如何利用AI进行学习
Twenty years ago, I founded a social enterprise. I wanted to change the world. And we were funding millions of meals to the underprivileged.
We were providing tens of thousands of vaccines across parts of Africa. And we were funding schools in the slums of India.
Now I thought that I was doing quite a good job and having a lot of impact in all of these areas, until one day I was working with ministers in the UK.
And they said that 20 percent of students leave secondary schools in the UK and they're not able to read and write well enough.
翻译&笔记
二十年前，我创办了一家社会企业。我想要改变世界。我们为弱势群体提供了数百万份膳食。
我们在非洲部分地区提供了数万剂疫苗。我们还资助了印度贫民窟的学校。
我觉得我做得相当不错，在所有这些领域都产生了很大的影响，直到有一天，我在英国与一些部长共事时。
他们告诉我，英国有20%的学生在中学停止学业，而他们基本的读写都有问题。
underprivileged：adj.n.弱势的（群体）
privilege：n.v.特权，优待
Now I thought with brick and mortar schools and qualified teachers, if they're not able to do that in the UK.
Then I'm not having the impact that I wanted to have in those schools, in the slums in India.
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我想这些英国的学校有整齐的校舍和合格的教师，但还是没能达到教育的目的。
那么在印度那些贫民窟学校我想产生的影响就更无从谈起。
brick and mortar：实体店
qualified：adj.合格的，胜任的；v.取得资格
qualify：v.取得资格
So what's going on? What's the problem? We need to fix it. So I went to schools.
I went to schools and I asked lots of questions. And I found two critical problems on the front line of education.
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到底是怎么回事？问题出在哪里？我们需要解决这个问题。于是，我走访了各个学校。
我走访了各个学校，提出了许多问题。我发现了教育一线存在的两个关键问题。
The first is that they continue to have the one-size-fits-all delivery of education to a classroom of around 30 to 35 people.
The second, I think you will agree with me, should be headline news every single day. 74 percent of teachers want to quit their jobs in the next three years.
翻译&笔记
首先，他们仍然沿用千篇一律的教学模式，给大约30到35名学生的班级授课。
其次，我想你会同意我的看法，这应该上每天的头条新闻。74%的教师打算在未来三年内辞职。
Why? It's because of workload. They spend so much time micro-marking, micro-assessing, trying to figure out where every child is at.
They are teachers by day, and they are data analysts by night. And not one of them signed up to do that night job.
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为什么？因为工作量太大。他们花费大量时间事无巨细地标记、评估，试图了解每个孩子的学习进度。
他们白天是老师，晚上是数据分析师。没有人愿意做晚上的那份工作。
signed up to：报名参加
So walking around schools, I had a smartphone in my hand, and we had machine-learning applications telling us how we should shop, how we should save and sleep.
And I thought, why don't we have this technology in the classroom, telling us how we should learn?
We've got to build that technology. But we can't use any old machine learning recommendation engine.
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当我走访学校时，手里拿着智能手机，上面有机器学习应用程序告诉我们应该如何购物、存钱、睡觉。
我想，为什么在课堂上没有这种技术，告诉我们应该如何学习呢？
我们必须开发这种技术。但是，我们不能用那些陈旧的机器学习推荐引擎。
We need to combine artificial intelligence with neuroscientific theory and the learning sciences to learn how every single brain in this room learns.
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我们需要将人工智能与神经科学理论和学习科学相结合，来了解在座各位的大脑是如何学习的。
Because if we can fix learning, we can improve outcomes. We can personalize education for every single one of us and provide intelligent insights to teachers to reduce the workload.
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因为如果我们能够改进学习方式，就能提升学习成果。我们可以为每个人提供个性化教育，为教师提供智能化的分析以减少其工作量。
outcomes：产出；结果；outcome的复数
So 12 years ago, I built a team. They built the technology. It exists, students use it in over 140 countries.
We've collected over 40 billion data points on how children learn. And I'm going to show you a couple of the things that I've learned about learning on the way.
