May 20, 2025 • 28 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 how AI can enhance teaching methods to prepare teachers for the classroom.
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Episode Transcript
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Speaker 1 (00:00):
Welcome to Tech Stuff, a production from iHeartRadio. This season
on Smart Talks with IBM, Malcolm Glabwell is back, and
this time he's taking the show on the road. Malcolm
is stepping outside the studio to explore how IBM clients
are using artificial intelligence to solve real world challenges and
transform the way they do business, from accelerating scientific breakthroughs
(00:23):
to reimagining education. It's a fresh look at innovation in action,
where big ideas meet cutting edge solutions. You'll hear from
industry leaders, creative thinkers, and of course, Malcolm Glabwell himself
as he guides you through each story. New episodes of
Smart Talks with IBM drop every month on the iHeartRadio app,
(00:43):
Apple Podcasts, or wherever you get your podcasts. Learn more
at IBM dot com slash smart Talks.
Speaker 2 (00:51):
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 that 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
(01:13):
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 Lowenberg 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,
(01:37):
which was about even and odd numbers.
Speaker 3 (01:40):
I would like to hear from as many people as
possible what comments you had, reactions you had to being
in that meeting yesterday.
Speaker 2 (01:47):
A little boy with black hair raises his hand. His
name is Sean.
Speaker 4 (01:51):
I don't have anything about the meeting yesterday that I
was just thinking about sitting im.
Speaker 2 (01:56):
Sean was thinking about the number six.
Speaker 4 (02:00):
I was thinking that it's a it's an idd. It
can be an odd number two because there could.
Speaker 5 (02:04):
Be two, two, four, six, two, three, twos and two threes.
Speaker 4 (02:13):
It will be an odd.
Speaker 5 (02:14):
Anthonytina three thinks make it takes me two things.
Speaker 2 (02:21):
And 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.
(02:42):
But Deborahaul doesn't do that. She never tells him he's wrong. Instead,
she simply asks him to explain his thinking.
Speaker 6 (02:50):
And the two things that you put together to make
it were odd right, three and three of each old.
Speaker 4 (02:56):
And I think.
Speaker 2 (02:59):
Two bauld 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 he had imagined.
(03:20):
And now he gets it.
Speaker 4 (03:23):
I'm a thank you for winging in love.
Speaker 2 (03:26):
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
(03:46):
is season six of Smart Talks with IBM, where we
offer 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 Lorielle and Ferrari
and tell stories of how they're using artificial intelligence and
data to transform the way they do business. This episode
(04:09):
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 AI
(04:31):
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 student's odd theory about the number six second. She's
(04:53):
taking Sean seriously. She's not dismissing his theory. She's listening
to him and trying to you 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
can get to the right answer on his own. Improvisation,
(05:18):
empathy patients. That's responsive teaching.
Speaker 7 (05:22):
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 2 (05:40):
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 7 (05:49):
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 2 (06:00):
Told him he was wrong, that's right, and then he goes,
He goes, I didn't think of it that way.
Speaker 4 (06:05):
Again, I thank you for ringing it alone.
Speaker 2 (06:09):
You've expanded my understanding. Thank you for bringing it up again.
It's like this, I love I know.
Speaker 7 (06:18):
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 2 (06:32):
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 Debora Ball classroom, all I could
think was, I really really hope my daughters get to
(06:54):
experience a math class like that. Far too many kids
are convincing themselves at far too young age 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
(07:16):
expected to 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 need a way to practice. For as long
(07:41):
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,
with the rise of artificial intelligence, the scale and complexity
(08:02):
of the problems technology can help us solve has jumped
by many orders of magnitude. You must have worked with
a with a million customers who are experimenting with ll
m'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 to Brian Bissel, who works out of IBM's Manhattan office.
(08:23):
He helps IBM customers discover how best to get AI
to work for them.
Speaker 8 (08:28):
There is one, but I don't think I can talk
about it unfortunately.
