and disrupts—in both content and form—the metaphysical assumptions that continue to underwrite our understanding of writing, agency, and communication. The gesture at the “shifting terrain of authorship” and its declared aim of “exposing and disrupting … the metaphysical assumptions that continue to underwrite or understanding of writing…” resonates, as David has made clear on X, with thinkers like Barthes, Foucault, and Derrida, i.e, what he here calls “theoretical innovations in poststructuralism” and elsewhere simply “the death of the author”. Now, I disagree with David about the consequences of poststructuralism for both authorship in general and the place of large language models in that terrain. Let me say that again: I disagree with David. What he has done with this paper is to attempt to force me (and others like me) into accepting his conclusion simply by reading and interpreting his text. While even his journal won’t let him do it, he has asked his reader to recognize ChatGPT as his co-author. If his conclusion is to “disrupt” the metaphysics of authorship, he would have won his argument the moment I begin to read his text as though it might possibly mean something. On these grounds, then, I refuse to the paper. There is much more to be said. And I have not said even this much as efficiently as I could. The issue is serious and the consequences are worth thinking about. And in future posts I will deal with the problem of AI authorship more generally and without direct reference to this paper. David asked me to make an argument. And I have at least outlined one here, stuffing it with more documentation than might be useful to the reader. At least we’re now on the same page, I hope. I think this paper is a bad idea — indeed, it is an infamous device — and, as I said on X, it is unethical in at least one simple sense: it sets a bad example for others, and especially, students to follow. In my opinion, even reading it, is bad form. But talking about our reasons for not reading something is entirely in order. Finally, let me address something that of course troubles me in this whole exercise: Do all these words not vindicate David’s little stunt? Is he not drawing attention to an issue and getting us talking about important things? Well, I would not condone the literal murder of a writer to draw attention to the literary “death of the author”. And I might even pen a lengthy denunciation of the act if someone I otherwise respected ever committed it. I would certainly refuse to read the manifesto he wrote in the blood of the victim!
With my blog more out of view than has been in the past, I’m finding that I approach writing with less urgency. This is a good thing, I think, but I will need to establish an entirely “private” writing practice if I want to make any thing of it. That is, I need to start … Continue reading Private and Publish →
With my blog more out of view than has been in the past, I’m finding that I approach writing with less urgency. This is a good thing, I think, but I will need to establish an entirely “private” writing practice if I want to make any thing of it. That is, I need to start thinking about writing more “publishable” pieces. I have put those two words in scare quotes to signal the uneasy tension between them and their difference from the mood of blogging. When I am writing a blogpost, as I am doing now, I almost feel like I’m thinking out loud, in public. In fact, my catchy title (of which I am at this moment still quite proud but may feel differently by the end) came to me while writing the first sentence of this paragraph. Blogging isn’t something we do in private and posting isn’t quite publishing. And writing essays and books has an altogether different feel. It requires days and weeks of lonely work, unobserved by others, sometimes making progress, sometimes losing ground. None of the struggle is visible to the reader except as a trace in the final product, often seen mostly in its imperfections. (Hopefully, most of the struggling ends up being successful, and therefore invisible.) There is some legitimate concern these days about the private/public distinction, the erosion of our privacy by forces outside of us. But perhaps we also need to understand the practices that maintain our privacy from the inside and, perhaps ironically, this doesn’t mean withdrawing from the world, staying out of public view. Rather, it means carefully selecting, in private, what to share with others, through publication. This idea that publishing a work properly, not merely posting it to the internet, constitutes an “interiority,” a privacy of the mind, is interesting to think about. It’s also an experience that I don’t give myself often enough. I like to think that I have a rich and interesting inner life, but, truth be told, I don’t work on it, I don’t elaborate it, nearly enough in prose. I need to set aside some hours in the morning to dwell upon my private thoughts with a public in mind. And the best way I know of to do this is to write down what I think for someone I respect. That is the basic scene of good prose writing. I once somewhat bitterly suggested that, in this age of “surveillance capitalism” (and, indeed, “central intelligence,”) if you want your privacy, you have to keep it like a secret. As invective against a certain kind of governance, I still think this is a nice way of putting it. But I need to go at this more constructively, with a healthier attitude. In fact, I need a more durable, longterm strategy. As an individual, then, maybe the best way to maintain your privacy is to open it like a book.
