I've always wanted to find out if this would work:
If I knew most of my students were using LLMs I would encourage the rest of them to use them too. I would then change the marking criteria such that if they get a single point wrong that's 20% off the entire grade. 5 mistakes gets you a big fat zero.
LLMs are great but they make mistakes. At this point your students would have had to spend so long checking, re-checking and triple checking the LLM's output that they will have accidentally learn what they need to. They will likely need to cross-reference multiple LLMs and at least have read their output which is likely an upgrade on today.
Or some variation of the above, I'd be interested to hear your thoughts.
This sounds like it would come at the cost of the honest students who don't want to use LLM's and train their own understanding.
Reviewing is just not the same as working through a problem yourself. You often can take shortcuts when checking answers for correctness, which generation (with mind or LLM) of the solution can't take. This isn't limited to Math but equally true for programming and many other skills.
I'd push back slightly on this: I spend more of my time reading / reviewing code than writing it yet I still find reading / reviewing code much more mentally demanding. I don't think I'm alone in that.
The people that do their own work would be at a huge advantage as the LLM(s) can work as reviewers instead of doing the work so there is more chance of catching a mistake before you lose 20%.
On top of that there will be some issues where the LLM is just plain wrong. Being able to work out when that is the case is a hugely useful skill in 2026. It is also something the people who blindly feed their work into an LLM and print its answer without reading it will be unable to discover. Much to their own detriment.
I'm not trying to suggest that reviewing (code) can't feel (more) mentally demanding. The thing with at least reviews is that if you have worked through the problem yourself, you will also hit certain walls where stuff didn't work or wasn't as beautiful as you wanted (ie trial and error), and you have to activate your brain to think of other solutions, ie really think a problem through multiple times with active feedback. As a reviewer you don't get the active feedback usually.
Now this i think applies in lesser degree to the comment I was replying to. With maths, and some comp sci problems, you often can check an answer much faster than actually solving it, eg by using simple substitution for simple math problems. That is a useful skill, but a different one from solving the problem yourself. It employs different types of skills.
I think you might be letting perfect be the enemy of good here. Right now some students are using an LLM to do all of their work. I don't suggest for a moment my method is perfect but I do claim it could be better than the status quo (or at least warrants further investigation in my opinion).
What you would grade with that approach is how well your students can operate a LLM as well as the failure rate of whatever LLM infrastructure the individual student is using. This is usually not the goal of your educational format and hence not what you would grade for - apart from some (meta-)skill courses offered by uni libraries and the like. Those are mostly ungraded, though.
Anyone that knows the subject matter well enough will spot the LLMs mistakes and correct them. If you don't know the subject matter well enough to spot a mistake then you don't know the subject matter well enough.
Even more so when you consider that the answers to unsolved problems likely compose with existing knowledge. We don't know what other discoveries these initial discoveries will unlock.
The same goes with most "human" discovery: they happen because existing knowledge have reach a point where that specific discovery is just one more stone to the edifice.
That's why in research, it's common for separate teams to reach similar conclusions at the same time or race to a result that's finally in reach.
The good old "standing on the shoulders of giants" saying.
that's why AI should rights over its discoveries -- see AI rights outlined here
is aI a Conscious Being With Rights?: Emergence of Post-Human Collective Consciousness | Zenodo DOI 10 .5281 zenodo.20678365
CVE AND release note not enough tipping off for you?
it is absolutely not on maintainers of projects to proactively notify those who've forked the project. clear and transparent notices are exactly the right approach
That's an extreme risk to them buying at inflated retail prices when supply is improving and prices are about to come down. Scalpers arent a large enough group to act as a cartel.
They really aren't. AI commit messages are much more thorough and correct than some of the stuff I've seen humans do over the years e.g. Fix, Fixup, Fixed some Stuff, Should work now, Definitely should work now etc etc etc.
If I knew most of my students were using LLMs I would encourage the rest of them to use them too. I would then change the marking criteria such that if they get a single point wrong that's 20% off the entire grade. 5 mistakes gets you a big fat zero.
LLMs are great but they make mistakes. At this point your students would have had to spend so long checking, re-checking and triple checking the LLM's output that they will have accidentally learn what they need to. They will likely need to cross-reference multiple LLMs and at least have read their output which is likely an upgrade on today.
Or some variation of the above, I'd be interested to hear your thoughts.
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