跟读练习: Is AI making us dumber? Maybe. | Charlie Gedeon | TEDxSherbrooke Street West - 通过YouTube学习英语口语
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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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为什么要通过这个视频练习口语?
在当今的学习环境中，人工智能（AI）为教育带来了前所未有的变革。通过看YouTube学英语，我们能够从一位讲者的演讲中获取宝贵的英语表达和思维方式。这个视频探讨了AI在教育上的作用，并对学习模式提出了深刻的见解。这种深度的讨论不仅能丰富我们的知识，还能帮助我们练习英语口语练习，提升我们的口语表达能力。模仿视频中的讲者进行英语影子跟读，能让我们掌握更自然的语调和表达方式，同时也能提高我们对复杂概念的理解能力。
语法与表达的语境分析
在这个视频中，讲者使用了一些关键的表达方式，以下是三个值得注意的结构：
- Why should...? - 用于提出疑问，引发思考，适合用在讨论教育的重要性时。
- It sounds like... - 用于表述看法和预测，展示演讲者对未来发展的看法。
- Now, for those of you... - 此句型为演讲者与听众建立联系，使讨论更具亲和力，适合在雅思口语练习中使用。
常见的发音陷阱
在视频中，讲者的某些单词和语调可能会给学习者带来发音挑战：
- Revolution - 注意音节的重音和中间的“lu”部分，确保发音清晰。
- Education - 发音时需要注意“ed”和“tion”的连接，可以通过反复shadowing site来加深印象。
- Motivate - 此单词的重音在第一音节，练习时可以尝试拉长其发音，以增加自己的自信心。
通过模仿和练习这些表达方式和发音，您将能更有效地提高自己的英语口语能力，并在实际交流中更加流利自信。结合这个视频进行英语影子跟读，让您的学习之路更加丰富多彩!
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。
如何在ShadowingEnglish上有效练习
- 选择您的视频： 挑选一段语音清晰、自然的YouTube视频。TED演讲，BBC新闻，电影片段，播客或雅思口语范例都很好。将URL粘贴到搜索栏中。从较短的视频（短于5分钟）以及您真正感兴趣的内容开始——兴趣是最重要的导师。
- 先听，理解上下文： 第一次听的时候，将速度保持在1倍速并仅仅倾听。还不要尝试重复。专注于理解其含义，收集新词汇，并注意讲话人如何强调单词，连读声音及使用停顿。
- 设置跟读模式：
- 等待模式：选择
+3s或+5s——在每句话播放完毕后，视频会自动暂停以便您有时间大声重复它。如果您想完全控制并在每次重复后由您自己点击下一步，请选择手动。 - 字幕同步：YouTube字幕有时会在音频前或后略微出现。使用
±100ms使它们完美对齐以助您准确跟读。
- 等待模式：选择
- 大声跟读（核心练习）： 这是真正发生改变的一步。当一个句子播放出来立刻——或在暂停期间——大声、清晰且自信地重复出来。千万不要只是张张嘴：要模仿说话者的准确节奏、重音、音高和连读。力求听上去就像说话者的影子，而不仅是逐字背诵。使用重复功能多次练习同一个句子，直到感觉自然为止。
- 提高难度： 当练习段落变得相对舒适后，就去挑战自我。将速度增加至 <code>1.25x</code> 或甚至 <code>1.5x</code> 以训练高速语言反射。或者将等待模式调整为 <code>关闭</code> 以进行连续跟读——这是最进阶同样收益最大的模式。持续的每日15–30分钟的练习将可以在几周内产生可见的效果。