This is how kids should be learning with Al | Priya Lakhani

孩子们应该这样用AI学习 | 普丽娅·拉卡尼

You're listening to ted talks daily where we bring you new ideas to spark your curiosity every day. I'm your host, elise hue. No two humans learn in exactly the same way. So what might happen when machines help us develop better tools to create personalized pathways to learning? In this talk, AI education entrepreneur priya lakani shows us how a one size fits all approach to the classroom, strains teachers and fails students and how, if properly designed, AI could amplify what students and teachers do best and reveal how irreplaceable we humans truly are.

您正在收听TED每日谈，我们每天为您带来激发好奇心的新想法。我是主持人Elise Hue。没有两个人的学习方式是完全相同的。那么，当机器帮助我们开发更好的工具来创建个性化的学习路径时，会发生什么？在这场演讲中，AI教育企业家普丽娅·拉卡尼向我们展示了课堂上的"一刀切"模式如何让教师不堪重负、让学生失败，以及如果设计得当，AI如何能够放大学生和教师最擅长的事情，并揭示我们人类是多么的不可替代。

20 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 that20% of students leave secondary schools in the UK and they're not able to read and write well enough. 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. So what's going on? What's the problem? We need to fix it.

20年前我创立了一个社会企业。我想改变世界，我们资助了数百万份餐食给弱势群体。我们在非洲部分地区提供了数万支疫苗，我们还资助了印度贫民窟的学校。当时我以为自己做得相当不错，在所有领域都产生了很大影响，直到有一天我与英国的大臣们合作，他们说英国有20%的学生在离开中学时读写能力不足。我想，在英国有实体学校和合格的教师尚且如此，那么我在印度贫民窟那些学校的影响力就没有达到我的预期。那么问题到底出在哪里？是什么问题？我们需要解决它。

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. The first is that they continue to have the one size fits all delivery of education. To a classroom of around30 to35 people. The second I think you will agree with me should be headline news every single day74% of teachers want to quit their jobs in the next three years. Why? It's because of workloads. 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.

于是我去了学校，我去学校问了很多问题，在教育一线发现了两个关键问题。第一是他们继续采用"一刀切"的教育方式。面向大约30到35人的班级。第二个问题，我想你们会同意我，应该每天都上头条新闻——74%的教师想在接下来三年内辞职。为什么？是因为工作量。他们花了大量时间进行细微评分、细微评估，试图弄清楚每个孩子的进度。他们白天是教师，晚上是数据分析师，而且没有一个人签约来做那份夜间工作。

So walking around schools, I had a smartphone in my hand and we have machine learning applications telling us how we should shop, how we should save and how we should 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. We need to combine artificial intelligence with neuroscientific theory and the learning sciences to learn how every single brain in this room learns 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.

我在学校里走动时，手里拿着智能手机，我们有机器学习应用告诉我们该如何购物、如何省钱、如何睡觉。我就想，为什么我们没有这种技术出现在教室里，告诉我们该如何学习呢？我们必须构建那种技术，但不能使用任何老旧的机器学习推荐引擎。我们需要将人工智能与神经科学理论及学习科学结合起来，以了解这个房间里每一个大脑是如何学习的，因为如果我们能修复学习过程，我们就能改善结果。我们可以为每一个人个性化教育，并为教师提供智能洞察以减轻工作量。

So12 years ago I built a team, they built the technology, it exists, students use it in over140 countries. We've collected over40 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. But before I do that, I thought it'd be really important to share with you some student feedback that I have on our platform. Yeah, it's really important because it tells us what children's expectations are when they use an AI education partner.

于是12年前我组建了一个团队，他们构建了这项技术，它现在已存在，学生在超过140个国家使用它。我们收集了超过400亿个关于儿童如何学习的数据点。我将向你们展示我在此过程中了解到的关于学习的一些事情。但在此之前，我认为与你们分享一些我们评台上收到的学生反馈非常重要。是的，这非常重要，因为它告诉我们孩子们在使用AI教育伙伴时的期望是什么。

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. 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 you100,000 pounds. I'm not joking. You just need to give me no work. Give me a button to do the work for me now these children and that sentiment very much ties in with a recent survey where children were asked how do you use AI llm chatbots with your homework. A staggering fifth of children admitted they get AI to do all of their work for them so they're not using AI to help them learn. They're using AI to actively avoid learning.