翻译&笔记
12年前，我组建了一个团队。他们开发了这项技术。目前，有140多个国家的学生在使用它。
我们已经收集了超过400亿个关于儿童学习方式的数据点。我想向大家分享我在这个过程中学到的几件事。
But before I do that, I thought it would be really important to share with you some student feedback that I have on our platform.
It's really important because it tells us what children's expectations are when they use an AI education partner.
So I get feedback like this. "I'm trying to say thank you." "It's lovely." "It's brilliant." "I think Century will help me achieve things that I thought were impossible."
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但在那之前，我认为与大家分享一些学生对我们平台的反馈非常重要。
这非常重要，因为它能告诉我们孩子们在使用人工智能教育伙伴时的期望。
我得到了这样的反馈：“我想说声谢谢。”“它太可爱了。”“太棒了。”“我认为Century将帮助我实现我认为不可能的目标。”
It's a golden child, right? My life's purpose has been fulfilled.
And then these sweet, lovely, innocent children send me messages like this. "I don't like this website, it makes me able to do my homework."
Wait. And then I'm being bribed. "I will give you 100,000 pounds, I'm not joking. You just need to give me no work. Give me a button to do the work for me."
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这孩子简直是个天使，对吧？我的人生目标实现了。
然后这些可爱、天真、纯真的孩子们给我发了这样的信息：“我不喜欢这个网站，它让我没有借 口不得不完成作业。”
等等。然后我被贿赂了。“我给你10万英镑，我没开玩笑。你只需要别给我布置作业。给我一个 按钮来帮我完成所有的功课。”
Now these children and that sentiment very much ties in with a recent survey where children were asked, how do you use AI LLMs, chatbots, with your homework?
A staggering fifth of children admitted they get AI to do all of their work for them.
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这些孩子和他们表达的这种情绪和最近的一项调查高度一致，当孩子们被问到：你如何在作业 中使用AI大语言模型和聊天机器人？
有高达五分之一的孩子承认，他们使用人工智能来完成所有作业。
tie in with：与...相关联；与...高度一致
a staggering fifth of：直译：惊人的五分之一
staggering：adj.大得惊人的；v.震惊；蹒跚，摇晃地走
So they're not using AI to help them learn. They're using AI to actively avoid learning.
Now I know some of you are frowning right now thinking, how dare they? I don't think they're that different from us.
Think about how we felt when we first used ChatGPT. This is my very scientific chart. I think that you all felt euphoric.
翻译&笔记
他们不是用AI来帮助他们学习，而是用AI来主动逃避学习。
我知道你们有些人现在肯定皱着眉头想：他们怎么敢？我觉得他们和我们没什么两样。
想想我们第一次使用ChatGPT时的感受。这是我精心绘制的图表。我想你们当时都欣喜若狂。
You thought, "Wow, I'm going to look like a genius. I never need to do any work ever again. This is amazing." Yeah? And then it hallucinated and confabulated.
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你会想：“哇，我简直太聪明了！我再也不用工作了！这太棒了！”是吧？然后它就开始胡编乱造。
hallucinate：vt.使产生幻觉；vi.出现幻觉
confabulate：vi.闲谈；虚构
And you were, like, "Big tech, seriously, you had one job to do, Sam Altman, with all that money, and it's making stuff up," right?
And then for the lawyer who shared it in a courtroom and got fined, sheer humiliation and embarrassment for those people.
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你会想：“科技大佬们，没开玩笑吧？萨姆·奥尔特曼，你们拿着那么多钱，就为了做一件事，结果就是胡编乱造？”
然后还有那个在法庭上用了AI并被罚款的律师，对那些人来说，绝对是羞辱和尴尬。
sheer：adj.（用于强调）纯粹的，绝对的；adv.陡峭地；v.突然转向；n.精纺织物；偏航
And I think we've ended up with this sort of sinking realization of acceptance, right, that the shortcuts don't really replace the work.
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我想我们大家现在已经幡然醒悟：捷径并不能真正取代工作。
sinking：adj.颓丧的；n.沉没；v.使下沉
realization：n.认识，领悟
acceptance：n.接受，容忍
They're very helpful. But we still need to learn, we need to produce, and we need to think.
