How University Students Use Claude

I felt that during my time in university. I absolutely loved reading and working through dense math text books but the moment there was a time constraint the struggle turned into chaos.

I guess I'd qualify to you as someone "AI crutched" but I mostly use it for research and bouncing ideas (or code complete, which I've mentioned before - this is a great use of the tool and I wouldn't consider it a crutch, personally).

For instance, "parse this massive log output, and highlight anything interesting you see or any areas that may be a problem, and give me your theories."

Lots of times its wrong. Sometimes its right. Sometimes, its response gives me an idea that leads to another direction. It's essentially how I was using google + stack overflow ten years ago - see your list of answers, use your intuition, knowledge, and expertise to find the one most applicable to you, continue.

This "crutch" is essentially the same one I've always used, just in different form. I find it pretty good at doing code review for myself before I submit something more formal, to catch any embarrassing or glaringly obvious bugs or incorrect test cases. I would be wary of the dev that refused to use tools out of some principled stand like this, just as I'd be wary of a dev that overly relied on them. There is a balance.

Now, if all you know are these tools and the workflow you described, yea, that's probably detrimental to growth.

I agree in principal - the process of problem solving is the important part.

However I think LLMs make you do more of this because of what you can offload to the LLM. You can offload the simpler things. But for the complex questions that cut across multiple domains and have a lot of ambiguity? You're still going to have to sit down and think about it. Maybe once you've broken it into sufficiently smaller problems you can use the LLM.

If we're worried about abstract problem solving skills that doesnt really go away with better tools. It goes away when we arent the ones using the tools.

The dangers I've found personally are more around how it eases busywork, so I'm more inclined to be distracted doing that as though it delivers actual progress.

Do you google if 5 is less than 6 or do you just memorize that?

If you believe that creativity is not based on a foundation of memorization and experience (which is just memorization) you need to reflect on the connection between those.

The issue is that, when presented with a situation that requires writing legibly, spelling well, or reading a map, WITHOUT their AI assistants, they will fall apart.

The AI becomes their brain, such that they cannot function without it.

I'd never want to work with someone who is this reliant on technology.

Bullshit! You cannot do second order reasoning with a set of facts or concepts that you have to look up first.

Google Search made intuition and deep understanding and encyclopedic knowledge MORE important, not less.

People will think you are a wizard if you read documentation and bother to remember it, because they're still busy asking Google or ChatGPT while you're happily coding without pausing

Like, Socrates may have been against writing because he thought it made your memory weak, but at least I, an individual, am perfectly capable of manufacturing my own writing implements with a modest amount of manual labor and abundantly-available resources (carving into wood, burning wood into charcoal to write on stone, etc.). But I ain't perfectly capable of doing the same to manufacture an integrated circuit, let alone a digital calculator, let alone a GPU, let alone an LLM. Anyone who delegates their thought to a corporation is permanently hitching their fundamental ability to think to this wagon.

What I don't like are all the hidden variables in these systems. Even GPS, for example, is making some assumptions about what kind of roads you want to take and how to weigh different paths. LLMs are worse in this regard because the creators encode a set of moral and stylistic assumptions/dictates into the model and everybody who uses it is nudged into that paradigm. This is destructive to any kind of original thought, especially in an environment where there are only a handful of large companies providing the models everyone uses.

It’s not that LLM’s are particularly different it’s that people are less able to determine when they are messing up. A search engine fails and you notice, an LLM fails and your boss, customer, ect notices.

This may eventually apply to all human labor.

I was thinking, even if they pass laws to mandate companies employ a certain fraction of human workers... it'll be like it already is now: they just let AI do most of the work anyway!

Because understanding how addition works is instrumental to understanding more advanced math concepts. And being able to perform simple addition quickly, without a calculator is a huge productivity boost for many tasks.

In the world of education and intellectual development it's not about getting the right answer as quickly as possible. It's about mastering simple things so that you can understand complicated things. And often times mastering a simple thing requires you to manually do things which technology could automate.

I'm the polar opposite. And I'm AI researcher.

The reason you can't answer your kid when he asks about LLMs is because the original position was wrong.

Being able to write isn't optional. It's a critical tool for thought. Spelling is very important because you need to avoid confusion. If you can't spell no spell checker can save you when it inserts the wrong word. And this only gets far worse the more technical the language is. And maps are crucial too. Sometimes, the best way to communicate is to draw a map. In many domains like aviation maps are everything, you literally cannot progress without them.

