シャドーイング練習: The AI Atrophy Problem How CIOs Fight It - 動画で英語スピーキングを学ぶ
レッスンを作成中...
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(bright curious music) ABBIE LUNDBERG: AI is helping your team work faster, but is it hurting how they think?
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I'm Abbie Lundberg, Editor in Chief at the MIT Sloan Management Review, and we are here at the annual MIT Sloan CIO Symposium asking IT and business leaders what one thing do you do to keep critical thinking sharp as AI takes over more and more work?
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Let's hear what they had to say.
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MONICA CALDAS: The way we think about it is, we put mechanisms in place to enable and remind people what we have to do.
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So for example, when we think about the evolution of technology, we have an emerging technology radar that we incorporate into our ways of working where we talk about what are the emerging techs, where are they going?
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So for example, quantum computing.
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And then we engage in different ways to both go deeper on the topic, but also then extrapolate what is the meaning of that to our business aspirations.
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That's a great example of a way where, yes, you can go and do research on an AI tool, fill in the blank, whatever your preferred chat engagement is, but you also have to have your own point of view.
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So, how do you use a variety of different sources of information to develop your own point of view?
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Critical thinking is now more important than it ever has been because general information is so easily available.
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But how do you actually connect that to the problems that you are trying to solve?
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MICHAEL SCHRAGE: When you get an output or an answer from a Claude or a Grok or a Perplexity that you really, really like, that you really, really resonate with, enjoy it for a moment and then stress it, conflict it, counter it, say, this is terrific, but what are the three most important arguments against what you've said?
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Or are you sure you're not hallucinating?
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When I'm in a more innovative or creative mood, I'll adopt certain kinds of persona.
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What would the biggest criticism of Peter Drucker or a Marshall McLuhan might have for this?
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The point is, you have to build in, forgive me, a dialectical stress test.
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You cannot trust the output to be accurate and precise.
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The overarching takeaway I would urge people to consider is, don't view these outputs as answers.
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View them as insights.
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View them as hypotheses that you should test, and stress test, and yes, iterate around.
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MELISSA SWIFT: So, when I get an AI output, what I do is I actually put on this persona to evaluate it.
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I pretend I'm a super critical outside reviewer, that I'm going to check every detail, right?
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I'm going to vet the ideas, I'm going to look at the sources.
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I put myself in that mindset every time I review AI output.
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And that reengages my critical thinking skills.
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So I don't get lulled by kind of a smooth, shiny output that might not be right.
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KERI PEARLSON: What I've noticed is that there is a marked difference in the activities that people do when they start to use AI.
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The promise is that AI will make our work faster and more streamlined.
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But the flip side is that we don't practice some of the skills that the AI has taken over.
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Now, how can we reverse that?
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Well, first of all, we want to make sure that people who use AI tools actually have the ability to validate the output that comes out of AI.
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If you don't know the basic skills for doing research, for example, and you use AI to do your research, then you have no ability to know if the outcome is valid.
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Is it reasonable? Has it been hallucinated?
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And we want to make sure that workers don't have that outcome.
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So, validating your output, practicing the skills once in a while to make sure you know how to do it.
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Following the thread, even asking AI, how did you come to this conclusion?
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Probing AI as if it were a strong assistant, and not a replacement for the work, is one way we can make sure to keep our skillset strong.
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MEGHNA SHAH: The one thing me and my teams do a lot is we emphasize that AI should accelerate thinking.
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It should not really replace thinking.
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That the output that you're getting from your AI model should really be a starting point.
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It shouldn't be the final answer, right?
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Because creative thinking is no longer going to be about, can you create content from scratch, right?
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It's really going to be, can you ask better questions?
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Can you apply reasoning?
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Can you apply the business context and the judgment that is needed with the response that you're getting from the LLM?
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So, it's going to be a lot of those pieces that feed into the critical thinking part of it.
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I think in today's day and age, when data is available so freely, I don't think having access to intelligence is going to be a differentiator.
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What's going to be a differentiator is how strategically and how responsibly you can actually apply that intelligence is going to be a critical differentiator.
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We also encourage no blind trust flows.
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So, even though we are using AI for coding, our engineers are still checking code and reviewing code before it goes into production.
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Our product managers are still reviewing requirements and validating that this is really what the customers need.
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And our architects are also reviewing architecture to see is this really fitting with what the organization needs and the long-term implications.
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So, there's a lot of those pieces to ensure that there's always critical thinking and a human in the loop and the human brain in the loop.
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GEORGE WESTERMAN: So much of what we do is data analysis, writing, summarizing information.
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And so the first thing that my team and I always have to ask is, how do we know that that is correct?
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We're always asking that question, always digging a little bit deeper to test a few things to make sure. But then, you can't ask the LLMs.
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The LLMs will tell you it's correct and they'll give you a really good reasoning, even though that may not be the situation.
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So, that's the first step.
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The other step we're trying to be really careful about is, is this the kind of question, the kind of prompt, that we can expect the engines to be good at?
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Because we don't want to set ourselves up in a situation where it's trying to do something it's not built for, and then we have a giant mess in our hands.
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MAX CHAN: At the end of the day, making sure that there is accountability owned by a human is critical.
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And with that understanding, we make sure that anyone who is leveraging AI, who is using AI to deliver a piece of code, to change a design, they must have the foundation.
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We make sure that employees and leadership do not forget the fact that, you know, being able to do something in AI does not mean that they have the skill to do it, they have the foundation to do it.
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And so that is really one thing that we continue to ensure we keep an eye on.
