Speaker 1: In the world of educational research, there is a famous
Speaker 1: video of a boy named Sean. I don't mean famous
Speaker 1: in a sense that it has a million views on YouTube.
Speaker 1: I mean it in the circle of people who think
Speaker 1: about teaching and how to make teaching better. The video
Speaker 1: has been written about in journal articles and shown over
Speaker 1: and again in college classrooms. It's a ten minute clip
Speaker 1: of a third grade class somewhere in Michigan. It was
Speaker 1: filmed in January of nineteen ninety, so the video is
Speaker 1: a bit grainy. The teacher's name is Deborah Lowenburg Ball.
Speaker 1: She's a professor at Michigan State University who is part
Speaker 1: of her research teaches a one hour math class at
Speaker 1: a local elementary school on the day in question. Miss
Speaker 1: Ball begins by asking her students about the previous day's lesson,
Speaker 1: which was about even and odd numbers.
Speaker 2: And I would like to hear from as many people
Speaker 2: as possible what comments you had, reactions you had to
Speaker 2: be in that meeting yesterday.
Speaker 1: A little boy with black hair raises his hand. His
Speaker 1: name is Sean.
Speaker 3: I know I don't have anything about the meeting yesterday,
Speaker 3: but I was just thinking about.
Speaker 1: Sit Sean was thinking about the number six.
Speaker 3: I was thinking that it's a it's an ID. It
Speaker 3: can be an odd number two because there could be two, two, four, six, two,
Speaker 3: three tudents and two threes, it would be an add anthonisina.
Speaker 2: She thinks.
Speaker 3: To make it takes me two things.
Speaker 1: Sean doesn't understand what odd and even means. He thinks
Speaker 1: that just because you can break down six in an
Speaker 1: odd number of parts and an even number of parts,
Speaker 1: that six must exist in some magical middle category. And
Speaker 1: when you listen to the Sean videotape, you keep waiting
Speaker 1: for the teacher to say, oh, no, Sean, you misunderstand.
Speaker 1: But Deborah Ball doesn't do that. She never tells him
Speaker 1: he's wrong. Instead, she simply asks him to explain his thinking.
Speaker 4: Me and the two things that you put together to
Speaker 4: make it were odd, right, three and three of each
Speaker 4: child any think proba.
Speaker 1: Two or easy bald and asked the class to give
Speaker 1: their views. Other students jump up and explain their theories
Speaker 1: on the blackboard. For the next fifteen minutes, she definitely
Speaker 1: guides the class through an in depth investigation of what
Speaker 1: she calls Shawn numbers, until Sewn himself realizes that the
Speaker 1: real meaning of odd and even is something different than
Speaker 1: he had imagined, and now he gets it.
Speaker 3: I've been thank you for waring in love.
Speaker 1: I don't want to focus just on how little Sean
Speaker 1: finally made his own way to the right answer. I'm
Speaker 1: interested in what his teacher did to get him there.
Speaker 1: Deborah Ball worked magic. She never told Sean the right answer,
Speaker 1: She just led him to a place where he could
Speaker 1: discover it for himself. My name is Malcolm Glawo. This
Speaker 1: is season six of Smart Talks with IBM, where we
Speaker 1: are for our listeners a glimpse behind the curtain of
Speaker 1: the world of technology and artificial intelligence. In this season,
Speaker 1: we're going to visit companies as varied as Lail and
Speaker 1: Ferrari and tell stories of how they're using artificial intelligence
Speaker 1: and data to transform the way they do business. This
Speaker 1: episode is about the promise of a radical new idea
Speaker 1: called responsive teaching, the kind of teaching that took place
Speaker 1: that day in Shawn's classroom, and whether artificial intelligence can
Speaker 1: help us train the next generation of teachers to be
Speaker 1: as good as Deborah Ball. Before we talk about how
Speaker 1: AI could transform the way we train teachers, I want
Speaker 1: to go back for a moment to the famous video
Speaker 1: of Sean. In the video, the teacher Deborah Ball doesn't
Speaker 1: have a predetermined plan that she's imposing on the class.
