August 11, 2025 • 20 mins
A workforce fluent in AI techniques will be essential to ensure U.S. leadership in artificial intelligence continues. Jeremy Waisome, an assistant professor at the University of Florida, discusses the Shark AI project, which has introduced artificial intelligence and machine learning techniques to thousands of middle school students.
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(00:03):
This is the Discovery Files podcastfrom the U.S.
National Science Foundation
To ensure U.S.
leadership in artificial intelligencecontinues, educators, innovators
and workers alike
need to have the skill set necessaryto contribute to an AI driven economy.
We’re joined todayby Jeremy Waisome, the Thomas O.
Hunter Rising Star assistant professorin the Department of Engineering Education
(00:26):
at the University of Florida, where she ispart of the collaboration behind Shark
AI, a program designed to introduceartificial intelligence and,
more specifically, machine learningtechniques to middle school students.
Dr. Waisomethank you so much for joining me today.
Thank you for having me.
I want to start with asking youwhat is shark AI
and how is ithelping the STEM workforce of tomorrow?
(00:48):
The shark AIproject is a multidisciplinary
team of researchers who are interested in
how we integrate artificial intelligenceconcepts into middle school classrooms.
And the idea came from a prior NSFfunded project
where they engaged studentswith fossil shark teeth and saw that
(01:10):
there was a lot of interest around sharkteeth and middle schoolers in Florida.
That doesn’t sound surprising,but it is something that
when we leave the state of Florida,people are like, why shark teeth?
Are people forgetting about Shark Week?
Like, there’s kind of a cultural thingthat everybody gets in hysteria about it.
I think people believe it came likethe impetus was Shark Week and I mean.
(01:33):
The megalodon is kind of like our theme,like our logo.
It’s in our logo. It’steeth is in our logo.
So I would say it’s tertiary out there.
Peripherylike I don’t know it’s somewhere nearby.
But it’s definitely not why we chose it.
Were there challengesgetting teachers on board.
Oh Yeah.
So the projectbasically we created a curriculum
(01:57):
that middle school science teacherscan use to integrate into their classrooms
that are aligned with Florida statescience standards.
And also there’slike a national standard for science.
And so we identifiedall of the different ways that the modules
could be integrated into the classroomto fulfill the standards that they need
(02:17):
you know,for the testing at the state level.
Our first year was rough,
and it was 2021when we were awarded the project.
So our first cohort,we didn’t have a lot of time
to like onboard themand get them interested in the project.
There are incentives involved withthe teachers being a part of the project,
(02:40):
but post Covid, nobody was really tryingto be physically in a space with anyone.
We ended up, I think, with ten teachers,that first cohort, which was remarkable
given what I was hearing fromother projects at the time.
They do a weeklongsummer professional development
like very intensive, introducing themto the curriculum, how to use it.
(03:01):
These teachers, they’re in-serviceteachers, right?
So they’ve been workingas science teachers
and have not been trainedin artificial intelligence
And they may not know anythingabout shark teeth and shark anatomy.
And so this is very foreign to them.
It was also foreignto a few of us on the team.
(03:22):
So it was really interestingworking with that small group.
And all of them ended up implementing itin their classrooms.
How hands on is it withthe artificial intelligence for students?
So they are firstjust observing and understanding,
but we walk them through more specifically
(03:43):
what machine learningis within artificial intelligence.
And then even narrowand then that computer vision.
And so initially a huge issuewe had was working
with the districts because we were workingwith teachers from all over the state.
We at the University of Floridahave a land grant mission.
And so we are embeddedin all the counties in the state.
(04:06):
And so we wanted to recruit teachers
from titleone schools across the state of Florida.
And so we ended up with several differentcounties involved, which meant several
different bureaucratic systemsto go through, right, and negotiate with.
And I don’t think that we realizedhow much effort it would take
just to allow the studentsto use the software that we wanted to use.
(04:30):
The idea is by the end of the time,
with our curriculum,they are developing their own machine
learning model using Google’steachable machine.
And so it is very hands on.
It’s not like, let’s just talk about it.
It’s here’s a phone, here’syour laptop with your webcam.
We want to showyou applications on your phone.