Now when we read those long answers that an LLM chatbot gives us, it feels very fluent when you read it, doesn't it?
The problem is that fluency we often mistake for learning, and that is why people we know, not us, of course.
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它们当然很有帮助。但我们仍然需要学习，仍然需要输出，仍然需要思考。
那些大语言模型的聊天机器人给出详细答案，读起来非常流畅，对吗？
问题在于，我们常常把这种流畅误认为是习得，这就是为什么我们认识的人，当然不是我们，
But they end up with this sort of illusion of competence, like they know everything, right?
What we actually know about learning is that learning requires what researchers call a "productive struggle." It's this sort of mental effort, right, that builds understanding.
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最终会产生一种能力错觉，觉得他们什么都知道。
我们对学习的正确认识是，学习需要研究人员所说的“富有成效的努力”的过程。这是一种心智 的付出，在此之上才能建立理解。
Now my top learning techniques, I've got four of them that all involve a productive struggle, and they improve outcomes. We've seen them work.
Three of them are about memory. This is really important. Memory and understanding are two sides of the same coin.
翻译&笔记
下面是最重要的几种学习技巧，其中四种都涉及到富有成效的努力，并且效果显著，经过了我们实践的检验。
其中三种都与记忆有关。这至关重要。记忆和理解是一枚硬币的两面。
If you think about it, we draw on what we remember in order to shape what we think. If we can't recall it, we can't use it. So the first important one is retrieval.
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仔细想想，我们正是依靠记忆来构建思维。如果我们无法回忆起某些信息，就无法运用它们。 最重要的是检索能力。
draw on：接近；利用；依靠
This is simply the act of recalling from our brains. The students in a study were given a passage like this one.
And it's the students who only read it once, but then tried to recall it from their memory, who could remember it far better than students who just read it over and over and over again.
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这其实就是从大脑中提取信息的过程。参加研究的学生拿到了这样一段话。
那些只读了一遍，然后尝试回忆的学生，比那些反复阅读的学生，记住的内容要多得多。
The second is spacing, and this is essentially students who then space their learning over time.
So rather than cramming things all in one go, students that can do that active process of retrieval over time.
Because then you're essentially going through that productive struggle over and over again.
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然后是间隔，也就是让学生将学习内容分散到不同的时间段。
与其试图一口吃个大胖子，不如让学生在一段时间内去主动回忆。
因为这样他们就能反复经历这种富有成效的学习过程。
essentially：adv.根本上；大体上
essential：adj.n.必需（的）
cramming：v.把...塞满；死记硬背
all in one go：一口气
The third, we don't like this one, but it's just generation, right? So students in a study were given word pairs like rapid-fast and cold and hot.
But then another set of students were just given the first word. And then a cue like the F, they had to come up with "fast."
Students who have to generate the answers themselves, even if they get them wrong initially, create a stronger memory trace. They remember more in the end.
翻译&笔记
第三点，我们不太喜欢，但它其实就是产出，对吧？参加研究的学生拿到了很多成对的单词组 ，比如rapid-fast和cold-hot。
而另一组学生则只得到第一个单词，以及第二个单词的提示，比如F，他们必须想出“fast”。
那些自己想出答案的学生，即使他们可能第一次做错了，也产生了更强的记忆印记。最终，他们记住的内容更多。
And then the fourth is reflection. When we reflect on our work and we are given structured feedback in three very specific ways:
How am I learning right now? What is my learning goal, and then what are the gaps to get to that goal, what do I need to do? Those students improve their outcomes.
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第四是反思。当我们反思自己的学习，我们会获得来自三个方面的结构化反馈：
我现在的学习情况如何？我的学习目标是什么，还有哪些差距？要实现目标，还需要做什么？ 这些学生都提高了他们的成绩。
Now you'll find that these four techniques have something in common. They are harder. They all involve a productive struggle.
We know sustained mental effort strengthens the parts of the brain, and it's positively correlated with growth in the brain.