Speaker 1: She's improvising, making up her approach as she goes along,
Speaker 1: responding to her students odd theory about the number six. Second,
Speaker 1: she's taking Sean seriously. She's not dismissing his theory. She's
Speaker 1: listening to him and trying to understand the problem from
Speaker 1: his perspective. And Thirdly, and most importantly, she's not force
Speaker 1: feeding him the right answer. She's being patient. She's waiting
Speaker 1: to see if with just the right subtle hints, he
Speaker 1: can get to the right answer on his own. Improvisation,
Speaker 1: empathy patients. That's responsive teaching.
Speaker 5: What I think about in terms of responsiveness is more
Speaker 5: like I think that students need to have a sense
Speaker 5: of agency in what happens in the classroom, and like
Speaker 5: authentic agency where they can be legitimized as knowers.
Speaker 1: I spoke to a physicist at Seattle Pacific University named
Speaker 1: Amy Robertson, a longtime advocate for responsive teaching. She uses
Speaker 1: the Sean video in her classroom.
Speaker 5: You have to trust that kids have a way of
Speaker 5: doing that, and that like heard. What she mostly did
Speaker 5: was to facilitate a conversation and to say you have
Speaker 5: to listen to them talk.
Speaker 1: No one told him he was wrong, that's right. And
Speaker 1: then he goes, He goes, I didn't think of it
Speaker 1: that way. Again, I thank you for leaving alone. You've
Speaker 1: expanded my understanding. Thank you for bringing it up again.
Speaker 6: It's like this, I love.
Speaker 5: I know responsive teaching, as I think about it is
Speaker 5: kind of rooted in this, like Eleanor Duckworth's work around
Speaker 5: the Having of Wonderful Ideas, where she says, like the
Speaker 5: goal of education is for students to have wonderful ideas
Speaker 5: and to have a good time having them.
Speaker 1: I love that. I've never heard that. What a beautiful,
Speaker 1: succinct way of summing up the purpose of education. Yes,
Speaker 1: responsive teaching is beautiful. It's rare to find a new
Speaker 1: teaching idea that everyone loves. This is one of those
Speaker 1: rare ideas. Watching the Ball classroom, all I could think was,
Speaker 1: I really really hope my daughters get to experience a
Speaker 1: math class like that. Far too many kids are convincing
Speaker 1: themselves at far too young an age that math isn't
Speaker 1: for them, and responsive teaching is a way to solve
Speaker 1: that problem. But here is the issue. It's really really
Speaker 1: hard to teach responsive teaching. Robertson says that teaching exists
Speaker 1: in a cultural environment where the teacher is expected to
Speaker 1: be the source of truth. That teaching is about the
Speaker 1: immediate correction of error and not letting a child wander
Speaker 1: down the pathway of their own Misunderstanding. Responsive teaching is
Speaker 1: deeply counterintuitive, and the only way to understand its beauty
Speaker 1: is to do it over and over again. Aspiring teachers
Speaker 1: need a way to practice. For as long as there
Speaker 1: has been technology, people have turned to digital machines to
Speaker 1: solve problems. My father was a mathematician and I remember
Speaker 1: him coming home in the nineteen seventies with a big
Speaker 1: stack of computer cards in his briefcase that he used
Speaker 1: to program the main frame back of the office. Today,
Speaker 1: with the rise of artificial intelligence, the scale and complexity
Speaker 1: of the problems technology can help us solve has jumped
Speaker 1: by many orders of magnitude. You must have worked with
Speaker 1: a million customers who are experimenting with llm's. Has there
Speaker 1: been one use case that you were like, WHOA, I
Speaker 1: had no idea or just simply that's clever. I'm speaking
Speaker 1: to Brian Bissell, who works out of IBM's Manhattan office.
Speaker 1: He helps IBM customers discover how best to get AI
Speaker 1: to work for them.
Speaker 7: There is one, but I don't think I can talk
Speaker 7: about it unfortunately.
Speaker 1: Wait, you can't tease me like that, can you wait?
Speaker 1: Disguise disguise it for me.
