May 20, 2025 • 27 mins
Malcolm Gladwell visits Kennesaw State University to learn about Jiwoo, an AI Assistant that helps future teachers practice responsive teaching by simulating classroom interactions with students. Discover AI’s impact on teaching methods to prepare teachers for the classroom.
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Speaker 1 (00:02):
In the world of educational research, there is a famous
video of a boy named Sean. I don't mean famous
in a sense that it has a million views on YouTube.
I mean it in the circle of people who think
about teaching and how to make teaching better. The video
has been written about in journal articles and shown over
and again in college classrooms. It's a ten minute clip
(00:24):
of a third grade class somewhere in Michigan. It was
filmed in January of nineteen ninety, so the video is
a bit grainy. The teacher's name is Deborah Lowenburg Ball.
She's a professor at Michigan State University who is part
of her research teaches a one hour math class at
a local elementary school on the day in question. Miss
Ball begins by asking her students about the previous day's lesson,
(00:47):
which was about even and odd numbers.
Speaker 2 (00:50):
And I would like to hear from as many people
as possible what comments you had, reactions you had to
be in that meeting yesterday.
Speaker 1 (00:57):
A little boy with black hair raises his hand. His
name is Sean.
Speaker 3 (01:01):
I know I don't have anything about the meeting yesterday,
but I was just thinking about.
Speaker 1 (01:06):
Sit Sean was thinking about the number six.
Speaker 3 (01:10):
I was thinking that it's a it's an ID. It
can be an odd number two because there could be two, two, four, six, two,
three tudents and two threes, it would be an add anthonisina.
Speaker 2 (01:28):
She thinks.
Speaker 3 (01:29):
To make it takes me two things.
Speaker 1 (01:32):
Sean doesn't understand what odd and even means. He thinks
that just because you can break down six in an
odd number of parts and an even number of parts,
that six must exist in some magical middle category. And
when you listen to the Sean videotape, you keep waiting
for the teacher to say, oh, no, Sean, you misunderstand.
(01:52):
But Deborah Ball doesn't do that. She never tells him
he's wrong. Instead, she simply asks him to explain his thinking.
Speaker 4 (02:00):
Me and the two things that you put together to
make it were odd, right, three and three of each
child any think proba.
Speaker 1 (02:09):
Two or easy bald and asked the class to give
their views. Other students jump up and explain their theories
on the blackboard. For the next fifteen minutes, she definitely
guides the class through an in depth investigation of what
she calls Shawn numbers, until Sewn himself realizes that the
real meaning of odd and even is something different than
(02:29):
he had imagined, and now he gets it.
Speaker 3 (02:33):
I've been thank you for waring in love.
Speaker 1 (02:36):
I don't want to focus just on how little Sean
finally made his own way to the right answer. I'm
interested in what his teacher did to get him there.
Deborah Ball worked magic. She never told Sean the right answer,
She just led him to a place where he could
discover it for himself. My name is Malcolm Glawo. This
(02:57):
is season six of Smart Talks with IBM, where we
are for our listeners a glimpse behind the curtain of
the world of technology and artificial intelligence. In this season,
we're going to visit companies as varied as Lail and
Ferrari and tell stories of how they're using artificial intelligence
and data to transform the way they do business. This
(03:18):
episode is about the promise of a radical new idea
called responsive teaching, the kind of teaching that took place
that day in Shawn's classroom, and whether artificial intelligence can
help us train the next generation of teachers to be
as good as Deborah Ball. Before we talk about how
(03:41):
AI could transform the way we train teachers, I want
to go back for a moment to the famous video
of Sean. In the video, the teacher Deborah Ball doesn't
have a predetermined plan that she's imposing on the class.
She's improvising, making up her approach as she goes along,
responding to her students odd theory about the number six. Second,
(04:03):
she's taking Sean seriously. She's not dismissing his theory. She's
listening to him and trying to understand the problem from
his perspective. And Thirdly, and most importantly, she's not force
feeding him the right answer. She's being patient. She's waiting
to see if with just the right subtle hints, he
(04:23):
can get to the right answer on his own. Improvisation,
empathy patients. That's responsive teaching.
Speaker 5 (04:32):
What I think about in terms of responsiveness is more
like I think that students need to have a sense
of agency in what happens in the classroom, and like
authentic agency where they can be legitimized as knowers.
Speaker 1 (04:50):
I spoke to a physicist at Seattle Pacific University named
Amy Robertson, a longtime advocate for responsive teaching. She uses
the Sean video in her classroom.
Speaker 5 (05:00):
You have to trust that kids have a way of
doing that, and that like heard. What she mostly did
was to facilitate a conversation and to say you have
to listen to them talk.
Speaker 1 (05:09):
No one told him he was wrong, that's right. And
then he goes, He goes, I didn't think of it
that way. Again, I thank you for leaving alone. You've
expanded my understanding. Thank you for bringing it up again.
Speaker 6 (05:23):
It's like this, I love.
Speaker 5 (05:27):
I know responsive teaching, as I think about it is
kind of rooted in this, like Eleanor Duckworth's work around
the Having of Wonderful Ideas, where she says, like the
goal of education is for students to have wonderful ideas
and to have a good time having them.
