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In one of my classes the approach was the opposite, I’m expected to do Ph.D level work as an undergrad and am expected to use AI.

In a different one she just said so long as you say AI was used you’re fine to use it.

In the rest of them AI is considered cheating.

To say we have discrepancies in the rules in an understatement. No one seems to have the exact answer on how to do it. I personally feel like expecting Ph.D level work is the best method as of now, I’ve learned more by using AI to do things about my head than hard core studying for a semester.

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If it’s any consolation, this problem of discrepancies in rules is very common at universities now.

I teach at two universities in Japan and occasionally give lectures on AI issues at others, and the consensus I get from the faculty and students I talk with is that there is no consensus about what to do about AI in higher education.

Education in many subjects has been based around students producing some kind of complex output: a written paper, a computer program, a business plan, a musical composition. This has been a good method because, when done well, students could learn and retain more from the process of creating such output than they would from, say, studying for and taking in-class tests. Also, the product often mirrored what the students would be doing in their future lives, so they were learning useful skills as well.

AI throws a huge spanner into that product-based pedagogy, because it allows students to short-cut the creation process and thus learn little or nothing. Also, it is no longer clear how valuable some of those product-creation skills (writing, programming, planning) will be in the years ahead.

And while the fundamental assumptions behind some widely used teaching methods are being overthrown, many educators, students, and administrators remain attached to the traditional ways. That’s not surprising, as AI is so new and advancing so rapidly that it’s very difficult to say with any confidence how education needs to change. But, in my opinion at least, it does need to change at a very fundamental level. That change won’t be easy.

It's not inherently contradictory, just like using a calculator could be considered cheating depending on the context. If you're just learning basic arithmetic, a calculator is cheating since it shortcuts the path to learning. OTOH in calculus, a calculator is necessary. You still have to have a deep understanding of the concepts and functions to succeed.

It's still a new tech so I'm not surprised a lot of teachers have different takes on it. But when it comes to education, I feel like different policies are reasonable. In some cases it's more likely to shortcut learning, and in other cases it's more likely to encourage learning. It's not entirely one or the other.

A better example might be physics and math classes. I was learned derivatives and integrals at the same time in those two classes, but the math one required we learn how it all works (using limits to understand why the derivative rules work, without using calculators, for example), while in physics we just memorized the rules and were expected to use the calculator.

I always thought they should teach calculus first.

Why do you need a calculator for calculus?!?

Gotta add up all the curves

Exactly, AI is the next calculator. Right now the consensus is that it just does the work for you, in my opinion that says more about us not having the right questions than actual laziness. In a world where the only questions are basic arithmetic, calculators do all the work for you. My opinion is that the future what used to be done by academics will be done by high schoolers and new academics will be producing work at a rate no one could’ve ever predicted.

For example, the professor who’s leading me in this project had a fellowship at a certain university in England and said he exclusively coded using claude code for a month straight, their purpose was to solve a vaccine for a specific disease and by using AI tools such as claude code they’re several months ahead of schedule.

I'm really not seeing how you can do PhD level work as an undergrad. You wouldn't have the foundational knowledge necessary to do PhD level work, and you have no idea how much of what you're learning is accurate.

Without going into too much detail, when I said “Ph.D level” I’m meaning active research that adds a meaningful contribution to a field. I’ll probably be posting on here in a couple months about it but I’ve been doing thousands of tests with beefy GPUs on a certain theory we have about small 9b LLMs under certain external constraints.

Am I saying I’m as knowledgeable or capable as a Ph.D right now? Absolutely not. There’s just not really a terminology that correctly describes accelerated learning and iteration by use of AI since the technology is so new. I can’t speak for others but as someone who’s a senior in my physics degree, I’ve been actually learning faster by using AI. It’s either a mental crutch or mental accelerator. The difference is in if you want it to completely do work for you or if you try to learn and follow along.

It’s a very under explored and new area right now, how higher learning is effected by using AI as a tool instead of as a cheating device, but historically, new tools like the calculator or computer have done a lot to accelerate learning once new rules are in place.

For what it is worth, no graduate student would say they are doing 'Ph.D level' research. It is called 'graduate level research' or just, you know, 'research'

Sounds like a fun project, I wish you the best. I ran a similar program (independent study that encouraged freshman/sophomore undergraduates to explore using microprocessors, at the time the EE curriculum was completely focused on analog circuit theory and ended at boolean logic) and it went well enough that it eventually became part of the official undergraduate curriculum.

It's not terribly uncommon for an undergrad to claim they're doing "PhD level work".

Undergrad research is pretty common and it's not all that hard to get your name on a paper as an undergrad. A lot of undergrads think that doing work that gets your name on a paper, equates to PhD level work.

Now at least you're an adult already. Imagine what mixed messages schoolchild's are receiving from their teachers...

> I’m expected to do Ph.D level work as an undergrad and am expected to use AI.

Nice idea. What class and what work are you doing then?

For that specific one, it’s more of an independent project analyzing complex systems for 6 credits, I’m gonna be expected to submit a paper to arXiv on the subject with the professor as a co-author (fingers crossed). He said I can use claude code or any AI. I’m required to do X amount of hours per week and then submit a thorough report after about 2 months.

>I’ve learned more by using AI to do things about my head than hard core studying for a semester.

How do you know you actually learned, instead of being fed slop by the AI that isn't true at all? If you didn't study, then I doubt you'll really know if the AI is lying to you or not. I have to wonder if your teacher will too, sounds like they have kind of checked-out from actually teaching.