By: Noa Guest, Junior Computer Science Major
In my last blog post, I wrote about my experience as a Computer Science student in a job and academic environment that is rapidly pushing GenAI to be the “next big thing.” I discussed how professors should implement GenAI based on the level of the course. Yet I find that, as a student, we don’t simply need a green or red light when it comes to using AI. We also need professors to strike a balance in their rhetoric about AI and the future of the job market. Professors and instructors should avoid doomsday pronouncements about how our jobs post-graduation will be “stolen by AI” and instead should focus on providing them a skillset that will help them navigate the changing employment landscape.
Students cannot avoid hearing the “your job will belong to AI” rhetoric anywhere in universities or society in general these days. The more a student hears about how they are fighting a losing battle against large language models, the more they will feel inclined to use GenAI “because it does not matter anyway.” Professors should realize that they are teaching the first generation of students who will be competing with computers for jobs. Therefore, they should design assignments that help students develop skills they can use to outperform AI.
Not only do we need assignments that help us develop skills in an AI job market, we need to hear professors articulate meaningful reasons why we should or shouldn’t use the tool. The most helpful words from my English classes are that AI-use in the course should help us work with AI because it is not going anywhere. In one of my favorite English assignments, we were instructed to find flaws in GenAI output so that we can better understand when it does not work. What made this effective was that our professor explained the reason we were doing the task; partly, we were learning to spot inaccuracies in the output so we can fix them, and partly we were learning about how to make a compelling argument to an employer for why we should avoid AI for some task. Comparatively, it feels like my Computer Science professors have almost given up, suggesting we use it on assignments where it is unnecessary or to prepare for internship interviews. Young people want advice on how to work with GenAI or how to actively move a workplace away from it. When professors give students the option to use AI, they should explain the advantages and disadvantages of using it for that purpose. For any task a student must complete with AI, the professors should encourage students to outperform AI or to notice the downsides of incorporating it into a professional workflow.
Many of my classmates have come up with very inventive ways to study with AI or to complete menial parts of their assignments. But they do not usually have the skills to explain why it is bad for a particular task, and many do not believe they can outperform AI. The AI jobs argument is based on the idea that the “simplistic” jobs will be outsourced to AI to save costs. Jobs that are often cited as on-the-line are junior developer, professional writing jobs, journalism, graphic design, and more. Many of these are jobs the average college student would be taking immediately after graduation. But AI cannot outperform skilled graduates if they are given the skills to counter AI and the secure foundations needed to be great at what they do. These skills and this confidence must come from their instructors and mentors.
If there is one thing to take away from how quickly GenAI rose in the professional and academic worlds, it is that educators can do a big part in developing young people into being better than the machines trying to “replace them.” AI must appear in assignments that encourage personal growth rather than reliance on GenAI. Professors and educators are the younger generation’s biggest weapon against AI, and working together on this effort is imperative to the future of our workforce.
