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The most valuable lesson you can learn from a major in engineering

The+most+valuable+lesson+you+can+learn+from+a+major+in+engineering
Tyler Vu

Engineering education has a problem—an apathy problem. This apathy varies, of course, but especially in the classes more focused on math and theory, students treat engineering education as a pill they have to swallow. I remember when I took dynamics that the general sentiment among students was one of passing by any means necessary.

Why does this sentiment exist among students? Well, ask them. The most common answer you’ll get is that “we won’t need to know most of this in our actual careers,” which is honestly true. I’m not an expert on the “real world,” being a college student myself, but I’ve talked to engineering graduates. Most of them would agree that when you start a career in engineering, you know nothing. What you learn in college is simply the “language” of your field. That language is the tool required to learn the concepts and skills you actually need to know for your job.

But this disconnect between curriculum and career has widened as software and technology keep improving. Engineering software is so complicated, the theory so complex, that you don’t just need a master’s in engineering to understand it. You need a master’s in multiple kinds of engineering, and probably a master’s in a very specific type of computer science as well. In the face of that complexity, is it really worth your time to fully understand the process?

And, to touch on possibly the most over-discussed subject in the past year, another important factor in the specialization and automation of engineering is machine learning language models such as ChatGPT. To be clear, the likes of ChatGPT will never replace engineers because machine learning models cannot themselves be legally held liable for mistakes, which means that they cannot—legally or morally—be given a professional engineer’s stamp.

However, a much more realistic concern is the inevitability of machine learning models being used as tools to supplement design. This introduces the “black box” problem, the idea that the process by which machine learning models reach their conclusions is almost impossible to figure out, which makes catching mistakes much more difficult. What this means is that in engineering design the importance of critical thinking is about to increase. It’s no longer critical to understand the steps to reach an output from an input. Today, the much more important concept to understand is how the input and output are related, and, critically, which inputs are selected and why. To meet the requirements of the current day, an engineer must know not just what they’re designing or how, but also why.

To understand the importance of “why” in engineering, let’s talk about an important example: highways. To tell it one way, the history of highways in the United States has been one of connection. If your grandparents lived in the U.S., you could ask them about road trips, and they’d probably tell you about the first cross-country road trip they ever took on the brand-new Interstate Highway System. It’s possibly the most critical piece of infrastructure that we have; not only do commuters and travelers rely on it, but American shipping via trucks would also be impossible without it.

And yet, to tell it another way, the history of highways has been a history of destruction. Those highways had to go somewhere, and whatever was in the way had to be demolished. This footprint is larger than you may think: In addition to direct spatial conflicts, living near a highway can lead to long-term respiratory problems, and it massively drives down quality of life and property values. Now, if you were an engineer in 1950s America, and you had to choose which neighborhood to demolish in order to build a highway, where might you choose? Here’s a hint: Go to Google and search up the name of any city and “black neighborhood highway construction.”

We’ve got our own story of destructive highways in Cleveland with an ironic twist: One of the only stretches of highway that got canceled due to community backlash, the Clark Freeway, was to run through the comparatively rich and white suburbs of Shaker Heights. The sad truth about highways in the U.S. is that the engineering design decisions about where they should go were not apolitical. The decision makers failed to rise above the political biases of their time.

This story will have been familiar to anyone who took ENGR 398: Professional Communication for Engineers, but I’d like to go one layer deeper. Adjusted for inflation, the U.S. Interstate Highway System as a whole cost more than $500 billion, not counting the money spent maintaining it. In comparison, the International Space Station has cost somewhere near $100 billion. Why did politicians and engineers decide that the largest highway network in the world was worth the price tag? Why not, say, an equally expensive and robust rail network accompanied by much smaller and cheaper highways? Whom has that fateful decision served, and how has it affected the collective and individual decisions that we’re able to make today?

These are the questions that every engineer should ask and answer. Sometimes, I get asked why I have a major in civil engineering but a minor in sociology. The answer is because the built world and the social world inform each other. Our design decisions influence our social beliefs, and, in turn, our social beliefs influence our design decisions. The emphasis on “why” that I’ve been discussing has a name among sociologists; they call it “sociological imagination.” Just like how a scientist should be able to justify the intellectual value of their research, an engineer should be able to justify the political and social value of the world they want to build.

However, returning to our curriculum here at CWRU, I don’t think that we should abandon all our technical standards and instead take only classes on software and sociology. But we should be shifting the way we think about our education. When you learn equations, you should not just be thinking about how they work, but also about what purpose they serve. College may end up being the only time in your life when you need to know what the fourth order Runge-Kutta method is, but it certainly won’t be the only time in your life when you need to be curious. So never ever stop asking why.

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About the Contributor
Tyler Vu, Graphic Designer
Tyler Vu (he/him) is a second-year majoring in mechanical and aerospace engineering. He spends his free time driving around Cleveland to find cool places to eat, going to the gym, losing to the elderly in pickleball or bothering his friends.

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