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AI Copilots Are Changing How Coding Is Taught
Less Emphasis on Syntax, More on Problem SolvingThe fundamentals and skills themselves are evolving. Most introductory computer science courses focus on code syntax and getting programs to run, and while knowing how to read and write code is still essential, testing and debugging—which aren’t commonly part of the syllabus—now need to be taught more explicitly.
Zingaro, who coauthored a book on AI-assisted Python programming with Porter, now has his students work in groups and submit a video explaining how their code works. Through these walk-throughs, he gets a sense of how students use AI to generate code, what they struggle with, and how they approach design, testing, and teamwork.
educators are modifying their teaching strategies. “I used to have this singular focus on students writing code that they submit, and then I run test cases on the code to determine what their grade is,” says Daniel Zingaro, an associate professor of computer science at the University of Toronto Mississauga. “This is such a narrow view of what it means to be a software engineer, and I just felt that with generative AI, I’ve managed to overcome that restrictive view.”
“We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,” says Matthews.Matthews adds that generative AI “can short-circuit the learning process of students relying on it too much.” Chang agrees that this overreliance can be a pitfall and advises his fellow students to explore possible solutions to problems by themselves so they don’t lose out on that critical thinking or effective learning process. “We should be making AI a copilot—not the autopilot—for learning,” he says.
Productive Procrastination
I do a pretty good job of channeling my procrastination into adjacent creative tasks which, in the end, influence, shape, and improve the chunks of work I do complete. And that looks like productivity from the outside. But trust me, from the inside, it usually just feels like avoidance and procrastination. But I’ve learned to accept that’s the cost of doing the kind of work I feel good at, so I let it be what it is.
The particularly nice thing about coding is that it offers many little “wins”: I get a function working, I figure out a piece of design
“Can’t face work? Then cultivate some side projects — and channel your work-avoidance into fun opportunities to learn” and once you’re done, you’ll 1) have something productive to show for it, and 2) be much more fit, rested, and ready to tackle that project at work.
In other words: rather than fight your penchant for procrastination, work with it. It’s a judo move: don’t fight your enemy, use its momentum against it for your benefit.
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