CheatGPT

It seems ChatGPT should be treated the same way institutions treat plagiarism. It's fine to use LLMs for inspiration but the work you submit must be meaningfully your own.

Of course it's not perfect, it never will be. But ultimately it's the student that suffers when they plagiarize. Professors ought have this conversation with students at the same times they're discussing the value of learning & education. I suspect we'll build tools that match answers against the text that LLMs spit out and that will make it easy for people to detect when answers are being pasted verbatim from LLMs.

The other broader spicier thought that comes to mind is that LLMs may push the Overton Window for what's considered entry level and that's probably ultimately good tough it won't be without some sacrifice. This would mean CS departments potentially need to do a curriculum iteration to accommodate. Perhaps there are new, slightly more sophisticated, entry level problems that can be tackled where the commodity solutions haven't yet been entombed in LLMs. Maybe assessments will shift to be less about regurgitating the solution to a common problem and instead to fixing problems with some localized toy implementation or some fictitious API that LLMs won't know about.

Abstraction works because you are able to treat an abstraction as a black box and concern yourself only with its input and output. A segment of code written by an LLM is qualitatively a very different thing; it's more like an open box of crap that you have to inspect and put together yourself, which requires knowledge of the contents, which requires experiencing the drudgery.

I think most just want a decent job to pay bills after. The rest love the field, the theory of cs, and may or may not continue into academics or research.

I am trying to think about use of LLMs in education as similar to calculators. This post reflects how I was taught: students couldn’t use calculators to solve problems initially. For example, no calculators to do addition and subtraction when you’re learning to add or subtract. But also, probably not when learning to multiply or divide. Doing addition and subtraction builds skills and intuition for what multiplication and division are. The same is true with understanding fractions.

Moving up the math hierarchy to algebra, though, this changes. Algebra is at first about the concept of solving equations, and the core idea is that “to solve the equation, you can do whatever you want to one side, as long as you do it to the other.” The mechanics of addition or subtraction, for the most part, no longer matter. Go ahead and use a calculator to solve obscure divisions and multiplications so you can better understand algebra (though it feels appropriate to note that a student who is good at arithmetic can still outpace a calculator for problems that are likely to be used pedagogically, since the numbers are easy).

In this example, algebra is to calculus what arithmetic is to algebra. A calculus teacher cares little if his students can solve equations; he expects they can. They’re instead learning integrals and derivatives and series, and I doubt a calculus teacher would begrudge their students using a calculator to solve a difficult equation.

The problems with treating LLMs this way are many. They are not calculators. You cannot (trivially, or maybe at all) understand how an LLM works. You cannot (trivially) fact check it if it spits out an absurd-sounding answer. You cannot limit the LLM to what your own, personal abilities to trivially do are. We need AI that cites sources, so we can debug when it’s wrong. We need understandable AI for the same reason, not the obscure black boxes we have today. We need AIs that not only can solve our problems, but that can also help us to solve our own problems. When we have those things, AI will be much more useful for education and for widespread use.

It will be a hard sell since it or similar tech has already been long embraced commercially. A bit like forcing students to use an abacus while calculators are around.

The student should still be responsible for what they submit and all of its errors of course. But what should really change is our methods of assessing students. The University model for undergrads has already been long broken in need of fixing. I don't think this will significantly impact grad students yet.