(04:50):
We want you to know that you’re engaging
with artificial intelligence every day,whether you are aware of it or not.
And here are some examples of that.
Here are other examples of machinelearning that aren’t computer vision.
Then also like these are the featuresthat you want to change
to improve your model. And here’s how.
And here’s what bias is and how you canintroduce that into your data.
(05:11):
All of those thingswe want the kids to understand.
On the other hand,thinking about the shark teeth aspect
kind of what are maybeif you could do a broad example of what
the curriculum is, what are theykind of teaching the machine?
So we start with an intro to AIbecause ultimately that’s the goal, right?
Like we want them to understand that.
So the first module is really likewhat is intelligence.
(05:32):
What does it mean to be intelligent.
And then how are machines intelligent.
Right. How does those things differ?
You would be amazed to hear
what the students have to sayabout all of those things.
And then from there we go into like
classification, data collection,those types of concepts.
And so the students receivea kit of like 16 teeth, and they’re
(05:57):
able to use the teeth to determinedifferent features of the teeth.
So first we just give them the teeth.
And we’re like group thesehow you think they should be grouped.
Now we know they’refrom different species of sharks.
But we also know that like the shark teeth
were specifically chosenbecause they have different functions.
There are some eagle ray teeth in therethat look like flat plates,
(06:20):
which don’t look like teeth, and oftenstudents are just like, what is this?
Like this isn’t a shark tooth.This isn’t supposed to be in there.
But an eagle ray is technically a sharkby classification.
So like,
it’s a lot of different ways that we’reintroducing these concepts to them.
So is it by species that we want toorganize it by?
Is it by the function of the teeththat we want to organize it by?
(06:42):
Why do those things matterto paleontologists
and why is it important for science.
So that’s very different than like, oh,these were the same color.
And so I put them together
because they’re the same coloror these were similar in size.
So I group them that way.
So we teach them about those thingsas well.
So having done it a couple of years now,what aspect like the teeth versus
(07:05):
the AI, what aspect of it didthe kids get the most excited about?
They’re very excited about the teeth.
I so like the
the when they get to the teeth, it’s it’sabsolute mayhem.
I would say it’s are we going to be able
to keep track of the teethor is a tooth going to disappear?
Right. Yeah. Because it’s kids like,ooh, this is exciting.
(07:25):
Yep. Pocket.
Yeah.
There’s a 3D printed tooththat we include in the kit.
And it’s a full megalodon tooth.
And so sometimes thingsgrow legs and walk away.
And we had to like replenishour supply which we get the teeth
actually a lot of them come from amateurfossil hunters.
(07:45):
And folks who just like in their freetime, are roaming the riverbanks
and like the PeaceRiver down in South Florida or some people
up here in Gainesville,you know, going around our rivers.
But yeah, that’s a good day. And then
the at the very end,
you know, when we’re doing the with themcoming up with their own model, right.
(08:07):
It gives them autonomyto make decisions and differs by teacher.
Right.
Like some of them put parameters aroundwhat they expect their model to be around.
But like we’ve had hot dog or sandwich,
sneaker classification,
not necessarily something sciency,but something that they felt like
(08:29):
would be interesting to them as kidsand brought them joy.
And the whole idea is for themto think about artificial intelligence
and understand itso that one, they’re not afraid of it,
but too, they also can recognize itwhen they’re engaging with it.
And you get a little bit
of scientific method in there,a little bit of classification stuff.
And I think with the hot dog or sneakers,you still get all those concepts.
(08:51):
Yeah.
But kind of in a little different package.
You said there was ten teachersthe first year.
You’ve done it for years now I believe.
How many were repeats.
How many broadlyhave you gotten in the last year?
So what I saw recently from our data iswe had in total 60 teachers.
That was our target.
(09:12):
We had several repeat teachers.
So one thing that we decided to do,given that we had so few
So what I saw recently from our data iswe had in total 60 teachers.
That was our target.We had several repeat teachers.
So one thing that we decided to do,given that we had
so few in the first cohort, was retaina couple of teachers as teacher leaders
and empower themto help our future teachers cohorts.
And so brought them backand they talked about their experience.
