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