Speaker 2 (08:31):
Wait, wait, you can't tease me like that, can you?
Speaker 4 (08:34):
Wait?
Speaker 2 (08:35):
Disguise disguise it for me? Just give me a general.
Speaker 8 (08:39):
It was about the ability to pull certain types of
information out of documents that you 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 2 (08:51):
Bissile'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 Basil saw lots of opportunities in education.
Speaker 8 (09:08):
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,
(09:28):
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 2 (09:41):
To Basil, that's the wrong use of AI. That's technology
making is dumber. What we really want is technology that
makes us smarter. Basil explained to me that there are
now two big tools being used for AI productivity, AI agents,
and AI assistance. Let's start with the AI agents. AI
(10:01):
agents can reason plan and collaborate with other AI tools
to autonomously perform tasks for a user. Mis Will gave
me an example of how college freshmen might use an
AI agent As.
Speaker 8 (10:13):
A new student. You may not know how do I
do with my health and wellness issue? So many the
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 2 (10:28):
Missill 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 school work.
Speaker 8 (10:44):
We can see patterns of how agents and assistants can
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 2 (11:06):
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 performed tasks
at your request. They're programmed to answer your questions, and
as it turns out, AI assistants are now being used
(11:27):
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 5 (11:49):
Yeah, so it was offered by Coursera. It was designed
by IBM. It was AI Foundation for everyone.
Speaker 2 (12:01):
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 it was.
Speaker 5 (12:10):
Not to know it was like fourteen weeks.
Speaker 2 (12:13):
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 5 (12:36):
So when you think about the teacher education and a
major challenge that teacher education phase is that we need
children to practice with. We need instructors who will give
the instruction on the pedagogical skills. So when you look
at the teacher education program, we have coursework in field experience,
(13:00):
and in those two areas there is something missing all
the time.
Speaker 2 (13:05):
Li says that pre service teachers often lack access to
both students and experienced teachers during their education.
Speaker 5 (13:13):
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 2 (13:26):
The first AI assistant Lee created is g wuji 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.
(13:47):
So how are Gabriel and Noah different from.
Speaker 5 (13:50):
G Wu gabrielle My first one 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, and who
(14:12):
would not sure much of their thoughts. So we wanted
that characteristic in some characters, and we use Gabrielle to
have that characteristic.
Speaker 2 (14:24):
And Noah. What'snaah's personality?
Speaker 3 (14:27):
How do he playful? Cheery, bright and energetic?
Speaker 2 (14:32):
That's Sean English professor, Lee's fellow researcher, and jewuj.
Speaker 5 (14:37):
Is articulated and kind of smart, but she has her
own way of thinking.
Speaker 2 (14:44):
I would end up spending a lot of time with Jeewu.
She's something of a character. I asked Sean about the
process of creating these AI assistants. What does building the
content side of the AI assistant entail?
Speaker 4 (14:58):
Sean?
Speaker 3 (14:59):
It 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,
(15:21):
and all that kind of stuff.
Speaker 2 (15:22):
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 3 (15:41):
And from a technological standpoint, while AI is a fantastic tool,
AI can hallucinate, which means 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,
(16:03):
that's a little crazy. I don't think that's exactly what
we're going for here.
Speaker 2 (16:07):
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 6 (16:26):
I was just kind of excited to see what the
program was and what it was going to be doing.
Speaker 2 (16:31):
This is Logan Hovis, a junior at Missouri on the
path to becoming an elementary school teacher.
Speaker 6 (16:37):
Obviously a little skeptical when he said it was sos 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
(16:58):
way that you really said that and you feel like
you're talking to a kid.
Speaker 2 (17:02):
Her point wasn't that jie Wu and her fellow avatars
were equivalent to real kids. Of course not, but for
someone starting out, someone who is already nervous about being
plunged into a classroom of nine year olds, Jeewu was
like a warm up before a baseball game.
