Ask HN: What is it like being in a CS major program these days?

We don't have a coherent message yet. Currently there's a significant mismatch between what we're teaching and the reality of the computing profession our students are entering. That's already true today. Now imagine 2030, when the students we admit today will start graduating. We're having students spend far too much time practicing classical programming, which is both increasingly unnecessary and impedes the ability to effectively teach other concepts. You learn something about resource allocation from banging out malloc by hand, but not as much as you could if you properly leveraged coding agents.

Degree programs also take time and energy to update, and universities just aren't designed to deal with the speed of the changes we're witnessing. Research about how to incorporate AI in computing education is outdated before the ink is dry. New AI degrees that are now coming online were designed several years ago and don't acknowledge the emergent behavior we've seen over the past year. Given the constraints faculty operate under, it's just hard to keep up. I'm not defending those constraints: We need to do better at adapting for the foreseeable future. Creating the freedom to innovate and experiment within our educational systems is a bigger and more fundamental challenge than people realize, and one that's not getting enough attention. We have a huge task ahead to update both how and what we teach. I'm incorporating coding agents into my introductory course (https://www.cs124.org/ai) and designing a new conversational programming course for non-technical students. And of course I'm using AI to accelerate all of this work.

Emotionally, most of my colleagues seem to be stuck somewhere on the Kübler-Ross progression: denial (coding agents don't work), anger (coding agents are bad), bargaining (but we still need to teach Python, right?), depression (computing education is over). We're scared and confused too: acceptance is hard when you don't know what's happening next. That makes it hard to effectively communicate with our students, even if there's a clear basis for connection. Also keep in mind that many computing faculty don't code, and so lack a first-hand perspective on what's changing. (One of the more popular posts about how to use AI effectively on our faculty Slack was about correcting LaTex formatting for a paper submission. Sigh.)

Here's what I'm telling students. First, if you use AI to complete an assignment that wasn't designed to be completed with AI, you're not going to learn much: not much about the topic, or about how to use AI, since one-shotting homework is not good prompting practice. Second, you have to learn how to use these new tools and workflows. Most of that will need to be done outside of class. Start immediately. Finally, speak up! Pressure from students is the most effective driver of curricular change. Don't expect that the faculty teaching your courses understand what's happening.

Personally I've never been more excited to teach computing. I'm a computing educator: I've always wanted my students to be able to build their castles in the sky. It was so hard before! It's easier now. Cue frisson. That's going to invite all kinds of new people with new ideas into computing, and allow us to focus on the meaningful stuff: coming up with good ideas, improving them through iterative feedback, understanding other problem domains, and caring enough to create great things.

- most students use AI to do pretty much all their labs and assignments. Most also use AI tools to help with studying for exams. Students seem pretty dependent on these tools at this point and their writing and coding skills without them have deteriorated substantially imo.

- Curriculums haven't changed much. Professors still put an emphasis on understanding theory and not just letting the LLM think for you.

- Almost every professor is vocally against the use of AI, whether its for writing reports or generating code. Some are ok with using AI as a studying tool or verifying that your solution to a homework problem is correct.

- A friend of mine was taking a course that's meant to be about building software in a practical context, agile, etc. The professor for that course encouraged students to use AI as much as possible for their projects, so I guess the permissibility of AI depends on whether the course is meant to be theoretical or practical.

- A lot of professors don't bother to take the time to explain why the material is relevant in the AI era, but a few do. The argument is that even if AI can physically write our code, real engineers still need to be able to verify that it works and that sort of thing. I think in general, professors want us to keep holding onto hope even if the future seems bleak. I had one professor tell us that engineers will likely be the last group of people to be replaced by AI, so having a thorough understanding of technical domains will still be important for years to come.

- From my point of view, it doesn't seem like students give much respect to the course content. The sentiment I'm seeing is that since AI can solve a lot of math and programming problems, acquiring a deep understanding of these domains is irrelevant.

- A lot of students feel overwhelmed with how competitive the tech job market is and it's seemingly all they think about. Any time I'm in the engineering building all I hear people talking about are interviews, internships, or their lack thereof.

- Students seem pretty divided as to whether they should be optimistic or pessimistic. Some think software engineering is already dead, some think it'll be the last profession to be replaced by AI.

- Some students are more willing to do things like side projects now that they have AI at their disposal. Most students don't seem to be fully up to date on the latest tools though. As of last fall, ChatGPT and Gemini were pretty ubiquitous, but only about ~20% of CS students (as a rough estimate) were using Cursor, and even fewer using tools like Codex and Claude Code, definitely less than 5%. I haven't been in school for the last few months so these numbers are likely higher now.

- Building a startup is trendier now than it was a year or two ago. Granted its a very small minority at my school, but still noteworthy nonetheless.

Hard to deal with I would expect.

Focus on fundamentals that are timeless and can be applied to any level of AI is my recommendations: - What are algorithms - Theory of databases - P, NP, etc - Computer architecture - O-notation - Why not to use classes - Type theory - And adjacent fields: Mathematics, Engineering, etc...

It is sort of like teaching computer graphics during the start of the video card era - 1996 to 2001. There was for about 5 years really rapid change, where it went from CPU-based rendering, to texturing on the card, to vertex transformations, to assembly programming on the GPU to high level languages (Cg, HLSL, etc.) But the fundamentals didn't change from 1996 to 2001 - so focus on that.