Speaker 7: Just give me a general It was about the ability
Speaker 7: to pull certain types of information out of documents that
Speaker 7: you wouldn't think you would be able to get the
Speaker 7: model to do, and be able to do that at
Speaker 7: a very large scale.
Speaker 1: Bissil's point was that we are well past the stage
Speaker 1: where anyone wonders whether AI can be useful. The real
Speaker 1: question now is what problems do we want to use
Speaker 1: it to solve? Where it can make the biggest difference,
Speaker 1: And Bissil saw lots of opportunities in education.
Speaker 7: I have two kids, one in middle school and one
Speaker 7: who just graduated high school, and I'm well aware of
Speaker 7: students using things like chat GPT to do their homework.
Speaker 7: And it's very easy to take tools like that and
Speaker 7: even IBM's own large language models, and just take a problem,
Speaker 7: a piece of homework, something you want written, and drop
Speaker 7: it into that and have it generate the answer for
Speaker 7: you and the student. The user in that case hasn't
Speaker 7: done any work, they haven't put any real thought into it.
Speaker 1: To Bissil, that's the wrong use of AI. That's technology
Speaker 1: making is dumber. What we really want is technology that
Speaker 1: makes us smarter. This will explain to me that there
Speaker 1: are now two big tools being used for AI productivity,
Speaker 1: AI agents and AI assistants. Let's start with AI agents.
Speaker 1: AI agents can reason plan and collaborate with other AI
Speaker 1: tools to autonomously perform tasks for a user. This will
Speaker 1: gave me an example of how college freshmen might use
Speaker 1: an AI agent.
Speaker 7: As a new student, you may not know how do
Speaker 7: I do with my health and wellness issue? Some of
Speaker 7: your credits are going to get for this given class.
Speaker 7: You could talk to someone and find out some of that,
Speaker 7: but maybe it's a little bit sensitive and you don't
Speaker 7: want to do that.
Speaker 1: Bisill told me you could build an AI agent, a
Speaker 1: resource for new students that helps them navigate a new campus,
Speaker 1: register for classes, access the services they need, and even
Speaker 1: schedule appointments on their behalf, which in turn buys them
Speaker 1: more time to focus on their actual schoolwork.
Speaker 7: We can see patterns of how agents and assistants can
Speaker 7: help help employees and customers and end users be more productive,
Speaker 7: automate workflows so they're not doing certain types of repetitive
Speaker 7: work over and over again, and streamlining their lives and
Speaker 7: making data more accessible to them twenty four hours a day.
Speaker 1: But Bissel says you can also use AI assistance in
Speaker 1: the education space. AI assistants are reactive as opposed to
Speaker 1: AI agents, which are proactive. AI assistants only perform tasks
Speaker 1: at your request. They're programmed to answer your questions, and
Speaker 1: as it turns out, AI assistants are now being used
Speaker 1: to further the responsive teaching revolution, which is why I
Speaker 1: found myself on a beautiful Georgia spring day not long
Speaker 1: ago on the campus of Kansas State University, sitting in
Speaker 1: the classroom with two researchers, one of them Professor Dabe Lee.
Speaker 1: Let's go into the journey of building this thing. You
Speaker 1: started by taking a course. What was the course you took?
Speaker 2: So it was offered by Coursera, It was designed by IBM.
Speaker 2: It was AI Foundation for everyone.
Speaker 1: In her AI Foundation's course, Lee learned how to build
Speaker 1: an AI assistant using IBM watson X that course took
Speaker 1: how long to take.
Speaker 2: It was not to know it was like fourteen mix.
Speaker 1: Lee's idea was to train an AI assistant on classroom
Speaker 1: data to play the role of Sean, a digital persona
Speaker 1: of a nine year old who likes math but doesn't
Speaker 1: always understand math, and that AI assistant, she thought could
Speaker 1: be used to train preservice teachers or teachers in training
Speaker 1: who are preparing to enter one of the most challenging
Speaker 1: professions in the modern world.
