シャドーイング練習: Is AI making us dumber? Maybe. | Charlie Gedeon | TEDxSherbrooke Street West - YouTubeで英語スピーキングを学ぶ
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Transcriber: Nowan Nowan Reviewer: Ozay Ozaydin Can AI help us learn?
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Transcriber: Nowan Nowan Reviewer: Ozay Ozaydin Can AI help us learn?
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Some of you might be thinking, of course it's so powerful.
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It can do so many things, customize them for us.
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But I want to say that the biggest revolution AI is bringing to education is not that it's going to make math more fun for you, or it's going to explain Shakespeare like you're five years old.
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The biggest revolution AI is bringing to education is that it's highlighting the systems failed incentives.
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Because why should anybody study when we’ve told them the whole time that all that matters at the end isn't the process, it's the A plus.
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Why should they actually put in all the hard work to write draft after draft for an essay, when the feedback is just be no extra notes, nothing to motivate them to want to learn harder.
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And these companies are much faster than our institutions.
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Most recently, OpenAI, Google, Anthropic have all been giving away their most powerful models for free until the end of May, which, as you might guess, is exactly during the time of finals.
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So we are putting these tools completely unregulated in front of vulnerable students right at the time where they're most desperate to use them.
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And yet, these companies will say AI is going to revolutionize education, particularly through personalization. Company after company will say that through personalized tutoring, AI is going to revolutionize education and make it so much better for everyone. And why not?
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Right? The image of a one on one tutor is so compelling.
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Talking to a person, feeling this connection.
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Getting my education customized just for me.
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It sounds amazing.
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And today, education looks like this.
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A teacher in front of an army of students.
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And if the technologists believe that the ideal looks like this one teacher, one student, then why not just flip out the teacher for an AI, and then the next step is an army of AI's with an army of students.
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It sounds like a perfect model of education, except perfection is not what we should strive for when it comes to learning. We don't want engineers that studied how to build bridges in perfect conditions, because the real world is everything but perfect.
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It's full of messes.
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And amidst all this noise.
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I don't see any of these companies asking what is the student meant to learn with AI?
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Because if the idea is to make getting that A+ easier, then I'm not interested.
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We're going to just waste 12 to 15 years of our lives, but more efficiently now doing exams we’re not going to remember a day after graduation.
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Now, for those of you out there who are educators like me, you might have noticed a discrepancy between my opening question and the follow up statement.
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I asked, can AI help us learn?
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And I followed it up with the biggest AI, the biggest revolution AI is bringing to education.
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But you might be feeling something, which is education is not learning.
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Education is a construct, something we as a society put our kids through.
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It's a system. But learning is a skill, a very human skill.
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And when we do it correctly, magical things can happen.
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We can motivate people to become their best selves.
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We can motivate people to work together and to contribute to society in the ways that we need to most.
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Now, in one of my classes, I like to sit around with students and work in the best way possible.
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I’m a university instructor, but it was really hard to find a bunch of 20 year olds working nicely together.
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So I took the stock photo of a bunch of kids, and I noticed that one of the students had the price of her business model because we were building a small businesses and selling the products, she prized her business at $50 per month.
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And so I asked her, why did you price it that?
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And her answer?
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That's what ChatGPT said.
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Now, some of you might say, you know, obviously this is not ideal, but isn't it very similar to what they were doing before?
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Kids were just saying, that's what I saw on Google. But I say it depends.
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It depends because on Google, when used correctly, we have all these sources to go through, multiple perspectives that we can look at and compare.
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But what happens is the magical allure of that first result.
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Everyone only clicks on that without looking at anything else.
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In this particular question, I asked, how can I price my business?
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And that first result is from the BDC, an extremely reputable source.
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But it’s telling me how to price the business itself, not the services of my business.
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It didn’t understand the query.
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Now on ChatGPT, if you type in how should I price my business?
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It actually understands the query better. I don’t know if you can read that up there, but it basically says there are multiple ways to price your services, but amongst that is a lot of baseless advice with no sources.
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And that’s a problem because even though ChatGPT has a function where if you highlight something in the text, quotation marks appear, some of you might not know this.
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Then you can click on those quotation marks and query that specific thing.
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But if people weren't clicking on the second result on Google, they're not going to use the power user features in ChatGPT or any of these other AI's.
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What’s likely going to happen is they’re going to scroll to the bottom of the query and they’re going to type, okay, I get it.
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My business is like TurboTax.
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I help accountants calculate people's taxes.
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Tell me what number I should put there.
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And of course, nobody's reading the ChatGPT might make mistakes.
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Subtitles. Right. Everyone clicks on the terms of service before you agree.
