A smart approach that does not solve the AI problem - actually flipped classrooms work worse now due to AI usage. My own experience with flipped classrooms (which seems to be shared by quite a few people who have tried it out): they only work well if all students actually read/watch the materials beforehand. In small, advanced courses, intrinsic motivation may be sufficient - but in most cases you need some extrinsic coercion - such as a mandatory quiz about the materials or hand-written lecture notes that need to be shown at each in-person session. With AI, some people don't watch the lectures but let ChatGPT give them a summary which they submit. Then these people poison your in-person session with their lack of knowledge and motivation.

CS Professor here: just yesterday I did the discussion of a course projects' (Parallel Computing), and one of the three groups that I did yesterday have clearly gone the ChatGPT way. They couldn't even understand the choices the LLM made regarding the architecture, etc. The way to "catch" these students is similar to what we did in the past when students copied from other students which is "to give them rope to hang" - ask for clarifications until they follow unintended paths that lead nowhere. To fellow professors, when you're suspicious my suggestion is to appeal to their honesty (like "let's be honest, how much of this code is yours, and how much is ChatGPT's?") and offer some empathy and understanding (like understanding they may had multiple deadlines in the same week, etc.). Nevertheless, don't miss the chance to give them the lesson on how is the correct way of doing things. The way to catch these students is to find the same signs of yesteryear copying from other students (which in essence is what copying from an LLM is, although the number has increased because they found us professors unprepared for the volume). The other two groups also used LLM but in a high-level and architectural way. They were clearly responsible for the code (even if they didn't wrote it 100% manually) and could explain their reasoning and strategies used to solve the problems. Me and my colleagues still have a lot of projects to review, and I asked them to keep the score of the number of projects like these, but so far, the score is 1 in 3 (33%).

> The other two groups also used LLM but in a high-level and architectural way. Sounds more like the score is 3/3 (100%) Would you have accepted them cooy-pasting code from libraries together to build their project? If not, why is using LLM generated code different?

> Would you have accepted them cooy-pasting code from libraries together to build their project? Yes, if they are "responsible" for the code delivered, where responsible means they understand the code, the architecture, the decisions made, etc. In this case, the students had to invent multiple strategies to solve a specific problem. The "successful" groups did a mix of generated and hand-crafted code (don't know percentages), implemented different strategies and knew their plus and minuses, could change the code in a timely manner to accommodate some of my requests, etc. The "unsuccessful" group couldn't do any of that. I'm not anti-AI (and really, what could I do if I were?) since I use it myself, I'm just anti-slop, especially from my students. But in reality I've been slowly transitioning from group projects (for a subset of the grade) to "practical tests", where they must implement a significant subset of a larger project in a 2h class. Still experimenting though.

What is the policy and guidance you gave the students regarding LLM use?

> Some of the numbers that you saw from the number of students who receive failing grades were because we caught them (cheating) and prosecuted them and are sending their cases to the center for student conduct,” Garcia said. According to Garcia, nearly 30 students in CS 10 were caught cheating on take-home exams in spring 2026.

In my uni, rates of honor code violations in introductory CS classes were high even before AI. I was a section-leader for the CS106 series at Stanford, and the honor code violations were common. In 2015, ~20% of one intro class was suspected of an honor code violation [1]. Often, the CS department comprised the majority of honor code violations in a given quarter. There are several reasons for this: 1. Cheating in CS is easier to detect. MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism in coding assignments. Personally I witnessed more honor-code violations in math problem sets, but there was no feasible way for professors to detect this. 2. Problems in programming assignments are (usually) very tangibly wrong. I can bullshit my way through an essay with shoddy research, I can hand-wave a proof that is definitely wrong but will probably garner at least some points. But when your program is crashing or not compiling, and the due date is approaching, it produces a very immediate and undeniable sense of failure and pressure to cheat. The thing is, many students would get a decent chunk of credit even for failing code, but this is not immediately obvious. 3. The ability to cheat is more available. Math problem sets tend to change quarter by quarter. It's basically impossible to cheat on a prose essay short of straight up paying someone to write it for you, or fabricating sources. But for CS classes, especially at prominent universities, there are plenty of solutions online. Much of it is people who aren't event at Stanford implementing the assignments for fun or self-learning, and sharing it with their peers. Which, to be clear, isn't unethical or bad - it's the responsibility of Stanford students to refrain from looking at those solutions. But nonetheless, it's a contributing factor. 1. https://stanforddaily.com/2015/03/29/increase-in-cs-106-hono... 2. https://theory.stanford.edu/~aiken/moss/

> MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism He apparently also makes (I would assume a satisfying amount of) money selling the same technology to law firms for copyright/patent analysis: https://www.similix.com (I love these ultra minimal HTML sites, ex. https://www.hwaci.com (SQLite commercial licensing) for another example. It just has this subtle smugness, like you either don't need any new clients or virtually all of the market is your client.)