Speaker 2: So when you think about the teacher education and a
Speaker 2: major challenge that teacher education face is that we need
Speaker 2: children to practice with. We need instructors who if the
Speaker 2: instruction on the pedagogical skills. So when you look at
Speaker 2: the teacher education program, we have coursework in field experience,
Speaker 2: and in those two areas there is something missing all
Speaker 2: the time.
Speaker 1: Li says that pre service teachers often lack access to
Speaker 1: both students and experienced teachers during their education.
Speaker 2: So what we try to resolve is that we have
Speaker 2: this virtual student for pre service teacher to work with
Speaker 2: so that they can practice their responsive teaching skills.
Speaker 1: The first AI assistant Lee created is g Wu. G
Speaker 1: Wu emulates the persona of a nine year old third
Speaker 1: grade girl. Then, with the help of one of her collaborators,
Speaker 1: a researcher at Canazon named Sean English, she created two
Speaker 1: more AI assistants, Gabriel and Noah, each of which have
Speaker 1: their own distinctive characteristics. So how are Gabriel and Noah different.
Speaker 2: From ji Wu gab Real my first one? He is
Speaker 2: very short answered. If you ask an open ended question,
Speaker 2: he will answer it in a close way. So I
Speaker 2: use that characteristic. And that's the problem that most teachers
Speaker 2: actually base. They're asked children who are shay, who are reserved,
Speaker 2: and who would not sure much of their thoughts. So
Speaker 2: we wanted that characteristic in some characters, and we use
Speaker 2: gabrielle to have that characteristic.
Speaker 1: And Noah. What'snawah's personality?
Speaker 6: How do he playful, cheering, right and energetic?
Speaker 1: That's Sean English, Professor Lee's fellow researcher.
Speaker 2: And Jiwu Ju is articulated and kind of smart, but
Speaker 2: she has her own way of thinking.
Speaker 1: I would end up spending a lot of time with
Speaker 1: ji Wu. She's something of a character. What Sean about
Speaker 1: the process of creating these AI assistants? What does building
Speaker 1: the content side of the AI assistant entail Sean what it.
Speaker 6: Sets up a series of actions, effectively, which are response cases.
Speaker 6: You can kind of think of them as you have
Speaker 6: a series of questions that you tie to an intent,
Speaker 6: and then that intent has reactions from the bot, and
Speaker 6: so effectively, if we were looking to say, make a
Speaker 6: hello action, we would have all the different ways that
Speaker 6: people could say Hello, Hello, what's up, how you doing,
Speaker 6: and all that kind of stuff.
Speaker 1: Sean says, the longer the list of potential responses, the better.
Speaker 1: But AI's responses don't just follow the list. The AI
Speaker 1: assistant uses those suggested responses to come up with a
Speaker 1: universe of other responses, and in that process sometimes it
Speaker 1: comes up with things that just don't make sense.
Speaker 6: And from a technological standpoint, while AI is a fantastic tool,
Speaker 6: AI can hallucinate, which I mean just give things that
Speaker 6: it's just straight up made up. There's a famous example
Speaker 6: of this called the three rs is where you ask
Speaker 6: a popular large language model how many RS are in strawberry,
Speaker 6: and it gives you the wrong answer and he repeats
Speaker 6: that result repetitively. You always want to have a human
Speaker 6: interacting with the system to be able to go Hey,
Speaker 6: that's a little crazy. I don't think that's exactly what
Speaker 6: we're going for here.
Speaker 1: That's why it's good to have someone like Sean English
Speaker 1: around to step in and get the model back on track,
Speaker 1: and over time, when the model has enough training, it's
Speaker 1: ready for the teachers in training. One of the rollouts
Speaker 1: of Jiwu, Gabriel and Noah was with the teacher training
Speaker 1: program at the University of Missouri.
Speaker 4: I was just kind of excited to see what the
Speaker 4: program was and what it was going to be doing.
Speaker 1: This is Logan Hovis, a junior at Missouri on the
Speaker 1: path to becoming an elementary school teacher.