I just asked Microsoft’s Copilot to write me a 1000-word essay about the normative implications of Quine’s naturalized epistemology, giving it a prompt of less than 20 words. It immediately complied and, within a few seconds, generated a coherent essay that could easily earn a decent grade in an undergraduate philosophy course. What I mean … Continue reading A Simple Test →
I just asked Microsoft’s Copilot to write me a 1000-word essay about the normative implications of Quine’s naturalized epistemology, giving it a prompt of less than 20 words. It immediately complied and, within a few seconds, generated a coherent essay that could easily earn a decent grade in an undergraduate philosophy course. What I mean is that if a student had written the same essay under closed-book conditions, given three hours at the end of a course, it would clearly have demonstrated a familiarity with the texts studied (Copilot was able to correctly cite the two key texts that I would have) and an understanding of the issues involved. The exact grade would of course depend on the level of the course and the standards of the teacher, but the student would certainly have had to attend the class and at least skimmed the readings to pull it off. I mention this, not to counter those who still insist that AI is not capable of doing their assignments, but to answer those that would have us abandon as meaningless any assignment that an AI can easily do. Keep in mind that my little test used a very low-level model (Copilot is available for free to all staff and students at CBS) and my prompt consisted of a one-sentence question along with the instruction to generate a 1000-word essay (it went over by about 100 words). A sophisticated student, faced with a 5000-word term paper at the end of a course they had not followed very closely, would be able to provide a better model with the course syllabus, learning objectives, and even the actual readings. Given a few hours, and assuming an above-average intelligence, they could no doubt cobble something together that would be quite impressive by pre-2022 standards. This ability to fake a semester’s worth of learning over a weekend is the problem we have to have face, I think. In the future, I think universities will have to make students sit for written exams, on-site and off-line, more often. A degree that does not require at least half of a student’s total grades to come from such performances cannot be taken seriously. In fact, transcripts should make it very clear which grades were earned through homework (where AI support should be presumed) and which were earned through invigilated examination. That is, it should be clear whether the graduate of a given program is capable of writing coherently about their subject themselves. Their future employers can use that information as they please. The simple test that I propose, then, is a 20-word question with no further context than the course that the student has taken. The student is given three hours and up to 1000 words to demonstrate what they have learned by answering the question to the best of their ability. Understanding the question (and its significance) is itself part of the competence being examined. Under these conditions, I am convinced that the instructor who designed and taught the course can easily determine whether the learning objectives have been met, just as a music teacher can evaluate a student’s ability simply by giving them some sheet music and an instrument to play it on, or a drafting teacher can evaluate drawing ability by giving a student a piece of paper and an object as model. The fact that an AI can also do these things does not make it less impressive when a student can muster their flesh and bones, their brain and their heart, to do it. An education, after all, consists of disciplining the body so as to liberate the mind. It’s important that we show our students what they are capable of.
Quine suggested that we think of our knowledge as an ever-changing “web of belief”. These beliefs have ontological implications, which is to say that they commit us to the existence of “things” of various kinds, such as furniture, corporations, and even numbers. Some of our beliefs we hold very lightly, others more firmly, and we … Continue reading On Holding Beliefs →
Quine suggested that we think of our knowledge as an ever-changing “web of belief”. These beliefs have ontological implications, which is to say that they commit us to the existence of “things” of various kinds, such as furniture, corporations, and even numbers. Some of our beliefs we hold very lightly, others more firmly, and we keep our commitments accordingly. We are not always very explicit about either our beliefs or our ontological commitments — indeed, we may sometimes be entirely unaware of them — but they can often be gleaned from our words and actions even by complete strangers. Granted, there will always be some “indeterminacy” about exactly what we believe and what we think reality consists of. But our peers, at least, usually have a pretty good sense how we parse our experience into objects of belief. After all, they live in the same world that we do and, for the most part, parse it like we would. An education is both a revision and a disciplining of our beliefs. We not only come to believe things we hadn’t before believed, and stop believing things we once thought were true, we also learn to hold our beliefs more, let’s say, intelligently. Educated people are, ideally, less afraid of being wrong about something they believe. They have experienced it often enough. Having come to believe something through deliberate effort, they know what sort of doubts may be raised. They sometimes face those doubts very explicitly through the criticism they receive from their peers. And they are not afraid of this criticism either. Just as they are familiar with the experience of being wrong themselves, they are familiar with the errors and misconceptions of others. They hold their beliefs in the face of doubt and criticism, at least until it becomes too much. Then they willingly discard the discredited notion. I said that we may hold our beliefs firmly or lightly. But it is important to remember that while the strength of a belief may be continuous, its attitude is discrete. We believe something or not. We think that something is true or we do not. We may believe something only for a moment, and hold the belief so lightly that even the slightest doubt removes it altogether, but, while we believed, we believed that something was the case. A belief is a “propositional attitude”; it is a particular take on the reality in which we live. One of the most important lessons of higher education consists in appreciating the contingency of our beliefs. We believe any one thing only because we believe many other things. And that means that revising one belief will often require us to revise others. Holding beliefs intelligently, then, means being careful about our revisions, always considering the implications. There is no shame in refusing to believe something that requires too radical a change in our existing web of belief, even if the evidence for the proposition is, and even in our own eyes, rather strong. In fact, sometimes we are put in the uncomfortable position of thinking something is true that we can’t quite bring ourselves to believe. The problem lies elsewhere in the web, and it will take us a little while to make all the necessary adjustments, to make room for the new among the old. Give it time. And give your peers time to do likewise when it happens to them. No one has ever been right about everything. That much is probably obvious. What may be less obvious is that the main purpose of an education is not to make you right about as many things as possible. It is to teach you how to be wrong.
