While the expansion of AI usage has the potential to complement and assist the work of humans, it has already replaced jobs in certain industries. That has implications for learning — especially in subjects like computer science.
LLMs, a specifically tailored subset of machine learning models trained on large data sets to recognize complex patterns, have the capability to efficiently write lines of code. Mass layoffs at popular tech companies, including Microsoft, Amazon, and Meta, are the result of the insurgence of AI programming tools, which have automated the process of coding.
Computer science teacher Mr. Patrick Maloney acknowledges that learning programming today is vastly different than before.
“Students today can just ask AI, ‘Hey, code this loop for me,’ and it can just do it,” Maloney said. “But there’s also a lot more opportunity for students to seek help and guide their learning in ways that aren’t just from the teacher.”
Maloney believes that the debate of AI in computer science is complicated.
“It’s this tricky subject, as the teacher in me does not want to like AI because it’s a crutch that can easily be abused,” he said. “But the computer science teacher in me is wondering: how can we leverage this to move the kids further along or lead to more creative opportunities?”
Maloney suggests that one way AI can be used productively in the computer science classroom is by looking for syntax errors. Finding small errors in lines of code can be meticulous, and sometimes neither the students nor the teacher can pinpoint the error.
“Now we can ask ChatGPT, as a last resort, ‘Where’s our mistake?’ and it’s really helpful,” he said. “It saves us a lot of time. It’s like having another teacher in the room that is infinitely smarter than me, whom I could leverage in a situation where we need it.”
For computer science to successfully integrate AI and for the industry to grow from it, Maloney explains that there still must be an understanding of the foundations of computer science, regardless of whether AI is used or not.
“Anything that I teach, which is all very introductory in the grand scheme of computer science, AI could probably do. However, as someone in computer science, it’s not whether or not you can do it; it’s whether or not you can actually interpret it, because AI is not perfect,” Maloney said.
He compares AI in computer science to calculators in math.
“You spend your first years of school mentally learning how to multiply, how to divide, and then you can use a calculator to save you from doing that,” Maloney said. “Maybe all of the stuff that we’re learning in this class will be pushed to having AI do it, but if you don’t actually understand what it’s doing, then you’re not going to be a computer scientist.”
Junior John Mathew, who has taken AP Computer Science Principles, agrees, offering advice to students interested in learning more about computer science.
“Just trying to get the quick and easy answer from ChatGPT instead of using it to learn what coding is useless,” Mathew said. “Use it to your benefit by actually trying to understand the code rather than just getting an easy, quick answer.”