Speaker 4: Obviously a little skeptical when he said it was sosed to,
Speaker 4: you know, be like talking to a student. You're like,
Speaker 4: there's no way this AI thing is going to totally
Speaker 4: sound like a second grader or a third grader, Like
Speaker 4: it's going to sound like an adult, or it's going
Speaker 4: to sound like a robot that knows all the answers.
Speaker 4: And it really didn't. It really was like talking to
Speaker 4: a child. It was very very well developed in the
Speaker 4: way that you really sit there and you feel like
Speaker 4: you're talking to a kid.
Speaker 1: Her point wasn't that Jiwu and her fellow avatars were
Speaker 1: equivalent to real kids, of course not, but for someone
Speaker 1: starting out, someone who was already nervous about being plunged
Speaker 1: into a classroom of nine year olds, Geewu was like
Speaker 1: a warm up before a baseball game.
Speaker 4: What I can think of is like, you know how,
Speaker 4: when you're at batting practice for baseball or softball, you
Speaker 4: have those automatic pitchers that throw them because you're working
Speaker 4: on your skill as the hitter. What can I do differently?
Speaker 4: What am I doing wrong? But that doesn't replace the
Speaker 4: game and what you do in a game. But this
Speaker 4: is you getting to practice your own skills to be
Speaker 4: better when you go in a game. And I think
Speaker 4: that's kind of what the AI software feels like for us.
Speaker 1: In batting practice, the pitches don't come as hard and
Speaker 1: fast as the pitch is in a real game, but
Speaker 1: you get to stand at the plate and the picture
Speaker 1: throws you dozens of balls over and over again in
Speaker 1: a concentrated block that allows you to work on your
Speaker 1: swing closely and carefully.
Speaker 4: There's a lot less stimulus going on around because the
Speaker 4: classroom is very, very busy. It's wonderful, it's beautiful, but
Speaker 4: it's very, very busy, so sometimes it's hard to keep
Speaker 4: you know, that focus in on the tasks that they're
Speaker 4: doing at hand, and also in the teacher setting, you're
Speaker 4: also kind of always looking around making sure that other
Speaker 4: students are doing what they're supposed to be doing, but
Speaker 4: also like if they need any help, if everything's going
Speaker 4: okay in the classroom. So being on the Jiwu chat,
Speaker 4: it was just nice that you didn't have to do
Speaker 4: any of the extra work to keep the focus on there,
Speaker 4: and it also felt you didn't have to feel the
Speaker 4: student's nervousness of being one on one with you. And
Speaker 4: also as a tea shirt, it was a lot less
Speaker 4: pressure too, because I was like, Okay, I'm taking this serious.
Speaker 4: This is a student I'm questioning.
Speaker 8: But I also know I'm probably not going to hurt
Speaker 8: someone's feelings right now, and that's terrify. I'm going to
Speaker 8: ask the wrong question and have set the child because
Speaker 8: I've done that.
Speaker 1: We think of the typical use of AI as a
Speaker 1: tool for speeding things up. That's what we always hear
Speaker 1: that the introduction of AI to problem X gave an
Speaker 1: answer in minutes. When solving problem X used to take weeks,
Speaker 1: but we shouldn't forget another use that it allows us
Speaker 1: to slow things down. Hovis, if she wanted to, could
Speaker 1: spend a whole weekend practicing with g Wu. A real
Speaker 1: nine year old will get frustrated on board with the
Speaker 1: fumbling novice after ten minutes, but g Wu g Wu
Speaker 1: will happily answer questions for as long as it takes
Speaker 1: for the people who want to learn to be responsive
Speaker 1: to learn how to be responsive. At the end of
Speaker 1: my time at Kenesas State, Sean and Dabe led me
Speaker 1: to a small table where Dabe had set up her laptop.
Speaker 1: In the corner of the screen was a chat box
Speaker 1: of the sort we've all seen and used a thousand times.
Speaker 1: Jiwu began she had been given a math problem.
Speaker 3: Who are of three fourth cup of a flower to
Speaker 3: the ball? Thanks to the added another three six is cup.
Speaker 3: It's a cotal amount of flower. Who use greater or
Speaker 3: dan or a less than war cup? How much flower
Speaker 3: they can loose?
