쉐도잉 연습: AI biết code, vì sao vẫn phải học lập trình? | đi-code ep 35 - 영상으로 영어 말하기 배우기

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Hi, I'm Chris Peach.

I'm a professor here at Stanford University.

I teach some large intro to computer science classes, some intro to math for AI.

Code in Place, if people don't know it, it's an online class where you can learn to program.

And the special thing about Code in Place is that it's the class in the world with the most teachers, and there's about 17,000 students and more than a thousand teachers.

We've been doing Code in Place for six years, so we did Code in Place before Cursor and Cloud Code and Code in Place after.

A few observations.

One, our enrollment basically doubled oh my gosh, all these people want to learn how to code.

You can expand the question.

You could say, should I learn to program?

You can also say, should I learn probability?

Like AI can code, but AI can also do probability.

Should I learn to write?

AI can write.

I think the wrong answer would be no, no, no. We're not giving up on the next generation being smart.

Yes, you should learn how to formalize an argument.

Yes, you should learn the depth of probabilistic reasoning.

And yes, you should learn how to program.

If AI is able to do those things, your abilities may be magnified.

But I imagine in the future, it will still be important to be smart in those spaces.

I'm seeing more people with a motivational crisis than I have in the past, and that makes sense.

There's more uncertainty in the world.

You know, you can think about what can I contribute with AI of 2026, but I think students are faced with a much harder problem of thinking about, well, if I'm starting a four-year program,

I have to think about what jobs are going to exist in 2030

when AI is four years more advanced

and that's a lot of uncertainty for students and I empathize with this quite a lot.

I think naturally that leads to some motivational problems.

When am I actually getting something out of AI and when have I given away too much of the growth?

I suppose if I start outsourcing, at what point will I no longer be able to do that?

Like that really critical piece.

I think all students have felt like this.

Like if you have AI write too many of your essays, At what point are you no longer able to write an essay?

If you have AI write too much of your code, at what point can you no longer do that valuable piece of the architecture?

So I suppose that's the part where like, I think it's fun to use AI.

I think people should be playing around with it.

But you should be self-aware.

And you should be self-aware of like, are you also growing alongside the AI?

And you should care so much about your own personal growth.

I was born in Nairobi, Kenya.

When I was 12 I moved to Kuala Lumpur, Malaysia.

I ended up coming to the US for university.

I was just a curious human.

I wasn't set on being a professor from day one.

I just liked learning and I liked interesting problems.

When I came to Stanford, I'd done a little bit of coding but I really didn't know how to program.

But like I had to fill an elective

so I just had to take a class

and I was like okay I'll do the programming class and my teacher did the most wonderful thing.

They said, at this point, I'm gonna have a challenge.

Everyone in class, go make the most wonderful things with what you've learned in the first two weeks of programming.

And I found myself able to put like 40 hours of extra work beyond my normal schooling into this challenge

because I was so excited.

And then eventually I discovered that I was

so curious about how people learned and I decided professor was the right thing for me.

So Carol speaks this thing called Python, which we're going to be using as our programming language throughout the course.

So Carol is our lovable robot, and Carol lives in a world we think of the world as kind of having a north, west, south, and east, and having compass directions.

Come on, Carol, turn left, and then turn left, and then turn left, and we're out of the kid turn right.

It's the class in the world with the most teachers.

There's one teacher for for every 10 students, and there's about 17,000 students and more than a thousand teachers.

So what problem was I trying to solve?

Let's go back in time.

It's early days in the pandemic.

I'm about to teach Stanford's flagship intro to coding class, and I've been told that everything's gonna be online.

And in this moment, we're thinking, the world is suffering.

While we're putting the class online, is there something

that we can also do to help the world we can just put our videos online

and we thought people might get a little bit out of it

but we know that it would be a lot less than what our Stanford students get

because our Stanford students get the special sauce of Stanford education

and the special sauce of Stanford education for intro CS is

you get a section leader you get somebody who's just a

little bit older than you a little bit further along in their career who's going to take time to help you grow.

One of the common misconceptions is just thinking that AI tutors will solve everything.

We basically have AI tutors already, but that isn't moving the needle in the way people expected.

So over the last six years, so we've now done this six times, we've tried a lot of different experiments where we gave people different dosage of AI, and we have learned something very surprising.

If we give people AI and just like here's a chat bot, use it to learn, predictably people will drop out.

People get demotivated.

It is demotivating to have AI thrown at you at the wrong moment of your learning.

We have found very nuanced ways where we can use AI that actually helps people learn.

But if you contrast that with humans, so if I throw AI at you, you're probably going to become a little bit demotivated statistically.

But what happens if I throw a human at you?