Speaker 1 (05:42):
I love that. I've never heard that. What a beautiful,
succinct way of summing up the purpose of education. Yes,
responsive teaching is beautiful. It's rare to find a new
teaching idea that everyone loves. This is one of those
rare ideas. Watching the Ball classroom, all I could think was,
I really really hope my daughters get to experience a
(06:04):
math class like that. Far too many kids are convincing
themselves at far too young an age that math isn't
for them, and responsive teaching is a way to solve
that problem. But here is the issue. It's really really
hard to teach responsive teaching. Robertson says that teaching exists
in a cultural environment where the teacher is expected to
(06:27):
be the source of truth. That teaching is about the
immediate correction of error and not letting a child wander
down the pathway of their own Misunderstanding. Responsive teaching is
deeply counterintuitive, and the only way to understand its beauty
is to do it over and over again. Aspiring teachers
(06:47):
need a way to practice. For as long as there
has been technology, people have turned to digital machines to
solve problems. My father was a mathematician and I remember
him coming home in the nineteen seventies with a big
stack of computer cards in his briefcase that he used
to program the main frame back of the office. Today,
(07:07):
with the rise of artificial intelligence, the scale and complexity
of the problems technology can help us solve has jumped
by many orders of magnitude. You must have worked with
a million customers who are experimenting with llm's. Has there
been one use case that you were like, WHOA, I
had no idea or just simply that's clever. I'm speaking
(07:29):
to Brian Bissell, who works out of IBM's Manhattan office.
He helps IBM customers discover how best to get AI
to work for them.
Speaker 7 (07:38):
There is one, but I don't think I can talk
about it unfortunately.
Speaker 1 (07:41):
Wait, you can't tease me like that, can you wait?
Disguise disguise it for me.
Speaker 7 (07:47):
Just give me a general It was about the ability
to pull certain types of information out of documents that
you wouldn't think you would be able to get the
model to do, and be able to do that at
a very large scale.
Speaker 1 (08:01):
Bissil's point was that we are well past the stage
where anyone wonders whether AI can be useful. The real
question now is what problems do we want to use
it to solve? Where it can make the biggest difference,
And Bissil saw lots of opportunities in education.
Speaker 7 (08:18):
I have two kids, one in middle school and one
who just graduated high school, and I'm well aware of
students using things like chat GPT to do their homework.
And it's very easy to take tools like that and
even IBM's own large language models, and just take a problem,
(08:38):
a piece of homework, something you want written, and drop
it into that and have it generate the answer for
you and the student. The user in that case hasn't
done any work, they haven't put any real thought into it.
Speaker 1 (08:52):
To Bissil, that's the wrong use of AI. That's technology
making is dumber. What we really want is technology that
makes us smarter. This will explain to me that there
are now two big tools being used for AI productivity,
AI agents and AI assistants. Let's start with AI agents.
AI agents can reason plan and collaborate with other AI
(09:15):
tools to autonomously perform tasks for a user. This will
gave me an example of how college freshmen might use
an AI agent.
Speaker 7 (09:23):
As a new student, you may not know how do
I do with my health and wellness issue? Some of
your credits are going to get for this given class.
You could talk to someone and find out some of that,
but maybe it's a little bit sensitive and you don't
want to do that.
Speaker 1 (09:38):
Bisill told me you could build an AI agent, a
resource for new students that helps them navigate a new campus,
register for classes, access the services they need, and even
schedule appointments on their behalf, which in turn buys them
more time to focus on their actual schoolwork.
Speaker 7 (09:54):
We can see patterns of how agents and assistants can
help help employees and customers and end users be more productive,
automate workflows so they're not doing certain types of repetitive
work over and over again, and streamlining their lives and
making data more accessible to them twenty four hours a day.
Speaker 1 (10:17):
But Bissel says you can also use AI assistance in
the education space. AI assistants are reactive as opposed to
AI agents, which are proactive. AI assistants only perform tasks
at your request. They're programmed to answer your questions, and
as it turns out, AI assistants are now being used
(10:37):
to further the responsive teaching revolution, which is why I
found myself on a beautiful Georgia spring day not long
ago on the campus of Kansas State University, sitting in
the classroom with two researchers, one of them Professor Dabe Lee.
Let's go into the journey of building this thing. You
started by taking a course. What was the course you took?
Speaker 2 (11:00):
So it was offered by Coursera, It was designed by IBM.
It was AI Foundation for everyone.
Speaker 1 (11:12):
In her AI Foundation's course, Lee learned how to build
an AI assistant using IBM watson X that course took
how long to take.
Speaker 2 (11:20):
It was not to know it was like fourteen mix.
Speaker 1 (11:23):
Lee's idea was to train an AI assistant on classroom
data to play the role of Sean, a digital persona
of a nine year old who likes math but doesn't
always understand math, and that AI assistant, she thought could
be used to train preservice teachers or teachers in training
who are preparing to enter one of the most challenging
professions in the modern world.
Speaker 2 (11:46):
So when you think about the teacher education and a
major challenge that teacher education face is that we need
children to practice with. We need instructors who if the
instruction on the pedagogical skills. So when you look at
the teacher education program, we have coursework in field experience,
(12:10):
and in those two areas there is something missing all
the time.
Speaker 1 (12:16):
Li says that pre service teachers often lack access to
both students and experienced teachers during their education.
Speaker 2 (12:23):
So what we try to resolve is that we have
this virtual student for pre service teacher to work with
so that they can practice their responsive teaching skills.
Speaker 1 (12:36):
The first AI assistant Lee created is g Wu. G
Wu emulates the persona of a nine year old third
grade girl. Then, with the help of one of her collaborators,
a researcher at Canazon named Sean English, she created two
more AI assistants, Gabriel and Noah, each of which have
their own distinctive characteristics. So how are Gabriel and Noah different.
Speaker 2 (13:00):
From ji Wu gab Real my first one? He is
very short answered. If you ask an open ended question,
he will answer it in a close way. So I
use that characteristic. And that's the problem that most teachers
actually base. They're asked children who are shay, who are reserved,
(13:22):
and who would not sure much of their thoughts. So
we wanted that characteristic in some characters, and we use
gabrielle to have that characteristic.
Speaker 1 (13:34):
And Noah. What'snawah's personality?
Speaker 6 (13:38):
How do he playful, cheering, right and energetic?
Speaker 1 (13:42):
That's Sean English, Professor Lee's fellow researcher.
Speaker 2 (13:45):
And Jiwu Ju is articulated and kind of smart, but
she has her own way of thinking.