LLMs are no different. They can do a little bit of thinking for us and help us along the way. But we need to understand what's going on to ask the right questions and to understand their answers.

Children will lack the critical thinking for solving complex problems, and even worse, won't have the work ethic for dealing with the kinds of protracted problems that occur in the real world.

But maybe that's by design. I think the ownership class has decided productivity is more important than societal malaise.

It's been my experience that LLMs are only better than me at stuff I'm bad at. It's noticeably worse than me at things I'm good at. So the answer to your question depends: can your child get good at things while leaning on an LLM?

I don't know the answer to this. Maybe schools need to expect more from their students with LLMs in the picture.

This is not conjecture by the way. As a TA, I have observed that half of the undergraduate students lost the ability to write any code at all without the assistance of LLMs. Almost all use ChatGPT for most exercises.

Thankfully, cheating technology is advancing at a similarly rapid pace. Glasses with integrated cameras, WiFi and heads-up display, smartwatches with polarized displays that are only readable with corresponding glasses, and invisibly small wireless ear-canal earpieces to name just a few pieces of tech that we could have only dreamed about back then. In the end, the students stay dumb, but the graduation rate barely suffers.

I wonder whether pre-2022 degrees will become the academic equivalent to low-background radiation steel: https://en.wikipedia.org/wiki/Low-background_steel

I think all of the above do one thing brilliantly, built self confidence.

Its easy to get bullshitted if what youre able to hold in your head is effectively nothing.

And those people are wrong, in a similar way to how it's wrong to say: "There's no point in having very much RAM, as you can just page to disk."

It's the cognitive equivalent of becoming morbidly obese (another popular decision in today's world).

As to writing, just the action of writing something down with a pen, on paper, has been proven to be better for memorization than recording it on a computer [1].

If we're not teaching these basic skills because an LLM does it better, how do learn to be skeptical of the output of the LLM. How do we validate it?

How do we bolster ourselves against corporate influences when asking which of 2 products is healthier? How do we spot native advertising? [2]

[0]: https://www.nature.com/articles/531573a

[1]: https://www.sciencedirect.com/science/article/abs/pii/S00016...

[2]: Example: https://www.nytimes.com/paidpost/netflix/women-inmates-separ...

I don't know. I really feel like the auto-correct features are out to get me. So many times I want to say "in" yet it gets corrected to "on", or vice-versa. I also feel like it does the same to me with they're/their/there. Over the past several iOS/macOS updates, I feel like I've either gotten dumber and no longer do english gooder, or I'm getting tagged by predictive text nonsense.

1. You won’t always have an LLM. It’s the same reason I still have at least my wife’s phone number memorized.

2. So you can learn to do it better. See point 1.

I wasn’t allowed to use calculators in first and second grade when memorizing multiplication tables, even though a calculator could have finished the exercise faster than me. But I use that knowledge to this day, every day, and often I don’t have a calculator (my phone) handy.

It’s what I tell my kids.

Au contraire! It is quite wrong and was wrong then too. "Rote memorisation" is a slur for knowledge. Knowledge is still important.

Knowledge is the basis for skill. You can't have skill or understanding without knowledge because knowledge is illustrative (it gives examples) and provides context. You can know abstract facts like "addition is abelian" but that is meaningless if you can't add. You can't actually program if you don't know the building blocks of code. You can't write a C program if you have to look up the function signature of read(2) and write(3) every time you need to use them.

You don't always have access to Google, and its results have declined procipitously in quality in recent years. Someone relying on Google as their knowledge base will be kicking themselves today, I would claim.

It is a bit like saying you don't need to learn how to do arithmetic because of calculators. It misses that learning how to do arithmetic isn't just important for the sake of being able to do it, but for the sake of building a comfort with numbers, building numerical intuition, building a feeling for maths. And it will always be faster to simply know that 6x7 is 42 than to have to look it up. You use those basic arithmetical tasks 100 times every time you rearrange an equation. You have to be able to do them immediately. It is analogous.

Note that I have used illustrative examples. These are useful. Knowledge is more than knowing abstract facts like "knowledge is more than knowing abstract facts". It is about knowing concrete things too, which highlight the boundaries of those abstract facts and illustrate their cores. There is a reason law students learn specific cases and their facts and not just collections of contextless abstract principles of law.

