The growing number of students using the AI program ChatGPT as a shortcut in their coursework has led some college professors to reconsider their lesson plans for the upcoming fall semester.
College professors are going back to paper exams and handwritten essays to fight students using ChatGPT::The growing number of students using the AI program ChatGPT as a shortcut in their coursework has led some college professors to reconsider their lesson plans for the upcoming fall semester.
In the real world, will those students be working from a textbook, or from a browser with some form of AI accessible in a few years?
What exactly is being measured and evaluated? Or has the world changed, and existing infrastructure is struggling to cling to the status quo?
Were those years of students being forced to learn cursive in the age of the computer a useful application of their time? Or math classes where a calculator wasn’t allowed?
I can hardly think just how useful a programming class where you need to write it on a blank page of paper with a pen and no linters might be, then.
Maybe the focus on where and how knowledge is applied needs to be revisited in light of a changing landscape.
For example, how much more practically useful might test questions be that provide a hallucinated wrong answer from ChatGPT and then task the students to identify what was wrong? Or provide them a cross discipline question that expects ChatGPT usage yet would remain challenging because of the scope or nuance?
I get that it’s difficult to adjust to something that’s changed everything in the field within months.
But it’s quite likely a fair bit of how education has been done for the past 20 years in the digital age (itself a gradual transition to the Internet existing) needs major reworking to adapt to changes rather than simply oppose them, putting academia in a bubble further and further detached from real world feasibility.
If you’re going to take a class to learn how to do X, but never actually learn how to do X because you’re letting a machine do all the work, why even take the class?
In the real world, even if you’re using all the newest, cutting edge stuff, you still need to understand the concepts behind what you’re doing. You still have to know what to put into the tool and that what you get out is something that works.
If the tool, AI, whatever, is smart enough to accomplish the task without you actually knowing anything, what the hell are you useful for?
As an anecdotal though, I once saw someone simply forwarding (ie. copy and pasting) their exam questions to ChatGPT. His answers are just ChatGPT responses, but paraphrased to make it look less GPT-ish. I am not even sure whether he understood the question itself.
In this case, the only skill that is tested… is English paraphrasing.
I’ll field this because it does raise some good points:
It all boils down to how much you trust what is essentially matrix multiplication, trained on the internet, with some very arbitrarily chosen initial conditions. Early on when AI started cropping up in the news, I tested the validity of answers given:
For topics aimed at 10–18 year olds, it does pretty well. It’s answers are generic, and it makes mistakes every now and then.
For 1st–3rd year degree, it really starts to make dangerous errors, but it’s a good tool to summarise materials from textbooks.
Masters+, it spews (very convincing) bollocks most of the time.
Recognising the mistakes in (1) requires checking it against the course notes, something most students manage. Recognising the mistakes in (2) is often something a stronger student can manage, but not a weaker one. As for (3), you are going to need to be an expert to recognise the mistakes (it literally misinterpreted my own work at me at one point).
The irony is, education in its current format is already working with AI, it’s teaching people how to correct the errors given. Theming assessment around an AI is a great idea, until you have to create one (the very fact it is moving fast means that everything you teach about it ends up out of date by the time a student needs it for work).
However, I do agree that education as a whole needs overhauling. How to do this: maybe fund it a bit better so we’re able to hire folks to help develop better courses - at the moment every “great course” you’ve ever taken was paid for in blood (i.e.
50 hour weeks teaching/marking/prepping/meeting arbitrary research requirement).
(1) seems to be a legitimate problem. (2) is just filtering the stronger students from the weaker ones with extra steps. (3) isn’t an issue unless a professor teaching graduate classes can’t tell BS from truth in their own field. If that’s the case, I’d call the professor’s lack of knowledge a larger issue than the student’s.
You may not know this, but “Masters” is about uncovering knowledge nobody had before, not even the professor. That’s where peer reviews and shit like LK-99 happen.
It really isn’t. You don’t start doing properly original research until a year or two into a PhD. At best a masters project is going to be doing something like taking an existing model and applying it to an adjacent topic to the one it was designed for.
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Is AI going to go away?
In the real world, will those students be working from a textbook, or from a browser with some form of AI accessible in a few years?
