> Society can't throw up its hands in shock as students outsource their thinking to simulation machines when fifty years of neoliberalism has masticated education into something homogenised, metricised and machinic. Meanwhile, so-called Ed Tech has claimed for decades that learning is informational rather than relational and ripe for technical disruption.
So university cannot effectively resist AI without resisting these ideas first. I hope it can be done.
I can kind of see the author's point, though I don't agree with it. AI is what made the problems obvious. Students didn't start cheating with the appearance of AI chatbots.
My view is that university classes should be taught in such a way that students can use AI as much or as little as they desire in order to learn the material. Evaluation should primarily be done in the classroom without access to AI. 90% of the grade in my undergraduate course comes from in-person exams. I don't care how they learned the material. This can be a problem for composition classes, for instance, but the problem existed long before the chatbots.
> AI is actually a giant material infrastructure with huge demands for energy, water and concrete, while the supply chain for specialised computer chips is entangled with geopolitical conflict. It also means that the AI industry will beg, borrow and steal, or basically just steal, all the text, images and audio that it can get its spidery hands on.
Sure. We don't know yet how the economics will play out. We don't know the actual cost of LLM and other AI services, we only know what companies are currently charging for them, but they're competing for mindshare so the prices are most definitely being held low. To a large extent, the whole thing has demonstrated what can be done in the short-term in the absence of copyright restrictions, and now we have to see the long-term effects of the removal of copyright restrictions.
I agree with many of the points in the article but don't understand how that turns into a recommendation to "resist".
Thank you. This author's writing is a careful exemplar of everything rotten about higher ed. The article is a great compilation of features, stylistic and material, that need to be crushed and discarded from the university.
That's a lot of words for one practical recommendation: University councils for discussing 'AI conviviality'.
I think the author presents a one dimensional view of AI bad and fails to see the bigger picture, which is ironic considering all the fine words he uses.
I agree that AI tools can potentially weaken some of our lower level cognitive functions, but on the flip side the AI tools also enable us to operate on higher levels of ability, planning, conceptualization and execution.
This is undoubtedly a point of inflexion for Universities: they should be working out how they can achieve a new deal for students and society that is far more nuanced and constructive than mere 'resistance' against AI.
>Generative AI's main impact on higher education has been to cause panic about students cheating, a panic that diverts attention from the already immiserated experience of marketised studenthood. It's also caused increasing alarm about staff cheating, via AI marking and feedback, which again diverts attention from their experience of relentless and ongoing precaritisation.
>The hegemonic narrative calls for universities to embrace these tools as a way to revitalise pedagogy, and because students will need AI skills in the world of work. A major flaw with this story is that the tools don't actually work, or at least not as claimed.
>AI summarisation doesn't summarise; it simulates a summary based on the learned parameters of its model. AI research tools don't research; they shove a lot of searched-up docs into the chatbot context in the hope that will trigger relevancy. For their part, so-called reasoning models ramp up inference costs while confabulating a chain of thought to cover up their glaring limitations.
If AI tools do not actually work, how are students able to cheat with them? It seems like that would be a problem that would solve itself - a student would attempt to use AI to cheat, it would fail to complete the assignment, and the student would get a bad grade.
Teach, and require, students to handwrite (without a machine such as a smartphone or a PC nearby) whatever quizzes and homework and exams they need to do. Of course, this would mean professors and TAs would have to actually go back to hand-scoring work, instead of lazily leaving it up to their classroom management software.
Beyond that written work, more of what universities examine of students should be students actually standing up and speaking about their work (without a machine assisting them).
> The way this technology works means that generative AI applied to anything is a form of slopification, of turning things into slop. However, where AI is undoubtedly successful is as a shock doctrine, as a way to further precaritise workers and privatise services.
You can't say both that AI produces worse results, and that it will be used to manipulate the job market: savvy companies would outcompete by not adopting AI, and hiring up the victims of AI layoffs. If either of his statements is true, the other is false.
This whole article, man. I don't know where to start with it. It definitely reminds me of grad school. In a bad way.
The university has been on a glide path toward irrelevance for quite a while—long before AI was a going concern—and the humanities and social sciences, in particular, have been skimming the treetops since at least when I was in school at the turn of the century. The role of the university is to teach and do research. AI can be a tremendous asset for both of those, and it's not going away, so deal with that reality.
The role of university isn't to resist nor embrace.
When I was in university, I did finance + economics + a bunch of other random stuff from CS to archaeology to philosophy.
One subject that was interesting from a technology standpoint was statistics. I took university at a point when ML was a thing, but LLMs obviously weren't. R was a thing, Python was beginning to get popular in the domain, you potentially had all sorts of tech to help with stats.
Introduction to stats, no technology was allowed. Every single problem was done by hand. Every single quiz and test, no calculators, no multiple choice, just problems to work through by hand. If you cheated on assignments, you'd obviously fail tests (which were >50% of the course). Problem solved. We had to learn without aids. Second stats course, everything was allowed. Did all my assignments with R. The point was simply learning. First theory, then how it's done on the real world.
University absolutely should teach all the theories, concepts and history before AI. And then it should also teach how to use AI, since it's a thing in the real world.
People just need to stop thinking about university as all about grades and check marks, and learn to learn.