What went well,what didn’t go so great that also
(09:34):
that first group helped uscompletely revise our curriculum.
You would think we’re universityprofessors.
We know what’s best and that is not true.
I like to describe the environmentthat we were in with the middle schoolers.
It’s like going to war, like it was.
It was a lot for me.
And I, I think the other facultywould say the same thing, like we’re
(09:57):
dealing with quote unquote young adultsand they function pretty independently.
But the little ones, it’s like,how do you do this every day?
Like multiple times a day?
So that was really havingtheir perspective on like,
this is never going to work ina classroom was really helpful.
So that revision our curriculum is online.
(10:19):
So if anyone wants to look at it they can.
And so that that is somethingthat I think was super helpful for us.
We’ve reached over 2000 students,which I think is the more
powerful statistic of this project,is engaging with lots of students.
In this last wrap up session that we had,one of the teachers told me
(10:42):
I visited their classroom virtually,and two of her kids decided
they were going to submit somethingto the science fair,
and they wanted their school,and they wanted the region,
and they went allthe way to the state level.
And they claim it’s because of me.
I don’t know if that’s true.
That’s what the teacher said.
For me, like I measure the successof a project is can I reach one kid?
(11:05):
Can I inspire one of the students?
And she’s like,these weren’t my gifted kids.
These were just kidswho really latched on.
In some ways.
That's what it is.When it's in the educational field.
How like, how do you get the peopleto get excited about something.
Apparently you just introduce shark teethand that's it.
As we head towards other areasthat you’ve worked in,
(11:27):
I want to ask you about more broaderAI in academia,
like how are you seeing it used insay your education background?
How are you seeing peopleuse AI in general?
I’ve been working with our University,
so UF is really interestedin being an AI university.
And what that means variesby who you talk to you about it.
The first thing is
(11:48):
most of the faculty are unfamiliarwith artificial intelligence also.
And so in the same way, we want kidswho are going through the K-12 system
to have some exposure towhat AI is and how it’s impacting them.
We have to do the same thing
at the collegiate levelto help the faculty get on board it.
And soand one of the things that I suggested
(12:10):
was that we work with facultylearning communities.
And so faculty learningcommunities are designed to bring together
interdisciplinary groups of facultyaround a central theme or topic
and help them kind of come up
with a shared goal and like, get there.
So in this case,we ended up with two communities.
(12:33):
One was for experts in AI.
And what do the experts need to doto help that community on our campus?
The other is for novices,folks who were like,
I think I could be interestingand relevant to my work.
How do we help other peoplelearn about the resources around campus?
What do we design?
And so those two groupskind of came together
(12:53):
and created a guide for facultywho were really interested
in utilizing artificial intelligencein their classrooms. .
And I think that that’s remarkable.
And I know that it was transformativefor several faculty,
but I see it being used in every domain.
We have a lot of smart vehicleslike smart transportation in my field.
(13:15):
So coming from civil engineering,there’s a lot of that going on.
Another project that I work on that NSFsupported
as well, is looking in agriculture.
If we embed small camerasto look at root systems,
can we predict crop health and production?
Can we maybe mitigate somethingthat’s in the soil that we can’t see
(13:39):
because it’snot manifesting itself above ground?
Obviously there’s drones and thingslike that that are looking at crops.
There’s a unmanned vehiclethat will look at strawberry
crops and pick the ones that are fresh,but not the ones that aren’t ripe yet,
and package them all in this giantrobot truck, which is really cool.
But also, how is it doing that? Right?
(14:00):
And it’s using computer vision, which isthe same thing we’re talking to you about.
You’ve talked a lotabout working with younger students today.
How importantor why is mentorship important to you?
Oh my goodness, I wouldn’t be where I amif I didn’t
have phenomenal mentors.
And you know, I credit mebeing at the University of Florida
(14:21):
to one of my mentors who I metwhen I was a high school student.
I would say going to these Stem
outreach activities on the weekendsreally inspired me.
Seeing kids learn something is exciting.
Having that light bulbgo off in their brain about something
that’s complex to most people,but they’ve gotten it.
(14:44):
It’s really exciting.
And, you know, if you look at the dataright now, we do not have the workforce.