Speaker 1 (13:54):
I would end up spending a lot of time with
ji Wu. She's something of a character. What Sean about
the process of creating these AI assistants? What does building
the content side of the AI assistant entail Sean what it.
Speaker 6 (14:09):
Sets up a series of actions, effectively, which are response cases.
You can kind of think of them as you have
a series of questions that you tie to an intent,
and then that intent has reactions from the bot, and
so effectively, if we were looking to say, make a
hello action, we would have all the different ways that
people could say Hello, Hello, what's up, how you doing,
(14:31):
and all that kind of stuff.
Speaker 1 (14:32):
Sean says, the longer the list of potential responses, the better.
But AI's responses don't just follow the list. The AI
assistant uses those suggested responses to come up with a
universe of other responses, and in that process sometimes it
comes up with things that just don't make sense.
Speaker 6 (14:51):
And from a technological standpoint, while AI is a fantastic tool,
AI can hallucinate, which I mean just give things that
it's just straight up made up. There's a famous example
of this called the three rs is where you ask
a popular large language model how many RS are in strawberry,
and it gives you the wrong answer and he repeats
that result repetitively. You always want to have a human
interacting with the system to be able to go Hey,
(15:13):
that's a little crazy. I don't think that's exactly what
we're going for here.
Speaker 1 (15:17):
That's why it's good to have someone like Sean English
around to step in and get the model back on track,
and over time, when the model has enough training, it's
ready for the teachers in training. One of the rollouts
of Jiwu, Gabriel and Noah was with the teacher training
program at the University of Missouri.
Speaker 4 (15:36):
I was just kind of excited to see what the
program was and what it was going to be doing.
Speaker 1 (15:42):
This is Logan Hovis, a junior at Missouri on the
path to becoming an elementary school teacher.
Speaker 4 (15:47):
Obviously a little skeptical when he said it was sosed to,
you know, be like talking to a student. You're like,
there's no way this AI thing is going to totally
sound like a second grader or a third grader, Like
it's going to sound like an adult, or it's going
to sound like a robot that knows all the answers.
And it really didn't. It really was like talking to
a child. It was very very well developed in the
(16:08):
way that you really sit there and you feel like
you're talking to a kid.
Speaker 1 (16:12):
Her point wasn't that Jiwu and her fellow avatars were
equivalent to real kids, of course not, but for someone
starting out, someone who was already nervous about being plunged
into a classroom of nine year olds, Geewu was like
a warm up before a baseball game.
Speaker 4 (16:27):
What I can think of is like, you know how,
when you're at batting practice for baseball or softball, you
have those automatic pitchers that throw them because you're working
on your skill as the hitter. What can I do differently?
What am I doing wrong? But that doesn't replace the
game and what you do in a game. But this
is you getting to practice your own skills to be
better when you go in a game. And I think
that's kind of what the AI software feels like for us.
Speaker 1 (16:52):
In batting practice, the pitches don't come as hard and
fast as the pitch is in a real game, but
you get to stand at the plate and the picture
throws you dozens of balls over and over again in
a concentrated block that allows you to work on your
swing closely and carefully.
Speaker 4 (17:09):
There's a lot less stimulus going on around because the
classroom is very, very busy. It's wonderful, it's beautiful, but
it's very, very busy, so sometimes it's hard to keep
you know, that focus in on the tasks that they're
doing at hand, and also in the teacher setting, you're
also kind of always looking around making sure that other
students are doing what they're supposed to be doing, but
also like if they need any help, if everything's going
(17:30):
okay in the classroom. So being on the Jiwu chat,
it was just nice that you didn't have to do
any of the extra work to keep the focus on there,
and it also felt you didn't have to feel the
student's nervousness of being one on one with you. And
also as a tea shirt, it was a lot less
pressure too, because I was like, Okay, I'm taking this serious.
(17:53):
This is a student I'm questioning.
Speaker 8 (17:55):
But I also know I'm probably not going to hurt
someone's feelings right now, and that's terrify. I'm going to
ask the wrong question and have set the child because
I've done that.
Speaker 1 (18:06):
We think of the typical use of AI as a
tool for speeding things up. That's what we always hear
that the introduction of AI to problem X gave an
answer in minutes. When solving problem X used to take weeks,
but we shouldn't forget another use that it allows us
to slow things down. Hovis, if she wanted to, could
(18:27):
spend a whole weekend practicing with g Wu. A real
nine year old will get frustrated on board with the
fumbling novice after ten minutes, but g Wu g Wu
will happily answer questions for as long as it takes
for the people who want to learn to be responsive
to learn how to be responsive. At the end of
(18:47):
my time at Kenesas State, Sean and Dabe led me
to a small table where Dabe had set up her laptop.
In the corner of the screen was a chat box
of the sort we've all seen and used a thousand times.
Jiwu began she had been given a math problem.
Speaker 3 (19:06):
Who are of three fourth cup of a flower to
the ball? Thanks to the added another three six is cup.
It's a cotal amount of flower. Who use greater or
dan or a less than war cup? How much flower
(19:27):
they can loose?
Speaker 1 (19:28):
That's a simulation of Jiwu speaking. We pause her for
a second. So Jeewu is trying to solve this problem,
and the first thing she does is she draws a
rectangle on the screen. This is a common tactic of
nine year olds. Try to visualize the fractions, and she
divides it into four pieces. And now she's gonna color
(19:52):
in three of the four pieces. Yes, so she's representing.
This is quite good. She's representing three quarters on the screen.
Speaker 2 (20:00):
This is.
Speaker 3 (20:02):
Three six.
Speaker 1 (20:05):
So now, Jiwu, there's another rectangle with six boxes and
colors in three of them.
Speaker 3 (20:12):
Okay, the together that makes sikes come off.