Speaker 6 (17:17):
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 2 (17:42):
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 pitcher
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 6 (17:58):
There's a lot less stimuli 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
(18:20):
okay in the classroom, So being on the ji Wu 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 teacher, it was a lot less pressure too,
(18:40):
because I was like, Okay, I'm taking this series. This
is a student I'm questioning, but I also know I'm
probably not going to hurt someone's feelings right now, and
that's terrifying to think I'm going to ask the wrong
question and upset the child because I've done that.
Speaker 2 (18:55):
We think that 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
(19:16):
spend a whole weekend practicing with gi Wu. A real
nine year old will get frustrated on board with the
fumbling novice after ten minutes, but gie wuji 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 my
(19:37):
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.
Ji Wu began. She had been given a math problem.
Speaker 4 (19:53):
A rule kodo who out of grude force? How half
a flower? Okay? Do ball? Thanks? Another three? Six is cup?
It's a total amount of flower the use greater or
dan or a less than war cop how much flower
(20:17):
can use?
Speaker 2 (20:18):
That's a simulation of gi Wu speaking. We pause it
for a second. So Jiwu 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
(20:42):
in three of the four pieces. Yes, so she's representing.
This is quite good. She's representing three quarters on the screen.
Speaker 4 (20:48):
Okay, this is three six.
Speaker 2 (20:55):
So now Jiwu does another rectangle with six boxes and
colors in three of.
Speaker 4 (21:02):
Them, okay, together makes sikes come off.
Speaker 2 (21:11):
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
(21:32):
ji Wu, Martin has less than one cup. And she
thinks she solved the problem.
Speaker 5 (21:36):
Yes, okay, so it's less than one cup.
Speaker 2 (21:39):
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
(22:01):
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 5 (22:17):
Yeah, seriously did Yeah, that's a good question.
Speaker 2 (22:21):
Jewu doesn't mess around. She answers immediately. So Ju says,
the blue and red pieces are not the same sizes.
Speaker 5 (22:28):
Oh so you understand now, gu knows that size differences.
Speaker 2 (22:34):
So she's pretty smart here.
Speaker 5 (22:36):
Yeah.
Speaker 2 (22:37):
Then I asked, if they are not the same size,
do you think you can add them together? Jiwu answered
right away. Ji Wu says, I have learned that I
could add any numbers in grade two. So three p
three is six and four to six is ten.
Speaker 5 (22:52):
Yeah, so she is using the knowledge of adding intiquers
into adding fractures.
Speaker 2 (22:59):
Now I'm stuck. 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
(23:21):
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. Daby and Sean had built a
mentor into the system, an experienced, responsive teacher who supervises
(23:44):
the session and offers advice. My mentor noticed that I
was struggling, told me to simplify my question. Grader Dabe
was trying to help me too, She suggested, why not
just ask ji Wu if three quarters is bigger or
smaller than one half?
Speaker 5 (24:02):
So we are trying to help her to think about
faction in a more conceptual way.
Speaker 2 (24:08):
This time, Jeewu 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, gi Wu.
Speaker 5 (24:25):
It's good she's understanding. She's realizing her misconception. So she's
getting confused.
Speaker 2 (24:31):
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 5 (24:45):
So she's the point that oh this, I have something
wrong here.
Speaker 7 (24:49):
That's a good sign.
Speaker 2 (24:51):
She's getting there.
Speaker 5 (24:52):
Yeah, she's getting there, but.
Speaker 2 (24:53):
I still have to get her. She has to get
the six pieces out of ten out of her head. Yeah,
I have no idea. I didn't 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, very
(25:14):
notable progress. My conversation with gie 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,
(25:36):
And when 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 baseball's all day long. My second thought
(25:56):
was that 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
(26:17):
of the classroom. 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
(26:38):
learning is born in pleasure, 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
(27:00):
teachers turn themselves into even better teachers. Smart Talks with
IBM is produced by Matt Romano, Amy Gains McQuaid, Lucy Sullivan,
(27:24):
and Jake Harper were edited by Lacy Roberts. Engineering by
Nina Birt Lawrence, Mastering by Sarah Brugerer 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
(27:45):
the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.
