Dr. Daniel Zingaro and Dr. Leo Porter are co-authors of the book Learn AI-Assisted Python Programming.
Leo will teach an introductory computer science course this quarter at UCSD using this book. We discuss how tools like GitHub Copilot let people new to programming focus on breaking down problems instead of language syntax.
Dr. Zingaro is an Associate Professor of Computer Science at University of Toronto Mississauga and Dr. Porter is an Associate Professor at University of California San Diego.
This episode was originally posted on Software Engineering Radio.
Topics covered:

Making programming more accessible
Teaching problem decomposition instead of language syntax
The importance of reading and testing untrusted generated code
The rise of throwaway or one-off code
Concerns about relying on commercial tools
Rethinking how to assess students

Related Links

Learn AI-Assisted Python Programming
Leo Porter
Daniel Zingaro
GitHub Copilot

Transcript
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Note the timestamps and audio for this transcript will not completely match.
Intro
[00:00:00] Jeremy: Today I'm talking to Dr. Leo Porter. He's an associate teaching professor of computer science at the University of California San Diego, and he co-founded the computing education research laboratory there.
I'm also joined by Dr. Daniel Zingaro who is an associate teaching professor of computer science at the University of Toronto. And he's also the author of the book, learn to Code by Solving Problems and the Book, Algorithmic Thinking. They are co-authors of the book, learn AI Assisted Python programming.
Leo and Dan, welcome to Software Engineering Radio.
[00:00:37] Leo: Thank you for having us, Jeremy. I really appreciate your podcast, so thanks. Great to be here.
[00:00:41] Dan: Thanks Jeremy.
Writing a book for Leo's CS1 class
[00:00:43] Jeremy: The first thing we could start with is, is why this book? And, and why now? How did you decide on like, okay, this is the thing we need to do now.
[00:00:51] Leo: So, uh, this is Dan. Uh, so Dan, um, like really early when LLMs first kind of were coming out and being seen on the scene for programming, uh, he started playing with them, uh, for programming projects. And I think Dan really quickly realized that they'd had this, a big impact on how we teach programming. so he reached out to me, uh, and said, I really need to give em a try.
And, uh, after I played with them for a little while, I had the exact same realization that this is gonna change, uh, how we teach programming, uh, in a pretty dramatic way. So having realized that, having realized that we had to change our, uh, introductory CS1 courses, we knew we needed to do that, but in order to teach that class, we'd have to have a book that we could assign our students that that would go along with the class.
And so we knew we had to change the class, but we also knew we had to have a book for it. And given the, the timeline to write books, we started in the book first. Um, and so that's how it got started.
LLMs for Syntax, Humans for breaking down problems
[00:01:45] Dan: I guess we figured out that our course had to change first, before we knew exactly, um, how it had to change. One thing we, um, learned early on was that the kinds of assignments we give in our introductory courses, they're just solved by, by these tools like ChatGPT and copilot.
So, uh, we knew something had to change, and then it is just a matter of figuring out what. And so we spent, um, quite a bit of time with these tools and we started to realize that what's gonna change is the skills that our students need to learn, uh, to be effective using these tools. So like b before these tools, we would spend a lot of time teaching syntax.
Um, and students struggle quite a bit with learning syntax, which I mean, it's very, it's, it's very frustrating, right? Cuz you can't even do anything until you get the syntax right? And you're getting all these errors like missing colons