>For instance, I'm not too concerned about my child's ability to write very legibly (most writing is done on computers),

Writing legibly is important for many reasons. Note taking is important and often isn't and can't be done with a computer. It is also part of developing fine motor skills generally.

>spell very well (spell check keeps us professional),

Spell checking can't help with confusables like to/two/too, affect/effect, etc. and getting those wrong is much more embarrassing than writing "embarasing" or "parralel". Learning spelling is also crucial because spelling is an insight into etymology which is the basis of language.

>reading a map to get around (GPS), etc

Reliance on GPS means never building a proper spatial understanding. Many people that rely on GPS (or being driven around by others) never actually learn where anything is. They get lost as soon as they don't have a phone.

>but I think my children will have a different perspective (similar to how I feel about memorizing poetry and languages without garbage collection).

Memorising poetry is a different sort of thing--it is a value judgment not a matter of practicality--but it is valuable in itself. We have robbed generations of children of their heritage by not requiring them to learn their culture.

I try to explain by shifting the focus from neurological to musculoskeletal development. It's easy to see that physical activity promotes development of children's bodies. So although machines can aid in many physical tasks, nobody is suggesting we introduce robots to augment PE classes. People need to recognize that complex tasks also induce brain development. This is hard to demonstrate but has been measured in extensive tasks like learning languages and music performance. Of course, this argument is about child development, and much of the discussion here is around adult education, which has some different considerations.

With previous technological advancements, humans had places to intellectually "flee", and in fact, previous advancements were often made for the express purpose of freeing up time for higher level pursuits. The invention of computers, for example, let mathematicians focus on much higher level skills (although even there an argument can be made that something has been lost with the general decrease in arithmetic abilities amoung modern mathematicians).

Large language models don't move humans further up the value chain, though. They kick us off of it.

I hear lots of people prosletizing wonderful futures where humans get to focus on "the problems that really matters", like social structures, or business objectives; but there's no fundamental reason that large language models can't replace those functionalities aswell. Unlike, say, a Casio, which would never be able to replace a social worker no matter how hard you tried.

Take rote memorization. It is hard. It sucks in so many ways (just because you memorized something doesn't mean you can reason using that information). Yet memorization also provides the foundations for growth. At a basic level, how can you perform anything besides trivial queries if you don't know what you are searching for? How can you assess the validity of a source if you don't know the fundamentals? How can you avoid falling prey to propaganda if your only knowledge of a subject is what is in front of your face? None of that is to say that we should dismiss search and depend upon memorization. We need both.

I can't tell you what to say to your children about LLMs. For one thing, I don't know what is important to them. Yet it is important to remember that it isn't an either-or thing. LLMs are probably going to be essential to manage the profoundly unmanagable amount of information our world creates. Yet it is also important to remember that they are like the person who memorizes but lacks the ability to reason. They may be able to impress people with their fountain of facts, yet they will be unable to create a mark on the world since they will lack the ability to create anything unique.

Not that these aren't noble things or worth doing, but they won't impact your life too much if you're not interest in penmanship, spelling, or cartography. <<<

For me it is the second order benefits, notably the idea of "attention to detail" and "a feel for the principles". The principles of each activity being different: writing -> fine motor control, spelling -> word choice/connotation, map -> sense of direction, (my own insert here) money handling -> cost of things

All of them involve "attention to detail" because that's what any activity is - paying attention to it.

But having built up the experience in paying attention to [xyz], you can now be capable when things go wrong.

IE catch disputable transaction on the credit card, or note being told by the shop clerk "No Returns" when their policy says otherwise, un-losting yourself when the phone runs out of battery in the city.

Notably, you don't have to be trained for the details in traditional ways like writing the same sentence 100 times on a piece of paper. Learning can be fun and interesting.

Children can write letters to their friends well before they get their own phone. Geocaching/treasure hunts(hand drawn mud maps!)/orienteering for map use.

As for LLM ... well currently "attention to detail" is vital to spot the (handwave number) 10% of when it goes wrong. In the future LLMs may be better.

But if you want to be better than your peers at any given thing - you will need an edge somewhere outside of using an LLM. Yet still, spelling/word choice/connotations are especially linked to using an LLM currently.

Knowing how to "pay attention to detail" when it counts - counts.