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And the other thing is with the speed of accelerations, right?
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AI accelerations that is taking place.
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We want to ensure that we have the ability to judge, applying judgment to make sure that we slow down where we have to.
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It's not because everything needs to go so fast.
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It's where do we need to slow down to gain that competitive edge?
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VANESSA ESCRIVÁ GARCÍA: Okay, we believe more than ever that the key is the training of the people and to make sure that people understand that we need to make the right questions.
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And AI, it's just the medium to obtain an objective.
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And one of the examples for that is that we have started with the training with the top of the company.
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The executive level have received this training, and we have a roadmap for it to make sure that 100% of the company is going to receive this training.
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And to understand that the very, very important thing is that the human is going to be the one that decide, and the AI is going to be the medium for this decision.
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KABIR NAGRECHA: You know, I think what we really push for on this team is questioning assumptions.
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Questioning assumptions about what can and cannot be done by AI today.
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What we assumed three months ago is impossible is now completely feasible, and that another part of our workflow could be completely automated.
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That self-questioning, that self introspection, is a big part of critical thinking around AI.
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THOMAS H. DAVENPORT: Hey, I'm a professor.
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I really torture my students to get them to use AI in an appropriate fashion.
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I'd say, "Every assignment that you do with AI," -- and I teach about AI, and of course I have to let them use AI, I've forced them use AI -- but they have to show the different prompts that they use.
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They have to show how they added value to the output, the edits that they made.
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They have to show that they checked the citations on the comments that the language model is making.
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And they can't stand doing this.
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One said to me, "It was easier when I could just paraphrase a Wikipedia article." But I think we all need to learn how to deal with AI in this way.
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And as painful as it might be, as productivity-lowering as these activities are, I think they're critical to our success as a species.
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(bright curious music)
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このレッスンの語彙とスピーキングのポイント
このC1レベルのスピーキングレッスンは、動画「The AI Atrophy Problem How CIOs Fight It」を教材にしています。 繰り返し出てくる語は次のとおりです：output, critical, thinking。 この動画には、シャドーイング用の文が87文、単語が1651語あります。 音声の長さは9:55です。 話す速さは1分あたり約166語で、日常会話に近い自然なペースです。 単語の83%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。 全87文のうち22文が疑問文なので、疑問文のイントネーションや短い受け答えの練習に向いています。
この動画の重要語彙
動画の中で特に難しい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| acceleration 名詞 | /əkˌsɛl.əˈɹeɪ.ʃən/ | 加速 |
| emerge 動詞 | /ɪˈmɝd͡ʒ/ | 現れる |
| aspiration 名詞 | /ˌæspəˈɹeɪʃən/ | 希望 |
| automate 動詞 | /ˈɔ.təˌmeɪt/ | 自動化する |
| compute 動詞 | /kəmˈpjuːt/ | 計算する |
| dialectical 形容詞 | 弁証法の | |
| introspection 名詞 | /ɪntɹəˈspɛkʃən/ | 内観 |
| paraphrase 動詞 | /ˈpæɹəfɹeɪz/ | 言い換える, 引用する |
| reviewer 名詞 | 評論家 | |
| summarize 動詞 | /ˈsʌməˌɹaɪz/ | 要約する |
| workflow 名詞 | /ˈwɝkfloʊ/ | ワークフロー |
| accelerate 動詞 | /ɪkˈsɛl.əˌɹeɪt/ | 加速する |
| emphasize 動詞 | /ˈɛm.fə.saɪz/ | 強調する, アクセントをつける |
| feasible 形容詞 | /ˈfiː.zɪ.bəl/ | 実行できる, 実現可能な |
| mindset 名詞 | /ˈmaɪn(d)ˌsɛt/ | 考え方, 心理 |
動画に出てくる句動詞
| 単語 | 意味 |
|---|---|
| take over 動詞 | 引き受ける |
| slow down 動詞 | 遅らせる |
繰り返し練習したい文
動画に出てくる短く完結した文です。日常会話でそのまま使えます。
- Let's hear what they had to say.
- Or are you sure you're not hallucinating?
- It's really going to be, can you ask better questions?
- Can you apply reasoning?
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | has been · you've said · I've noticed |
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | been hallucinated · is needed · cannot be done |
| 関係詞節 who / which + 節 — 人や物について情報を加える | people who use · anyone who is |
注意したい発音
話し手はdon't, I'm, you'reなど、短縮形や弱形を29回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, assumption /əˈsʌm(p).ʃ(ə)n/, aspiration /ˌæspəˈɹeɪʃən/, introspection /ɪntɹəˈspɛkʃən/
- 長い単語（アクセントの位置に注意）: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, hallucinate /həˈl(j)uːsɪneɪt/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, aspiration /ˌæspəˈɹeɪʃən/, extrapolate /ɛkˈstɹæp.əˌleɪt/
日本語話者が苦手な音：
- /r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, extrapolate /ɛkˈstɹæp.əˌleɪt/, streamline /ˈstriːmlaɪn/, accelerate /ɪkˈsɛl.əˌɹeɪt/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: validate /ˈvæl.ɪ.deɪt/, overarching /ˌoʊvəɹˈɑɹt͡ʃɪŋ/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, evaluate /ɪˈvaljʊeɪt/
- /f/ — 「フ」ではなく、上の歯と下唇で出す: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, paraphrase /ˈpæɹəfɹeɪz/, workflow /ˈwɝkfloʊ/, emphasize /ˈɛm.fə.saɪz/, feasible /ˈfiː.zɪ.bəl/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。acceleration, emerge, aspirationなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。