We need to maintain our competitivenessinternationally in these disciplines.
We just don’t unless unless folksare really interested in coming to college
and learning these skills andthese problems aren’t going to go away.
(15:05):
In fact, they’regoing to get worse. Hurricane.
If you haven’t noticed, there’s
a lot of natural disastersgoing on around the country.
NHERI Facilities support all of that.
How do we ensure that natural hazardsdon’t impact the livelihood
of our populace?
What do we do to design infrastructureto withstand that?
(15:27):
If I can’t inspire a kidto want to come and be a civil engineer,
it’s goingto be really hard for us to have,
you know, those of us who,have these degrees really fill the need.
Like we just don’t have it right nowand it’s just going to get worse
if we can’t inspire them.
And so I always try to work with kids
in some way, shape or form,and be a role model.
(15:51):
But that wouldn’t have happened
if I didn’t have peoplewho did the same thing for me.
And so this is insome ways a service for me, but it’s
also a way for me to get dataand conduct research
and be really engaged in the workforcedevelopment research space.
How has NSF supportimpacted your career? Wow.
(16:12):
I would say it’ssignificantly changed my trajectory.
So first, as a grad student,I received the NSF bridge
to the doctorate fellowship,and that really helped me
get established at my universityas a student researcher.
That’s what funded methose first couple of years working
(16:34):
with the faculty member on that device,and allowed me to kind of figure out
where I needed to go to do the workthat I was really interested in doing.
So you get more flexibility
when you have external fundingthan when you’re funded by a university.
So that’s that’s the first thing.
Then as a faculty member,I should say as a
(16:55):
as a postdoc,it completely changed my life, right?
Because I was ableto go across the country
talking about the work that we were doingand meet people that work with folks
and develop relationships and partnershipswith people who support me
in more ways than I could describe.
(17:17):
And as a faculty member,
it gave me a lot of leverage to say like,this is, my work is valued,
right?
And here are the waysin which it’s valued.
There are numerical thingsrelated to that, right?
There’s the money side of things,but there’s also the impact
and how you’re able to changepeople’s lives.
(17:39):
And the work would not be easy
to do without the support from NSF.
We are paying teachersand helping them integrate these concepts
that they would have to find other fundsor resources to learn these concepts,
and they’re not going to do it.
If I have to choose between somethingthat’s going to like enhance
(18:01):
my classroom or somethingthat’s going to help me,
I’m going to choose my classroomevery time if I’m one of those teachers.
And so this helps us do thatand do it well.
As we wrap up here, I want to ask youabout the future a little bit.
What do you think is the overall impact
or what has been the impact of the sharkAI program?
(18:22):
For the teachers?
I think it helped them understandthat they can do things
that they might have otherwise said,I can’t do that.
Like it’s too hard.
And artificial intelligenceis definitely something that I think
they were brave to choose to trust usand to engage with that.
And so most of them saidthey want to continue
(18:45):
working with the curriculumand what we created.
And I’m so gratefulthat they still want to engage with us.
And they don’t hate us.
And they, you know, like this
is something that really has impactedthe students lives.
And so even though the project will end,they are going to continue to use it.
(19:05):
They’re fossils.So the teeth are going to last.
And hopefully they continue to be ableto teach those courses to those kids.
And they learn a little bit aboutFlorida history and AI at the same time.
For the kids, some of them have said,I didn’t think that I was good
at computing or computer things,
(19:26):
like, I’mnot someone who is good at these things,
and so there’s these misconceptionsthat students have about themselves too,
that they can’t do it.
And then if you break it downto like the easiest
terms, it’s like, oh,this isn’t really that hard.
I can follow this set up procedures.
We’re not asking them to bake a cake.
(19:47):
In this case, we’reasking them to just follow the steps
and they can do that well.
And I’m hopeful that it will inspire themto consider moving into these areas.
We need more computer scientists.
So I think that that will will definitelychange how our society functions.
If we’re able to develop more youthwho have this passion for STEM.
(20:10):
Special thanks to Jeremy Waisome.
For the Discovery Files, I’m Nate Pottker.
Watch video versionsof these conversations on our @NSFscience
YouTube channel.