Speaker 1 (20:21):
So then she counts up all the colored boxes and
that's her numerator, and counts up the total number of
boxes and that's her denominator. Ji Wu had counted the
colored boxes and landed on an answer. When you add
three quarters of a cup and three sixth of a cup,
you get six tenths of a cup. So, according to
(20:42):
ji Wu, Martin has less than one cup. And she
thinks she solved the problem.
Speaker 2 (20:46):
Yes, okay, so it's less than one cup.
Speaker 1 (20:49):
Yeah, so she says it's less than one cup. Now,
oh my god, this is hard. So the question is
what do I, as a teacher say to Jiwu. We
were off. The rules were simple. I couldn't give ji
Wu the answer or explain to her what she was
doing wrong. I had to be Deborah Ble. I had
to help her find the way herself. The chat box
(21:11):
in the corner of the screen was waiting for my
first question. I thought for a moment and started typing,
do you think the boxes in the red rectangle are
the same size as the boxes in the blue rectangle?
Then I turned to Sean and dabey, is that a
good question?
Speaker 6 (21:27):
Yeah?
Speaker 7 (21:28):
Serious thing.
Speaker 2 (21:30):
Yeah, that's a good question.
Speaker 1 (21:32):
Jewu doesn't mess around. She answers immediately. So Ju says,
the blue and red pieces are not the same sizes.
Speaker 2 (21:39):
Oh, so you understand now Ju knows that side differences.
Speaker 1 (21:44):
So she's pretty smart here. Yeah. Then I asked, if
they are not the same size, do you think you
can add them together? Ji Wu answered right away. Jiwu says,
I have learned that I could add any numbers in
grade too, So three p three is six and four
to six is ten.
Speaker 2 (22:02):
Yeah. So she is using the knowledge of edding intiquers
into edding fractions.
Speaker 1 (22:09):
Now I'm stumped. So now I have to somehow lead
her to figure out a way to get her to
understand that we're dealing with a different kind of problem,
a harder problem. Amy Robertson had told me that learning
how to do responsive teaching properly was really hard, and
now I understood why I had to put my mind
(22:31):
inside the mind of a nine year old. I had
to internalize her knowledge base and assumptions, and keep in mind,
I haven't been nine for a very long time. I
honestly had no idea what to say next. I thought
for a moment, I asked what I quickly realized was
a hopelessly convoluted question. Dobby and Sean had built a
mentor into the system, an experienced responsive teacher who supervises
(22:54):
the session and offers advice. My mentor noticed that I
was struggling, told me to implify my question.
Speaker 2 (23:01):
Remember she was a thirdth grader.
Speaker 1 (23:04):
Dabe was trying to help me too. She suggested, why
not just ask jie Wu if three quarters is bigger
or smaller than one half?
Speaker 2 (23:12):
So we are trying to help her to think about
faction in a more conceptual way.
Speaker 1 (23:18):
This time, Jiwu understood. She wrote back, three quarters is
larger than one half? I wrote back, is three six
of a cup bigger or smaller than one half? Jewu said,
I'm confused. Oh no, I've confused, Or.
Speaker 2 (23:35):
Gi Wu, it's good she's understanding, she's realizing her misconception,
so she's getting confused.
Speaker 1 (23:41):
She says, I'm confused. Three quarters is pretty close to
one and adding three six would make it go over one. Oh,
so she's got the answer. Yeah, But then she says,
but there are six pieces out of ten, which is
less than one, so I don't get it.
Speaker 2 (23:55):
So she's the point that, oh this, I have something
wrong here. That's a good sign.
Speaker 1 (24:01):
She's getting there.
Speaker 2 (24:02):
She's getting there.
Speaker 1 (24:03):
But I still have to get her to She has
to get the six pieces out of ten out of
her head. Yeah, I have no idea how to do that,
and she thinks she's confused when she has. Actually she's
figured out the answer. Yeah she did. So we have advance.
Even in my stumbling and bumbling, we've made some progress, and.
Speaker 6 (24:24):
Very notable progress.
Speaker 1 (24:25):
As my conversation with ji Wu went on for some time,
and eventually I got there. Ji Wu found her way
to the right answer. She said, I have more than
one cup of flower. The mentor chimed in. I got
a little emoji that made me feel good, And when
(24:46):
it was over, I realized two things. The first was
I needed more batting practice, much more, and that batting
practice was really, really easy to do, because someone has
gone to the trouble of building me my very own
baseball diamond and given me a pitcher who had thrown
me baseballs all day long. My second thought was that
(25:07):
I've been thinking about AI all wrong. I have interpreted
a lot of the talk about the promise of AI
to be about replacing human expertise. I had actually thought
when I first heard about Dabe's project that that's what
Dabe and Sean were doing, creating an AI to teach
students by passing the teacher altogether. But if you did
it that way, you had missed the magic of the classroom.
(25:29):
Remember Eleanor Duckworth's quote, the goal of education is for
students to have wonderful ideas and have a good time
having them. I think we often focus on the first
part of that formulation, the wonderful ideas, but neglect the second,
the good time having them. Real learning is born in pleasure,
(25:50):
in community, in playful discussion, in a group of kids
coming together to solve a problem, And all of that
magic only comes from human interaction from a teacher who
is skilled enough to inspire a class of nine year
olds We don't want AI assistants to replace the teacher.
We want AI assistants to help teachers turn themselves into
(26:13):
even better teachers. Smart Talks with IBM is produced by
Matt Ramano, Amy Gains, McQuaid, Lucy Sullivan, and Jake Harper.
(26:35):
We're edited by Lacy Roberts, Engineering by Nina Bird Lawrence,
mastering by Sarah Bugier, Music by Gramoscope. Special thanks to
Tatiana Lieberman and Cassidy Meyer. Smart Talks with IBM is
a production of Pushkin Industries and Ruby Studio at iHeartMedia.
To find more Pushkin podcasts, listen on the iHeartRadio app,
(26:57):
Apple Podcasts, or wherever you get your podcasts. I'm Malcolm Gabo.