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你会发现这四种方法有一个共同点：它们都更具挑战性。它们都需要富有成效的努力。
我们知道，持续的脑力劳动会锻炼到大脑的各个部分，并且与大脑的生长呈正相关。
There was an amazing study in my home city of London with black taxi drivers.
Now if you're a cabbie in London, you have to pass a test called The Knowledge.
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在我的家乡伦敦，有一项针对出租车司机的惊人研究。
如果你是伦敦的出租车司机，你必须通过一项“专业知识”考试。
You have to memorize 26,000 streets in the city of London. You're not allowed to use navigation apps.
Wow, exactly, right? Isn't that crazy? Yeah, no Uber drivers for them, right?
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你必须记住伦敦市内26000条街道。你不允许使用导航应用程序。
哇，没错，对吧？是不是很疯狂？他们没有优步司机。
And so neuroscientists scanned their brains and they found that parts of the hippocampus in the brain, this is the part of the brain that's responsible for spatial memory and navigation, were larger in parts with experienced cabbies.
Because you have to build all of those mental models, you have to generate new routes every time you have a new passenger.
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神经科学家扫描了他们的大脑，发现经验丰富的出租车司机大脑中负责空间记忆和方向感的海 马体部分区域更大。
因为你必须在脑子里建立所有这些模型，每次载客时都必须生成新的路线。
And so they say that that growth, because of the positive correlation with what they have to do, is really meaningful and telling, and it is no different for learning.
Durable learning does not come from shortcuts. It comes from certain types of effort. And this is why AI is amazing for education.
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他们认为，这种成长，与他们必须做的事呈正相关，这是非常有意义和有说服力的，对学习来说也是如此。
持久的学习并非来自捷径，而是来自特定的努力。这就是人工智能在教育领域大放异彩的原因。
Because AI can spot patterns in how we all learn. It can spot patterns in how concepts across subjects connect.
It can predict if you don't know something and provide you with that material at the right time, it can provide us with timely, targeted interventions and give teachers those insights.
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因为人工智能能够发现我们所有人通用的学习模式。它可以发现不同科目之间的概念相互联系的模式。
它可以预测你不懂的东西，然后在恰当的时间，给你提供所需的材料；它可以为我们提供及时 、有针对性的干预措施，并为教师提供这些信息。
spot patterns in：发现...的规律/模式
It can predict when you're just about to forget something and give you that material at just the right time, it can force you to generate an answer rather than just reveal the answer.
And it can provide amazing, structured feedback against expertly designed rubrics from teachers. So AI well-designed can be phenomenal in education.
翻译&笔记
它可以预测你何时会开始遗忘，并在那个时候提供恰当的材料，它可以促使你自己得出答案，而不是直接给你答案。
它还可以根据教师精心设计的评分标准，提供结构化反馈。精心设计的人工智能可以在教育领域大显身手。
against：prep.反对，违背；针对
phenomenal：adj.现象级，非凡的
And we've seen it work. Now a lot of people come to me, students and adults, and they say, but why bother? Because we've got GPS, right?
We have AI, we can Google the answer to absolutely anything, so we don't need to do this anymore. That's not true.
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我们已经见证了它的成效。有很多人来找我，有学生，有成人，他们说，但又何必呢？因为我们有GPS，对吧？
我们有人工智能，我们可以用谷歌搜索任何问题的答案，所以我们不需要再这样学习了。事实并非如此。
If you think about AI, AI is our history predicting our future. It is brilliant at spotting patterns in data.
It has been amazing as a partner in remarkable breakthroughs like drug discovery and protein folding, new materials and crystals.
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如果你想想人工智能，人工智能是在用我们的历史来预测未来。它非常擅长从数据中发现规律。
它曾作为合作伙伴，在药物研发、蛋白质折叠、新材料和晶体等方面取得了重大突破。
But the thing is, none of that happens with AI in isolation. We humans, we frame the questions. We set the goals, we chose the data sets.
We decide which discoveries matter. Our knowledge is not just trivia. It is the raw material of thinking and discovery. AI is not there to replace our expertise.