I'm Malcolm Glapo. This is a paid advertisement from IBM.
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Welcome to Tech Stuff, a production from iHeartRadio. This season
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Speaker 2 (00:51):
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 that 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
(01:13):
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 Lowenberg 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,
(01:37):
which was about even and odd numbers.
Speaker 3 (01:40):
I would like to hear from as many people as
possible what comments you had, reactions you had to being
in that meeting yesterday.
Speaker 2 (01:47):
A little boy with black hair raises his hand. His
name is Sean.
Speaker 4 (01:51):
I don't have anything about the meeting yesterday that I
was just thinking about sitting im.
Speaker 2 (01:56):
Sean was thinking about the number six.
Speaker 4 (02:00):
I was thinking that it's a it's an idd. It
can be an odd number two because there could.
Speaker 5 (02:04):
Be two, two, four, six, two, three, twos and two threes.
Speaker 4 (02:13):
It will be an odd.
Speaker 5 (02:14):
Anthonytina three thinks make it takes me two things.
Speaker 2 (02:21):
And 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.
(02:42):
But Deborahaul doesn't do that. She never tells him he's wrong. Instead,
she simply asks him to explain his thinking.
Speaker 6 (02:50):
And the two things that you put together to make
it were odd right, three and three of each old.
Speaker 4 (02:56):
And I think.
Speaker 2 (02:59):
Two bauld 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 he had imagined.
(03:20):
And now he gets it.
Speaker 4 (03:23):
I'm a thank you for winging in love.
Speaker 2 (03:26):
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
(03:46):
is season six of Smart Talks with IBM, where we
offer 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 Lorielle and Ferrari
and tell stories of how they're using artificial intelligence and
data to transform the way they do business. This episode
(04:09):
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 AI
(04:31):
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 student's odd theory about the number six second. She's
(04:53):
taking Sean seriously. She's not dismissing his theory. She's listening
to him and trying to you 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
can get to the right answer on his own. Improvisation,
(05:18):
empathy patients. That's responsive teaching.
Speaker 7 (05:22):
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 2 (05:40):
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 7 (05:49):
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 2 (06:00):
Told him he was wrong, that's right, and then he goes,
He goes, I didn't think of it that way.
Speaker 4 (06:05):
Again, I thank you for ringing it alone.
Speaker 2 (06:09):
You've expanded my understanding. Thank you for bringing it up again.
It's like this, I love I know.
Speaker 7 (06:18):
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 2 (06:32):
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 Debora Ball classroom, all I could
think was, I really really hope my daughters get to
(06:54):
experience a math class like that. Far too many kids
are convincing themselves at far too young age 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
(07:16):
expected to 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 need a way to practice. For as long
(07:41):
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,
with the rise of artificial intelligence, the scale and complexity
(08:02):
of the problems technology can help us solve has jumped
by many orders of magnitude. You must have worked with
a with a million customers who are experimenting with ll
m'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 to Brian Bissel, who works out of IBM's Manhattan office.
(08:23):
He helps IBM customers discover how best to get AI
to work for them.
Speaker 8 (08:28):
There is one, but I don't think I can talk
about it unfortunately.
Speaker 2 (08:31):
Wait, wait, you can't tease me like that, can you?
Speaker 4 (08:34):
Wait?
Speaker 2 (08:35):
Disguise disguise it for me? Just give me a general.
Speaker 8 (08:39):
It was about the ability to pull certain types of
information out of documents that you 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 2 (08:51):
Bissile'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 Basil saw lots of opportunities in education.