Speaker 1: That's a simulation of Jiwu speaking. We pause her for
Speaker 1: a second. So Jeewu is trying to solve this problem,
Speaker 1: and the first thing she does is she draws a
Speaker 1: rectangle on the screen. This is a common tactic of
Speaker 1: nine year olds. Try to visualize the fractions, and she
Speaker 1: divides it into four pieces. And now she's gonna color
Speaker 1: in three of the four pieces. Yes, so she's representing.
Speaker 1: This is quite good. She's representing three quarters on the screen.
Speaker 2: This is.
Speaker 3: Three six.
Speaker 1: So now, Jiwu, there's another rectangle with six boxes and
Speaker 1: colors in three of them.
Speaker 3: Okay, the together that makes sikes come off.
Speaker 1: So then she counts up all the colored boxes and
Speaker 1: that's her numerator, and counts up the total number of
Speaker 1: boxes and that's her denominator. Ji Wu had counted the
Speaker 1: colored boxes and landed on an answer. When you add
Speaker 1: three quarters of a cup and three sixth of a cup,
Speaker 1: you get six tenths of a cup. So, according to
Speaker 1: ji Wu, Martin has less than one cup. And she
Speaker 1: thinks she solved the problem.
Speaker 2: Yes, okay, so it's less than one cup.
Speaker 1: Yeah, so she says it's less than one cup. Now,
Speaker 1: oh my god, this is hard. So the question is
Speaker 1: what do I, as a teacher say to Jiwu. We
Speaker 1: were off. The rules were simple. I couldn't give ji
Speaker 1: Wu the answer or explain to her what she was
Speaker 1: doing wrong. I had to be Deborah Ble. I had
Speaker 1: to help her find the way herself. The chat box
Speaker 1: in the corner of the screen was waiting for my
Speaker 1: first question. I thought for a moment and started typing,
Speaker 1: do you think the boxes in the red rectangle are
Speaker 1: the same size as the boxes in the blue rectangle?
Speaker 1: Then I turned to Sean and dabey, is that a
Speaker 1: good question?
Speaker 6: Yeah?
Speaker 7: Serious thing.
Speaker 2: Yeah, that's a good question.
Speaker 1: Jewu doesn't mess around. She answers immediately. So Ju says,
Speaker 1: the blue and red pieces are not the same sizes.
Speaker 2: Oh, so you understand now Ju knows that side differences.
Speaker 1: So she's pretty smart here. Yeah. Then I asked, if
Speaker 1: they are not the same size, do you think you
Speaker 1: can add them together? Ji Wu answered right away. Jiwu says,
Speaker 1: I have learned that I could add any numbers in
Speaker 1: grade too, So three p three is six and four
Speaker 1: to six is ten.
Speaker 2: Yeah. So she is using the knowledge of edding intiquers
Speaker 2: into edding fractions.
Speaker 1: Now I'm stumped. So now I have to somehow lead
Speaker 1: her to figure out a way to get her to
Speaker 1: understand that we're dealing with a different kind of problem,
Speaker 1: a harder problem. Amy Robertson had told me that learning
Speaker 1: how to do responsive teaching properly was really hard, and
Speaker 1: now I understood why I had to put my mind
Speaker 1: inside the mind of a nine year old. I had
Speaker 1: to internalize her knowledge base and assumptions, and keep in mind,
Speaker 1: I haven't been nine for a very long time. I
Speaker 1: honestly had no idea what to say next. I thought
Speaker 1: for a moment, I asked what I quickly realized was
Speaker 1: a hopelessly convoluted question. Dobby and Sean had built a
Speaker 1: mentor into the system, an experienced responsive teacher who supervises
Speaker 1: the session and offers advice. My mentor noticed that I
Speaker 1: was struggling, told me to implify my question.
Speaker 2: Remember she was a thirdth grader.
Speaker 1: Dabe was trying to help me too. She suggested, why
Speaker 1: not just ask jie Wu if three quarters is bigger
Speaker 1: or smaller than one half?