Please subscribe wherever you get podcastsand if you like our program, share
with a friend and considerleaving a review. Discover how the U.S.
National Science Foundationis advancing research at NSF.gov.
This is the Discovery Files podcastfrom the U.S.
National Science Foundation
To ensure U.S.
leadership in artificial intelligencecontinues, educators, innovators
and workers alike
need to have the skill set necessaryto contribute to an AI driven economy.
We’re joined todayby Jeremy Waisome, the Thomas O.
Hunter Rising Star assistant professorin the Department of Engineering Education
(00:26):
at the University of Florida, where she ispart of the collaboration behind Shark
AI, a program designed to introduceartificial intelligence and,
more specifically, machine learningtechniques to middle school students.
Dr. Waisomethank you so much for joining me today.
Thank you for having me.
I want to start with asking youwhat is shark AI
and how is ithelping the STEM workforce of tomorrow?
(00:48):
The shark AIproject is a multidisciplinary
team of researchers who are interested in
how we integrate artificial intelligenceconcepts into middle school classrooms.
And the idea came from a prior NSFfunded project
where they engaged studentswith fossil shark teeth and saw that
(01:10):
there was a lot of interest around sharkteeth and middle schoolers in Florida.
That doesn’t sound surprising,but it is something that
when we leave the state of Florida,people are like, why shark teeth?
Are people forgetting about Shark Week?
Like, there’s kind of a cultural thingthat everybody gets in hysteria about it.
I think people believe it came likethe impetus was Shark Week and I mean.
(01:33):
The megalodon is kind of like our theme,like our logo.
It’s in our logo. It’steeth is in our logo.
So I would say it’s tertiary out there.
Peripherylike I don’t know it’s somewhere nearby.
But it’s definitely not why we chose it.
Were there challengesgetting teachers on board.
Oh Yeah.
So the projectbasically we created a curriculum
(01:57):
that middle school science teacherscan use to integrate into their classrooms
that are aligned with Florida statescience standards.
And also there’slike a national standard for science.
And so we identifiedall of the different ways that the modules
could be integrated into the classroomto fulfill the standards that they need
(02:17):
you know,for the testing at the state level.
Our first year was rough,
and it was 2021when we were awarded the project.
So our first cohort,we didn’t have a lot of time
to like onboard themand get them interested in the project.
There are incentives involved withthe teachers being a part of the project,
(02:40):
but post Covid, nobody was really tryingto be physically in a space with anyone.
We ended up, I think, with ten teachers,that first cohort, which was remarkable
given what I was hearing fromother projects at the time.
They do a weeklongsummer professional development
like very intensive, introducing themto the curriculum, how to use it.
(03:01):
These teachers, they’re in-serviceteachers, right?
So they’ve been workingas science teachers
and have not been trainedin artificial intelligence
And they may not know anythingabout shark teeth and shark anatomy.
And so this is very foreign to them.
It was also foreignto a few of us on the team.
(03:22):
So it was really interestingworking with that small group.
And all of them ended up implementing itin their classrooms.
How hands on is it withthe artificial intelligence for students?
So they are firstjust observing and understanding,
but we walk them through more specifically
(03:43):
what machine learningis within artificial intelligence.
And then even narrowand then that computer vision.
And so initially a huge issuewe had was working
with the districts because we were workingwith teachers from all over the state.
We at the University of Floridahave a land grant mission.
And so we are embeddedin all the counties in the state.
(04:06):
And so we wanted to recruit teachers
from titleone schools across the state of Florida.
And so we ended up with several differentcounties involved, which meant several
different bureaucratic systemsto go through, right, and negotiate with.
And I don’t think that we realizedhow much effort it would take
just to allow the studentsto use the software that we wanted to use.
(04:30):
The idea is by the end of the time,
with our curriculum,they are developing their own machine
learning model using Google’steachable machine.
And so it is very hands on.
It’s not like, let’s just talk about it.
It’s here’s a phone, here’syour laptop with your webcam.
We want to showyou applications on your phone.
(04:50):
We want you to know that you’re engaging
with artificial intelligence every day,whether you are aware of it or not.
And here are some examples of that.
Here are other examples of machinelearning that aren’t computer vision.
