AI Is Changing Career Pathways. We Need to Prepare Students for What Comes Next.

If we want students to be ready for work, college and university, we need to start helping them understand how AI is changing the fields they are about to enter.

I’ve spent much of my career working at the intersection of education, technology, career preparation and innovation. One thing I’ve learned over the years is that schools are constantly being asked to prepare students for a future that is arriving faster than the education system can comfortably adapt. 

AI is one of those moments. 

There is no shortage of conversation about artificial intelligence in education, and much of it is focused on whether students should use AI, how schools should regulate it, or whether it creates new challenges around academic integrity. Those are important but obvious conversations.  I think there is another one we need to be having, particularly in Career and Technical Education: 

How is AI changing the careers our students are actually preparing for?

Because it is. And it is not just changing jobs in technology. AI is finding its way into construction, culinary arts, cybersecurity, engineering, health and biotechnology, media production, supply chain, manufacturing, public safety and many other career fields. For high school students today, this matters. Some of our students will move directly into employment after high school. Some will enter apprenticeships or technical training. Others will continue into community college, college or university. All of them are likely to encounter AI as part of their next step.

A student entering construction may work with AI-supported estimating, scheduling, safety documentation or project controls. That same student might eventually enter a construction management, architecture or engineering program where AI-supported design and analysis tools are becoming part of the field. A cybersecurity student might encounter AI-supported network monitoring, code review, vulnerability analysis or incident response in an entry-level role, while a student moving into a college or university cybersecurity program will increasingly need to understand how to use similar tools without losing sight of the underlying systems, logic and evidence. 

The same pattern is emerging in engineering, media, biotechnology, supply chain and other pathways.

That is why I don’t believe we should frame this simply as “AI job readiness.” It is really “next-step readiness.” 

We need students prepared for their first job, an apprenticeship, the college classroom, the university program and the career(s) that follow. The lines between career readiness and postsecondary readiness are becoming much less clear, especially as the same technologies start showing up in both workplaces and higher education.

At the same time, I don’t believe the answer is to turn every CTE or CTS course into an AI course. Foundational skills are fundamental:

• A construction student still needs to learn construction. 

• A culinary student still needs to understand food preparation, safety and kitchen operations. 

• A cybersecurity student still needs to understand networks and systems. 

• An engineering student still needs mathematics, measurement, design and problem-solving. 

In fact, as AI becomes more capable, those foundational skills will become even more important. If AI produces an estimate, someone needs to know whether the estimate makes sense. If AI recommends a security response, someone needs enough cybersecurity knowledge to recognize a bad recommendation. If AI suggests an ingredient substitution, someone still needs to understand food safety, allergens and how ingredients actually behave.

The goal, then, isn’t to teach students to hand their work over to AI. It is to teach them enough about their field that they can use AI intelligently. Students need to understand where AI can help, where it can save time, where it can support analysis and where human knowledge and judgment still need to take over.

This is where we are focusing a lot of our work at Work ED. Rather than teaching AI as a separate subject, we help school districts to introduce applied AI where it is actually showing up in the work students are learning to do. 

• A construction student reviews an AI-assisted quantity takeoff. 

• A cybersecurity student evaluates an AI-generated security alert. 

• A culinary student verifies an AI-generated allergen recommendation. 

• An engineering student reviews an AI-supported design change. 

• A media student evaluates AI-generated content for authenticity, copyright or accuracy.

• …and so on

The important part isn’t simply using AI as a tool. It is what the student does with the information the tool provides. 

At Work ED, our approach is built around a practical learning cycle: Analyze and Check. Decide and Explain. Act and Adjust. 

Students look at the evidence, compare the AI-supported output against it, identify what may be missing or incorrect, make a decision and explain their reasoning. They also learn to recognize when something needs to be escalated to a teacher, supervisor, technician or other qualified professional. This is much closer to how we believe AI will actually function in workplaces than simply teaching students to become better prompt writers.

One of the most important skills in this work may actually be learning when not to trust AI. AI is incredibly useful. It can help people work faster, find patterns, organize information, generate ideas and automate routine tasks. 

But useful is not the same as correct. 

Students need experience asking whether the recommendation is supported by evidence, what information may be missing, whether there is a privacy or safety concern and whether they are qualified to make the decision themselves.

Those habits matter just as much in college and university as they do in the workplace. A student who can question an AI-generated explanation, verify sources, test assumptions and articulate their own reasoning will be better prepared for a lab, a research assignment, a technical program, an internship or a workplace. 

This is why I see AI literacy as something much bigger than knowing how to use a particular platform. Increasingly, it is about judgment.

Career and Technical Education teachers are central to making this work. CTE teachers came into education because they know their industry. They understand how the work gets done, what good work looks like and where safety, quality and professional judgment matter. We shouldn’t suddenly expect them to become AI specialists as well. Our job at Work ED is to make this easier by giving teachers pathway-specific lessons, realistic workplace scenarios, student tasks, assessments and implementation support that can fit into the programs they are already teaching. The teacher remains the expert in the classroom and the technical pathway remains the foundation. We are not trying to replace what works in CTE. We are trying to help teachers keep it current.

I also think we need to rethink what we mean when we say a student is “ready.” 

For a long time, readiness has largely been communicated through courses, grades, credits and transcripts. Those still matter, but employers and postsecondary institutions increasingly want to know what students can actually do. 

• Can this student solve a problem? 

• Can they work with evidence? 

• Can they explain their reasoning? 

• Can they use technology responsibly? 

• Can they demonstrate a skill rather than simply say they completed a course?

That is why we support school districts and their CTE teachers to connecting this work to student evidence, micro-credentials, portfolios, work-based learning and employer validation. 

The goal is to help students leave high school with more than a list of courses they completed. 

We want them to be able to show what they know, what they can do and how they have applied it. That evidence can have value whether the student’s next stop is an employer, an apprenticeship, a community college, a college or a university.

I have seen a lot of technology come into education over the years. Some of it changed teaching and learning substantially. Some of it didn’t live up to the hype. I don’t think the right response to AI is to chase every new tool or try to predict exactly what the workplace will look like five or ten years from now. We won’t get all of that right. What we can do is prepare students to adapt by giving them strong technical foundations, helping them understand how technology is changing their field, teaching them to question and verify information, and giving them opportunities to apply those skills in situations that look and feel like real work.

That is the direction we are taking at Work ED. We work with leading school districts in Canada and the United States to help Career Pathway teachers introduce applied AI in practical ways and to strengthen the connections between high school learning, employment, apprenticeships, college and university.

For me, the goal is simple. I want students to leave high school understanding their field, understanding how AI is changing it, knowing when AI can help and knowing when they need to rely on their own knowledge and judgment. 

Most importantly, I want them to feel prepared for whatever comes next.

That feels like a pretty good definition of career, college, university, and life readiness, wouldn’t you agree?