No man ever knows enough about any art. I have seen young men with most brilliant endowment who have failed to consider the length of the journey. (Ezra Pound) In my last post, I suggested that logic lets us improve the consistency of our beliefs and that such improvement is what science is really all … Continue reading Discipline and Discourse →
No man ever knows enough about any art. I have seen young men with most brilliant endowment who have failed to consider the length of the journey. (Ezra Pound) In my last post, I suggested that logic lets us improve the consistency of our beliefs and that such improvement is what science is really all about. In this post I want to emphasize that this is an ongoing, collective enterprise. We do not discover the truth, the whole truth, and nothing but the truth, and certainly not once and for all. Rather we are constantly revising our system of beliefs, adding truths, removing falsehoods, and, no doubt, introducing new errors as we go. That’s true of everyone from the individual student to the whole of science. We manage the process through discipline and discourse, governed by disciplines and discourses. That’s not just a clever play on words. A “discipline” can be understood as a community of researchers working a range of problems, framed by accepted theories and guided by trusted methods, and the problems, theories, and methods may change over time, but only as a result of the steady and careful (“disciplined”) work of the members of the community. Moreover, they can’t just make discoveries, report them, and expect to be believed. They must persuade their peers that their results are both significant and reliable. That is, they must discuss their results with each other and be open to criticism. This conversation is itself disciplined by what we call “discourse,” which are the not merely logical but also rhetorical conditions under which scholars revise each other’s beliefs about the world. Students experience this in the microcosms of their minds and classes. Their own personal web of belief is constantly revised as they read, write, and talk about their subjects, and the conversation among them (for they can see each other as “peers” too) also changes as their progress through the curriculum. They are not merely accumulating “truths” one at a time, by addition only. Rather, they are learning and unlearning and learning things again and again, always trying to maintain some semblance of consistency among their own beliefs (again, personally, in their own minds) and the beliefs of their peers (in their classes). Though they sometimes fail to notice it, they are also building craft skills: they are becoming better readers and writers and talkers about their subject, they are disciplining their minds and bodies, fashioning themselves as scholars of their subjects. There is no end to this process. Science never reaches the final truth on all things and the student never stops learning, even when she makes full professor. And all the while, as science progresses and careers develop, new generations of students are being invited into the conversation, encouraged to change their minds again and again, belief by belief, always (I hope) supported in their efforts to maintain consistency and therefore rejecting some of the things we are trying to teach them until they’ve made the necessary adjustments elsewhere in their web. (I put this hope in careful parentheses because lately I’ve been getting the impression that students and teachers are getting impatient with each other. Some teachers don’t want to be questioned and some students just want to be told what to be believe for the exam. Hopefully, there are still some patient people in academia who understand that forming a belief “correctly” can take a little time, sometimes longer than a semester.) All research is embedded in an ongoing conversation. It has been going on as long as there has been language, I would think, but since the advent of writing, it has been increasingly disciplined. Modern research is beholden to often very specific discourses that determine the meaning of words and the significance of statements. It even determines limits to what can be said. Fortunately, none of the conventions that govern discourse are absolute and they are likely to change under the pressure of the need to acknowledge particular truths. The good researcher is simply the person who applies that pressure, carefully and consistently, over the course of their career.