This is a paid advertisement from ib M. The conversations
on this podcast don't necessarily represent ib m's positions, strategies,
or opinions.
In the world of educational research, there is a famous
video of a boy named Sean. I don't mean famous
in a sense that it has a million views on YouTube.
I mean it in the circle of people who think
about teaching and how to make teaching better. The video
has been written about in journal articles and shown over
and again in college classrooms. It's a ten minute clip
(00:24):
of a third grade class somewhere in Michigan. It was
filmed in January of nineteen ninety, so the video is
a bit grainy. The teacher's name is Deborah Lowenburg Ball.
She's a professor at Michigan State University who is part
of her research teaches a one hour math class at
a local elementary school on the day in question. Miss
Ball begins by asking her students about the previous day's lesson,
(00:47):
which was about even and odd numbers.
Speaker 2 (00:50):
And I would like to hear from as many people
as possible what comments you had, reactions you had to
be in that meeting yesterday.
Speaker 1 (00:57):
A little boy with black hair raises his hand. His
name is Sean.
Speaker 3 (01:01):
I know I don't have anything about the meeting yesterday,
but I was just thinking about.
Speaker 1 (01:06):
Sit Sean was thinking about the number six.
Speaker 3 (01:10):
I was thinking that it's a it's an ID. It
can be an odd number two because there could be two, two, four, six, two,
three tudents and two threes, it would be an add anthonisina.
Speaker 2 (01:28):
She thinks.
Speaker 3 (01:29):
To make it takes me two things.
Speaker 1 (01:32):
Sean doesn't understand what odd and even means. He thinks
that just because you can break down six in an
odd number of parts and an even number of parts,
that six must exist in some magical middle category. And
when you listen to the Sean videotape, you keep waiting
for the teacher to say, oh, no, Sean, you misunderstand.
(01:52):
But Deborah Ball doesn't do that. She never tells him
he's wrong. Instead, she simply asks him to explain his thinking.
Speaker 4 (02:00):
Me and the two things that you put together to
make it were odd, right, three and three of each
child any think proba.
Speaker 1 (02:09):
Two or easy bald and asked the class to give
their views. Other students jump up and explain their theories
on the blackboard. For the next fifteen minutes, she definitely
guides the class through an in depth investigation of what
she calls Shawn numbers, until Sewn himself realizes that the
real meaning of odd and even is something different than
(02:29):
he had imagined, and now he gets it.
Speaker 3 (02:33):
I've been thank you for waring in love.
Speaker 1 (02:36):
I don't want to focus just on how little Sean
finally made his own way to the right answer. I'm
interested in what his teacher did to get him there.
Deborah Ball worked magic. She never told Sean the right answer,
She just led him to a place where he could
discover it for himself. My name is Malcolm Glawo. This
(02:57):
is season six of Smart Talks with IBM, where we
are for our listeners a glimpse behind the curtain of
the world of technology and artificial intelligence. In this season,
we're going to visit companies as varied as Lail and
Ferrari and tell stories of how they're using artificial intelligence
and data to transform the way they do business. This
(03:18):
episode is about the promise of a radical new idea
called responsive teaching, the kind of teaching that took place
that day in Shawn's classroom, and whether artificial intelligence can
help us train the next generation of teachers to be
as good as Deborah Ball. Before we talk about how
(03:41):
AI could transform the way we train teachers, I want
to go back for a moment to the famous video
of Sean. In the video, the teacher Deborah Ball doesn't
have a predetermined plan that she's imposing on the class.
She's improvising, making up her approach as she goes along,
responding to her students odd theory about the number six. Second,
(04:03):
she's taking Sean seriously. She's not dismissing his theory. She's
listening to him and trying to understand the problem from
his perspective. And Thirdly, and most importantly, she's not force
feeding him the right answer. She's being patient. She's waiting
to see if with just the right subtle hints, he
(04:23):
can get to the right answer on his own. Improvisation,
empathy patients. That's responsive teaching.
Speaker 5 (04:32):
What I think about in terms of responsiveness is more
like I think that students need to have a sense
of agency in what happens in the classroom, and like
authentic agency where they can be legitimized as knowers.
Speaker 1 (04:50):
I spoke to a physicist at Seattle Pacific University named
Amy Robertson, a longtime advocate for responsive teaching. She uses
the Sean video in her classroom.
Speaker 5 (05:00):
You have to trust that kids have a way of
doing that, and that like heard. What she mostly did
was to facilitate a conversation and to say you have
to listen to them talk.
Speaker 1 (05:09):
No one told him he was wrong, that's right. And
then he goes, He goes, I didn't think of it
that way. Again, I thank you for leaving alone. You've
expanded my understanding. Thank you for bringing it up again.
Speaker 6 (05:23):
It's like this, I love.
Speaker 5 (05:27):
I know responsive teaching, as I think about it is
kind of rooted in this, like Eleanor Duckworth's work around
the Having of Wonderful Ideas, where she says, like the
goal of education is for students to have wonderful ideas
and to have a good time having them.
Speaker 1 (05:42):
I love that. I've never heard that. What a beautiful,
succinct way of summing up the purpose of education. Yes,
responsive teaching is beautiful. It's rare to find a new
teaching idea that everyone loves. This is one of those
rare ideas. Watching the Ball classroom, all I could think was,
I really really hope my daughters get to experience a
(06:04):
math class like that. Far too many kids are convincing
themselves at far too young an age that math isn't
for them, and responsive teaching is a way to solve
that problem. But here is the issue. It's really really
hard to teach responsive teaching. Robertson says that teaching exists
in a cultural environment where the teacher is expected to
(06:27):
be the source of truth. That teaching is about the
immediate correction of error and not letting a child wander
down the pathway of their own Misunderstanding. Responsive teaching is
deeply counterintuitive, and the only way to understand its beauty
is to do it over and over again. Aspiring teachers
(06:47):
need a way to practice. For as long as there
has been technology, people have turned to digital machines to
solve problems. My father was a mathematician and I remember
him coming home in the nineteen seventies with a big
stack of computer cards in his briefcase that he used
to program the main frame back of the office. Today,
(07:07):
with the rise of artificial intelligence, the scale and complexity
of the problems technology can help us solve has jumped
by many orders of magnitude. You must have worked with
a million customers who are experimenting with llm's. Has there
been one use case that you were like, WHOA, I
had no idea or just simply that's clever. I'm speaking
(07:29):
to Brian Bissell, who works out of IBM's Manhattan office.