Or how many are normally caught cheating? Did they use AI to detect AI using cheaters?

And if cheating was triggered using AI detectors, was it real? AI detectors are pretty mid in practice - they tend to have a lot of false positives for "B" students who are okay, but can still be struggled to be more coherent than AIs are. There are some specific triggers that AIs are way more likely to do than students, but a lot of AI detectors will trigger on this "almost there, but you're still struggling" level of essay writing that might get a B, B-. I could expect the same might be true for CS students even though I haven't seen how AI detectors work for CS/math homework.

You'd be amazed at how many students we know are obviously cheating because the logs reveal that they copy pasted a long, complete answer within seconds of opening a problem for the first time, full of sophisticated code constructs that we didn't teach them, and lot's of nicely formatted comments. Sometimes they even copy/paste the entire GPT output and then format it down.

This has been my wife’s experience as a college math professor. Instead of code it’s extremely formal problems with way more steps than the student normally performs using notation never taught in class. It’s not that students didn’t cheat before, LLMs have just lowered the bar so far many can’t complete a live test in a class that requires effort.

When I was a section leader (not at Berkeley, though) we used MOSS: https://theory.stanford.edu/~aiken/moss/ It's not AI, its a deterministic program that analyzes compiled code for similarity.

It's too damn tempting to not use. You have a magical machine that, on command, will spit out the answer to your question in 10 seconds, whereas you'd need to spend hours to do the assignment the Good Old Fashioned Way. Even students who aren't just there for the prestigious degree are falling victim to this. When you're up against a deadline - and unless you're very good at time management you're frequently up against a deadline - it's going to be an irresistible lever to pull. In times past, cheating would mean copying an answer off the Internet or off a friend, both of which are easy to detect. More sophisticated cheaters might spend an hour rewriting the solution to make it less obvious they cheated, but at some point the cost of cheating (time + risk of getting caught) starts exceeding the cost of just doing the assignment. AI changes this - you get a customized answer that doesn't show up in a database with no extra work. The thing is, students fail to realize just what using AI robs them of. Struggling with the assignment is the entire point. You don't learn if the assignments are too easy; you need to have some challenge to push your brain to understand the material more deeply and to build those pathways to apply the knowledge in novel ways. You become more efficient and effective over time as that knowledge settles in and you get more proficient - one of the reasons why time-bounded exams still make sense (being fast is also a proxy measure for understanding).

With AI, they fail later (during the exams), where as without using AI previously, they'd fail early and either course-correct, or drop out early (and suffer less of the consequences). Not sure what the solution is - there's no possibility of stopping students using AI to complete their homework/assignments etc. But let me flip the question - do they need to be stopped? Why not let them fail at the exam? As long as the exam acts as a filter, their usage of AI to "cheat" their learning is inconsequential to anyone but themselves.

You can use an llm to get out of doing homework but you can also use it to ask every question you would ever wanted in a 1-1 tutoring session. The problem is kids will use it to cheat on their homework. If we can’t deal with that problem then a ban is necessary. But these things can be phenomenal teachers if you use them properly.

As an educator, this is exactly what I struggle with. I'm pulling out all the stops to give students every chance to do the hard work and not lean on AI. But there's a good chunk of the class who don't listen to reason. I haven't figured it out yet. They know, logically, they can't pass an interview, but that's apparently a "tomorrow" problem. The smart ones either use it not at all, or use it to positive effect, like you're saying.

And how do you propose that to work if the internet is still full of AI services that just give you the answer or write your essay? The only way an Illustrated Primer can work if you can’t trivially cheat. Which is to say, it solves nothing compared to the current situation.

I can't agree. That's similar to past arguments for banning books and the internet. Plagiarism isn't new, and those things enabled it too.

I don't like the framing of calling it academic dishonesty. If it were one or two students doing it, sure. But there is no reason to believe that 2026 Berkeley freshmen are fundamentally more dishonest than 2025 Berkeley students. When so many are doing it, it suggests a sea change in the understanding of what is honest or dishonest in that particular community. That sort of thing should be treated more like a "disease": something that should be treated, than a "crime": something that will be punished.

Professors suddenly realized everyone was cheating and started paying attention, but the cheating isn't new ... A lot of faculty are happy when their students get good grades because they interpret it as I'm such a good teacher instead of I should pay more attention to how they cheat . AI woke some of them up to reality.

Dude, cheating in CS10 of all classes?