The introduction of ChatGPT last fall thrust artificial intelligence into the national consciousness, putting an exclamation mark on questions about how automation will affect the job prospects for today鈥檚 students. This has particular salience given that concerns about the cost of college have prompted parents and policymakers to embrace career and technical education programs, which prepare students for the workforce. How should we think about the intersection of these two trends? Is AI going to gut the kinds of jobs that CTE will prepare students for, or is CTE a key to preparing students for an AI-infused future? I鈥檝e been wondering about all of this and thought it worth reaching out to someone who鈥檚 actually studied it. Cameron Sublett is an associate professor and director of the Educational Leadership & Policy Studies Department at the University of Tennessee, Knoxville and has written, among many other studies, the 鈥淭ime and Place: An Examination of Career and Technical Education Course Taking and Labor Markets Across Two High School Cohorts鈥� and the 鈥淐ommunity College Career and Technical Education and Labor Market Projections: A National Study of Alignment.鈥� Here鈥檚 what he had to say.
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I鈥檝e been on ChatGPT a lot lately and鈥攁pparently鈥擨鈥檓 not the only one. I鈥檓 not actually using it (though I intend to); I鈥檓 there to gawk over what it can do鈥攁nd, spoiler, it goes well beyond producing first-year term papers. At a recent social gathering, one of my colleagues demonstrated that鈥攊f given a fictional research question鈥攖he generative artificial intelligence behind ChatGPT can write nearly flawless computer code for a certain syntax-based statistical package commonly used among policy-researcher types, like myself. It was humbling; I鈥檝e spent years learning to write such code, to middling ability. As you might imagine, this demonstration led to some inevitable鈥攁nd now ubiquitous鈥攈and-wringing about automation and the implications for society.
After Career and Technical Education (CTE) month in February, my mind naturally returned to an area of inquiry I鈥檝e had for some time now: To what degree can automation affect the career outcomes of graduates of CTE programs? I鈥檝e done some preliminary digging and have an idea, but a quick CTE primer is a useful starting point.
Today鈥檚 鈥渃areer and technical education鈥� is yesterday鈥檚 鈥渧ocational education,鈥� though not really. Like previous iterations, contemporary CTE focuses on equipping high school and community college students with technical skills that are closely tethered to specific workforce applications鈥攖hink carpentry or plumbing. By contrast, courses and programs within the 鈥渁cademic鈥� curriculum emphasize subject-matter knowledge and the development of broadly applicable skills鈥攖hink history, science, language studies, etc.
Modern-day CTE advocates would argue the similarities to former vocational education models end there, however, and would likely (and rightly) assert that making the 鈥渁cademic鈥� versus 鈥渧ocational鈥� education distinction is a bit anachronistic given the college- and career-readiness movement, and material changes to federal CTE legislation have, over time, successfully blurred the lines between the two. There鈥檚 a collective (and bipartisan!) sense that these changes have steered CTE in a positive direction, toward 鈥渞elevance and rigor,鈥� and away from its 鈥溾�� of tracking disadvantaged students into low-wage, low-opportunity occupations.
My recent ChatGPT experience has me wondering about this consensus opinion, however. Let me explain.
To begin, jobs requiring skills that are difficult to automate with available technologies at of automation. These skills include things like two-way communication, critical thinking, creativity, planning, management, and problem-solving. These are transferable skills, not technical skills. Career and technical education courses and programs need to equip students with both. Not only will the combination of technical and transferable skills help CTE students compete for the automation-resilient jobs of today (which tend to require bachelor鈥檚 degrees), the combination will give them greater agility when automation threats come knocking tomorrow.
This shouldn鈥檛 be a stretch; a key element of contemporary, 鈥渞igorous and relevant鈥� CTE is a push to better integrate academic content within technical learning contexts. The concern I have is that 鈥渁cademic integration鈥� is mostly open to interpretation, and there鈥檚 not a lot of guidance for how to do it well across the16 different trades-based (e.g., Architecture & Construction, and Manufacturing), service-based (e.g., Education & Training and Human Services) and tech-based (e.g., Information Technology and Science, Technology, Engineering and Mathematics (STEM)) CTE fields of study or 鈥渃areer clusters.鈥� There鈥檚 also little accountability for academic integration baked into federal policy. Consequently, states, districts, schools, and teachers take different approaches to academic integration, and some approaches are more successful than others.
The importance of鈥攁nd challenges to鈥攃arving out space in every CTE classroom in every CTE career cluster for the development of transferable, nontechnical skills becomes especially salient when you analyze automation risks across the different CTE career clusters. To do this, I merged Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics (OEWS) data with an available automation-risk that assigns each occupation an individual risk score. This particular index has a base of 100; occupations with a score above this base have higher risks of automation, and occupations below the base have lower risks of automation. I calculated the average automation risk (weighted by total 2019 employment) for each CTE career-cluster area by entry education level (see Figure 1). Several things stand out.
First, average automation risks decrease as education level goes up, largely because jobs requiring bachelor鈥檚 degrees a greater number of transferable skills that are less easy to automate. Second, some CTE career-cluster areas have average automation risks that are low: Education & Training, Health Sciences, Information Technology, and Science, Technology, Engineering and Math. Other CTE career-cluster areas have automation risks that are high: Architecture & Construction, Hospitality & Tourism, Manufacturing, and Transportation, Distribution & Logistics. Third, the gap between the lowest and highest levels of education is greatest in clusters with the highest aggregate automation risk, which suggests the academic-integration hurdle is higher in these clusters compared with others.
All this matters because existing research CTE participation can be stratified by race, gender, income, and rurality. Consequently, some student groups may be overrepresented in at-risk clusters. In other words, exposure to automation risk can be correlated with student characteristics. And if our efforts to equip these students with automation-resilient, transferable skills are not successful in these clusters, we risk the possibility of, once again, funneling disadvantaged students into low-wage, low-opportunity occupations. CTE鈥檚 鈥渄ark history鈥� becomes its future.
Can contemporary CTE shield students against risks posed by automation? Absolutely. In theory, CTE students should be better prepared for automation. The pieces are there; done right, academic integration, work-based learning, the Comprehensive Local Needs Assessment, and apprenticeship models can work to close the gap between the skills students have and the skills employers need, today and tomorrow. And the 鈥渟pecial populations鈥� set-aside now within federal CTE legislation that requires providers to allocate funds toward recruiting low-income, disabled, and racially marginalized students into CTE should help diversify cluster pipelines and mitigate tracking. It鈥檚 always been important to get these things right, but the arrival of ChatGPT means it鈥檚 now more important than ever.