He helps IBM customers discover how best to get AI
to work for them.
Speaker 7 (07:38):
There is one, but I don't think I can talk
about it unfortunately.
Speaker 1 (07:41):
Wait, you can't tease me like that, can you wait?
Disguise disguise it for me.
Speaker 7 (07:47):
Just give me a general It was about the ability
to pull certain types of information out of documents that
you wouldn't think you would be able to get the
model to do, and be able to do that at
a very large scale.
Speaker 1 (08:01):
Bissil's point was that we are well past the stage
where anyone wonders whether AI can be useful. The real
question now is what problems do we want to use
it to solve? Where it can make the biggest difference,
And Bissil saw lots of opportunities in education.
Speaker 7 (08:18):
I have two kids, one in middle school and one
who just graduated high school, and I'm well aware of
students using things like chat GPT to do their homework.
And it's very easy to take tools like that and
even IBM's own large language models, and just take a problem,
(08:38):
a piece of homework, something you want written, and drop
it into that and have it generate the answer for
you and the student. The user in that case hasn't
done any work, they haven't put any real thought into it.
Speaker 1 (08:52):
To Bissil, that's the wrong use of AI. That's technology
making is dumber. What we really want is technology that
makes us smarter. This will explain to me that there
are now two big tools being used for AI productivity,
AI agents and AI assistants. Let's start with AI agents.
AI agents can reason plan and collaborate with other AI
(09:15):
tools to autonomously perform tasks for a user. This will
gave me an example of how college freshmen might use
an AI agent.
Speaker 7 (09:23):
As a new student, you may not know how do
I do with my health and wellness issue? Some of
your credits are going to get for this given class.
You could talk to someone and find out some of that,
but maybe it's a little bit sensitive and you don't
want to do that.
Speaker 1 (09:38):
Bisill told me you could build an AI agent, a
resource for new students that helps them navigate a new campus,
register for classes, access the services they need, and even
schedule appointments on their behalf, which in turn buys them
more time to focus on their actual schoolwork.
Speaker 7 (09:54):
We can see patterns of how agents and assistants can
help help employees and customers and end users be more productive,
automate workflows so they're not doing certain types of repetitive
work over and over again, and streamlining their lives and
making data more accessible to them twenty four hours a day.
Speaker 1 (10:17):
But Bissel says you can also use AI assistance in
the education space. AI assistants are reactive as opposed to
AI agents, which are proactive. AI assistants only perform tasks
at your request. They're programmed to answer your questions, and
as it turns out, AI assistants are now being used
(10:37):
to further the responsive teaching revolution, which is why I
found myself on a beautiful Georgia spring day not long
ago on the campus of Kansas State University, sitting in
the classroom with two researchers, one of them Professor Dabe Lee.
Let's go into the journey of building this thing. You
started by taking a course. What was the course you took?
Speaker 2 (11:00):
So it was offered by Coursera, It was designed by IBM.
It was AI Foundation for everyone.
Speaker 1 (11:12):
In her AI Foundation's course, Lee learned how to build
an AI assistant using IBM watson X that course took
how long to take.
Speaker 2 (11:20):
It was not to know it was like fourteen mix.
Speaker 1 (11:23):
Lee's idea was to train an AI assistant on classroom
data to play the role of Sean, a digital persona
of a nine year old who likes math but doesn't
always understand math, and that AI assistant, she thought could
be used to train preservice teachers or teachers in training
who are preparing to enter one of the most challenging
professions in the modern world.
Speaker 2 (11:46):
So when you think about the teacher education and a
major challenge that teacher education face is that we need
children to practice with. We need instructors who if the
instruction on the pedagogical skills. So when you look at
the teacher education program, we have coursework in field experience,
(12:10):
and in those two areas there is something missing all
the time.
Speaker 1 (12:16):
Li says that pre service teachers often lack access to
both students and experienced teachers during their education.
Speaker 2 (12:23):
So what we try to resolve is that we have
this virtual student for pre service teacher to work with
so that they can practice their responsive teaching skills.
Speaker 1 (12:36):
The first AI assistant Lee created is g Wu. G
Wu emulates the persona of a nine year old third
grade girl. Then, with the help of one of her collaborators,
a researcher at Canazon named Sean English, she created two
more AI assistants, Gabriel and Noah, each of which have
their own distinctive characteristics. So how are Gabriel and Noah different.
Speaker 2 (13:00):
From ji Wu gab Real my first one? He is
very short answered. If you ask an open ended question,
he will answer it in a close way. So I
use that characteristic. And that's the problem that most teachers
actually base. They're asked children who are shay, who are reserved,
(13:22):
and who would not sure much of their thoughts. So
we wanted that characteristic in some characters, and we use
gabrielle to have that characteristic.
Speaker 1 (13:34):
And Noah. What'snawah's personality?
Speaker 6 (13:38):
How do he playful, cheering, right and energetic?
Speaker 1 (13:42):
That's Sean English, Professor Lee's fellow researcher.
Speaker 2 (13:45):
And Jiwu Ju is articulated and kind of smart, but
she has her own way of thinking.
Speaker 1 (13:54):
I would end up spending a lot of time with
ji Wu. She's something of a character. What Sean about
the process of creating these AI assistants? What does building
the content side of the AI assistant entail Sean what it.
