How Will AI Affect CTE?人工智能如何影響職業技術教育？

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.

最近我一直在搗鼓ChatGPT，顯然這么干的人不止我一個。其實我并沒有在正經地使用ChatGPT（雖然我有這個打算）， 我只是來湊個熱鬧，圍觀一下它到底能做什么——劇透一下，它的功能可遠遠不止撰寫大一期末論文那么簡單。在最近的一次社交聚會上，一位同事展示說，如果給它一個虛構的研究問題，ChatGPT背后的生成式人工智能就能編寫出近乎完美的電腦代碼，形成一個基于句法的統計數據包，供我這樣的政策研究人員使用。這實在讓人自愧不如，我花了好些年學習編寫類似的代碼，卻始終水平一般。正如你可能猜想到的，這樣的展示引發關于自動化及社會影響的憂慮，這種憂慮是難免的，如今無處不在。

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.

自動化會在多大程度上影響職業技術教育（CTE） 畢業生的職業出路呢？我已經做了一些初步的調查，并有點自己的想法，但簡短介紹一下CTE有助于開個好頭。

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“職業技術教育”就是以前所說的“職業教育”，但也不盡相同。與以往的職業教育一樣，當代CTE側重于讓高中生和社區大學的學生掌握和特定勞動力應用密切相關的專門技能——比如木工和管道工。相比之下，“學術”教育中的課程和專業則更注重學科知識和普適性技能的培養——比如歷史、科學、語言研究等。

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.

不過，當代的CTE提倡者可能會認為CTE和以前的職業教育模式相似之處僅止于此，并且，他們還可能會（正確地）斷言，區分“學術”教育和“職業”教育已經不合時宜了，一是由于上大學和就業準備運動，二是由于隨著時間推移，聯邦CTE立法上的實質性變化也早已成功模糊了二者間的界限。人們達成了一個共識（并且是兩黨共識！），那就是這些改變已將CTE引上了正軌，使之朝著“實用、嚴謹”的方向發展，遠離了將弱勢群體學生輸送到收入低微、前途渺茫的職業中去的“黑歷史”。

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

然而，我最近對 ChatGPT 的體驗讓我對這一共識產生了疑問。且聽我道來：

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，所需技能難以借助現有技術實現自動化的工作崗位被自動化替代的風險更低。這些技能包括雙向溝通能力、批判性思考能力、創造力、規劃能力、管理能力和問題解決能力。這些都是可遷移技能而非專門性技能。職業技術教育課程和專業需要讓學生同時具備這兩方面的能力。專門性技能和可遷移技能的結合不僅有助于CTE學生競爭當今能適應自動化的工作（這些工作往往需要學士學位），而且未來面臨自動化威脅時，這種結合也能使他們具備更強的靈活性。

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.

這個應該不難做到，當代“嚴謹、實用”的CTE教育中一個關鍵要素就是推動學術內容更好地融入專業技術學習的環境中。我所擔心的是對“學術融合”多半各有各的解讀，對于不同的CTE學習領域，或者說“職業集群”并沒有太多關于怎么做的指導。“職業集群”或基于行業，如建筑與施工和制造業；或基于服務，如教育培訓和公眾服務業；或基于技術，如信息技術和科學、技術、工程與數學（STEM）。同時在聯邦政策中，對學術融合的歸責條文也很少。

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.

當我們分析不同CTE職業集群的自動化風險時，在每一個CTE職業集群教育中的每一間CTE教室里為可遷移、非專門性技能的發展留出空間，就顯得尤為重要并極具挑戰性。為此，我將美國勞工統計局的職業就業和工資統計數據與現有的自動化風險指數進行了合并。我按入門受教育水平計算了每個CTE職業集群領域的平均自動化風險，其中有幾點值得注意。

First， average automation risks decrease as education level goes up， largely because jobs requiring bachelor’s degrees involve 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 aggregate20 automation risk， which suggests the academic-integration hurdle is higher in these clusters compared with others.

第一，平均自動化風險隨著受教育水平的提高而降低，這主要是因為需要學士學位的工作涉及更多的可遷移技能，而這些技能不太容易自動化。第二，部分CTE職業集群的平均自動化風險較低，比如教育與培訓、健康科學、信息技術以及STEM。而另一部分CTE職業集群的平均自動化風險則較高，比如建筑與施工、酒店與旅游管理、制造業以及運輸、分銷和物流業。第三，在總體自動化風險最高的集群中，最低和最高受教育水平之間的差距最大，這表明這些集群與其他集群相比，實現學術融合的障礙更大。

All this matters because existing research indicates CTE participation can be stratified21 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， funneling22 disadvantaged students into low-wage， low-opportunity occupations. CTE’s “dark history” becomes its future.

這幾點發現很重要，因為現有研究表明，學生參與CTE培訓的情況可能因種族、性別、家庭收入和城鄉身份而出現分層。因此，某些學生群體在高風險群組中可能會有過高的占比。換句話說，受自動化風險影響的概率可能與學生特征相關。如果我們不能使這些處于高風險群組的學生群體掌握具有自動化彈性的可遷移技能，就有可能再次將弱勢學生群體輸送到工資低、機會少的職業中，CTE 的“黑歷史”就將重演。

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 apprenticeship23 models can work to close the gap between the skills students have and the skills employers need， today and tomorrow. And the “special populations” 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 pipelines24 and mitigate tracking. It’s always been important to get these things right， but the arrival of ChatGPT means it’s now more important than ever.

當代的 CTE 究竟能否保護學生免于自動化帶來的風險？當然可以。從理論上講，CTE學生應對自動化風險的準備應該更加充分。學術整合，實操學習，地方需求綜合評估，學徒模式，這些要素都有了，將它們恰當地整合運用就能縮小學生所會技能與雇主所需技能之間的差距，對于當下和未來皆是如此。目前聯邦職業技術教育立法中為“特殊人群”預留機會的計劃要求校方撥出專款招收低收入、殘疾和種族邊緣化的學生接受職業技術教育，這應有助于實現職業集群人才渠道的多樣化，并減少分流現象。處理好這些問題一直都很重要，但 ChatGPT 的到來意味著，現在這些事比以往任何時候都更加重要。

（譯者為“《英語世界》杯”翻譯大賽獲獎者）

1 gawk over呆呆地盯著看，此處意為看熱鬧，圍觀。 2 spoiler劇透，爆料。 3 go well beyond遠超出。 4 humbling使人謙遜的，羞辱人的。 5 middling中等的；普通的。 6 ubiquitous似乎無所不在的；十分普遍的。 7 hand-wringing（因焦急或煩惱）扭絞雙手，來自習語wring your hands。

8 iteration迭代，重復，此處指之前的職業教育。 9 carpentry木工。 10 plumbing管道工程。 11 anachronistic過時的。

12 bipartisan為兩黨派所支持的。 13 steer引導，引領。 14 rigor嚴謹。 15 tracking（學校）按能力分班（或分組）。 16 resilient可迅速恢復的；有適應力的。 17 agility敏捷，靈活。

18 stretch有難度的工作或任務。 19 bake into加入，使進入……當中。

20 aggregate總數；合計。 21 stratify使分層。

22 funnel輸送，傳輸。 23 apprenticeship學徒制。 24 pipeline渠道，方式。