Then also like these are the featuresthat you want to change
to improve your model. And here’s how.
And here’s what bias is and how you canintroduce that into your data.
(05:11):
All of those thingswe want the kids to understand.
On the other hand,thinking about the shark teeth aspect
kind of what are maybeif you could do a broad example of what
the curriculum is, what are theykind of teaching the machine?
So we start with an intro to AIbecause ultimately that’s the goal, right?
Like we want them to understand that.
So the first module is really likewhat is intelligence.
(05:32):
What does it mean to be intelligent.
And then how are machines intelligent.
Right. How does those things differ?
You would be amazed to hear
what the students have to sayabout all of those things.
And then from there we go into like
classification, data collection,those types of concepts.
And so the students receivea kit of like 16 teeth, and they’re
(05:57):
able to use the teeth to determinedifferent features of the teeth.
So first we just give them the teeth.
And we’re like group thesehow you think they should be grouped.
Now we know they’refrom different species of sharks.
But we also know that like the shark teeth
were specifically chosenbecause they have different functions.
There are some eagle ray teeth in therethat look like flat plates,
(06:20):
which don’t look like teeth, and oftenstudents are just like, what is this?
Like this isn’t a shark tooth.This isn’t supposed to be in there.
But an eagle ray is technically a sharkby classification.
So like,
it’s a lot of different ways that we’reintroducing these concepts to them.
So is it by species that we want toorganize it by?
Is it by the function of the teeththat we want to organize it by?
(06:42):
Why do those things matterto paleontologists
and why is it important for science.
So that’s very different than like, oh,these were the same color.
And so I put them together
because they’re the same coloror these were similar in size.
So I group them that way.
So we teach them about those thingsas well.
So having done it a couple of years now,what aspect like the teeth versus
(07:05):
the AI, what aspect of it didthe kids get the most excited about?
They’re very excited about the teeth.
I so like the
the when they get to the teeth, it’s it’sabsolute mayhem.
I would say it’s are we going to be able
to keep track of the teethor is a tooth going to disappear?
Right. Yeah. Because it’s kids like,ooh, this is exciting.
(07:25):
Yep. Pocket.
Yeah.
There’s a 3D printed tooththat we include in the kit.
And it’s a full megalodon tooth.
And so sometimes thingsgrow legs and walk away.
And we had to like replenishour supply which we get the teeth
actually a lot of them come from amateurfossil hunters.
(07:45):
And folks who just like in their freetime, are roaming the riverbanks
and like the PeaceRiver down in South Florida or some people
up here in Gainesville,you know, going around our rivers.
But yeah, that’s a good day. And then
the at the very end,
you know, when we’re doing the with themcoming up with their own model, right.
(08:07):
It gives them autonomyto make decisions and differs by teacher.
Right.
Like some of them put parameters aroundwhat they expect their model to be around.
But like we’ve had hot dog or sandwich,
sneaker classification,
not necessarily something sciency,but something that they felt like
(08:29):
would be interesting to them as kidsand brought them joy.
And the whole idea is for themto think about artificial intelligence
and understand itso that one, they’re not afraid of it,
but too, they also can recognize itwhen they’re engaging with it.
And you get a little bit
of scientific method in there,a little bit of classification stuff.
And I think with the hot dog or sneakers,you still get all those concepts.
(08:51):
Yeah.
But kind of in a little different package.
You said there was ten teachersthe first year.
You’ve done it for years now I believe.
How many were repeats.
How many broadlyhave you gotten in the last year?
So what I saw recently from our data iswe had in total 60 teachers.
That was our target.
(09:12):
We had several repeat teachers.
So one thing that we decided to do,given that we had so few
So what I saw recently from our data iswe had in total 60 teachers.
That was our target.We had several repeat teachers.
So one thing that we decided to do,given that we had
so few in the first cohort, was retaina couple of teachers as teacher leaders
and empower themto help our future teachers cohorts.
And so brought them backand they talked about their experience.
What went well,what didn’t go so great that also
(09:34):
that first group helped uscompletely revise our curriculum.
You would think we’re universityprofessors.
We know what’s best and that is not true.
