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.

Mandarin and other Chinese languages and dialects have been considered serious, practical majors for some time because of the potential professional value of speaking in China. But why would the ability to speak in Francophone Africa be less valuable, unless you think Africa will never produce economic muscle to match its population?

We have a prevalent concept of the “practical college major” in our society, but that concept is vague, not buttressed with evidence, and shifts according to whim and prejudice. And the ultimate point of stressing the practicality of certain majors while denigrating the frivolity of others is to blame people for economic conditions they can’t control.

In the 2000s and 2010s, dozens of new schools of pharmacy were opened thanks to the perception that pharmacy was a safe field for young graduates. Thousands of newly minted pharmacists flooded the market. Somehow, administrators in higher education were surprised to find that these new graduates had a harder time finding a good job than previous generations. But this is an inevitable outcome of telling young people an academic field is a practical choice, since you’re making that field more attractive and thus increasing the competition they have to face in the labor market.

programming, like all skills, is subject to the simple constraints of supply and demand, and thus the practicality of studying the major is a moving target.

I have never — never — found a consistent and coherent definition of a “practical major,” anywhere. The meaning of the term floats around depending on the whims of the person using it, and those whims are usually dependent on mockery. The entire concept seems to exist simply to serve as an instrument to blame people for their own economic misfortune.

Some will say that a practical major is one that gives you the best opportunity for secure employment. Setting aside the fact that life spent in singular pursuit of money is soul-deadening, this strikes me as great advice for people in late adolescence who are in possession of a time machine. For the rest of us, perhaps we should build a society where the educational path chosen early in life is less consequential for lifetime economic security, and where we’re all more free to study what we actually care about.

Technology can change the economy faster than any person can reasonably be expected to keep up with. Nobody knows for sure which fields might be disrupted by AI, which skills rendered unmarketable. But if the effects are as big as some predict, a lot of people are suddenly going to find their once-practical path has become fraught and unsustainable. The question is, are we callous enough to blame them for it?