我收到的反馈是这样的。"我正想表达感谢。它很棒。它太出色了。我认为Century将帮助我完成那些我认为不可能的事情。它是个宝贝。孩子说得对。我的人生目标已经实现了。" 然后这些可爱、天真无邪的孩子会给我发这样的信息。"我不喜欢这个网站。它让我能做我的作业，等等，我这是被贿赂了。我给你10万英镑。我不是在开玩笑。你只需要不给我布置作业。给我一个按钮替我做作业。" 现在，这些孩子以及这种情绪，与最近一项调查非常吻合，该调查询问孩子们如何将AI大语言模型聊天机器人用于作业。惊人的五分之一的孩子承认他们让AI替他们完成所有作业，所以他们不是在用AI帮助学习。他们是在用AI积极逃避学习。

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. Use chat gpt I think that you all felt euphoric. 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 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. 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. They're very helpful. But we still need to learn, we need to produce and we need to think.

我知道你们中有些人现在正皱着眉头想他们怎么敢这样。我不认为他们与我们有多大不同。想想我们第一次使用ChatGPT时的感受。我想你们都感到欣喜若狂。你想，哇，我要看起来像个天才了。我再也不需要做任何工作了。这太神奇了，是的。然后它出现了幻觉和虚构，你就想，大科技公司啊，说真的，你们就这点事要做。萨姆·奥尔特曼有那么多钱，它却在胡编乱造，对吧？还有那位在法庭上使用了它结果被罚款的律师，对那些人来说是纯粹的羞辱和尴尬。我认为我们最终有了这种沉痛的、接受性的认识，对吧，捷径并不能真正替代工作。它们很有帮助。但我们仍然需要学习，需要产出，需要思考。

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, is that fluency we often mistake for learning. And that is why people we know, not us, of course, 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 called a productive struggle. It's this sort of mental effort, right, that builds understanding.

现在，当我们阅读大语言模型聊天机器人给出的那些长答案时，读起来感觉非常流畅，不是吗？问题在于，我们常常将这种流利性误认为是学习。这就是为什么我们认识的一些人——当然不是我们——最终陷入了这种能力幻觉，好像他们什么都懂，对吧？我们实际上所了解的学习是：学习需要研究者所称的"高效挣扎"。正是这种脑力劳动，构建了理解。

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. If you think about it, we draw on what we remember in order to shape what we think. If we can't it, we can't use it. So the first important one is retrieval. This is simply the act of recalling from our brains. The students in a study were given a passage, right? 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.

我最推崇的学习技巧有四个，它们都涉及"高效挣扎"，并且能提高学习效果。我们见证了它们有效。其中三个与记忆有关。这非常重要。记忆和理解是同一枚硬币的两面。想想看，我们依靠我们所记得的来塑造我们所思考的。如果我们记不住，就无法运用它。所以第一个重要的是提取。这只是从我们大脑中回忆的行为。一项研究中的学生拿到了一段文章，对吧？结果是那些只读一遍但随后尝试从记忆中回忆的学生，比那些只是反复读、反复读的学生能记住的要多得多。

The second is spacing and this is essentially students who then spaced 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. 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 que 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.

第二是间隔。这本质上是那些将学习间隔开的学生。随着时间推移。所以，与其一次性填鸭，学生如果能在一段时间内主动进行提取过程，那么你基本上就是在反复经历那种高效挣扎。第三个我们不太喜欢，但就是生成，对吧。一项研究中的学生拿到的是像"rapid - fast"和"cold - hot"这样的词对。但另一组学生只拿到第一个词和一个提示，比如"f"，他们必须自己想出"fast"。必须自己生成答案的学生，即使一开始答错了，也能创造更强的记忆痕迹。最终他们记住的更多。

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. 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.