Speaker 2: So we are trying to help her to think about
Speaker 2: faction in a more conceptual way.
Speaker 1: This time, Jiwu understood. She wrote back, three quarters is
Speaker 1: larger than one half? I wrote back, is three six
Speaker 1: of a cup bigger or smaller than one half? Jewu said,
Speaker 1: I'm confused. Oh no, I've confused, Or.
Speaker 2: Gi Wu, it's good she's understanding, she's realizing her misconception,
Speaker 2: so she's getting confused.
Speaker 1: She says, I'm confused. Three quarters is pretty close to
Speaker 1: one and adding three six would make it go over one. Oh,
Speaker 1: so she's got the answer. Yeah, But then she says,
Speaker 1: but there are six pieces out of ten, which is
Speaker 1: less than one, so I don't get it.
Speaker 2: So she's the point that, oh this, I have something
Speaker 2: wrong here. That's a good sign.
Speaker 1: She's getting there.
Speaker 2: She's getting there.
Speaker 1: But I still have to get her to She has
Speaker 1: to get the six pieces out of ten out of
Speaker 1: her head. Yeah, I have no idea how to do that,
Speaker 1: and she thinks she's confused when she has. Actually she's
Speaker 1: figured out the answer. Yeah she did. So we have advance.
Speaker 1: Even in my stumbling and bumbling, we've made some progress, and.
Speaker 6: Very notable progress.
Speaker 1: As my conversation with ji Wu went on for some time,
Speaker 1: and eventually I got there. Ji Wu found her way
Speaker 1: to the right answer. She said, I have more than
Speaker 1: one cup of flower. The mentor chimed in. I got
Speaker 1: a little emoji that made me feel good, And when
Speaker 1: it was over, I realized two things. The first was
Speaker 1: I needed more batting practice, much more, and that batting
Speaker 1: practice was really, really easy to do, because someone has
Speaker 1: gone to the trouble of building me my very own
Speaker 1: baseball diamond and given me a pitcher who had thrown
Speaker 1: me baseballs all day long. My second thought was that
Speaker 1: I've been thinking about AI all wrong. I have interpreted
Speaker 1: a lot of the talk about the promise of AI
Speaker 1: to be about replacing human expertise. I had actually thought
Speaker 1: when I first heard about Dabe's project that that's what
Speaker 1: Dabe and Sean were doing, creating an AI to teach
Speaker 1: students by passing the teacher altogether. But if you did
Speaker 1: it that way, you had missed the magic of the classroom.
Speaker 1: Remember Eleanor Duckworth's quote, the goal of education is for
Speaker 1: students to have wonderful ideas and have a good time
Speaker 1: having them. I think we often focus on the first
Speaker 1: part of that formulation, the wonderful ideas, but neglect the second,
Speaker 1: the good time having them. Real learning is born in pleasure,
Speaker 1: in community, in playful discussion, in a group of kids
Speaker 1: coming together to solve a problem, And all of that
Speaker 1: magic only comes from human interaction from a teacher who
Speaker 1: is skilled enough to inspire a class of nine year
Speaker 1: olds We don't want AI assistants to replace the teacher.
Speaker 1: We want AI assistants to help teachers turn themselves into
Speaker 1: even better teachers. Smart Talks with IBM is produced by
Speaker 1: Matt Ramano, Amy Gains, McQuaid, Lucy Sullivan, and Jake Harper.
Speaker 1: We're edited by Lacy Roberts, Engineering by Nina Bird Lawrence,
Speaker 1: mastering by Sarah Bugier, Music by Gramoscope. Special thanks to
Speaker 1: Tatiana Lieberman and Cassidy Meyer. Smart Talks with IBM is
Speaker 1: a production of Pushkin Industries and Ruby Studio at iHeartMedia.
Speaker 1: To find more Pushkin podcasts, listen on the iHeartRadio app,
Speaker 1: Apple Podcasts, or wherever you get your podcasts. I'm Malcolm Gabo.
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Speaker 1: on this podcast don't necessarily represent ib m's positions, strategies,
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