We keep hearing the argument that AI datasets are built via "stealing"; it is as if the Fair use doctrine does not exist. Large copyright holders are the obvious beneficiaries of such denials, and perhaps the OP isn't just a mindless parrot on this point, but an active participant in intentional subterfuge. Copyright infringement can occur at the output, not at the input of AI models, people. For the author to be ethically consistent on this point, they would never use a university library ever again.
this article contains some reasonable points about problems caused both by concrete advances in AI and by rhetoric about AI used to justify the decisions of the powerful. the focus is especially on AI-based end-user technology being sold to consumers and institutions.
it seems to miss the point that some members of the university community may actually do research on AI, and may not feel inclined to ask for approval from the “People’s Council” to get permission to think about concepts in statistics, computer science, or decision theory.
there is a relatively simple way to somewhat deal with AI in a sensible way.
stop using evaluations as filters. sharing marks or even reporting them.
simply give marks to the students for self-evaluations.
That until they can make something of real value towards the later years. And evaluate that. It could be of the form of they want, they could use AI at will for that, but it would be presented in person in front of a panel. You could even have a few of these walls but all of them should be toward the end, where AI does not trivialize the task.
Let students repeat if they want, pass if they want, go back a grade if they want, even skip a grade if they want. You will have to make them aware that there will be huge walls at the end though.
That would be my naive approach to that. Until something better gets established.
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[ 2.8 ms ] story [ 28.0 ms ] threadSo university cannot effectively resist AI without resisting these ideas first. I hope it can be done.
My view is that university classes should be taught in such a way that students can use AI as much or as little as they desire in order to learn the material. Evaluation should primarily be done in the classroom without access to AI. 90% of the grade in my undergraduate course comes from in-person exams. I don't care how they learned the material. This can be a problem for composition classes, for instance, but the problem existed long before the chatbots.
> AI is actually a giant material infrastructure with huge demands for energy, water and concrete, while the supply chain for specialised computer chips is entangled with geopolitical conflict. It also means that the AI industry will beg, borrow and steal, or basically just steal, all the text, images and audio that it can get its spidery hands on.
Sure. We don't know yet how the economics will play out. We don't know the actual cost of LLM and other AI services, we only know what companies are currently charging for them, but they're competing for mindshare so the prices are most definitely being held low. To a large extent, the whole thing has demonstrated what can be done in the short-term in the absence of copyright restrictions, and now we have to see the long-term effects of the removal of copyright restrictions.
I agree with many of the points in the article but don't understand how that turns into a recommendation to "resist".
Sadly, this article, or lets be honest, rant, is not a contribution to that.
I think the author presents a one dimensional view of AI bad and fails to see the bigger picture, which is ironic considering all the fine words he uses.
I agree that AI tools can potentially weaken some of our lower level cognitive functions, but on the flip side the AI tools also enable us to operate on higher levels of ability, planning, conceptualization and execution.
This is undoubtedly a point of inflexion for Universities: they should be working out how they can achieve a new deal for students and society that is far more nuanced and constructive than mere 'resistance' against AI.
>The hegemonic narrative calls for universities to embrace these tools as a way to revitalise pedagogy, and because students will need AI skills in the world of work. A major flaw with this story is that the tools don't actually work, or at least not as claimed.
>AI summarisation doesn't summarise; it simulates a summary based on the learned parameters of its model. AI research tools don't research; they shove a lot of searched-up docs into the chatbot context in the hope that will trigger relevancy. For their part, so-called reasoning models ramp up inference costs while confabulating a chain of thought to cover up their glaring limitations.
If AI tools do not actually work, how are students able to cheat with them? It seems like that would be a problem that would solve itself - a student would attempt to use AI to cheat, it would fail to complete the assignment, and the student would get a bad grade.
Beyond that written work, more of what universities examine of students should be students actually standing up and speaking about their work (without a machine assisting them).
You can't say both that AI produces worse results, and that it will be used to manipulate the job market: savvy companies would outcompete by not adopting AI, and hiring up the victims of AI layoffs. If either of his statements is true, the other is false.
This whole article, man. I don't know where to start with it. It definitely reminds me of grad school. In a bad way.
The university has been on a glide path toward irrelevance for quite a while—long before AI was a going concern—and the humanities and social sciences, in particular, have been skimming the treetops since at least when I was in school at the turn of the century. The role of the university is to teach and do research. AI can be a tremendous asset for both of those, and it's not going away, so deal with that reality.
When I was in university, I did finance + economics + a bunch of other random stuff from CS to archaeology to philosophy.
One subject that was interesting from a technology standpoint was statistics. I took university at a point when ML was a thing, but LLMs obviously weren't. R was a thing, Python was beginning to get popular in the domain, you potentially had all sorts of tech to help with stats.
Introduction to stats, no technology was allowed. Every single problem was done by hand. Every single quiz and test, no calculators, no multiple choice, just problems to work through by hand. If you cheated on assignments, you'd obviously fail tests (which were >50% of the course). Problem solved. We had to learn without aids. Second stats course, everything was allowed. Did all my assignments with R. The point was simply learning. First theory, then how it's done on the real world.
University absolutely should teach all the theories, concepts and history before AI. And then it should also teach how to use AI, since it's a thing in the real world.
People just need to stop thinking about university as all about grades and check marks, and learn to learn.
it seems to miss the point that some members of the university community may actually do research on AI, and may not feel inclined to ask for approval from the “People’s Council” to get permission to think about concepts in statistics, computer science, or decision theory.
stop using evaluations as filters. sharing marks or even reporting them.
simply give marks to the students for self-evaluations.
That until they can make something of real value towards the later years. And evaluate that. It could be of the form of they want, they could use AI at will for that, but it would be presented in person in front of a panel. You could even have a few of these walls but all of them should be toward the end, where AI does not trivialize the task.
Let students repeat if they want, pass if they want, go back a grade if they want, even skip a grade if they want. You will have to make them aware that there will be huge walls at the end though.
That would be my naive approach to that. Until something better gets established.