Ah the calculator fallacy; hello my old friend.

So, a calculator is a great shortcut, but it's useless for most mathematics (i.e. proof!). A lot of people assume that having a calculator means they do not need to learn mathematics - a lot of people are dead wrong!

In terms of exams being about memory, I run mine open book (i.e. students can take pre-prepped notes in). Did you know, some students still cram and forget right after the exams? Do you know, they forget even faster for courseworks?

Your argument is a good one, but let's take it further - let's rebuild education towards an employer centric training system, focusing on the use of digital tools alone. It works well, productivity skyrockets, for a few years, but the humanities die out, pure mathematics (which helped create AI) dies off, so does theoretical physics/chemistry/biology. Suddenly, innovation slows down, and you end up with stagnation.

Rather than moving us forward, such a system would lock us into place and likely create out of date workers.

At the end of the day, AI is a great tool, but so is a hammer and (like AI today), it was a good tool for solving many of the problems of its time. However, I wouldn't want to only learn how to use a hammer, otherwise how would I be replying to you right now?!?

I think a central point you're overlooking is that we have to be able to assess people along the way. Once you get to a certain point in your education you should be able to solve problems that an AI can't. However, before you get there, we need some way to assess you in solving problems that an AI currently can. That doesn't mean that what you are assessed on is obsolete. We are testing to see if you have acquired the prerequisites for learning to do the things an AI can't do.

Here's a somewhat tangential counter, which I think some of the other replies are trying to touch on ... why, exactly, continue valuing our ability to do something a computer can so easily do for us (to some extent obviously)?

My theory prof said there would be paper exams next year. Because it's theory. You need to be able to read an academic paper and know what theoretical basis the authors had for their hypothesis. I'm in liberal arts/humanities. Yes we still exist, and we are the ones that AI can't replace. If the whole idea is that it pulls from information that's already available, and a researcher's job is to develop new theories and ideas and do survey or interview research, then we need humans for that. If I'm trying to become a professor/researcher, using AI to write my theory papers is not doing me or my future students any favors. Ststistical research on the other hand, they already use programs for that and use existing data, so idk. But even then, any AI statistical analysis should be testing a new hypothesis that humans came up with, or a new angle on an existing one.

So idk how this would affect engineering or tech majors. But for students trying to be psychologists, anthropologists, social workers, professors, then using it for written exams just isn't going to do them any favors.

As they are talking about writing essays, I would argue the importance of being able to do it lies in being able to analyze a book/article/whatever, make an argument, and defend it. Being able to read and think critically about the subject would also be very important.

Sure, rote memorization isn't great, but neither is having to look something up every single time you ever need it because you forgot. There are also many industries in which people do need a large information base as close recall. Learning to do that much later in life sounds very difficult. I'm not saying people should memorize everything, but not having very many facts about that world around you at basic recall doesn't sound good either.

It's an interesting point.. I do agree memorisation is (and always has been) used as more of a substitute for actual skills. It's always been a bugbear of mine that people aren't taught to problem solve, just regurgitate facts, when facts are literally at our fingertips 24/7.

In my experience, the best means of preparing for exams, as is universally advised, is to take previous or practice exams … which I think tells you pretty clearly what kind of task an exam actually is … a practiced routine in something that narrowly ranges between regurgitation and pretty short-form, practiced and shallow problem solving.

You are getting some flak, but imho you are right. The only thing an exam really tests is how well you do in exams. Of course, educators dont want to hear that. But if you take a deep dive into (scientific) literature on the topic, the question "What are we actually measuring here?" is raised rightfully so.

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:

1. 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.

2. For 1st--3rd year degree, it really starts to make dangerous errors, but it's a good tool to summarise materials from textbooks.

3. 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).

That sounds like AI. If you do your homework then even sitting in a regular exam you should score better than 20%. This exam being open book, it sounds like they were unfamiliar with the textbook and could not find answers fast enough.

It's a tough one because I cannot say with 100% certainty that AI is the issue. Anxiety is definitely a possibility in some cases, but not all; perhaps thinking time might be a factor, or even just good old copying and then running the work through a paraphraser. The large amount of absenses also means it was hard to benchmark those students based on class assessment (yes, we are always tracking how you are doing in class, not tp judge you, but just in case you need some extra help!).

However, AI is a strong contender since the "open book" part didn't include the textbook, it allowed the students to take a booklet into the exams with their own notes (including fully worked examples). They scored low because they didn't understand their own notes, and after reviewing the notes they brought in (all word perfect), it was clear they did not understand the subject.

Not the previous poster. I taught an introduction to programming unit for a few semesters. The unit was almost entirely portfolio based ie all done in class or at home.

The unit had two litmus tests under exam like conditions, on paper in class. We're talking the week 10 test had complexity equal to week 5 or 6. Approximately 15-20% of the cohort failed this test, which if they were up to date with class work effectively proved they cheated. They'd be submitting course work of little 2d games then on paper be unable to "with a loop, print all the odd numbers from 1 to 20"

If you don't mind me asking - what kind of degree was it, and what format were the exams?

"Sometimes" would be my answer. I caught students who colluded during online exams, and even managed to spot students pasting directly from an online search. Those were painful conversations, but I offered them resits and they were all honest and passed with some extra classes.

With AI, detection is impossible at the moment.

"Avoid at all costs because we hate marking it even more than you hate writing it"?

An in person exam can be done in a locked down IT lab, and this leads to a better marking experience, and I suspect a better exam experience!