I like to describe the environmentthat we were in with the middle schoolers.
It’s like going to war, like it was.
It was a lot for me.
And I, I think the other facultywould say the same thing, like we’re
(09:57):
dealing with quote unquote young adultsand they function pretty independently.
But the little ones, it’s like,how do you do this every day?
Like multiple times a day?
So that was really havingtheir perspective on like,
this is never going to work ina classroom was really helpful.
So that revision our curriculum is online.
(10:19):
So if anyone wants to look at it they can.
And so that that is somethingthat I think was super helpful for us.
We’ve reached over 2000 students,which I think is the more
powerful statistic of this project,is engaging with lots of students.
In this last wrap up session that we had,one of the teachers told me
(10:42):
I visited their classroom virtually,and two of her kids decided
they were going to submit somethingto the science fair,
and they wanted their school,and they wanted the region,
and they went allthe way to the state level.
And they claim it’s because of me.
I don’t know if that’s true.
That’s what the teacher said.
For me, like I measure the successof a project is can I reach one kid?
(11:05):
Can I inspire one of the students?
And she’s like,these weren’t my gifted kids.
These were just kidswho really latched on.
In some ways.
That's what it is.When it's in the educational field.
How like, how do you get the peopleto get excited about something.
Apparently you just introduce shark teethand that's it.
As we head towards other areasthat you’ve worked in,
(11:27):
I want to ask you about more broaderAI in academia,
like how are you seeing it used insay your education background?
How are you seeing peopleuse AI in general?
I’ve been working with our University,
so UF is really interestedin being an AI university.
And what that means variesby who you talk to you about it.
The first thing is
(11:48):
most of the faculty are unfamiliarwith artificial intelligence also.
And so in the same way, we want kidswho are going through the K-12 system
to have some exposure towhat AI is and how it’s impacting them.
We have to do the same thing
at the collegiate levelto help the faculty get on board it.
And soand one of the things that I suggested
(12:10):
was that we work with facultylearning communities.
And so faculty learningcommunities are designed to bring together
interdisciplinary groups of facultyaround a central theme or topic
and help them kind of come up
with a shared goal and like, get there.
So in this case,we ended up with two communities.
(12:33):
One was for experts in AI.
And what do the experts need to doto help that community on our campus?
The other is for novices,folks who were like,
I think I could be interestingand relevant to my work.
How do we help other peoplelearn about the resources around campus?
What do we design?
And so those two groupskind of came together
(12:53):
and created a guide for facultywho were really interested
in utilizing artificial intelligencein their classrooms. .
And I think that that’s remarkable.
And I know that it was transformativefor several faculty,
but I see it being used in every domain.
We have a lot of smart vehicleslike smart transportation in my field.
(13:15):
So coming from civil engineering,there’s a lot of that going on.
Another project that I work on that NSFsupported
as well, is looking in agriculture.
If we embed small camerasto look at root systems,
can we predict crop health and production?
Can we maybe mitigate somethingthat’s in the soil that we can’t see
(13:39):
because it’snot manifesting itself above ground?
Obviously there’s drones and thingslike that that are looking at crops.
There’s a unmanned vehiclethat will look at strawberry
crops and pick the ones that are fresh,but not the ones that aren’t ripe yet,
and package them all in this giantrobot truck, which is really cool.
But also, how is it doing that? Right?
(14:00):
And it’s using computer vision, which isthe same thing we’re talking to you about.
You’ve talked a lotabout working with younger students today.
How importantor why is mentorship important to you?
Oh my goodness, I wouldn’t be where I amif I didn’t
have phenomenal mentors.
And you know, I credit mebeing at the University of Florida
(14:21):
to one of my mentors who I metwhen I was a high school student.
I would say going to these Stem
outreach activities on the weekendsreally inspired me.
Seeing kids learn something is exciting.
Having that light bulbgo off in their brain about something
that’s complex to most people,but they’ve gotten it.
(14:44):
It’s really exciting.
And, you know, if you look at the dataright now, we do not have the workforce.
We need to maintain our competitivenessinternationally in these disciplines.
We just don’t unless unless folksare really interested in coming to college
and learning these skills andthese problems aren’t going to go away.
