シャドーイング練習: AI biết code, vì sao vẫn phải học lập trình? | đi-code ep 35 - 動画で英語スピーキングを学ぶ

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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.

100

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

101

But one challenge I would pose to anybody thinking about how

102

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

103

Sometimes I will inspire my students in a deep way

104

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

105

And I just show them something really neat

106

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

107

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.

112

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.

116

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

117

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

118

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

119

And that makes sense.

120

There's more uncertainty in the world.

121

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,

122

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

123

And that's a lot of uncertainty for students.

124

And I empathize with this quite a lot.

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In five to 10 years, many things will change.

126

The future has always been unpredictable.

127

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

128

Five to 10 years, people always get it wrong.

129

Here's an interesting anecdote though.

130

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

131

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

132

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.

133

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

134

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

135

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

136

But there was a lot of reasons that people underestimated.

137

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

138

Or the long tail sort of experiences.

139

There's always something different happening on a highway.

140

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.

141

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

142

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

143

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

144

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

145

I am getting more separated from problem solving myself.

146

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

147

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,

148

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

149

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

150

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

151

Like that really critical piece.

152

I think all students have felt like this.

153

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

154

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

155

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

156

I think people should be playing around with it.

157

But you should be self-aware

158

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

159

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

160

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

161

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

162

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

163

and break them down into small pieces?

164

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

165

how do you think about algorithms?

166

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

167

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

168

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

169

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

170

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

171

You get immediate falsifiable feedback.

172

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

173

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.

174

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

175

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

176

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

177

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

178

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

179

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

180

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.

181

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

182

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

183

I'll give your young engineers a particular challenge.

184

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

185

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

186

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

187

You might be able to make a high quality startup.

188

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

189

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

190

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

191

Like what is worth actually making?

192

What do users want?

193

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

194

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

195

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

196

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

197

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

198

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,

199

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?

200

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

201

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

202

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

203

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

204

Kids still need to learn multiplication.

205

Now there's a subtle difference.

206

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

207

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

208

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

209

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

210

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.

213

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.

220

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.

227

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.

251

Just take axioms.

252

You will become smarter than you were yesterday.

253

Start your day like that.

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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「AI biết code, vì sao vẫn phải học lập trình?」を教材にしています。この動画には、シャドーイング用の文が253文、単語が3395語あります。音声の長さは16:41です。話す速さは速く、1分あたり約203語です。音のつながりや弱く発音される音が多くなります。単語の84%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。

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
probability 名詞	/ˌpɹɑ.bəˈbɪl.ə.ti/	確率
multiplication 名詞	/ˌmʌltɪplɪˈkeɪʃən/	掛け算, 乗法
axiom 名詞	/ˈæk.si.əm/	公理
motivational 形容詞		動機付けの
outsourcing 名詞	/ˈaʊtˌsɔː(ɹ).sɪŋ/	アウトソーシング
tutor 名詞	/ˈtjuːtəː/	家庭教師
curiosity 名詞	/ˌkjʊɹ.iˈɑ.sə.ti/	好奇心
chatbot 名詞	/ˈtʃætbɑt/	チャットボット
empathize 動詞	/ˈɛmpəˌθaɪz/	同情する
ignite 動詞	/ɪɡˈnaɪt/	火を点ける
multiply 動詞	/ˈmʌltɪplaɪ/	掛ける
syntax 名詞	/ˈsɪn.tæks/	統語論, 構文論
motivate 動詞	/ˈmoʊ.tɪ.veɪt/	動機を与える
underestimate 動詞	/ʌndɚˈɛs.tɪ.meɪt/	過小評価する, 侮る
premise 名詞	/ˈpɹɛm.ɪs/	前提

動画に出てくる句動詞

単語	意味
go up 動詞	上がる, 登る

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。

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?

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
現在完了進行形 have/has been + -ing — 以前に始まり今も続いている動作	We've been doing · has been growing
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	have I given · have felt · you've learned
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	may be magnified · are faced · was born
関係詞節 who / which + 節 — 人や物について情報を加える	people who are · ones who are · teacher who really

注意したい発音

話し手はI'm, gonna, don'tなど、短縮形や弱形を90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。

「th」の音: empathize /ˈɛmpəˌθaɪz/, algorithm /ˈælɡəɹɪðm̩/, thrive /θɹajv/
「sh」と「zh」の音: multiplication /ˌmʌltɪplɪˈkeɪʃən/, foundational /faʊnˈdeɪ.ʃə.nəl/, inflection /ɪnˈflɛkʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, misconception /ˌmɪs.kənˈsɛp.ʃən/
長い単語（アクセントの位置に注意）: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, demotivate /ˌdiːˈmoʊtɪveɪt/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, uncertainty /ʌnˈsɜːtənti/, curiosity /ˌkjʊɹ.iˈɑ.sə.ti/

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: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, algorithm /ˈælɡəɹɪðm̩/, formalize /ˈfɔɹ.məlaɪːz/, grossly /ˈɡɹoʊsli/, predictably /pɹəˈdɪk.tə.bli/
/v/ — /b/ にならないように、上の歯を下唇に当てる: demotivate /ˌdiːˈmoʊtɪveɪt/, motivate /ˈmoʊ.tɪ.veɪt/, elective /ɪˈlɛktɪv/, lovable /ˈlʌvəbəl/, overestimate /ˌəʊvəɹˈɛstɪmeɪt/
/f/ — 「フ」ではなく、上の歯と下唇で出す: artful /ˈɑːtf(ʊ)l/, falsifiable /ˈfɒl.sɪˌfaɪ.ə.bəl/, formalize /ˈfɔɹ.məlaɪːz/, foundational /faʊnˈdeɪ.ʃə.nəl/, inflection /ɪnˈflɛkʃən/

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。probability, multiplication, axiom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。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: AI biết code, vì sao vẫn phải học lập trình? | đi-code ep 35 - 動画で英語スピーキングを学ぶ

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