A Lot of "Learn to Code"

A Lot of “Learn to Code”

Being surrounded by pragmatic midwesterners and Salvadoran immigrants in your extended family is the easiest way to regret choosing to major in English. When I was a freshmen in college still puzzling out my future, they told me that my ability to recite Chaucer in the original Middle English was not going to get me a job that would pay off my student loans and help me save for retirement. I continued studying English, but I began to worry that they had been correct when my B.A. finally threw me into the depths of the Great Recession.

Ultimately, degrees in computer science—and most definitely not in English—have long been promoted to college students as some of the most reliable routes to job stability in the twenty-first century.

Even with the recent round of IT layoffs, there are still plenty of coding jobs available in many industries, with competitive compensation. According to the Federal Reserve, the average starting salary for a person with a computer science degree is much higher than that of a mid-career English graduate.

For example, an entry-level software engineer at Google reportedly makes $184,000, and that’s before the free meals, massages, and other benefits. The development of computer science and STEM fields throughout the past 20 years has arguably characterized higher education more than anything else. Undergraduate computer science degree enrollment has surged by over 49% since 2016. In the meantime, enrollments in the humanities have declined rapidly across the country; In many instances, this has resulted in the elimination of entire departments.

However, that was prior to the generative AI period. In addition to writing entire essays quickly, ChatGPT and other chatbots can write code in any number of programming languages. Although you can’t directly enter “make me a video game” into ChatGPT and have it played on the other end, a lot of programmers have recently created simple AI-coded smartphone apps. In a cruel twist of irony, American workers who worked on software engineering projects believe artificial intelligence would have the largest influence on their jobs, according to a recent Pew Research Center survey. Too bad I never learned to code.

Even if they might not be as lucrative, jobs will still be obtained, according to Matt Welsh, a former Harvard computer science professor turned entrepreneur. According to his theory, automation will make it easier for more people to enter the profession of software development, pushing the machines’ output to ever-higher speeds. Because of this trend, highly qualified coders may become even more crucial to the software industry. Welsh does, however, add that a larger pool of talent “may change the economics of the situation,” perhaps resulting in lower salary and less job stability.

While mid-career developers must worry about the potential impact of automation on their jobs, students face an even more difficult task: they must consider the long-term effects of their decisions before beginning their careers. Professor Timothy Richards of computer science at the University of Massachusetts at Amherst told me, “I do worry about the question of what it will look like for a student to go through an undergraduate program in computer science, graduate with that degree, and go on into the industry.” Not only do educators like Richards struggle with the question of whether teaching pupils to code is still desirable, but it has also grown more difficult to do so.

Some of the fundamental assignments in any beginner course, such identifying issues with code blocks, can be completed using ChatGPT and other chatbots. Certain students may develop a habit of using ChatGPT to cheat on their assignments, which would lead to them finally receiving their diploma without ever learning how to complete the work themselves.

Richards has already started to modify his strategy. He now instructs his beginning programmers to utilize AI in the same manner that a math student would use a calculator, requiring them to reveal the precise commands they gave the computer and provide an explanation for their actions. Richards’s students now complete the majority of their work in the classroom, guided by him, rather than taking assignments home.

At least not in computer science, he said, “I don’t think we can really teach students in the way that we’ve been teaching them for a long time.”

Modest curriculum changes in computer science may not be sufficient to keep coding at the top of the higher education food chain. One profitable career choice that has emerged is “prompt engineering,” which involves feeding phrases to huge language models to make their responses sound more human. This type of work may be more appropriate for English majors than computer science graduates.

According to Ben Royce, a Columbia University specialist in artificial intelligence, “machines can’t be creative; at best, they’re very elaborate derivatives.” A fresh coding difficulty leaves chatbots unsure on how to proceed. They choke and spit. They fabricate things. As AI develops and becomes more proficient in coding, programmers might have to focus more on the aspects of their work that require intellectual creativity rather than just technical expertise. The people who can think more creatively—the fixers and inquirers—will be the ones who will be most resistant to automation in the workplace.

The possible demise of “learn to code” does not, I hope, imply that English majors were correct all along or that technologists are destined to become the architects of their own obsolescence. Instead, the chaos that artificial intelligence (AI) presents may indicate that students’ specific major choices are not as significant as their capacity to think critically about the range of issues that technology may be able to assist us solve.

A graduate in the humanities with little to no coding experience or a computer science graduate with plenty of it might be the seed of the next big Silicon Valley monster. Ultimately, the field has always involved more than merely picking up the basics of Python and C++. Its core is recognizing patterns and putting them together.

That is to say, the answer to the question of what comes next in higher education might be found in the things that machines are incapable of doing. Royce introduced me to Moravec’s paradox, which states that while AI excels in higher order thinking and activities that are typically thought to indicate cognitive ability (e.g., playing chess), it struggles with lower order tasks. In an AI future, the naturally curious impulses that have traditionally been the basis of human creativity are not only enduring, but they are becoming increasingly crucial. Students can get there in a variety of ways, thank goodness.

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So Much for ‘Learn to Code’ (msn.com)

A Lot of “Learn to Code”

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A Lot of “Learn to Code”

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