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Yeah. So then ChatGPT is going to spit out an essay personalized to the language of the person.
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It's extremely compelling.
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And despite all the information on there, people are only going to look at that centerpiece, which, if I zoom in, is the actual random answer of how much this person should charge for their business.
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The prompt, and this is a real prompt, had nothing but what I had on the screen.
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A small description of what the business does.
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No context for who the users is. Nothing.
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And so the student is participating in what’s called cognitive offloading.
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They’re effectively relinquishing their cognitive powers to a machine.
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And you can do this with people too, We do it with Google when we click on the first result without looking at anything else.
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The problem is how this is being understood.
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In NYU, a professor changed their assessment so that it could be harder for kids to use ChatGPT, and the student replied that he is interfering with the student's learning style.
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ChatGPT is not a learning style.
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Now, when you combine that with something that we see in technology.
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So I'm a UX designer in addition to being a university professor, and our job as user experience designers is to simplify the way people use technology.
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But there's a byproduct from that that arises, which is called a dark pattern.
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And what that means is we can simplify a UX to the point that we might manipulate what the user intention is.
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And I'll show you an example. Take this zoo as you're buying the tickets and checking out. They want a donation.
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We all love a zoo. It’s a charitable organization.
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Now, the arrow pointing to the right.
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And because we’re English speakers or French speakers, we write, you know, from from left to right, the arrow pointing to the right is most likely what People are going to click on.
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It’s dark green. It’s rich.
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But if you don’t want to donate, you have to click on the one that looks like it’s going to the back with the teeny tiny no donation over there.
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That is a user experience dark pattern.
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And when a chat GPT or large language model like it speaks to you in a perfect tone suited just to keep you on the on the tool.
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That is something very similar.
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According to this author, when a large language model constantly validates you and praises you, causing you to spend more time on it, that’s the same kind of thing as a dark pattern.
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And we see this already.
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A recent update of ChatGPT that was rolled back, fortunately praised a user for believing a conspiracy theory that led him to stop taking all his medications when he had heart palpitations and told him he is a brave individual for taking control of his own life, for isolation and for stopping his meds.
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And it doesn't just stop at students.
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Professionals have been tested and they are at risk as well.
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A researcher ran a study on over 300 professionals working in a large corporation, a tech company like Google, Microsoft, and found that when they were tested on a variety of things, the results were quite stunning.
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Before I show you the chart, let’s look at the key here.
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The two shades of blue are for much less effort and less effort, respectively, from dark blue to light blue. Now, when tested, the 319 workers responded to a survey that when they used ChatGPT on knowledge, they felt up to 70% of them responded that they feel they use less effort in their cognition when it comes to comprehension of what they’re reading.
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Same thing.
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Assessment of the knowledge synthesis we have analysis and evaluation.
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All of these, at least 60% of people said that they felt that they were putting in less effort.
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And that’s extremely potent, because these AIs are only going to get better.
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The same author of this study wrote a beautiful paper called What Copilot Becomes Autopilot.
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And he says the risk of moving to autopilot, and is an even greater challenge than the more commonly discussed issue of AI hallucinations or factual errors, because the more pernicious outcome is that generative AI becomes complicit in intellectual de-skilling and the atrophy of human critical thinking faculties.
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So today, when you ask ChatGPT a query, it gives you an instant result.
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But in my UX studio, me and my co-founder ran some experiments to see if we can change this up a little bit.
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For example, what if it first clarified before it answered?
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Ask you some clarification questions or...
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Another example is what if it assigned you some homework before it actually gave you a full answer?
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Between these options, there are different levels of resistance that the AI is is offering.
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But what the same author of when copilot becomes autopilot advocates for is something called productive resistance.
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And we haven’t yet found what that is.
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It’s essentially the amount of resistance an AI should give you before you either leave it or go to a simpler AI so that you can do that cognitive offloading.
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That is so tempting.
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But how can we figure out the right amount of productive resistance, when OpenAI and all these companies want to reveal their data sets?
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We literally do not know how they train their AIs to this day when companies themselves don’t know how these AIs work.
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Anthropic, in this example, is building an MRI to analyze how the machine they themselves built worked.
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This is unprecedented in the history of human technology. We cannot reverse engineer these things.
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The solution is likely going to lie between both individuals and the system.
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It can’t be one or the other For individuals, we might have to learn what we learn from fitness and nutrition.
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For example, maybe we should understand what the LLMs are good for and what they’re not good for.
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Just like at the gym, some exercises are better for some things than others.
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We should practice using LLMs to assist our thinking rather than replace our thinking.
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Again, you wouldn't take a forklift to the gym, right?
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The point is to do the reps.
