I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.

A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.

AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.

That’s not what it’s about. It’s actually from someone who genuinely wants to learn for the sake of learning who’s talking about how AI is creating havoc in her studies.

I don't think programming becoming 'easier' has make understanding programs any easier or less important. Even if all of the above goes away, I think computer science has taught me a lot about how to think about problems.

It was interesting to see things change towards the end. Some was the chaos from the lack of consistency the article highlights just starting to emerge. Mostly though, it was just the rampant surge in cheating and the classes not being prepared for it.

First off, congrats on spending time doing side projects in OpenProcessing. Keep working on projects that keep you motivated and keep you learning.

We've been there, lost in our learning paths. Even before AI there were times when we were overwhelmed with our CS journey. You'll overcome, don't you worry about that.

The CS degree was never the end of the learning journey. It was always _what you put into it_. You'll learn what you practice, and uni just happens to be a good place to practice things. Whether that's optimizing AI harnesses, understanding how CPU caches work, crunching pixels on a GPU or the principles of distributed computing... it will all make you a better thinker and good at what you do one day.

Stay curious.

I'm also now reminded that this is ran by that same guy who complained about the MIT license allowing people to do whatever they wish.

Provide examples.

It's hard for readers to gauge whether you're complaints are legitimate or overreactions.

TBH I’m also thinking about how to use AI at work for learning. (My work environment is 100% all-in on AI.) Everyone can see and admit that AI coding is making us lazier and that it produces too much code to do old-style line by line reviews. Some people are using agent teams to churn out millions of lines of code where there’s zero hope of reviewing it. We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix. Agents should get better at broad architecture in the future, but they’re definitely not there yet.

> The amount of work involved has also tripled

This has to be one of the big ironies of using AI. I’ve heard this from other students too. The problem is that old-style assignments where the level of difficulty was hand-crafted is way way too easy for an LLM, therefore teachers need to crank the level of difficulty so the student has to actually spend some time on the assignment. But LLMs are getting better every month and the difficulty is so high that no students can possibly do the assignment by hand and now small errors in assignment design can lead to large errors in effort and time required.

> Whereas before, assignments were designed by a person who thought them through at least once, then approved by other people in the department, now I doubt they're getting read more than once (if that). Errors abound, often with corrections coming a week or two before the deadline.

This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.

From the title I thought I was going to be reading an article wondering how AI would devalue a CS degree, but this is actually a story about a CS program falling apart because the instructors are flailing about without AI.

   File under "old man yells at clouds."

   I think it's worth asking what  a CS degree is for.  Is it
   to teach you how to  code and make you obviously hire-able
   by industry?  Maybe  it's to teach you how  to think about
   data and code?   If you think it's the former,  then AI is
   probably going  to be a  problem for you.  If  the latter,
   it's neutral to somewhat good.

   I've  been hiring  CS  grads for  4  decades (yikes!)  and
   there's  definitely  been  a   shift.   Not  all  existing
   programs are  bad.  Not all  young software devs  are bad.
   But...  the  percentage  of  recent  grads  that  seem  to
   understand  what data  processing  systems  are about  has
   definitely fallen.

   Us  old farts  in industry  must share  some of  the blame
   here.   Our industry  seems  to be  pretty  boom or  bust.
   Someone comes in with a stack of money and tells people to
   build { the web | blockchain | AI | virtual worlds | etc }
   and suddenly  there's a  rush.  At the  start of  the gold
   rush, we're not sure how  to build everything that's going
   to be the next google [[1]] so we fill the room with young
   kids  who have  seen  some recent  tooling  and will  work
   cheap.   We  don't  develop  them.  We  want  them  to  be
   compliant drones  dancing to  a tune played  by a  CTO and
   VP/Eng who got their jobs  because they're the children of
   the fund investors who gave the startup a bunch of money.

   And then  we go back  to Universities and tell  them "OMG!
   Your not bar-raising your grads by teaching them __fill in
   the blank__!" [[2]]

   So... I  don't doubt  it looks like  AI is  destroying the
   degree.   But maybe  it's just  making it  hard to  ignore
   previous problems with the uni to industry pipeline.

   And I didn't  mention the Mythical Man Month  once in this
   comment.  I'm getting better.

   1. I'm not  sure too many people want to  be Google at the
      moment, but  over the last 25  years, they've certainly
      done well.

   2. I'm  intentionally misspelling  "you're" because  in my
      time in industry,  I've found only about  15% of people
      would pass a 7th grade spelling test.

No one needs AI in universities or software engineering. It only benefits the middle managers and mediocre people who jump on any bandwagon in order to help their careers.

No software has gotten any better since AI, except that some forgotten bugs from the 1990s were found, just as 20000+ bugs have been found by the Luddite OssFuzz system before AI.

It's awkward, you're very unlikely to stop students from using AI for certain assignments (i.e: first programming class) for the good of their fundamentals, but there's also no other teaching tool being used to get them equivalent experience to solving assignments without AI.

I haven't seen a better example of "those who can't do, teach" than CS professors.

They gave Wesley a laser scalpel, to teach him to chisel wood, with whatever his mind could picture.

Wesley complained, he said, "dood, this isn't art! i'm not even chiseling!"

And the mentor-kidnapper said, "No, Wesley, you are doing art. The art is in your mind. The tool is just doing the work."

