쉐도잉 연습: The AI Atrophy Problem How CIOs Fight It - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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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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이 레슨의 어휘와 말하기 포인트
이 C1 수준 말하기 레슨은 영상 “The AI Atrophy Problem How CIOs Fight It”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: output, critical, thinking. 이 영상에는 섀도잉할 문장 87개와 단어 1651개가 있습니다. 말하는 구간의 길이는 9:55입니다. 화자는 분당 약 166단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 단어의 83%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요. 전체 87문장 중 22문장이 질문이어서 의문문 억양과 짧은 대답을 연습하기에 좋습니다.
이 영상의 핵심 어휘
영상에서 가장 어려운 단어 10개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| acceleration 명사 | /əkˌsɛl.əˈɹeɪ.ʃən/ | 가속 |
| prompt 동사 | /pɹɑmpt/ | 부추기다 |
| aspiration 명사 | /ˌæspəˈɹeɪʃən/ | 열망, 염원 |
| automate 동사 | /ˈɔ.təˌmeɪt/ | 자동화하다 |
| summarize 동사 | /ˈsʌməˌɹaɪz/ | 요약하다 |
| workflow 명사 | /ˈwɝkfloʊ/ | 워크플로 |
| emphasize 동사 | /ˈɛm.fə.saɪz/ | 강조(强調)하다 |
| hypothesis 명사 | /haɪˈpɒθɪsɪs/ | 가설 |
| evaluate 동사 | /ɪˈvaljʊeɪt/ | 평가하다 |
| architect 명사 | /ˈɑɹ.kɪˌtɛkt/ | 설계자, 계획자 |
따라 말해 볼 만한 문장
영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:
- 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?
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | has been · you've said · I've noticed |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | been hallucinated · is needed · cannot be done |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | people who use · anyone who is |
주의할 발음
화자는 don't, I'm, you're 같은 축약형과 약화된 형태를 29번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, assumption /əˈsʌm(p).ʃ(ə)n/, aspiration /ˌæspəˈɹeɪʃən/, introspection /ɪntɹəˈspɛkʃən/
- 긴 단어 — 강세 위치에 주의: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, hallucinate /həˈl(j)uːsɪneɪt/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, aspiration /ˌæspəˈɹeɪʃən/, extrapolate /ɛkˈstɹæp.əˌleɪt/
한국어 화자가 어려워하는 소리:
- /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, paraphrase /ˈpæɹəfɹeɪz/, workflow /ˈwɝkfloʊ/, emphasize /ˈɛm.fə.saɪz/, feasible /ˈfiː.zɪ.bəl/
- /v/ — ㅂ(/b/)과 구별하기: validate /ˈvæl.ɪ.deɪt/, overarching /ˌoʊvəɹˈɑɹt͡ʃɪŋ/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, evaluate /ɪˈvaljʊeɪt/
- /z/ — ㅈ이 아니라 성대를 울리는 /s/: paraphrase /ˈpæɹəfɹeɪz/, resonate /ˈɹɛz.əˌneɪt/, summarize /ˈsʌməˌɹaɪz/, emphasize /ˈɛm.fə.saɪz/, feasible /ˈfiː.zɪ.bəl/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, acceleration, prompt, aspiration 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.