Speaker 8 (09:08):
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,
(09:28):
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 2 (09:41):
To Basil, that's the wrong use of AI. That's technology
making is dumber. What we really want is technology that
makes us smarter. Basil explained to me that there are
now two big tools being used for AI productivity, AI agents,
and AI assistance. Let's start with the AI agents. AI
(10:01):
agents can reason plan and collaborate with other AI tools
to autonomously perform tasks for a user. Mis Will gave
me an example of how college freshmen might use an
AI agent As.
Speaker 8 (10:13):
A new student. You may not know how do I
do with my health and wellness issue? So many the
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 2 (10:28):
Missill 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 school work.
Speaker 8 (10:44):
We can see patterns of how agents and assistants can
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 2 (11:06):
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 performed tasks
at your request. They're programmed to answer your questions, and
as it turns out, AI assistants are now being used
(11:27):
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 5 (11:49):
Yeah, so it was offered by Coursera. It was designed
by IBM. It was AI Foundation for everyone.
Speaker 2 (12:01):
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 it was.
Speaker 5 (12:10):
Not to know it was like fourteen weeks.
Speaker 2 (12:13):
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 5 (12:36):
So when you think about the teacher education and a
major challenge that teacher education phase is that we need
children to practice with. We need instructors who will give
the instruction on the pedagogical skills. So when you look
at the teacher education program, we have coursework in field experience,
(13:00):
and in those two areas there is something missing all
the time.
Speaker 2 (13:05):
Li says that pre service teachers often lack access to
both students and experienced teachers during their education.
Speaker 5 (13:13):
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 2 (13:26):
The first AI assistant Lee created is g wuji 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.
(13:47):
So how are Gabriel and Noah different from.
Speaker 5 (13:50):
G Wu gabrielle My first one 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, and who
(14:12):
would not sure much of their thoughts. So we wanted
that characteristic in some characters, and we use Gabrielle to
have that characteristic.
Speaker 2 (14:24):
And Noah. What'snaah's personality?
Speaker 3 (14:27):
How do he playful? Cheery, bright and energetic?
Speaker 2 (14:32):
That's Sean English professor, Lee's fellow researcher, and jewuj.
Speaker 5 (14:37):
Is articulated and kind of smart, but she has her
own way of thinking.
Speaker 2 (14:44):
I would end up spending a lot of time with Jeewu.
She's something of a character. I asked Sean about the
process of creating these AI assistants. What does building the
content side of the AI assistant entail?
Speaker 4 (14:58):
Sean?
Speaker 3 (14:59):
It 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,
(15:21):
and all that kind of stuff.
Speaker 2 (15:22):
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 3 (15:41):
And from a technological standpoint, while AI is a fantastic tool,
AI can hallucinate, which means 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,
(16:03):
that's a little crazy. I don't think that's exactly what
we're going for here.
Speaker 2 (16:07):
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 6 (16:26):
I was just kind of excited to see what the
program was and what it was going to be doing.
Speaker 2 (16:31):
This is Logan Hovis, a junior at Missouri on the
path to becoming an elementary school teacher.
Speaker 6 (16:37):
Obviously a little skeptical when he said it was sos 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
(16:58):
way that you really said that and you feel like
you're talking to a kid.
Speaker 2 (17:02):
Her point wasn't that jie Wu and her fellow avatars
were equivalent to real kids. Of course not, but for
someone starting out, someone who is already nervous about being
plunged into a classroom of nine year olds, Jeewu was
like a warm up before a baseball game.
Speaker 6 (17:17):
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 2 (17:42):
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 pitcher
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 6 (17:58):
There's a lot less stimuli 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
(18:20):
okay in the classroom, So being on the ji Wu 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 teacher, it was a lot less pressure too,
(18:40):
because I was like, Okay, I'm taking this series. This
is a student I'm questioning, but I also know I'm
probably not going to hurt someone's feelings right now, and
that's terrifying to think I'm going to ask the wrong
question and upset the child because I've done that.
