{"id":2430,"date":"2023-09-14T00:15:42","date_gmt":"2023-09-14T00:15:42","guid":{"rendered":"https:\/\/cehhs.utk.edu\/ero\/?p=2430"},"modified":"2024-02-05T10:35:12","modified_gmt":"2024-02-05T15:35:12","slug":"how-will-ai-automation-affect-career-and-technical-education","status":"publish","type":"post","link":"https:\/\/cehhs.utk.edu\/ero\/how-will-ai-automation-affect-career-and-technical-education\/","title":{"rendered":"How Will AI Automation Affect Career and Technical Education?"},"content":{"rendered":"\n

I\u2019ve been on ChatGPT a lot lately and\u2014apparently\u2014I\u2019m not the only one. I\u2019m not actually using <\/em>it (though I intend to); I\u2019m there to gawk over what it can do\u2014and, spoiler, it goes well beyond producing first-year term papers. At a recent social gathering, one of my colleagues demonstrated that\u2014if given a fictional research question\u2014the 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\u2019ve spent years learning to write such code, to middling ability. As you might imagine, this demonstration led to some inevitable\u2014and now ubiquitous\u2014hand-wringing about automation and the implications for society.<\/p>\n\n\n\n

After Career and Technical Education (CTE) month in February, my mind naturally returned to an area of inquiry I\u2019ve had for some time now: To what degree can automation affect the career outcomes of graduates of CTE programs? I\u2019ve done some preliminary digging and have an idea, but a quick CTE primer is a useful starting point.<\/p>\n\n\n\n

Today\u2019s \u201ccareer and technical education\u201d is yesterday\u2019s \u201cvocational education,\u201d 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\u2014think carpentry or plumbing. By contrast, courses and programs within the \u201cacademic\u201d curriculum emphasize subject-matter knowledge and the development of broadly applicable skills\u2014think history, science, language studies, etc.<\/p>\n\n\n\n

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 \u201cacademic\u201d versus \u201cvocational\u201d 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\u2019s a collective (and bipartisan!) sense that these changes have steered CTE in a positive direction, toward \u201crelevance and rigor,\u201d and away from its \u201cdark history<\/a>\u201d of tracking disadvantaged students into low-wage, low-opportunity occupations.<\/p>\n\n\n\n

My recent ChatGPT experience has me wondering about this consensus opinion, however. Let me explain.<\/p>\n\n\n\n

To begin, jobs requiring skills that are difficult to automate with available technologies are<\/a> at lower risk<\/a> of automation. These skills include things like two-way communication, critical thinking, creativity, planning, management, and problem-solving. These are transferable<\/em> skills, not technical<\/em> 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\u2019s degrees), the combination will give them greater agility when automation threats come knocking tomorrow.<\/p>\n\n\n\n

This shouldn\u2019t be a stretch; a key element of contemporary, \u201crigorous and relevant\u201d CTE is a push to better integrate academic content within technical learning contexts. The concern I have is that \u201cacademic integration\u201d is mostly open to interpretation, and there\u2019s 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 \u201ccareer clusters.\u201d There\u2019s 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.<\/p>\n\n\n\n

The importance of\u2014and challenges to\u2014carving out space in every<\/em> CTE classroom in every<\/em> 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 index<\/a> 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.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n

Note: AFNR = Agriculture, Food & Natural Resources, AC = Architecture & Construction, AV = Arts, A\/V Technology & Communication, BM = Business Management & Administration, ED = Education & Training, FIN = Finance, GOV = Government & Public Administration, HS = Health Science, HOSP = Hospitality & Tourism, HUM = Human Services, IT = Information Technology, LAW = Law, Public Safety, Corrections & Security, MAN = Manufacturing, MARK = Marketing, STEM = Science, Technology, Engineering & Math, TRAN = Transportation, Distribution & Logistics. Source: Bureau of Labor Statistics Occupational Employment and Wage Statistics.<\/p>\n\n\n

First, average automation risks decrease as education level goes up, largely because jobs requiring bachelor\u2019s degrees involve<\/a> 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.<\/p>\n\n\n\n

All this matters because existing research indicates<\/a> 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\u2019s \u201cdark history\u201d becomes its future.<\/p>\n\n\n\n

Can contemporary CTE shield students against risks posed by automation? Absolutely. In theory, CTE students should be better<\/em> 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 \u201cspecial populations\u201d 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\u2019s always been important to get these things right, but the arrival of ChatGPT means it\u2019s now more important than ever.<\/p>\n","protected":false},"excerpt":{"rendered":"

I\u2019ve been on ChatGPT a lot lately and\u2014apparently\u2014I\u2019m not the only one. I\u2019m not actually using it (though I intend to); I\u2019m there to gawk over what it can do\u2014and, spoiler, it goes well beyond producing first-year term papers. At a recent social gathering, one of my colleagues demonstrated that\u2014if given a fictional research question\u2014the […]<\/p>\n","protected":false},"author":23,"featured_media":2432,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"editor_plus_copied_stylings":"{}","footnotes":"","_links_to":"","_links_to_target":""},"categories":[32,8],"tags":[],"class_list":["post-2430","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-posts","category-news"],"acf":[],"yoast_head":"\nHow Will AI Automation Affect Career and Technical Education? - Education Research & Opportunity Center<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cehhs.utk.edu\/ero\/how-will-ai-automation-affect-career-and-technical-education\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Will AI Automation Affect Career and Technical Education? - Education Research & Opportunity Center\" \/>\n<meta property=\"og:description\" content=\"I\u2019ve been on ChatGPT a lot lately and\u2014apparently\u2014I\u2019m not the only one. 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