I teach computing at the University of Illinois. I'm spending a lot of time thinking about how to adapt my own courses and our degree programs. I'm actually at a workshop about incorporating AI into computing education, so this was a timely post to find this morning.

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 Research about how to incorporate AI in computing education is outdated before the ink is dry.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.