Imagine you're just programming in Code in Place.

You might get a pop-up and it says, hey there's a teacher online and they'd like to spend 10 minutes with you.

Do you want to talk them.

If you hit yes, your probability of completing the course goes up 10 percentage points.

So you must be thinking, oh the humans must be saying the right things and the AI must be saying the wrong things.

We've looked at these conversations.

The AI was correct.

It wasn't hallucinating, not for intro programming.

And the humans weren't always correct.

But the human touch is special.

It's motivating and I think we all need motivation right now.

Everyone needs something to convince them, I'm not gonna make Claude do all the thinking for me.

Like to actually do the thinking yourself takes extra energy.

Crown jewel of education has always been motivation

and it's a lot more motivating for me to say I care about you being a smart person.

I'm not giving up on you being a smart person this time of AI.

Let's work on your foundations and then when you're done with your foundations I'll teach you how to code with AI.

That works so much better.

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When I look at chatbots I think they do a good job of answering my question.

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But one challenge I would pose to anybody thinking about how

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to make these work better for education is how do you get it to inspire?

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Sometimes I will inspire my students in a deep way

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and it could be like you come into my office and be like Hey, do you want to see something really cool about probability?

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And I just show them something really neat

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and they weren't even thinking about that wasn't the question

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that came in with but then they they feel that like love and like that passion, that inspiration and as I said

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if I can flip the switch of getting the student so curious that they can't help but learn.

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Like, the rest of the day, all they can think about is the problem that I just post to them or that cool thing I showed to them.

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If that curiosity gets ignited, then I feel like they'll get there.

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And when I look at current chatbots, they are not igniting curiosity that much.

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It's not like, you never show up to chat to people and it's like, hey, do you want to just see something that is going to make your mind explode that will, like, you know, pull you in?

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Now, as a teacher, I can do that because I I have some context on my students.

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I know largely where they are and largely where they're trying to go.

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So I can be very delicate in the choice of the inspiring example or the inspiring challenge to pose to my students.

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If you just think an AI tutor will solve the clarity problem, you might miss the bigger piece of the puzzle.

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And I feel like if we leverage this, we can have a nicer world.

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I'm seeing more people with a motivational crisis than I have in the past.

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And that makes sense.

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There's more uncertainty in the world.

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You know, you can think about what can I contribute with AI of 2026, but I think students are faced with a much harder problem of thinking about, well, if I'm starting a four-year program,

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I have to think about what jobs are gonna exist in 2030 when AI is four years more advanced.

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And that's a lot of uncertainty for students.

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And I empathize with this quite a lot.

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In five to 10 years, many things will change.

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The future has always been unpredictable.

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It's always been the case that if you ask people to project what jobs will be the right jobs.

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Five to 10 years, people always get it wrong.

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Here's an interesting anecdote though.

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So when I was young, I'm an old man now, but when I was young and as in my PhD, it was around the time that one of my now colleagues

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was making some of the first major milestones in self-driving cars and this is back in like 2011-2012.

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And at that moment you would see this car drive and you think, oh my god, what does it mean to be a a taxi driver, or what does it mean to be a truck driver.

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But in fact, what happened is the truck driver profession has been growing at a very healthy rate.

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Now, I don't know what the future holds for truck drivers.

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Maybe one day we'll come to an inflection point.

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But there was a lot of reasons that people underestimated.

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They underestimated like, well, if you have valuable cargo, you need a person who's responsible.

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Or the long tail sort of experiences.

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There's always something different happening on a highway.

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99% of the experiences can be the same, but like that 1% of things that are different, it's so hard to have an AI master all of them.

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I think one day eventually we'll have fully self-driving cars

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and we'll live in a world where all our cars are driven by an AI system.

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But what I was surprised about was how grossly we overestimated how quickly we'd get there.

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I think everyone who's worked deeply with AI has had this experience of by outsourcing a lot of thinking to AI,

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I am getting more separated from problem solving myself.

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A good example right now is I program with AI a lot, but I happen to know a lot about programming and architecture.

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And if I don't know a lot about programming architecture, AI will start to make some poor decisions, which I might not experience the first time I make a prototype,

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but like five weeks down the line when students are actually using my thing, they might start to hit weird bugs.

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And if I don't understand the architecture, I can't help them.

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I suppose if I start outsourcing, at what point will I no longer be able to do that.

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Like that really critical piece.

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I think all students have felt like this.

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Like if you have AI write too many of your essays, at what point are you no longer able to write an essay?

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If you have AI write too much of your code, at what point can you no longer do that valuable piece of the architecture?

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So I suppose that's the part where like I think it's fun to use AI.

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I think people should be playing around with it.

