htmx ~ The University In The AI Era
The University In The AI Era
Carson Gross<br>June 11, 2026
As I mentioned in “Yes, And”, I teach computer science at Montana State<br>University.
In that earlier essay, I say that computer science is probably still a reasonably good area to study, but that you<br>should also expand your skills beyond “just” computer science to help make yourself more employable in the future.
In this essay I want to think more about what AI means for universities in general and computer science programs in<br>particular.
Note: I apologize that this is a longer essay. I have provided a Table of Contents to help you navigate it.
Table of Contents
First: Is The University Still Relevant?
Writing Code
Signaling Competence in an AI World
Towards An AI-accepting CS Curriculum
Current Changes
Homework Is No Longer A Strong Signal
Homework Can Be More Ambitious & Realistic
AI is a Great TA
The Return of Butt-in-chair, Handwritten Tests
Demos & Visualizations Are Cheap
Class Content Should Be In Markdown
Class Analysis & Improvements
Automate Everything
Upcoming Changes
Stronger Pseudocode Standards
AI & Non-AI Tracks
Open Source Work
Clearly, Honestly Communicating The Dangers of AI
Speculative Changes
The “CS+” Concept
Network Isolated Computers
Interview-Based Grading
Conclusion
First: Is The University Still Relevant?
An initial question that many people are asking is: in the era of AI, is the University still relevant?
This is not a new question. Many people have pointed to famous software industry figures who dropped out of college as<br>proof that a university education isn’t useful in technology. And most people who have worked in Silicon Valley know at<br>least one excellent engineer who either dropped out or simply never went to college.
So a college degree has never been a hard requirement for a successful career in technology. But, in reality, most<br>software engineers have some sort of college under their belt and many of the best developers have studied computer<br>science in their undergraduate education.
That being said, there is<br>clearly an emerging crisis<br>in Computer Science education that needs to be addressed in order to keep the university relevant in the post-AI world.
Writing Code
Historically, many computer science departments have looked at writing code as a secondary skill, to be picked up by<br>students on their own, while the department focuses more on the theoretical foundations of computer science.
Since I was mature enough to have an opinion on the matter, I have viewed this as wrongheaded: I think you need to learn<br>how to write code in order to appreciate those deeper theoretical foundations of computer science. If you can’t code up<br>a linked list or use a hash table effectively, learning about the big-O behavior of them is much more abstract and<br>difficult to grasp.
Ironically, in the era of AI, many professional environments are also starting to look at raw coding somewhat<br>skeptically, sometimes insisting that their own engineers not write code at all, but rather use agents to generate it.
This approach may work for more experienced seniors, who have already written a lot of code and know what reasonable<br>code looks like, but it puts junior developers in a bind: they don’t have pre-AI experience writing code, and now they<br>are going into environments where no one is writing code.
As I said in “Yes, And”, you must write the code if you want to develop the ability to read code.
How is that supposed to happen at companies where nobody is writing the code?
I think this presents an opportunity for Computer Science departments: we can be the places where young software<br>engineers write the code. By refocusing our curriculum on practical, code heavy assignments we can give students a safe<br>environment, free of the time pressures and demands of corporate work, to write the code.
This experience can then put them in position to go into environments that use AI more heavily with the confidence that<br>they know how to code and, because of that, are in a position to read and understand the code necessary for their<br>career.
Signaling Competence in an AI World
Now, of course, students are famously lazy and famously clever in figuring out how to be lazy. So, many students will<br>use AI to complete many of these code-heavy assignments. They will learn very little or nothing, but will get a good<br>grade because, let’s be honest, AI can perform at or above the level required for most reasonable undergraduate<br>projects.
Here another irony of the AI era becomes evident: Universities are now in a position to signal competence in a way that<br>nearly no other institution can. AI has made online testing pointless. I know this because the last semester I offered<br>online tests (which I like to do because it is convenient for my working students) the testing scores were through the<br>roof.
While I feel I am a pretty good teacher, this was clearly a case of AI being used by my students,...