Speaker 6 (14:09):
Sets up a series of actions, effectively, which are response cases.
You can kind of think of them as you have
a series of questions that you tie to an intent,
and then that intent has reactions from the bot, and
so effectively, if we were looking to say, make a
hello action, we would have all the different ways that
people could say Hello, Hello, what's up, how you doing,
(14:31):
and all that kind of stuff.
Speaker 1 (14:32):
Sean says, the longer the list of potential responses, the better.
But AI's responses don't just follow the list. The AI
assistant uses those suggested responses to come up with a
universe of other responses, and in that process sometimes it
comes up with things that just don't make sense.
Speaker 6 (14:51):
And from a technological standpoint, while AI is a fantastic tool,
AI can hallucinate, which I mean just give things that
it's just straight up made up. There's a famous example
of this called the three rs is where you ask
a popular large language model how many RS are in strawberry,
and it gives you the wrong answer and he repeats
that result repetitively. You always want to have a human
interacting with the system to be able to go Hey,
(15:13):
that's a little crazy. I don't think that's exactly what
we're going for here.
Speaker 1 (15:17):
That's why it's good to have someone like Sean English
around to step in and get the model back on track,
and over time, when the model has enough training, it's
ready for the teachers in training. One of the rollouts
of Jiwu, Gabriel and Noah was with the teacher training
program at the University of Missouri.
Speaker 4 (15:36):
I was just kind of excited to see what the
program was and what it was going to be doing.
Speaker 1 (15:42):
This is Logan Hovis, a junior at Missouri on the
path to becoming an elementary school teacher.
Speaker 4 (15:47):
Obviously a little skeptical when he said it was sosed to,
you know, be like talking to a student. You're like,
there's no way this AI thing is going to totally
sound like a second grader or a third grader, Like
it's going to sound like an adult, or it's going
to sound like a robot that knows all the answers.
And it really didn't. It really was like talking to
a child. It was very very well developed in the
(16:08):
way that you really sit there and you feel like
you're talking to a kid.
Speaker 1 (16:12):
Her point wasn't that Jiwu and her fellow avatars were
equivalent to real kids, of course not, but for someone
starting out, someone who was already nervous about being plunged
into a classroom of nine year olds, Geewu was like
a warm up before a baseball game.
Speaker 4 (16:27):
What I can think of is like, you know how,
when you're at batting practice for baseball or softball, you
have those automatic pitchers that throw them because you're working
on your skill as the hitter. What can I do differently?
What am I doing wrong? But that doesn't replace the
game and what you do in a game. But this
is you getting to practice your own skills to be
better when you go in a game. And I think
that's kind of what the AI software feels like for us.
Speaker 1 (16:52):
In batting practice, the pitches don't come as hard and
fast as the pitch is in a real game, but
you get to stand at the plate and the picture
throws you dozens of balls over and over again in
a concentrated block that allows you to work on your
swing closely and carefully.
Speaker 4 (17:09):
There's a lot less stimulus going on around because the
classroom is very, very busy. It's wonderful, it's beautiful, but
it's very, very busy, so sometimes it's hard to keep
you know, that focus in on the tasks that they're
doing at hand, and also in the teacher setting, you're
also kind of always looking around making sure that other
students are doing what they're supposed to be doing, but
also like if they need any help, if everything's going
(17:30):
okay in the classroom. So being on the Jiwu chat,
it was just nice that you didn't have to do
any of the extra work to keep the focus on there,
and it also felt you didn't have to feel the
student's nervousness of being one on one with you. And
also as a tea shirt, it was a lot less
pressure too, because I was like, Okay, I'm taking this serious.
(17:53):
This is a student I'm questioning.
Speaker 8 (17:55):
But I also know I'm probably not going to hurt
someone's feelings right now, and that's terrify. I'm going to
ask the wrong question and have set the child because
I've done that.
Speaker 1 (18:06):
We think of the typical use of AI as a
tool for speeding things up. That's what we always hear
that the introduction of AI to problem X gave an
answer in minutes. When solving problem X used to take weeks,
but we shouldn't forget another use that it allows us
to slow things down. Hovis, if she wanted to, could
(18:27):
spend a whole weekend practicing with g Wu. A real
nine year old will get frustrated on board with the
fumbling novice after ten minutes, but g Wu g Wu
will happily answer questions for as long as it takes
for the people who want to learn to be responsive
to learn how to be responsive. At the end of
(18:47):
my time at Kenesas State, Sean and Dabe led me
to a small table where Dabe had set up her laptop.
In the corner of the screen was a chat box
of the sort we've all seen and used a thousand times.
Jiwu began she had been given a math problem.
Speaker 3 (19:06):
Who are of three fourth cup of a flower to
the ball? Thanks to the added another three six is cup.
It's a cotal amount of flower. Who use greater or
dan or a less than war cup? How much flower
(19:27):
they can loose?
Speaker 1 (19:28):
That's a simulation of Jiwu speaking. We pause her for
a second. So Jeewu is trying to solve this problem,
and the first thing she does is she draws a
rectangle on the screen. This is a common tactic of
nine year olds. Try to visualize the fractions, and she
divides it into four pieces. And now she's gonna color
(19:52):
in three of the four pieces. Yes, so she's representing.
This is quite good. She's representing three quarters on the screen.
Speaker 2 (20:00):
This is.
Speaker 3 (20:02):
Three six.
Speaker 1 (20:05):
So now, Jiwu, there's another rectangle with six boxes and
colors in three of them.
Speaker 3 (20:12):
Okay, the together that makes sikes come off.
Speaker 1 (20:21):
So then she counts up all the colored boxes and
that's her numerator, and counts up the total number of
boxes and that's her denominator. Ji Wu had counted the
colored boxes and landed on an answer. When you add
three quarters of a cup and three sixth of a cup,
you get six tenths of a cup. So, according to
(20:42):
ji Wu, Martin has less than one cup. And she
thinks she solved the problem.