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我们决定哪些发现至关重要。我们的知识并非微不足道，而是思考和发现的土壤。人工智能并 非要取代我们的专业知识。
但关键在于，所有这些都不是人工智能独立完成的。是我们人类，提出问题，设定目标，选择数据集。
trivia：n.琐事；冷知识
It's there to allow our expertise to expand. And if you think about powered flight, penicillin, electricity, AI itself, humans learned.
They went through that productive struggle, right? They built domain expertise, and from that they took a leap in their imagination and they created innovations.
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它的存在是为了拓展我们的专业知识。想想动力飞行、青霉素、电力，以及人工智能本身，这些都是人类学习的结果。
他们经历了富有成效的奋斗，对吧？他们先积累专业知识，然后灵感迸发，迎来了创新。
So for students who want to cheat and want to use AI to do their homework, for us lifelong learners, right.
Who are reading and reading and reading and reinforcing that illusion of competence, just remember, you do not get the growth unless you go through the struggle.
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对于那些想作弊、想用人工智能代写作业的学生，以及我们这些终身学习者。
我们不停地阅读，并靠着这个来不断支撑自己无所不能的幻觉，请记住，只有经历磨砺，才能真正成长。
reinforce：v.加固；强化；增援
So whether AI is good or bad for education is totally up to you. Are we designing it well and are you using it to complement or to replace human cognition?
翻译&笔记
人工智能对教育究竟是好是坏，完全取决于你。我们把它设计得是否恰当，你是把它当作辅助，还是要它取代你的认知？
complement：v.补充；n.补足物；补集
So the next time you're learning and you want to invest in yourself, educate yourself, you want to grow and maybe take that leap in imagination, just remember, mental effort is not a flaw in the process.
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下次当你学习、想要投资自己、提升自我、想要成长，甚至想要灵感迸发时，请记住，脑力劳动并非学习过程中的缺陷。
flaw：n.缺陷；裂痕；v.削弱
It is a critical feature that allows learning to stick, allows us to build expertise and fuel human ingenuity.
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它是关键步骤，它让学到的东西生根发芽，让我们积累专业知识，迸发智慧光芒。
ingenuity：n.聪明才智，独创力，巧思；精巧的装置/方法
Thank you so much for listening to me, and good luck with your AI journey.
📌 summary
原版
The speech begins by describing the phenomenon that adolescents in the UK are using AI to avoid learning,
rather than helping them to learn. .It suggests that the significant outcome need to go through productive struggle
（that 引导的从句缺少谓语动词；significant outcome 表意模糊，原文强调的是“学习成果”或“真正的掌握”；）
Then the speech outlines several features of how to learn and build expertise:
1.Retrieval（检索） - The experiment illustrates that rapidly recalling material you have read once is more effective than reading it over and over again.
2.Spacing（分段） - Rather than learn something all in one go, active recall and retrieval over time are essential processes.
3.Generation（产出） - Do some practice and generate the answers by yourself. You may get them wrong initially but remain impressive memory trace and remember more in the end.
4.Reflection（反思） - Asking yourself: What's the target? How to get the target and the gap to the target.
In the end, the speech explain the relation between AI and human beings with giving some examples. In conclusion, AI should be used to complement, rather than replacing human thinking.
The speech begins by describing the phenomenon of adolescents in the UK using AI to avoid learning, rather than using it to help them learn. It argues that meaningful learning outcomes require a "productive struggle."
The speaker then outlines four key techniques for effective learning and building expertise:
1.Retrieval — Experiments show that rapidly recalling material after reading it once is more effective than reading it repeatedly.
2.Spacing — Rather than cramming all at once, active recall spread over time strengthens memory.
3.Generation — Attempting to generate answers yourself, even if initially wrong, creates a stronger memory trace.
4.Reflection — Asking yourself: What is my goal? What are the gaps? What do I need to do next?
Finally, the speech explains the relationship between AI and human cognition, using examples such as GPS and scientific breakthroughs. It concludes that AI should be used to complement, not replace, human thinking.
Note
It argues that：它认为
It suggests that：它表明