AI Doesn’t Need to Be a Standalone Pathway
With growing pressure on CTE leaders to figure out where artificial intelligence belongs, the default reaction is often to ask whether we need an entirely new AI pathway. Building a whole program from scratch requires significant alignment across master schedules, budgets, staffing, state requirements, and existing programs of study. Rather than rushing to build a standalone track, a more practical starting point may be asking how AI strengthens the career pathways districts already offer.
AI is not just creating specialized technical roles; it is changing work across industries. Research from Lightcast has found that more than half of job postings seeking AI skills are now outside traditional IT and computer science roles. That does not mean every occupation suddenly requires AI expertise. It does mean demand for AI skills is no longer confined to traditional technology jobs.
At the same time, the World Economic Forum continues to identify analytical thinking, collaboration, adaptability, and other human capabilities as critical (skills educators have always wanted for their students) alongside growing demand for AI and technology skills. National workforce trends like these can point us in a direction, but local labor-market data and employer advisory partners should ultimately help districts determine where AI belongs in their CTE programs.
That matters for CTE. Our students are entering business, healthcare, engineering, manufacturing, marketing, agriculture, finance, construction, cybersecurity, and many other fields where AI is increasingly becoming part of the work. Perhaps AI should not always sit off by itself.
AI Literacy Is Bigger Than CTE
I believe every student increasingly needs some level of AI literacy, and that responsibility is bigger than CTE. We should also be careful about saying that every student needs an AI pathway. They don’t. Schools will need to decide what all students should understand about using AI appropriately, evaluating its output, protecting information, recognizing bias, and knowing when human judgment matters.
CTE has a different—and potentially very powerful—role. CTE can give students the opportunity to use AI in the context of actual work.
A business student might use AI to research a market, analyze information, develop a product concept, or automate a workflow. A computer science student might move from understanding AI to building with Python or working with AI agents. Students in data-focused programs can begin to see how AI, analytics, databases, and decision-making intersect.
That is where AI becomes more than a tool students use to answer a question or complete an assignment faster. Students begin to understand how it is changing the work itself.
Start With Students, Not the Course Catalog
The question isn’t simply, “Do we have access to an AI course?” It is, “Is this the right AI learning experience for these students at this point in their pathway?”
Vanderbilt University’s Honors AI & AI Agents for High School Students Specialization caught my attention because it was actually designed with high school students in mind and does not require students to arrive as programmers. From there, the options become increasingly specialized.
Selected AI product management coursework may make sense for older students interested in business and technology, particularly when it is incorporated into a teacher-supported CTE program. Data science, AI agent development, and machine learning require progressively greater commitments of time, mathematics, programming, or teacher support.
A simple progression can help districts think through those differences:
Foundation → Application → Specialization
At the foundation level, students might develop AI and data literacy, understand how generative AI systems work and where they can fail, learn effective ways to interact with them, evaluate outputs and sources, explore concepts such as AI agents, and examine privacy, bias, intellectual property, accuracy, and ethics.
Application is where CTE can become especially powerful. Students begin using AI within the career area they are already studying rather than learning AI in isolation.
Some students will want to go further. They may move into Python development, data science, AI product management, automation and AI agent development, data architecture, or eventually machine learning. Not every student needs to reach that level, and not every district needs to offer all of it.
Maybe AI Belongs in a Pathway That Already Exists
If I were making this decision at the district level, I would start by looking at the CTE programs we already had. Where is AI already changing the work? Where would it strengthen an existing pathway? Which students need a foundation, and which students are ready to go deeper?
A business pathway might incorporate selected AI product management coursework. An IT or computer science program might add Python, AI agents, or data science. Other programs might embed AI applications that are increasingly relevant to the industry rather than creating an entirely separate course sequence.
Another district may decide there is enough student interest, staffing capacity, and local industry demand to build a dedicated emerging technology or AI sequence for juniors and seniors.
Those are very different solutions, and I think that is a good thing. The goal does not need to be getting every district to the same model. It should be finding the model that makes sense for its students, employers, and existing CTE programs.
Of course, adding AI coursework to an existing pathway does not automatically make that coursework part of a state-recognized program of study. Districts still have to do the alignment work.
Build for Skills That Will Outlast the Tools
There is a real risk in building a four-year pathway too tightly around today’s AI tools and terminology. By the time curriculum is developed, approved, staffed, and implemented, the platforms, terminology, and even some job descriptions may have changed.
Good CTE programs already know how to manage this problem. They build around durable competencies and industry needs, then update the technologies students use to develop those competencies.
AI should be approached the same way.
The more durable skills are the ones students can carry from one technology to another: asking good questions, analyzing information, working with data, evaluating AI output, solving problems, explaining their reasoning, and recognizing when AI should not be trusted or used at all. Just as important is whether students can take what they know and apply it to a problem they have not seen before.
Within CTE, those skills should show up in the work students actually do. Can a business student use AI to help solve a real business problem rather than simply generate a business plan? Can a data student analyze and defend conclusions rather than accept an AI-generated answer? Can a student build an AI-enabled tool and explain the decisions behind it? Can they identify when the technology produces something incomplete, biased, or simply wrong?
That is a much stronger measure of career readiness than whether a student knows how to use today’s most popular AI tool.
Industry credentials can absolutely be part of that work, but districts should distinguish among course-completion certificates, professional certificates, industry certifications, and credentials recognized by their state for CTE accountability. Whatever credential a student earns, I would be cautious about making the number of certificates the measure of success.
A credential becomes much more meaningful when students can demonstrate what they learned through a project, portfolio, capstone, internship, or other authentic application.
Making AI Work in Practice
Whatever model a district chooses, there is still a significant difference between a good idea on paper and something that works for students. Who teaches it? Where does it fit in the master schedule? What prerequisites do students need? Does the coursework meet state CTE or program-of-study requirements? And how will online learning connect to teacher instruction, projects, work-based learning, or other hands-on experiences?
Every state is different, and districts will need to determine how AI coursework fits within their own approval structures, graduation requirements, funding rules, and existing CTE programs.
Those practical considerations may ultimately help determine whether AI belongs inside an existing pathway, as an advanced option for some students, or as a new program of its own. The answer does not have to be the same everywhere.
For some districts, the right decision may be to start small—with one course, one pathway, or one pilot—and learn alongside local employers before expanding. Being intentional about AI does not have to mean being aggressive about implementation.
Adaptability and Flexibility: A Strong Fit for CTE
What I like about the AI content available through SchoolDay Academy, powered by Coursera, is that districts do not have to adopt a one-size-fits-all definition of an AI pathway.
A school might begin with Vanderbilt’s high-school-specific AI coursework. Another might use a single course to strengthen an existing business or technology pathway. A third could build a junior/senior sequence that eventually leads students into Python, data science, AI agents, or machine learning.
Courses can stand alone, be combined into a larger sequence, or support a blended model in which teachers add instruction, projects, discussion, and hands-on experiences.
That flexibility allows districts to start with the students, pathways, teachers, employer needs, and resources they already have rather than assuming every school needs the same four-year AI program.
SchoolDay can support that work by giving districts access to university- and industry-developed coursework they can adapt to existing CTE programs, local priorities, and student needs.
I don’t believe most districts need to begin by building a brand-new AI pathway. They need to look carefully at the pathways they already have, decide where AI genuinely adds value, establish a strong foundation, and create opportunities for students who are ready to go further.
That may be the new CTE question: Where does AI fit?