Speaker 2 (18:55):
We think that 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
(19:16):
spend a whole weekend practicing with gi Wu. A real
nine year old will get frustrated on board with the
fumbling novice after ten minutes, but gie wuji 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 my
(19:37):
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.
Ji Wu began. She had been given a math problem.
Speaker 4 (19:53):
A rule kodo who out of grude force? How half
a flower? Okay? Do ball? Thanks? Another three? Six is cup?
It's a total amount of flower the use greater or
dan or a less than war cop how much flower
(20:17):
can use?
Speaker 2 (20:18):
That's a simulation of gi Wu speaking. We pause it
for a second. So Jiwu 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
(20:42):
in three of the four pieces. Yes, so she's representing.
This is quite good. She's representing three quarters on the screen.
Speaker 4 (20:48):
Okay, this is three six.
Speaker 2 (20:55):
So now Jiwu does another rectangle with six boxes and
colors in three of.
Speaker 4 (21:02):
Them, okay, together makes sikes come off.
Speaker 2 (21:11):
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
(21:32):
ji Wu, Martin has less than one cup. And she
thinks she solved the problem.
Speaker 5 (21:36):
Yes, okay, so it's less than one cup.
Speaker 2 (21:39):
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
(22:01):
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 5 (22:17):
Yeah, seriously did Yeah, that's a good question.
Speaker 2 (22:21):
Jewu doesn't mess around. She answers immediately. So Ju says,
the blue and red pieces are not the same sizes.
Speaker 5 (22:28):
Oh so you understand now, gu knows that size differences.
Speaker 2 (22:34):
So she's pretty smart here.
Speaker 5 (22:36):
Yeah.
Speaker 2 (22:37):
Then I asked, if they are not the same size,
do you think you can add them together? Jiwu answered
right away. Ji Wu says, I have learned that I
could add any numbers in grade two. So three p
three is six and four to six is ten.
Speaker 5 (22:52):
Yeah, so she is using the knowledge of adding intiquers
into adding fractures.
Speaker 2 (22:59):
Now I'm stuck. 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
(23:21):
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. Daby and Sean had built a
mentor into the system, an experienced, responsive teacher who supervises
(23:44):
the session and offers advice. My mentor noticed that I
was struggling, told me to simplify my question. Grader Dabe
was trying to help me too, She suggested, why not
just ask ji Wu if three quarters is bigger or
smaller than one half?
Speaker 5 (24:02):
So we are trying to help her to think about
faction in a more conceptual way.
Speaker 2 (24:08):
This time, Jeewu 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, gi Wu.
Speaker 5 (24:25):
It's good she's understanding. She's realizing her misconception. So she's
getting confused.
Speaker 2 (24:31):
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 5 (24:45):
So she's the point that oh this, I have something
wrong here.
Speaker 7 (24:49):
That's a good sign.
Speaker 2 (24:51):
She's getting there.
Speaker 5 (24:52):
Yeah, she's getting there, but.
Speaker 2 (24:53):
I still have to get her. She has to get
the six pieces out of ten out of her head. Yeah,
I have no idea. I didn't 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, very
(25:14):
notable progress. My conversation with gie 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,
(25:36):
And when 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 baseball's all day long. My second thought
(25:56):
was that 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
(26:17):
of the classroom. 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
(26:38):
learning is born in pleasure, 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
(27:00):
teachers turn themselves into even better teachers. Smart Talks with
IBM is produced by Matt Romano, Amy Gains McQuaid, Lucy Sullivan,
(27:24):
and Jake Harper were edited by Lacy Roberts. Engineering by
Nina Birt Lawrence, Mastering by Sarah Brugerer 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
(27:45):
the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.
I'm Malcolm Glapo. This is a paid advertisement from IBM.
The conversations on this podcast don't necessarily represent IBM's positions, strategy,
our opinions,
TechStuff News
Hosts And Creators
Oz Woloshyn
Karah Preiss
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