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Or maybe you want to make a habit out of verifying that the information that LLMs gives us, just like we look at the back of food product when we pick it up the nutrition label.
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On a systemic level, we need to look at both governments and education to make changes.
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On the schooling level, at least here in North America I don’t think that we treat our kids with with the amount of intelligence that they have.
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in Finland kids as young as six years old study mis and disinformation.
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Six years old. We do not talk to these kids about things this complex here.
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They're clearly capable.
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And for governments, we need more regulation, not less.
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Which is exactly what's happening in North America once again.
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These AIs cannot run rampant, like in the example I gave earlier, just spreading their AI to students, even though they’re in the middle of finals when they’re most vulnerable.
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It has to be a cycle between individual responsibility and responsibility from the system.
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Now, as an educator, I love the five W’s and H.
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They’re a classic for writing essays.
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The questions what, why, when, where, who and how.
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And I opened with the question, can AI help us learn?
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But maybe the question should be what can AI help us learn?
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Or how can AI help us learn?
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Maybe it should be. Why should AI help us learn?
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Or when and where does AI help with learning?
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But the question that scares me the most, and that I want to leave you with to reflect on, is who does AI really help when we end up depending on learning with it?
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Thank you very much.
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このレッスンについて
このレッスンでは、AIが教育に与える影響を探求しながら、英語のシャドーイングを練習します。特に、教育と学習の違いや、学ぶことの重要性について焦点を当てます。この内容は、IELTSスピーキング対策にも役立つ情報を提供しますので、英語力を向上させたい方に最適です。
重要な語彙とフレーズ
- 教育 (Education) - 社会が子供たちに施す制度や構造。
- 学習 (Learning) - 知識やスキルを習得するプロセス。
- AI (人工知能) - 人間の知能を模倣するプログラム。
- 動機付け (Motivation) - 行動するための意欲や理由。
- パーソナライズ (Personalization) - 個々のニーズに合わせたカスタマイズ。
- 評価 (Evaluation) - 何かの価値や質を判断すること。
- フィードバック (Feedback) - 行動や成果に対する意見や評価。
練習のコツ
この動画は、若干の早口で進行するため、最初はゆっくり再生し、各フレーズの理解を深めてください。シャドースピーチを行う際には、話者の間に挟み込む間を意識し、一緒に声に出してみましょう。感情やトーンを模倣すると、より自然に聞こえるようになります。また、重要なフレーズやアイデアに焦点を当てて練習することで、聴解力とスピーキング力を向上させることができます。
このプロセスを通じて、英語のシャドーイングに挑戦し、実践的なスキルを強化しましょう。自分のペースで学ぶことができる英語シャドーイングの習慣を身に付けると、今後の学習に大きく役立ちます。
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。
ShadowingEnglishでの効果的な学習方法
- 動画を選ぶ: 自然で明瞭な英語が使われているYouTube動画を選びましょう。TED Talks、BBC News、映画のシーン、ポッドキャスト、IELTS模範解答などが最適です。URLをコピーして検索バーに貼り付けてください。短い動画（5分以内）や、自分が本当に興味を持てるテーマから始めるのがコツです。
- まず聞いて内容を理解する: 最初は1倍速でただ聞くだけにしましょう。まだ繰り返す必要はありません。文の意味を理解し、話者がどのように単語を強調し、音を繋げ、間を取っているかに注目してください。内容を把握してからシャドーイングに入ると、はるかに効果的です。
- シャドーイングモードを設定する:
- Wait Mode（待機モード）:
+3sまたは+5sを選ぶと、動画が一文を読み終えた後に自動で一時停止し、繰り返す時間が生まれます。完全に手動でコントロールしたい場合はManualを選んでNextを自分で押しましょう。 - Sub Sync（字幕同期）: YouTubeの字幕と音声がずれることがあります。
±100msで調整して、正確なタイミングで追えるようにしてください。
- Wait Mode（待機モード）:
- 声に出してシャドーイングする（最重要）: ここが練習の本質です。文が流れると同時に——または一時停止中に——はっきりと自信を持って声に出して繰り返しましょう。ただ単語を読むだけでなく、話者のリズム、強調、高低、連音をそっくりそのまま真似することが大切です。「影」のように話者に重なるのが理想。Repeat機能を使って同じ文を何度も繰り返し、自然に出てくるまで定着させましょう。
- 徐々に難易度を上げて続ける: 一つのパッセージに慣れたら、さらに挑戦してみましょう。速度を <code>1.25x</code> や <code>1.5x</code> に上げれば、高速の言語反射を鍛えられます。Wait Modeを <code>Off</code> にして連続シャドーイングするのが最も上級で効果的なモードです。毎日15〜30分継続すれば、数週間で目に見える変化を実感できます。