然后是第四个：反思。当我们反思自己的作业，并以三种非常具体的方式获得结构化反馈时：我目前是如何学习的？我的学习目标是什么？然后，要达到那个目标还存在哪些差距？我需要做什么？这些学生改善了他们的学习效果。现在你会发现这四种技巧有共同点。它们更难。它们都涉及高效挣扎。我们知道持续的脑力劳动会加强大脑的某些部分，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 you have to memorize26,000 streets in the city of London. You're not allowed to use navigation apps now. Wow, exactly right. Isn't that crazy? Yeah, no Uber drivers for them, right? And so neuroscientists scanned their brains and they found that parts of the hippocampi in the brain, this is the parts 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. 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.

在我家乡伦敦有一项关于黑色出租车司机的惊人研究。如果你在伦敦开出租车，你必须通过一项叫做"知识"的考试，必须记住伦敦市的26,000条街道。现在不允许使用导航应用。哇，完全正确。这不是很疯狂吗？是的，对他们来说没有优步司机，对吧？神经科学家扫描了他们的大脑，发现大脑中海马体的某些部分——这部分负责空间记忆和导航——在有经验的出租车司机脑中更大，因为你必须建立所有那些心理模型，每次有新乘客都必须生成新路线。他们说，这种增长，由于与他们必须做的事情呈正相关，非常有意义且有说服力，而且（对于学习来说）也并无不同。

For durable learning does not come from shortcuts, it comes from certain types of effort and this is why AI is amazing for education 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 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 redesigned rubrics from teachers saying AI well designed can be phenomenal in education and we've seen it work.

因为持久的学习并非来自捷径，而是来自特定类型的努力，这就是为什么AI对教育来说如此神奇。因为AI能发现我们所有人学习方式的模式。它能发现跨学科概念如何联系的模式。它能预测你是否不懂某些内容，并在恰当时机提供相关材料。它能为我们提供及时、有针对性的干预，并为教师提供这些洞察；它能预测你何时即将遗忘某物，并在最佳时刻给你那份材料。它能强迫你生成一个答案，而不仅仅是揭示答案。它还能根据教师专业重设的评估量规提供出色的结构化反馈。设计良好的AI在教育中可以是非凡的，我们已见证了它的成效。

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 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. 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 datasets, we decide which discoveries matter. Our knowledge is not just trivia, it is the raw material of thinking and discover. AI is not there to replace our expertise, it's there to allow our expertise to expand.

现在很多人来找我，学生和成年人都有，他们会说：但何必费心呢？因为我们有GPS了，对吧？我们有AI，我们可以谷歌任何事情的答案，所以我们不再需要做这个了。但如果你想一想，这并不正确。关于AI：AI是我们的历史在预测我们的未来。它擅长发现数据中的模式。作为合作伙伴，它在药物发现、蛋白质折叠、新材料和晶体等卓越突破中表现惊人。但问题是，所有这些都不是AI孤立完成的。我们人类，我们提出 问题，我们设定目标，我们选择数据集，我们决定哪些发现重要。我们的知识不仅仅是琐事，它是思考和发现的原材料。AI的存在不是为了取代我们的专业知识，而是为了让我们的专业知识得以扩展。

And if you think about powered to 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, so. 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.

如果你想想飞行技术、青霉素、电力、AI本身，人类都经历了那种高效挣扎，对吧？他们建立了领域专业知识，并由此在想象力上实现飞跃，创造了创新。那些想作弊、想用AI替他们做作业的学生——以及我们这些终身学习者，对吧？我们读啊读啊读，强化了那种能力幻觉。只需记住：除非经历挣扎，否则无法获得成长。

So whether AI is good or bad for education is totally up to you. Are we designing it well and you're you using it to complement or to replace human cognition? 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. It is a critical feature that allows learning to stick allows us to build expertise and fuel human ingenuity. Thank you so much for listening to me and good luck with your AI journey.

所以，AI对教育是好是坏，完全取决于你。我们是否设计得当，你是否用它来补充还是取代人类的认知？所以，下次当你学习并想要投资自己、教育自己时，你想要成长并可能在想象力上实现飞跃时，只需记住：脑力劳动不是这个过程的一个缺陷。它是一个关键特征，能让学习扎根，让我们建立专业知识，并激发人类的创造力。非常感谢你们听我演讲，祝你们的AI之旅好运。

That was priya lacani speaking at ted next2025.

以上是普丽娅·拉卡尼在2025年TED Next大会上的演讲。