ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: AI biết code, vì sao vẫn phải học lập trình? | đi-code ep 35
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253 ประโยค
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1
Hi, I'm Chris Peach.
2
I'm a professor here at Stanford University.
3
I teach some large intro to computer science classes, some intro to math for AI.
4
Code in Place, if people don't know it, it's an online class where you can learn to program.
5
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.
6
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.
7
A few observations.
8
One, our enrollment basically doubled oh my gosh, all these people want to learn how to code.
9
You can expand the question.
10
You could say, should I learn to program?
11
You can also say, should I learn probability?
12
Like AI can code, but AI can also do probability.
13
Should I learn to write?
14
AI can write.
15
I think the wrong answer would be no, no, no. We're not giving up on the next generation being smart.
16
Yes, you should learn how to formalize an argument.
17
Yes, you should learn the depth of probabilistic reasoning.
18
And yes, you should learn how to program.
19
If AI is able to do those things, your abilities may be magnified.
20
But I imagine in the future, it will still be important to be smart in those spaces.
21
I'm seeing more people with a motivational crisis than I have in the past, and that makes sense.
22
There's more uncertainty in the world.
23
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,
24
I have to think about what jobs are going to exist in 2030
25
when AI is four years more advanced
26
and that's a lot of uncertainty for students and I empathize with this quite a lot.
27
I think naturally that leads to some motivational problems.
28
When am I actually getting something out of AI and when have I given away too much of the growth?
29
I suppose if I start outsourcing, at what point will I no longer be able to do that?
30
Like that really critical piece.
31
I think all students have felt like this.
32
Like if you have AI write too many of your essays, At what point are you no longer able to write an essay?
33
If you have AI write too much of your code, at what point can you no longer do that valuable piece of the architecture?
34
So I suppose that's the part where like, I think it's fun to use AI.
35
I think people should be playing around with it.
36
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?
38
And you should care so much about your own personal growth.
39
I was born in Nairobi, Kenya.
40
When I was 12 I moved to Kuala Lumpur, Malaysia.
41
I ended up coming to the US for university.
42
I was just a curious human.
43
I wasn't set on being a professor from day one.
44
I just liked learning and I liked interesting problems.
45
When I came to Stanford, I'd done a little bit of coding but I really didn't know how to program.
46
But like I had to fill an elective
47
so I just had to take a class
48
and I was like okay I'll do the programming class and my teacher did the most wonderful thing.
49
They said, at this point, I'm gonna have a challenge.
50
Everyone in class, go make the most wonderful things with what you've learned in the first two weeks of programming.
51
And I found myself able to put like 40 hours of extra work beyond my normal schooling into this challenge
52
because I was so excited.
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And then eventually I discovered that I was
54
so curious about how people learned and I decided professor was the right thing for me.
55
So Carol speaks this thing called Python, which we're going to be using as our programming language throughout the course.
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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.
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Come on, Carol, turn left, and then turn left, and then turn left, and we're out of the kid turn right.
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It's the class in the world with the most teachers.
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There's one teacher for for every 10 students, and there's about 17,000 students and more than a thousand teachers.
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So what problem was I trying to solve?
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Let's go back in time.
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It's early days in the pandemic.
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I'm about to teach Stanford's flagship intro to coding class, and I've been told that everything's gonna be online.
64
And in this moment, we're thinking, the world is suffering.
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While we're putting the class online, is there something
66
that we can also do to help the world we can just put our videos online
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and we thought people might get a little bit out of it
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but we know that it would be a lot less than what our Stanford students get
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because our Stanford students get the special sauce of Stanford education
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and the special sauce of Stanford education for intro CS is
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you get a section leader you get somebody who's just a
72
little bit older than you a little bit further along in their career who's going to take time to help you grow.
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One of the common misconceptions is just thinking that AI tutors will solve everything.
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We basically have AI tutors already, but that isn't moving the needle in the way people expected.
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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.
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If we give people AI and just like here's a chat bot, use it to learn, predictably people will drop out.
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People get demotivated.
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It is demotivating to have AI thrown at you at the wrong moment of your learning.
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We have found very nuanced ways where we can use AI that actually helps people learn.
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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.
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But what happens if I throw a human at you?
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Imagine you're just programming in Code in Place.
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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.
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Do you want to talk them.
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If you hit yes, your probability of completing the course goes up 10 percentage points.
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So you must be thinking, oh the humans must be saying the right things and the AI must be saying the wrong things.
87
We've looked at these conversations.
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The AI was correct.
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It wasn't hallucinating, not for intro programming.
90
And the humans weren't always correct.
91
But the human touch is special.
92
It's motivating and I think we all need motivation right now.
93
Everyone needs something to convince them, I'm not gonna make Claude do all the thinking for me.
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Like to actually do the thinking yourself takes extra energy.
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Crown jewel of education has always been motivation
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and it's a lot more motivating for me to say I care about you being a smart person.
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I'm not giving up on you being a smart person this time of AI.
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Let's work on your foundations and then when you're done with your foundations I'll teach you how to code with AI.
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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?
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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
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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.
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.
125
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.
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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.
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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.
141
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.
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
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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?
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.
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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
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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.
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.
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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.
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.
211
Honestly, the people I've seen get most lost
212
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.
214
He was using AI, he was solving problems, he was learning amazing things.
215
I ask, hey, wonderful student, like, what are you thinking about?
216
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.
218
And like, you need pause.
219
I think about AI all the time.
220
I feel like I think about AI 10 times a day.