However, I am going to hazard a guess that you still care about your child's ability to do arithmetic, even though calculators make that trivial.

And if I'm right, I think it's for a good reason—learning to perform more basic math operations helps build the foundation for more advanced math, the type which computers can't do trivially.

I think this applies to AI. The AI can do the basic writing for you, but you will eventually hit a wall, and if all you've ever learned is how to type a prompt into ChatGPT, you won't ever get past that wall.

----

Put another way:

> So how do I answer my child when he asks "Why should I learn to do X if I can just ask an LLM and it will do it better than me"

"Because eventually, you will be able to do X better than any LLM, but it will take practice, and you have to practice now."

The same way you answer - "Why should I memorise this if I can always just look it up"

Because your perceptual experience is built upon your knowledge and experiences. The entire way you see the universe is altered based on these things, including what you see through your eyes, what you decide is important and what you decide to do.

The goal of life is not always "simply to do as little as possible", or "offload as much work as possible" but lots of the time includes struggling through the fundimentals so that you become a greater version of yourself, it is not the complete task that we desire, it is who you became while you did the work that we desire.

For instance your point about: > reading a map to get around (GPS)

https://www.statnews.com/2024/12/16/alzheimers-disease-resea...

After reading the above it dawned on me that the human brain needs to develop spatial awareness and not using that capability of the brain very slowly shuts it off. So I purposefully turn off the gps when I can.

I think not fully developing each of those abilities might have some negative effects that will be hard to diagnose.

If the expectation is X, and your tool gives you Y, then you’ve failed - no matter if you could have done X by hand from scratch or not, it doesn’t really matter, because what counts is whether the person checking your work can verify that you’ve produced X. You agreed to deliver X, and you gave them Y instead.

So why should you learn to do X when the LLM can do it for you?

Because unless you know how to do X yourself, how will you be able to verify whether the LLM has truly done X?

Your kid needs to learn to understand what the person grading them is expecting, and deliver something that meets those expectations.

That sounds like so much bullshit when you’re a kid, but I wish I had understood it when I was younger.

It's not true even though it's accepted. Rote memorization has a place in an education. It does strengthen learning and allow one to make connections between the things seen presently and things remembered, among other things.

It absolutely isn't.

That sounds like setting-up your child for failure, to put it bluntly.

How do you want to express a thought clearly if you already fail at the stage of thinking about words clearly?

You start with a fuzzy understanding of words, which you delegated to a spellchecker, added to a fuzzy understanding of writing, which you've delegated to a computer, combined with a fuzzy memory, which you've delegated to a search engine, and you expect that not to impact your child's ability to create articulate thoughts and navigate them clearly?

To add irony to the situation, the physical navigation skills have, themselves, been delegated to a GPS..

Brains are like muscles, they atrophy when not used.

Reverse that course before it's too late, or suffer (and have someone else suffer) the consequences.

Realistically it comes down to the idea that being an educated individual that knows how to think is important for being successful, and learning in school is the only way we know to optimize for that, even if it's likely not the most efficient way to do so.

It's sort of the mental analog of weight training. The only way to get better at weightlifting is to actually lift weight.

Academia needs to embrace this concept and not try to fight it. AI is here, it's real, it's going to be used. Let's teach our students how to benefit from its (ethical) use.

Flipped classroom is just having the students give lectures, instead of the teacher.

> Basically, a student's marks depend mostly (only?) on what they can do in a setting where AI is verifiably unavailable.

This is called "proctored exams" and it's been pretty common in universities for a few centuries.

None of this addresses the real issue, which is whether teachers should be preventing students from using AIs.

Surely this is sarcasm, but really your average schoolteacher is now a C student Education Major.

I feel AI has just revealed how poor the teaching is, though I don't expect any meaningful response to be made by teaching establishments. If anything AI will lead to bigger differences in student learning. Those who learn core concepts and to critically think will be become more valuable and the people who just AI everything will become near worthless.

Unis will release some handbook policy changes to the press and will continue to pump out the bell curve of students and get paid.

Quality of CS/Software Engineering programs vary that much.

Sure. Works in my IDE. "Create a linked list implementation, use that implementation in a method to reverse a linked list and write example code to demonstrate usage".

Working code in a few seconds.

I'm very glad I didn't have access to anything like that when I was doing my CS degree.