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But you should be self-aware

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and you should be self-aware of like are you also growing alongside the AI

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and you should care so much about your own personal growth.

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When you're learning how to program, largely you can separate it into two pieces.

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One piece is you're learning the syntax of how do we tell computers to do things

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and the other thing you're learning is basically problem-solving like how do you take big problems

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and break them down into small pieces?

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How do you set it up so that data can speak to algorithms?

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how do you think about algorithms?

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So I'm going to say AI is going to get really, really good at just the syntax.

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It's less important in the future that you've memorized every command.

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It's probably more important that you know how to problem solve.

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So while you're learning to program, really focus on that problem solving ability.

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There's one thing about coding that's special.

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You get immediate falsifiable feedback.

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Like if your logic is wrong, your thing doesn't work, and you get to see that and you get to iterate quickly.

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Whereas if you apply problem solving to life, you could make a poor decision, but the feedback cycle is so slow that you don't get to practice getting better and better at making decisions.

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So there's a couple of things about coding that makes it particularly good at teaching how to problem solve.

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The question, how did you become like a really high contributor engineer?

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You might not find my answer that surprising, but it's like, it's time on task.

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It's like, how much time are you spending actually creating things?

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And I'm going to separate you creating versus you giving it to CloudCode.

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Now, by the way, you know what I would do if I was a young person?

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I would make a lot of prototypes with CloudCode and I'd say, CloudCode, teach me all the most important things that you did in order to create this.

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And I would iterate that way and I'd get lots of experience

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so I can try and figure out one of the most important concepts.

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I'll give your young engineers a particular challenge.

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As I said, it's a confusing time, but there's an opportunity that didn't exist before.

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One of the things that's happened is barriers to entries have been cut.

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You could be a 12th grader, so an 18 year old, with a friend.

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You might be able to make a high quality startup.

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The two of you could make a pretty impressive code base that solves an interesting problem.

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There is a real art form to knowing what is a valuable problem to solve.

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And I think more and more juniors, engineers get to engage with that art form.

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Like what is worth actually making?

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What do users want?

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What's the feature that will help them make progress in whatever their problems are?

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So that ability to interface between what are computers able to do

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and what do humans actually need has always been a critical high order skill

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and I think if I were a junior engineer I would start working on that skill now.

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I wouldn't wait till I was a senior engineer.

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If you start with the premise that my children will become smart people and your children will become smart people, if you don't have children then maybe your nephews and nieces will become smart people,

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you start from the premise that the next generation will be filled with people who are smarter than we are, then you're like okay how do we get them to that point?

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And then you look at any subject, probability, computer science, and when you look at any subject there's often

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foundational concepts and then you'll have layers of complexity built on top of it.

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If you expect them to become smarter than you are, it's really hard to skip the foundations.

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And one way of thinking about that is we've had calculators to do multiplication for a long time.

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Kids still need to learn multiplication.

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Now there's a subtle difference.

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The concept of multiplication is so critical, but actually knowing how to do the rote.

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You know, if I asked you like what 13 times 7, go quick.

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That's not as important as just knowing what is multiplication.

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You can't skip the foundations, but you can maybe be more artful about what you focus on.

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I kind of take it as an axiom that I'm not giving up on the next generation.

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Honestly, the people I've seen get most lost

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and most demotivated in this mode of AI are sometimes the ones who are overthinking it.

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I had a student, he was just doing such wonderful things.

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He was using AI, he was solving problems, he was learning amazing things.

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I ask, hey, wonderful student, like, what are you thinking about?

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And he says, I actually don't think about it.

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I don't really think about the future of AI, and that allows me to thrive.

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And like, you need pause.

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I think about AI all the time.

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I feel like I think about AI 10 times a day.

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And then the simplicity of like, no, I'm just gonna be curious and learn.

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Since that day, I start my day with the axiom.

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I don't ask why I care about the next generation being smarter.

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I take it as a truth.

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I want this and I will work towards it.

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It's a tool and it will multiply humans.

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So when humans are at our best, we can use this tool to multiply us.

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Like the doctor who really cares about their patient now has a tool that they can do more, faster, more accurately.

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The teacher who really cares about their students, who is passionate about them learning, they can go go further with their students and they can do more.

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Also, I get to see young people all the time.

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And I would say that gives me inspiration, seeing their self-awareness, how critical their thinking,

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seeing them blossoming, it gives you optimism.

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If I was a young person right now, the most valuable thing is that you have the self-awareness.

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You should also have the goal that I will become smarter.

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Chris is not giving up on you.

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You should not give up on yourself either.

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I have two kids under five.

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And you know what?

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They're gonna live in an awesome world.

