College vs. University: Navigating Canadian Education in the Age of AI

For decades, Canada's post-secondary landscape has followed a clear division: universities teach you how to learn, while colleges teach you how to do. My father explained it simply: university prepares you to learn how to perform tasks, while college trains you to perform specific tasks directly. This system has served us well for nearly a century, producing both thoughtful professionals and skilled tradespeople.

But as I've discovered during my return to the IT field after a 15-year hiatus, we're facing a fundamental shift that challenges this traditional divide.

Learning to do, or learning to learn.

The Looming Disruption

The uncomfortable truth is that many of the university-track careers we're training students for today are marked for AI disruption. Editors, librarians, and countless white-collar professions requiring information processing are already seeing transformation or elimination.

What's particularly striking is that many traditional university-track jobs now face higher risk of AI replacement than hands-on college-trained positions. This isn't to suggest everyone should abandon university for college—but it does require us to reconsider what each path offers in our rapidly evolving landscape.

The Trades Paradox

Meanwhile, essential trades are experiencing critical shortages. My cousin in construction recently shared that our local college's carpentry program—normally graduating 60-70

people—will only produce 11 graduates this year. This shortage drives up building costs and creates bottlenecks across industries.

Yes, automation is changing trades too. I've seen modular home panels constructed entirely by machines, eliminating certain carpentry roles. But we still need plumbers, electricians, and specialized carpenters for tasks that won't be automated in the immediate future.

Learning How to Learn vs. Learning What's Already Obsolete

The university approach of teaching how to learn remains valuable. The problem arises when institutions focus on content that's rapidly becoming obsolete. As someone who's had

to completely relearn my field, I've experienced firsthand how quickly technical knowledge can expire.

Consider AI engineering itself. If you're planning to start university tomorrow to study AI engineering, you might already be too late. The field is evolving so rapidly that within five years, many traditional engineering roles—software, chemical, civil—will be profoundly transformed by the very technology they're creating.

The Path Forward: Curiosity as Compass

This brings me to perhaps the most valuable approach in our AI-transformed world: self-directed learning driven by curiosity. Living by the principle that curiosity propels both personal and societal growth has served me well throughout my career transitions.

Even traditionally secure professions—doctors, lawyers, financial managers—are facing AI disruption. Those requiring physical presence or human touch may persist longer, but the transformation is inevitable across sectors.

Choosing Your Path

Should this reality frighten prospective students? Not necessarily. Every technological revolution creates new opportunities alongside disruption. A university education still teaches you how to learn, which remains invaluable. But if immediate employment is your priority, college programs teaching practical skills may offer a more direct path in the short term.

The most important skill, regardless of which educational path you choose, will be adaptability—the ability to pivot as AI reshapes professional landscapes. From my vantage point on Prince Edward Island, watching these changes ripple across industries, I'm convinced that those who cultivate curiosity and flexibility will thrive regardless of their educational background.

After all, the distinction between "learning how to learn" and "learning how to do" matters far less when both what we know and what we do are continuously evolving.