Your post is based in a misguided idea that students actually care about some basic quality of their work.

most normal-difficulty undergraduate assignments are now doable reliably by AI with little to no human oversight. this includes both programming and mathematical problem sets.

for harder problem sets that require some insight, or very unstructured larger-scale programming projects, it wouldn't work so reliably.

but easier homework assignments serve a valid purpose to check understanding, and now they are no longer viable.

No, you see a plausible set of tokens that appear similar to how it's done, and as a beginner, you're not able to tell the difference between a good example and something that is subtly wrong.

So you learn something, but it's wrong. You internalize it. Later, it comes back to bite you. But OpenAI keeps the money for the tokens. You pay whether the LLM is right or not. Sam likes that.

It's the most efficient few-shot which beats the odds on any SotA model.

You need to know how to do things so you know when the AI is lying to you.

LLMs can't produce intellectual rigour. They get fine details wrong every time. So indeed using ChatGPT for doing your reasoning for you produces inferior results. By normalising non-rigorous yet correct sounding answers, we drive down the expectations.

To take a concrete example, if you tell a student to implement memcpy with chatgpt, and it will just give an answer which uses uint64 copying. The student has not thought from first principles (copy byte by byte? Improve performance? How to handle alignment?). This lack of insight in return to immediate gratification will bite later.

It's maybe not problem for non-STEM fields where this kind of rigor and insight is not required to excel. But in STEM fields, we write programs and prove theorems for insight. And that insight and the process of obtaining it is gone with AI.

The person you're responding to is talking about it from an educational perspective though. If your fundamentals aren't solid, you won't know that exponentially smoothed reservoir sampling backed by a splay tree is optimal for your problem, and ChatGPT has no clue either. Trying things, struggling, and failing is crucial to efficient learning.

Not to mention, you need enough brain power or expertise to know when it's bullshitting you. Just today it was telling me that a packed array was better than my proposed solution, confidently explaining why, and not once saying anything correct. No prompt changes could fix it (whether restarting or replying), and anyone who tried to use less brainpower there would be up a creek when their solution sucked.

Mind you, I use LLMs a lot, including for code-adjacent tasks and occasionally for code itself. It's a neat tool. It has its place though, and it must be used correctly.

Target the cheaters with pop quizzes. Prof can randomly choose 3 questions from assignments. If students cant get enough marks on 2/3 of them they are dealt a huge penalty. Students that actually work through the problems will have no problems with scoring enough marks on 2/3 of the questions. Students that lean irresponsibly on LLMs will lose their marks.

I'm more worried about those who will learn to solve the problems with the help of an LLM, but can't do anything without one. Those will go under the radar, unnoticed, and the problem is, how bad is it, actually? I would say that a lot, but then I realize I'm pretty useless driver without a GPS (once I get out of my hometown). That's the hard question, IMO.

FWIW, exams testing rote learning without the ability to look up things would have been much easier. It was really stressful to sit down and make major changes to your project to satisfy new unit tests, which often targeted edge cases and big O complexity to crash your code.

> We found that students primarily use Claude to create and improve educational content across disciplines (39.3% of conversations). This often entailed designing practice questions, editing essays, or summarizing academic material.

Sure, throwing a paragraph of an essay at Claude and asking it to turn it into a 3-page essay could have been categorized as "editing" the essay.

And it seems pretty naked the way they lump "editing an essay" in with "designing practice questions," which are clearly very different uses, even in the most generous interpretation.

I'm not saying that the vast majority of students do use AI to cheat, but I do want to say that, if they did, you could probably write this exact same article and tell no lies, and simply sweep all the cheating under titles like "create and improve educational content."

They never could be arsed to learn how to input their assignments into Wolfram Alpha. It was always the ux/ui effort that held them back.

This context is important: this taxonomy did not emerge from artificial intelligence nor cognitive science. So its levels are unlikely to map to how ML/AI people assess the difficulty of various categories of tasks.

Generative models are, by design, fast (and often pretty good) at generation (creation), but this isn't the same standard that Bloom had in mind with his "creation" category. Bloom's taxonomy might be better described as a hierarchy: proper creation draws upon all the layers below it: understanding, application, analysis, and evaluation.

There's something irreplaceable about the absoluteness of words on paper and the decisions one has to do to write them out. Conversational speak is, almost by definition, more relaxed and casual. The bar is lower and as such, the bar for thoughts is lower, in order of ease of handwaving I think it goes: mental, speech, writing.

