> He has conclusive evidence that at least 50 students cheated on the March midterm exam, making it the biggest known scandal at Brown and in the entire Ivy League
I'd like a citation for this being the "biggest known scandal" in the "entire Ivy League". Frequently such situations are kept somewhat quiet, for a variety of reasons. But fifty students is not a large number in courses that can enroll hundreds or up to a thousand students.
There are limits to what you can assess on timed assessments, and there are students whose performance on such assessments is not a good signal of their intellectual ability.
In addition, at many institutions such tests are given infrequently and can be worth a significant component of a student's overall grade, increasing both student stress levels and the tendency for such assessments to measure short term knowledge students have obtained by cramming, not more meaningful longer-term knowledge gains.
I see you're giving quizzes every three weeks, which is better than twice a semester, but still not what I would consider an ideal cadence. In my course weekly computer-based quizzes comprise 70% of a student's grade, but that's supported by a significant institutional investment in high-frequency computer-based testing: https://cbtf.illinois.edu/.
A group of people sitting together listening to one person talk are not socializing. Once one person stops monopolizing their collective attention, then they can socialize. The more effective alternatives to lecture bring learners into more engaged contact with each other.
You're describing your response to a performance, not to a learning opportunity. Is it fun to watch a great performer? Sure. Is that an effective way to learn? No.
Does lecturing have a place in disseminating ideas? Sure. I love that scene from Oppenheimer when he attends Heisenberg's lecture, being exposed to cutting edge ideas directly from the mouth of a truly remarkable professor. Watching that gave me a better appreciation of lecturing's original purpose and historical importance. But that's very different from teaching well-understood concepts and skills.
Lectures have been an incredibly ineffective way to learn forever. Faculty continue to lecture, and we continue to build lecture-style classrooms, further enshrining this poor approach. Active learning works, and yet both faculty and students dislike it. Faculty like to talk and pretend they're teaching, and students like to listen and pretend they're learning.
All to say—I wish it was this easy to change the academy. But it's not.
They have not succeeded in forcing me, yet. But it's sad how many computing faculty apparently can't operate the basic online infrastructure needed to support their courses. Not that universities make it easy for us.
And of course the other serious concern I have with Canvas is that they are likely using all the materials faculty upload to train their AI replacements. Many of my colleagues engage in dark humor about this but I haven't noticed much action.
I took a course from him as a graduate student. I was not (and am still not) a theoretician. But I enjoyed the class and Professor Rabin's lectures.
A friend of mine was one of his graduate students and a teaching assistant for the class. He pointed out to me once that Professor Rabin would state many of his points during lecture twice. Once I started listening more carefully, I found this to be true. It was both subtle and pedagogically effective.
English was not his first language, but he enjoyed his struggles with it. I remember him stumbling over the pronunciation of a word during class. Giving up with a smile, he said, "This is a word I know only from books."
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.
I've built several bespoke "apps" that are essentially Claude Code + a folder with files in it. For example, I have Claude Coach, which designs ultimate frisbee workouts for me. We started with a few Markdown files—one with my goals, one with information about my schedule, another with information about the equipment and facilities I have access to, and so on. It would access those files and use them to create my weekly workout plans, which were also saved as files under the same folder.
Over time this has become more sophisticated. I've created custom commands to incorporate training tips from YouTube videos (via YT-DLP and WhisperX) and PDFs of exercise plans or books that I've purchased. I've used or created MCP servers to give it access to data from my smart watch and smart scale. It has a few database-like YAML files for scoring things like exercise weight ranges and historical fitness metrics. At some point we'll probably start publishing the workouts online somewhere where I can view and complete them electronically, although I'm not feeling a big rush on that. I can work on this at my own pace and it's never been anything but fun.
I think there's a whole category of personal apps that are essentially AI + a folder with files in it. They are designed and maintained by you, can be exactly what you want (or at least can prompt), and don't need to be published or shared with anyone else. But to create them you needed to be comfortable at the command line. I actually had a chat with Claude about this, asking if there was a similar workflow for non-CLI types. Claude Cowork seems like it. I'll be curious to see what kinds of things non-technical users get up to with it, at least once it's more widely available.
As a teacher, I agree. There's a ton of covert AI grading taking place on college campuses. Some of it by actual permanent faculty, but I suspect most of it by overworked adjuncts and graduate student teaching assistants. I've seen little reporting on this, so it seems to be largely flying under the radar. For now. But it's definitely happening.
Is using AI to support grading such a bad idea? I think that there are probably ways to use it effectively to make grading more efficient and more fair. I'm sure some people are using good AI-supported grading workflows today, and their students are benefiting. But of course there are plenty of ways to get it wrong, and the fact that we're all pretending that it isn't happening is not facilitating the sharing of best practices.
Of course, contemplating the role of AI grading also requires facing the reality of human grading, which is often not pretty. Particularly the relationship between delay and utility in providing students with grading feedback. Rapid feedback enables learning and change, while once feedback is delayed too long, its utility falls to near zero. I suspect this curve actually goes to zero much more quickly than most people think. If AI can help educators get feedback returned to students more quickly, that may be a significant win, even if the feedback isn't quite as good. And reducing grading burden also opens up opportunities for students to directly respond to the critical feedback through resubmission, which is rare today on anything that is human-graded.
And of course, a lot of times university students get the worst of both worlds: feedback that is both unhelpful and delayed. I've been enrolling in English courses at my institution—which are free to me as a faculty member. I turned in a 4-page paper for the one I'm enrolled in now in mid-October. I received a few sentences of written feedback over a month later, and only two days before our next writing assignment was due. I feel lucky to have already learned how to write, somehow. And I hope that my fellow students in the course who are actual undergraduates are getting more useful feedback from the instructor. But in this case, AI would have provided better feedback, and much more quickly.