How Will AI Affect CTE?人工智能如何影响职业技术教育？

I’ve been on ChatGPT a lot lately and—apparently—I’m not the only one. I’m not actually using it （though I intend to）; I’m there to gawk over1 what it can do—and， spoiler2， it goes well beyond3 producing first-year term papers. At a recent social gathering， one of my colleagues demonstrated that—if given a fictional research question—the 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 humbling4; I’ve spent years learning to write such code， to middling5 ability. As you might imagine， this demonstration led to some inevitable—and now ubiquitous6—hand-wringing7 about automation and the implications for society.

最近我一直在捣鼓ChatGPT，显然这么干的人不止我一个。其实我并没有在正经地使用ChatGPT（虽然我有这个打算）， 我只是来凑个热闹，围观一下它到底能做什么——剧透一下，它的功能可远远不止撰写大一期末论文那么简单。在最近的一次社交聚会上，一位同事展示说，如果给它一个虚构的研究问题，ChatGPT背后的生成式人工智能就能编写出近乎完美的电脑代码，形成一个基于句法的统计数据包，供我这样的政策研究人员使用。这实在让人自愧不如，我花了好些年学习编写类似的代码，却始终水平一般。正如你可能猜想到的，这样的展示引发关于自动化及社会影响的忧虑，这种忧虑是难免的，如今无处不在。

To what degree can automation affect the career outcomes of graduates of Career and Technical Education （CTE） programs？ I’ve done some preliminary digging and have an idea， but a quick CTE is a useful starting point.

自动化会在多大程度上影响职业技术教育（CTE） 毕业生的职业出路呢？我已经做了一些初步的调查，并有点自己的想法，但简短介绍一下CTE有助于开个好头。

Today’s “career and technical education” is yesterday’s “vocational education，” though not really. Like previous iterations8， contemporary CTE focuses on equipping high school and community college students with technical skills that are closely tethered to specific workforce applications—think carpentry9 or plumbing10. By contrast， courses and programs within the “academic” curriculum emphasize subject-matter knowledge and the development of broadly applicable skills—think history， science， language studies， etc.

如今的“职业技术教育”就是以前所说的“职业教育”，但也不尽相同。与以往的职业教育一样，当代CTE侧重于让高中生和社区大学的学生掌握和特定劳动力应用密切相关的专门技能——比如木工和管道工。相比之下，“学术”教育中的课程和专业则更注重学科知识和普适性技能的培养——比如历史、科学、语言研究等。

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 “academic” versus “vocational” education distinction is a bit anachronistic11 given the college- and career-readiness movement， and mater-ial changes to federal CTE legislation have， over time， successfully blurred the lines between the two. There’s a collective （and bipartisan12！） sense that these changes have steered13 CTE in a positive direction， toward “relevance and rigor14，” and away from its “dark history” of tracking15 disadvantaged students into low-wage， low-opportunity occupations.

不过，当代的CTE提倡者可能会认为CTE和以前的职业教育模式相似之处仅止于此，并且，他们还可能会（正确地）断言，区分“学术”教育和“职业”教育已经不合时宜了，一是由于上大学和就业准备运动，二是由于随着时间推移，联邦CTE立法上的实质性变化也早已成功模糊了二者间的界限。人们达成了一个共识（并且是两党共识！），那就是这些改变已将CTE引上了正轨，使之朝着“实用、严谨”的方向发展，远离了将弱势群体学生输送到收入低微、前途渺茫的职业中去的“黑历史”。

My recent ChatGPT experience has me wondering about this consensus opinion， however. Let me explain.

然而，我最近对 ChatGPT 的体验让我对这一共识产生了疑问。且听我道来：

To begin， jobs requiring skills that are difficult to automate with available technologies are at lower risk 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-resilient16 jobs of today （which tend to require bachelor’s degrees）， the combination will give them greater agility17 when automation threats come knocking tomorrow.

首先，所需技能难以借助现有技术实现自动化的工作岗位被自动化替代的风险更低。这些技能包括双向沟通能力、批判性思考能力、创造力、规划能力、管理能力和问题解决能力。这些都是可迁移技能而非专门性技能。职业技术教育课程和专业需要让学生同时具备这两方面的能力。专门性技能和可迁移技能的结合不仅有助于CTE学生竞争当今能适应自动化的工作（这些工作往往需要学士学位），而且未来面临自动化威胁时，这种结合也能使他们具备更强的灵活性。

This shouldn’t be a stretch18; a key element of contemporary， “rigorous and relevant” CTE is a push to better integrate academic content within technical learning contexts. The concern I have is that “academic integration” is mostly open to interpretation， and there’s not a lot of guidance for how to do it well across the 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 “career clusters.” There’s also little accountability for academic integration baked into19 federal policy.

这个应该不难做到，当代“严谨、实用”的CTE教育中一个关键要素就是推动学术内容更好地融入专业技术学习的环境中。我所担心的是对“学术融合”多半各有各的解读，对于不同的CTE学习领域，或者说“职业集群”并没有太多关于怎么做的指导。“职业集群”或基于行业，如建筑与施工和制造业；或基于服务，如教育培训和公众服务业；或基于技术，如信息技术和科学、技术、工程与数学（STEM）。同时在联邦政策中，对学术融合的归责条文也很少。

The importance of—and challenges to—carving 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 index. I calculated the average automation risk for each CTE career-cluster area by entry education level. Several things stand out.