What exactly is being measured and evaluated? Or has the world changed, and existing infrastructure is struggling to cling to the status quo?
Were those years of students being forced to learn cursive in the age of the computer a useful application of their time? Or math classes where a calculator wasn’t allowed?
I can hardly think just how useful a programming class where you need to write it on a blank page of paper with a pen and no linters might be, then.
Maybe the focus on where and how knowledge is applied needs to be revisited in light of a changing landscape.
For example, how much more practically useful might test questions be that provide a hallucinated wrong answer from ChatGPT and then task the students to identify what was wrong? Or provide them a cross discipline question that expects ChatGPT usage yet would remain challenging because of the scope or nuance?
I get that it’s difficult to adjust to something that’s changed everything in the field within months.
But it’s quite likely a fair bit of how education has been done for the past 20 years in the digital age (itself a gradual transition to the Internet existing) needs major reworking to adapt to changes rather than simply oppose them, putting academia in a bubble further and further detached from real world feasibility.
If you’re going to take a class to learn how to do X, but never actually learn how to do X because you’re letting a machine do all the work, why even take the class?
In the real world, even if you’re using all the newest, cutting edge stuff, you still need to understand the concepts behind what you’re doing. You still have to know what to put into the tool and that what you get out is something that works.
If the tool, AI, whatever, is smart enough to accomplish the task without you actually knowing anything, what the hell are you useful for?
But that’s actually most of the works we have nowadays. IA is replacing repetitive works such as magazine writers or script writers
Writers are repetitive work???
Well, it seems they will be replaced, at least certain writers. https://www.npr.org/2023/05/20/1177366800/striking-movie-and-tv-writers-worry-that-they-will-be-replaced-by-ai Also, callcenters https://www.bbc.com/news/business-65906521 And junior programmers. The problem here it’s not my opinion, those already happened so its not debatable.
As an anecdotal though, I once saw someone simply forwarding (ie. copy and pasting) their exam questions to ChatGPT. His answers are just ChatGPT responses, but paraphrased to make it look less GPT-ish. I am not even sure whether he understood the question itself.
In this case, the only skill that is tested… is English paraphrasing.
Textbooks, like on physical paper, are never going to just go away. They offer way too many advantages over even reading digital books.
I’ll field this because it does raise some good points:
It all boils down to how much you trust what is essentially matrix multiplication, trained on the internet, with some very arbitrarily chosen initial conditions. Early on when AI started cropping up in the news, I tested the validity of answers given:
For topics aimed at 10–18 year olds, it does pretty well. It’s answers are generic, and it makes mistakes every now and then.
For 1st–3rd year degree, it really starts to make dangerous errors, but it’s a good tool to summarise materials from textbooks.
Masters+, it spews (very convincing) bollocks most of the time.
Recognising the mistakes in (1) requires checking it against the course notes, something most students manage. Recognising the mistakes in (2) is often something a stronger student can manage, but not a weaker one. As for (3), you are going to need to be an expert to recognise the mistakes (it literally misinterpreted my own work at me at one point).
The irony is, education in its current format is already working with AI, it’s teaching people how to correct the errors given. Theming assessment around an AI is a great idea, until you have to create one (the very fact it is moving fast means that everything you teach about it ends up out of date by the time a student needs it for work).
However, I do agree that education as a whole needs overhauling. How to do this: maybe fund it a bit better so we’re able to hire folks to help develop better courses - at the moment every “great course” you’ve ever taken was paid for in blood (i.e. 50 hour weeks teaching/marking/prepping/meeting arbitrary research requirement).
(1) seems to be a legitimate problem. (2) is just filtering the stronger students from the weaker ones with extra steps. (3) isn’t an issue unless a professor teaching graduate classes can’t tell BS from truth in their own field. If that’s the case, I’d call the professor’s lack of knowledge a larger issue than the student’s.
You may not know this, but “Masters” is about uncovering knowledge nobody had before, not even the professor. That’s where peer reviews and shit like LK-99 happen.
It really isn’t. You don’t start doing properly original research until a year or two into a PhD. At best a masters project is going to be doing something like taking an existing model and applying it to an adjacent topic to the one it was designed for.