(15:05):
In fact, they’regoing to get worse. Hurricane.
If you haven’t noticed, there’s
a lot of natural disastersgoing on around the country.
NHERI Facilities support all of that.
How do we ensure that natural hazardsdon’t impact the livelihood
of our populace?
What do we do to design infrastructureto withstand that?
(15:27):
If I can’t inspire a kidto want to come and be a civil engineer,
it’s goingto be really hard for us to have,
you know, those of us who,have these degrees really fill the need.
Like we just don’t have it right nowand it’s just going to get worse
if we can’t inspire them.
And so I always try to work with kids
in some way, shape or form,and be a role model.
(15:51):
But that wouldn’t have happened
if I didn’t have peoplewho did the same thing for me.
And so this is insome ways a service for me, but it’s
also a way for me to get dataand conduct research
and be really engaged in the workforcedevelopment research space.
How has NSF supportimpacted your career? Wow.
(16:12):
I would say it’ssignificantly changed my trajectory.
So first, as a grad student,I received the NSF bridge
to the doctorate fellowship,and that really helped me
get established at my universityas a student researcher.
That’s what funded methose first couple of years working
(16:34):
with the faculty member on that device,and allowed me to kind of figure out
where I needed to go to do the workthat I was really interested in doing.
So you get more flexibility
when you have external fundingthan when you’re funded by a university.
So that’s that’s the first thing.
Then as a faculty member,I should say as a
(16:55):
as a postdoc,it completely changed my life, right?
Because I was ableto go across the country
talking about the work that we were doingand meet people that work with folks
and develop relationships and partnershipswith people who support me
in more ways than I could describe.
(17:17):
And as a faculty member,
it gave me a lot of leverage to say like,this is, my work is valued,
right?
And here are the waysin which it’s valued.
There are numerical thingsrelated to that, right?
There’s the money side of things,but there’s also the impact
and how you’re able to changepeople’s lives.
(17:39):
And the work would not be easy
to do without the support from NSF.
We are paying teachersand helping them integrate these concepts
that they would have to find other fundsor resources to learn these concepts,
and they’re not going to do it.
If I have to choose between somethingthat’s going to like enhance
(18:01):
my classroom or somethingthat’s going to help me,
I’m going to choose my classroomevery time if I’m one of those teachers.
And so this helps us do thatand do it well.
As we wrap up here, I want to ask youabout the future a little bit.
What do you think is the overall impact
or what has been the impact of the sharkAI program?
(18:22):
For the teachers?
I think it helped them understandthat they can do things
that they might have otherwise said,I can’t do that.
Like it’s too hard.
And artificial intelligenceis definitely something that I think
they were brave to choose to trust usand to engage with that.
And so most of them saidthey want to continue
(18:45):
working with the curriculumand what we created.
And I’m so gratefulthat they still want to engage with us.
And they don’t hate us.
And they, you know, like this
is something that really has impactedthe students lives.
And so even though the project will end,they are going to continue to use it.
(19:05):
They’re fossils.So the teeth are going to last.
And hopefully they continue to be ableto teach those courses to those kids.
And they learn a little bit aboutFlorida history and AI at the same time.
For the kids, some of them have said,I didn’t think that I was good
at computing or computer things,
(19:26):
like, I’mnot someone who is good at these things,
and so there’s these misconceptionsthat students have about themselves too,
that they can’t do it.
And then if you break it downto like the easiest
terms, it’s like, oh,this isn’t really that hard.
I can follow this set up procedures.
We’re not asking them to bake a cake.
(19:47):
In this case, we’reasking them to just follow the steps
and they can do that well.
And I’m hopeful that it will inspire themto consider moving into these areas.
We need more computer scientists.
So I think that that will will definitelychange how our society functions.
If we’re able to develop more youthwho have this passion for STEM.
(20:10):
Special thanks to Jeremy Waisome.
For the Discovery Files, I’m Nate Pottker.
Watch video versionsof these conversations on our @NSFscience
YouTube channel.
Please subscribe wherever you get podcastsand if you like our program, share
with a friend and considerleaving a review. Discover how the U.S.
National Science Foundationis advancing research at NSF.gov.
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