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Like, we're gonna adapt.

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We're gonna figure things out.

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They're gonna still have curiosities.

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They're gonna still grow their minds.

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And we're gonna keep every day working towards that.

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The top engineer might not be the person who knows all the code.

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Maybe the top engineer is a person who can relate the real world human problems into the world of apps, into the world of data science,

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into the world of research.

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So go make stuff.

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Make stuff that people use, make stuff that people love,

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and in that process of iteration you have an opportunity to become excellent at coding and excellent at problem solving.

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Just take axioms.

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You will become smarter than you were yesterday.

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Start your day like that.

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이 레슨의 어휘와 말하기 포인트

이 C1 수준 말하기 레슨은 영상 “AI biết code, vì sao vẫn phải học lập trình?”을(를) 바탕으로 합니다. 이 영상에는 섀도잉할 문장 253개와 단어 3395개가 있습니다. 말하는 구간의 길이는 16:41입니다. 화자는 분당 약 203단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 단어의 84%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.

이 영상의 핵심 어휘

영상에서 가장 어려운 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
probability 명사	/ˌpɹɑ.bəˈbɪl.ə.ti/	확률
multiplication 명사	/ˌmʌltɪplɪˈkeɪʃən/	곱셈, 승법
uncertainty 명사	/ʌnˈsɜːtənti/	불확실
axiom 명사	/ˈæk.si.əm/	공리
outsourcing 명사	/ˈaʊtˌsɔː(ɹ).sɪŋ/	아웃소싱
tutor 명사	/ˈtjuːtəː/	튜터
curiosity 명사	/ˌkjʊɹ.iˈɑ.sə.ti/	호기심
chatbot 명사	/ˈtʃætbɑt/	챗봇
empathize 동사	/ˈɛmpəˌθaɪz/	동정하다
ignite 동사	/ɪɡˈnaɪt/	불을 지르다, 점화(點火)하다
multiply 동사	/ˈmʌltɪplaɪ/	곱하다
syntax 명사	/ˈsɪn.tæks/	통어론, 구문론
motivate 동사	/ˈmoʊ.tɪ.veɪt/	...에 동기를 주다
underestimate 동사	/ʌndɚˈɛs.tɪ.meɪt/	과소평가하다
algorithm 명사	/ˈælɡəɹɪðm̩/	알고리즘, 알고리듬

영상에 나오는 구동사

단어	뜻
go up 동사	올라가다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

You could say, should I learn to program?
You can also say, should I learn probability?
Should I learn to write?
Let's go back in time.
It's like, how much time are you spending actually creating things?

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료진행 have/has been + -ing — 전에 시작해 지금도 계속되는 동작	We've been doing · has been growing
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	have I given · have felt · you've learned
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	may be magnified · are faced · was born
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	people who are · ones who are · teacher who really

주의할 발음

화자는 I'm, gonna, don't 같은 축약형과 약화된 형태를 90번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: empathize /ˈɛmpəˌθaɪz/, algorithm /ˈælɡəɹɪðm̩/, thrive /θɹajv/
“sh”와 “zh” 소리: multiplication /ˌmʌltɪplɪˈkeɪʃən/, foundational /faʊnˈdeɪ.ʃə.nəl/, inflection /ɪnˈflɛkʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, misconception /ˌmɪs.kənˈsɛp.ʃən/
긴 단어 — 강세 위치에 주의: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, demotivate /ˌdiːˈmoʊtɪveɪt/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, uncertainty /ʌnˈsɜːtənti/, curiosity /ˌkjʊɹ.iˈɑ.sə.ti/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: artful /ˈɑːtf(ʊ)l/, falsifiable /ˈfɒl.sɪˌfaɪ.ə.bəl/, formalize /ˈfɔɹ.məlaɪːz/, foundational /faʊnˈdeɪ.ʃə.nəl/, inflection /ɪnˈflɛkʃən/
/v/ — ㅂ(/b/)과 구별하기: demotivate /ˌdiːˈmoʊtɪveɪt/, motivate /ˈmoʊ.tɪ.veɪt/, elective /ɪˈlɛktɪv/, lovable /ˈlʌvəbəl/, overestimate /ˌəʊvəɹˈɛstɪmeɪt/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: empathize /ˈɛmpəˌθaɪz/, formalize /ˈfɔɹ.məlaɪːz/, memorize /ˈmɛm.əˌɹaɪ̯z/, optimism /ˈɑptɪmɪzəm/, confuse /kənˈfjuːz/

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, probability, multiplication, uncertainty 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.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: AI biết code, vì sao vẫn phải học lập trình? | đi-code ep 35 - 영상으로 영어 말하기 배우기

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같은 채널

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