In today's tech world, job titles can shape perceptions almost as much as the work itself. A simple change in title, without altering the actual responsibilities, can influence how a role is valued both in reputation and in pay.
This keyword economy" rewards people who know how to reframe their skills in whatever language is hot that year. Yesterday it was "data engineer" and today it is "AI developer." And tomorrow, maybe something else? The toolbox does not necessarily change; the title just wears a new coat of paint. Yet, that new label often comes with a different salary attached.
The shift is not inherently deceptive. In a market where everyone is competing for attention, it is almost expected that professionals will package their abilities in terms that recruiters and companies want to hear. The problem lies in what happens when these same dynamic starts shaping the educational structures designed to prepare the workforce.
Adapting is inevitable. Doing it well is the hard part.
From Job Titles to Degree Titles
What began as a corporate trend has now made its way into the classroom. Where once students enrolled in Computer Science or Software Engineering, they now see options for bachelor's in Artificial Intelligence, bachelor's in data science, or bachelor's in Machine Learning.
On the surface, this sounds like progress, with universities keeping up with industry trends. But a closer look reveals that, in many cases, these "new" degrees are largely rebranded versions of existing programs, with only minor changes to the curriculum.
Instead of designing a complete program from the ground up, some institutions add a handful of courses to the existing structure, update the degree title, and present it as an entirely new academic degree. The outcome is a mismatch between the promise of the degree title and the actual learning experience.
When the Foundations Are Missing
Drafting an AI curriculum is not about tossing in a "Machine Learning 101" midway through. You need a staircase, not a ladder missing half its rungs. That means starting with the fundamentals in math, statistics, algorithms, programming, and systems architecture, and only then moving into neural networks, deep learning, and ethical considerations.
In a nutshell, it must follow a deliberate, structured sequence that makes sense to the students.
Without that deliberate buildup, students can graduate with an impressive title on paper but struggle to do the work their degree implies. Employers lose confidence, and graduates lose opportunities.
The Market-Academia Feedback Loop
The problem is self-reinforcing. Employers write job ads stuffed with trend-heavy keywords because that is what grabs clicks. Universities echo those words in their degree offerings to draw applicants. The cycle continues, both sides are incentivized to align with the language of the moment, but not necessarily to ensure depth of competence behind it.
If the content does not live up to the label, both graduates and employers face a skills gap. In the short term everybody feels like they are winning, students get degrees that seem to match job postings, and institutions fill classrooms. Long term though, you risk a workforce with the right buzzwords, but the wrong skill set.
Building More Than a Brand
Universities can't just sit still when new technologies keep reshaping the landscape. In tech, momentum is everything, and if you're not moving forward, you're already sliding backward. The tricky part is that adapting isn't just a matter of swapping in trendy course titles or giving degrees a sleek new label.
If a school wants to claim it offers a real AI or Data Science program, the bones of it must go deeper than branding. The learning path should make sense: start somewhere solid, build on it layer by layer, and eventually tackle the kind of advanced, sometimes messy applications graduates will face outside campus walls. And the ingredients matter: a bit of math, a lot of statistics, coding that gets used for something, and a habit of thinking about systems. These shouldn't be locked in separate boxes; the best results come when they spill into each other.
Just as critical is shaping how students think. Throw them into problem-solving drills, quirky puzzles, and scenario challenges that make them pause, scratch their heads, and reason their way through. And honestly, nothing beats work that's a little unpredictable: internships, real-world projects, the kind of assignments where theory bumps into reality and you must improvise. Let them try research too, whether it's hands-on experiments or big-picture theory, so they're not just keeping pace with the field but nudging it forward.
Beyond Labels
The trend of stuffing job titles with flashy keywords has already shown just how persuasive and occasionally deceptive language can be. When that same surface-level branding trick gets applied to academic degrees without real changes in coursework, the danger is pretty much identical. Catchy words might pull people in, but it is depth of skill, hard-earned experience, and a solid framework for learning that carry someone through in the long run. In the job market, it is about weighing genuine competence more heavily than the buzzwords on a resume.
In the end, the shape of tomorrow's digital world will not hinge on how we choose to label our roles or degrees. It will depend on whether we have truly equipped people to thrive in them.