Furthermore there's the concept of editing which I'm unsure how it could be carried out in a conversational sense in graceful manner. Being able to revise words, delete, move around, can't be done with conversation unless you count "forget I said that, it's actually more like this..." as suitable.

So, I would say that while I wouldn't fully dispute your claim that attributing authorship from text alone is impossible, it isn't yet totally clear one way or the other (to us, at least -- would welcome any outside research).

Long-term -- and that's long-term in AI years ;) -- gaze tracking and other biometric tracking will undoubtedly be necessary. At some point in the near future, many people will be wearing agents inside earbuds that are not obvious to the people around them. That will add another layer of complexity that we're aware of. Fundamentally, it's more about creating evidence than creating proof.

We want to give writers and students the means to create something more detailed than they would get from a chatbot out-of-the-box, so that mimicking the whole act of writing becomes more complicated.

It won't be long 'til we're at the point that embodied AI can be used for scalable face-to-face assessment that can't be cheated any easier than a human assessor.

This is at the root of the Dunnin-Kruger effect. When you read an explanation you feel like you understand it. But it's an illusion, because you never developed the underlying cognition, you just saw the end result.

Learning is not about arriving at the result, or knowing the answers. These are by products of the process of learning. If you just short cut to the end by products, you get the appearance of learning. And you might be able to play the system and come out with a diploma. But you didn't actually develop cognitive skills at all.

People who spent the past two years offloading their entry-level work onto LLMs are now taking 400-level systems programming courses and running face-first into a capability wall. I try my best to help, but there's only so much I can do when basic concepts like structs and pointer manipulation get blank stares.

> "Oh, the foo field in that struct should be signed instead of unsigned."

< "Struct?"

> "Yeah, the type definition of Bar? It's right there."

< "Man, I had ChatGPT write this code."

> "..."

Spaced repetition as it's more commonly known has been quite studied, and is anecdotally very popular on HN and reddit. Albeit more for some subject than others

when wikipedia was initially made, many schools/teachers explicitly denied wikipedia as a source for citing in essays. And obviously, plenty of kids just plagerized wikipedia articles for their essay topics (and was easily discovered at the time).

With the advent of LLM, this sort of pseudo-learning is going to be more and more common. The unsupervised tests (like online tests, or take home assignments) cannot prevent cheating. The end result is that students would pass, but without _actually_ learning the material at all.

I personally think that perhaps the issue is not with the students, but with the student's requirement for certification post-school. Those who are genuinely interested would be able to leverage LLM to the maximum for their benefit, not just to cheat a test.

what's the point of the teacher then? Courses could entirely be taught via LLM in this case!

A student's willingness to learn is orthogonal to the availability of cheating devices. If a student is willing, they will know when to leverage the LLM for tutoring, and when to practise without it.

A student who's unwilling cannot be stopped from cheating via LLM now-a-days. Is it worth expending resources to try prevent it? The only reason i can think of is to ensure the validity of school certifications, which is growing increasingly worthless anyway.

At best what you can learn specifically regarding critical thinking are some rules of thumb such as "compare at least three sources" and "ask yourself who benefits".

You used to _actually_ need to do the arithmetic, now you just need to understand when a calculator is not giving you what you expected. (Not that this is being taught either, lol)

You can get to the higher order thinking sooner than if you spent years grinding multiplication tables.

The problem is that there's a conflict of interest here. The extreme case proves it--leaving aside the feasibility of it, what if the only solution is a total ban on AI usage in education? Anthropic could never sanction that.

I think this is really still up for debate

We produce more output certainly but if it's overall lower quality than previous output is that really "improved productivity"?

There has to be a tipping point somewhere, where faster output of low quality work is actually decreasing productivity due to the efforts now required to keep the tower of garbage from toppling

> You should feel nothing. They knew they were cheating. They didn't give a crap about you.

Most of us (a) don't feel our students owe us anything personally and (b) want our students to succeed. So it's upsetting to see students pluck the low-hanging, easily picked fruit of cheating via LLMs. If cheating were harder, some of these students wouldn't cheat. Some certainly would. Others would do poorly.

But regardless, failing a student and citing students for plagiarism feel bad, even though basically all of us would agree on the importance and value of upholding standards and enforcing principles of honesty and integrity.