Speaker 2 (20:46):
Yes, okay, so it's less than one cup.
Speaker 1 (20:49):
Yeah, so she says it's less than one cup. Now,
oh my god, this is hard. So the question is
what do I, as a teacher say to Jiwu. We
were off. The rules were simple. I couldn't give ji
Wu the answer or explain to her what she was
doing wrong. I had to be Deborah Ble. I had
to help her find the way herself. The chat box
(21:11):
in the corner of the screen was waiting for my
first question. I thought for a moment and started typing,
do you think the boxes in the red rectangle are
the same size as the boxes in the blue rectangle?
Then I turned to Sean and dabey, is that a
good question?
Speaker 6 (21:27):
Yeah?
Speaker 7 (21:28):
Serious thing.
Speaker 2 (21:30):
Yeah, that's a good question.
Speaker 1 (21:32):
Jewu doesn't mess around. She answers immediately. So Ju says,
the blue and red pieces are not the same sizes.
Speaker 2 (21:39):
Oh, so you understand now Ju knows that side differences.
Speaker 1 (21:44):
So she's pretty smart here. Yeah. Then I asked, if
they are not the same size, do you think you
can add them together? Ji Wu answered right away. Jiwu says,
I have learned that I could add any numbers in
grade too, So three p three is six and four
to six is ten.
Speaker 2 (22:02):
Yeah. So she is using the knowledge of edding intiquers
into edding fractions.
Speaker 1 (22:09):
Now I'm stumped. So now I have to somehow lead
her to figure out a way to get her to
understand that we're dealing with a different kind of problem,
a harder problem. Amy Robertson had told me that learning
how to do responsive teaching properly was really hard, and
now I understood why I had to put my mind
(22:31):
inside the mind of a nine year old. I had
to internalize her knowledge base and assumptions, and keep in mind,
I haven't been nine for a very long time. I
honestly had no idea what to say next. I thought
for a moment, I asked what I quickly realized was
a hopelessly convoluted question. Dobby and Sean had built a
mentor into the system, an experienced responsive teacher who supervises
(22:54):
the session and offers advice. My mentor noticed that I
was struggling, told me to implify my question.
Speaker 2 (23:01):
Remember she was a thirdth grader.
Speaker 1 (23:04):
Dabe was trying to help me too. She suggested, why
not just ask jie Wu if three quarters is bigger
or smaller than one half?
Speaker 2 (23:12):
So we are trying to help her to think about
faction in a more conceptual way.
Speaker 1 (23:18):
This time, Jiwu understood. She wrote back, three quarters is
larger than one half? I wrote back, is three six
of a cup bigger or smaller than one half? Jewu said,
I'm confused. Oh no, I've confused, Or.
Speaker 2 (23:35):
Gi Wu, it's good she's understanding, she's realizing her misconception,
so she's getting confused.
Speaker 1 (23:41):
She says, I'm confused. Three quarters is pretty close to
one and adding three six would make it go over one. Oh,
so she's got the answer. Yeah, But then she says,
but there are six pieces out of ten, which is
less than one, so I don't get it.
Speaker 2 (23:55):
So she's the point that, oh this, I have something
wrong here. That's a good sign.
Speaker 1 (24:01):
She's getting there.
Speaker 2 (24:02):
She's getting there.
Speaker 1 (24:03):
But I still have to get her to She has
to get the six pieces out of ten out of
her head. Yeah, I have no idea how to do that,
and she thinks she's confused when she has. Actually she's
figured out the answer. Yeah she did. So we have advance.
Even in my stumbling and bumbling, we've made some progress, and.
Speaker 6 (24:24):
Very notable progress.
Speaker 1 (24:25):
As my conversation with ji Wu went on for some time,
and eventually I got there. Ji Wu found her way
to the right answer. She said, I have more than
one cup of flower. The mentor chimed in. I got
a little emoji that made me feel good, And when
(24:46):
it was over, I realized two things. The first was
I needed more batting practice, much more, and that batting
practice was really, really easy to do, because someone has
gone to the trouble of building me my very own
baseball diamond and given me a pitcher who had thrown
me baseballs all day long. My second thought was that
(25:07):
I've been thinking about AI all wrong. I have interpreted
a lot of the talk about the promise of AI
to be about replacing human expertise. I had actually thought
when I first heard about Dabe's project that that's what
Dabe and Sean were doing, creating an AI to teach
students by passing the teacher altogether. But if you did
it that way, you had missed the magic of the classroom.
(25:29):
Remember Eleanor Duckworth's quote, the goal of education is for
students to have wonderful ideas and have a good time
having them. I think we often focus on the first
part of that formulation, the wonderful ideas, but neglect the second,
the good time having them. Real learning is born in pleasure,
(25:50):
in community, in playful discussion, in a group of kids
coming together to solve a problem, And all of that
magic only comes from human interaction from a teacher who
is skilled enough to inspire a class of nine year
olds We don't want AI assistants to replace the teacher.
We want AI assistants to help teachers turn themselves into
(26:13):
even better teachers. Smart Talks with IBM is produced by
Matt Ramano, Amy Gains, McQuaid, Lucy Sullivan, and Jake Harper.
(26:35):
We're edited by Lacy Roberts, Engineering by Nina Bird Lawrence,
mastering by Sarah Bugier, Music by Gramoscope. Special thanks to
Tatiana Lieberman and Cassidy Meyer. Smart Talks with IBM is
a production of Pushkin Industries and Ruby Studio at iHeartMedia.
To find more Pushkin podcasts, listen on the iHeartRadio app,
(26:57):
Apple Podcasts, or wherever you get your podcasts. I'm Malcolm Gabo.
This is a paid advertisement from ib M. The conversations
on this podcast don't necessarily represent ib m's positions, strategies,
or opinions.
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