And so then Wesley threw the perfect wooden dolphin at the wall, infuriated that all the pleasure had been removed from the art. But he lived the rest of his life on that planet, making perfect wooden laser sculpted dolphins, cause that was his job and his boss told him too bad.

For a little while Wesley made popsicle stick art during his free time at night to try and feel the sensation of choice and control, but eventually just gave up and played mariokart until he was an old man.

AI clearly means you need to change how you teach and evaluate students. We are only a few years in though, and it's still extremely unclear what effective teaching with these tools looks like.

The worst part is that the evaluation loop is measured in multiple years! If you start trying some new education mechanism today you won't get a robust idea of how well it works until your current cohort graduate in 3-6 years time.

Students effectively all become guinea pigs for untested new ways of learning and teaching.

I keep getting excited because I can ask it questions and learn so much from it, but then I realize that a lot of what I'm learning is specific technical knowledge that I don't really need anymore.

That said, the important stuff in building systems has never been the coding. It's the wisdom to organize the system in such a way that it works well and is changeable. AI is still really bad at this. But it's super good at the for loops and if statements, which is actually the easy stuff.

At my uni they had us doing assignments in assembly on day one, before anything was actually taught. The lab handbook said "your first year programing courses will serve you well." lol. Apparently it used to be a 2nd year course and they just moved it to the first semester of the first year unmodified, for no apparent reason.

Had a similar experience at other universities and other courses. This was in the Netherlands so maybe it's different in other countries but my experience is that it functions mostly as a filter for people who already know the material rather than actually teaching you anything.

Over half of people fail the first year (and are banned from repeating it, for 5 years), which is considered normal and acceptable. So it's more of a selection mechanism than anything else.

So I said to a friend who's choosing a university right now, "just don't expect them to actually teach you anything", which is a pretty sad thing to say for an educational institution.

Despite the massive suck all around, I very much like the idea of university, and did enjoy many aspects of my time there, especially the projects. (And I've followed some lectures from other universities on YouTube, where I experienced actual learning, and -- god forbid -- actual joy!)

It is now, and always has been IMO, up to the individual whether or not they want to learn deeply the material being presented, or just do what is necessary to get the grade as a means to an end. In the past, accidently learning something deeply was a little more likely than having the answers at our fingertips via AI, but the possibility is still there depending on how you use the tool.

2) Learn AI to offload labour but not your brain - AI exhibits "Jagged Intelligence" and is "Probabilistic". So you must confine it within explicit guardrails. Always have the "Map of the entire Forest" in your Head and have AI contribute only in constructing "the Trees within the Forest" and that too where you know the peaks of its jagged intelligence lie.

3) Focus on end-to-end Software Engineering and learn how to do everything yourself with no external dependencies other than AI. Specifically, learn to map Requirements -> Formal Specifications and do Formal Verification against it. You need to become the overall "Systems Engineer" (as it used to designated in the old days).

4) In addition, if you use AI to learn about business functions viz. marketing/sales, accounting, legal etc. you are golden and can setup yourself easily as a OPC.

5) Because AI can go deep in any one particular area, cultivating cross and inter disciplinary knowledge, studies, skills are more important today. You must truly build a "M/T" shaped skillset.

6) Always keep in mind Grant Allen's dictum viz. "I have never let Schooling interfere with my Education". AI is an excellent Socratic dialogue partner to learn anything quickly. Practice Maieutic Prompting with AI so you can appropriately constrain it and accelerate your learning curve.

7) Never ever stop learning something actively (can be Mental, Verbal or Physical). The subject you are learning can be anything at all but the key is to do it with mindful self-effort. It has been shown that people who are curious, continuously learning and figuring things out for themselves keep their mental acuity well into old age.

Unis usually watch industrial revolutions from far and have several years to experiment and come up with an approach that mostly works before pushing students through those policies and coursework. This revolution threatens to destroy their business, so they feel an urgent need to do something, even if slapdash policies much better changed quickly.

AI is throwing a wrench in the normal workflows because:

(1) it can be seen as an existential threat to Unis (either their business model or the value of having their diploma),

(2) it is undercutting the value of a Uni degree because that diploma doesn't increase your odds of getting a good job right now,

(3) even people in the workplace using AI aren't working with consistent policies. Tokenmaxxing, then Token-efficiency. You might be prosecuted for a crime if you put confidential customer data into the LLM machine (my CEO sent an email warning about this to all employees), but all of your incentives are to move so fast that might be the only option available. Most of the time it has more to do with negotiating the uncertainty around what is the role of the human in the SDLC (ideation, planning, design, coding, testing, review, maintenance, iteration, coordination, etc) and what is the role of the many agents.

(4) Students are coming to Uni with AI as a habit and might need to change those habits to adhere to Uni policies. The little ones are getting access to LLMs early so the habits are formed early.

(5) Teachers / professors have always had to contend with cheating (how to detect and how to punish). Teachers can use a calculator to build the test (eg. Ensure the contrived numbers on a math equation are solvable and to verify the answers on their answer sheet). That doesn't mean that students should have access to a calculator if the purpose of that unit is to learn the concept without the use of tools.

As frustrating as it is for you, it is almost certainly as frustrating for the professors and department heads.

The cost cutting is a normal headache in all aspects of life. Welcome to capitalism.