221
And then the simplicity of like, no, I'm just gonna be curious and learn.
222
Since that day, I start my day with the axiom.
223
I don't ask why I care about the next generation being smarter.
224
I take it as a truth.
225
I want this and I will work towards it.
226
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.
228
Like the doctor who really cares about their patient now has a tool that they can do more, faster, more accurately.
229
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.
230
Also, I get to see young people all the time.
231
And I would say that gives me inspiration, seeing their self-awareness, how critical their thinking,
232
seeing them blossoming, it gives you optimism.
233
If I was a young person right now, the most valuable thing is that you have the self-awareness.
234
You should also have the goal that I will become smarter.
235
Chris is not giving up on you.
236
You should not give up on yourself either.
237
I have two kids under five.
238
And you know what?
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They're gonna live in an awesome world.
240
Like, we're gonna adapt.
241
We're gonna figure things out.
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They're gonna still have curiosities.
243
They're gonna still grow their minds.
244
And we're gonna keep every day working towards that.
245
The top engineer might not be the person who knows all the code.
246
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,
247
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,
250
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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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
บทเรียนฝึกพูดระดับ C1 นี้ใช้วิดีโอ “AI biết code, vì sao vẫn phải học lập trình?” เป็นสื่อ วิดีโอนี้มี 253 ประโยค และ 3395 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 16:41 ผู้พูดพูดเร็ว ประมาณ 203 คำต่อนาที จึงมีการเชื่อมเสียงและลดเสียงค่อนข้างมาก 84% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ยากที่สุด 15 คำในวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| probability คำนาม | /ˌpɹɑ.bəˈbɪl.ə.ti/ | ความน่าจะเป็น |
| uncertainty คำนาม | /ʌnˈsɜːtənti/ | ความไม่แน่นอน |
| axiom คำนาม | /ˈæk.si.əm/ | สัจพจน์, ฐานบท |
| tutor คำนาม | /ˈtjuːtəː/ | ครูพิเศษ |
| curiosity คำนาม | /ˌkjʊɹ.iˈɑ.sə.ti/ | ความอยากรู้อยากเห็น, ความอยากรู้ |
| multiply คำกริยา | /ˈmʌltɪplaɪ/ | คูณ |
| syntax คำนาม | /ˈsɪn.tæks/ | วากยสัมพันธ์ |
| underestimate คำกริยา | /ʌndɚˈɛs.tɪ.meɪt/ | ดูถูกดูแคลน |
| premise คำนาม | /ˈpɹɛm.ɪs/ | ข้อตั้ง |
| algorithm คำนาม | /ˈælɡəɹɪðm̩/ | ขั้นตอนวิธี, อัลกอริทึม |
| anecdote คำนาม | /ˈæn.ɪkˌdəʊt/ | เรื่องขำขัน, ชวนหัว |
| memorize คำกริยา | /ˈmɛm.əˌɹaɪ̯z/ | จดจำ |
| calculator คำนาม | /kæl.kjə.leɪ.tɚ/ | เครื่องคิดเลข |
| blossom คำกริยา | /ˈblɑ.səm/ | บาน |
| optimism คำนาม | /ˈɑptɪmɪzəm/ | สุทรรศนนิยม, สุนิยม |
ประโยคที่ควรฝึกพูดซ้ำ
ประโยคสั้น ๆ ที่สมบูรณ์จากวิดีโอ ซึ่งนำไปใช้ในบทสนทนาประจำวันได้:
- 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?
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Present perfect continuous have/has been + -ing — การกระทำที่เริ่มก่อนหน้านี้และยังดำเนินอยู่ | We've been doing · has been growing |
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | have I given · have felt · you've learned |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | may be magnified · are faced · was born |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | people who are · ones who are · teacher who really |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 90 ครั้ง เช่น I'm, gonna, don't ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “th”: empathize /ˈɛmpəˌθaɪz/, algorithm /ˈælɡəɹɪðm̩/, thrive /θɹajv/
- เสียง “sh” และ “zh”: multiplication /ˌmʌltɪplɪˈkeɪʃən/, foundational /faʊnˈdeɪ.ʃə.nəl/, inflection /ɪnˈflɛkʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, misconception /ˌmɪs.kənˈsɛp.ʃən/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, demotivate /ˌdiːˈmoʊtɪveɪt/, multiplication /ˌmʌltɪplɪˈkeɪʃən/, uncertainty /ʌnˈsɜːtənti/, curiosity /ˌkjʊɹ.iˈɑ.sə.ti/
เสียงที่คนไทยมักออกเสียงยาก:
- เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: empathize /ˈɛmpəˌθaɪz/, syntax /ˈsɪn.tæks/, premise /ˈpɹɛm.ɪs/, artful /ˈɑːtf(ʊ)l/, elective /ɪˈlɛktɪv/
- /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: demotivate /ˌdiːˈmoʊtɪveɪt/, motivate /ˈmoʊ.tɪ.veɪt/, elective /ɪˈlɛktɪv/, lovable /ˈlʌvəbəl/, overestimate /ˌəʊvəɹˈɛstɪmeɪt/
- /z/ — เสียงก้อง ไม่ใช่ /s/: empathize /ˈɛmpəˌθaɪz/, formalize /ˈfɔɹ.məlaɪːz/, memorize /ˈmɛm.əˌɹaɪ̯z/, optimism /ˈɑptɪmɪzəm/, confuse /kənˈfjuːz/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง probability, uncertainty, axiom เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