September 9, 2025 • 37 mins
What happens when K-12, higher education, manufacturing, and a startup tech company sit around the same table to talk about AI? This episode brings that rare collaboration to life.
Recorded live at TitletownTech—the venture studio founded by Microsoft and the Green Bay Packers—this panel features four leaders from distinctly different sectors, all navigating how AI is changing their world. From fault anomaly detection in industrial equipment to generative AI in K-12 classrooms, this episode is a crash course in what applied AI really looks like on the ground.
Panelists include:
- Mike Beighley, Superintendent, Whitehall School District
- Dr. Kate Burns, Provost & Vice Chancellor of Academic Affairs, University of Wisconsin–Green Bay
- Rick Roeske, Senior Director of Service and Solutions, BW Converting
- Alex Tyink, Founder & CEO, Fork Farms
Moderated by Matt Kirchner, Host of The TechEd Podcast
Through stories of innovation, disruption, and surprising lessons, these leaders share how they’re preparing students, supporting workers, and strengthening their communities with artificial intelligence.
Listen to learn:
- How a rural K-12 school is using AI to power personalized learning and student-led scheduling
- What happens when higher ed rethinks writing and assessment in the age of ChatGPT
- How manufacturers are using AI to capture tribal knowledge and improve customer relationships
- What it’s like to co-develop AI solutions inside the Microsoft AI Co-Innovation Lab
- Why human connection and relevance still matter more than ever in the AI-powered classroom
3 Big Takeaways from this Episode:
1. AI is expanding what’s possible in education by unlocking more personalized, student-centered learning. In both K-12 and higher ed, AI is giving educators the tools to meet students where they are—academically, emotionally, and logistically. From adaptive math instruction to AI-driven student support systems, the future of learning is more flexible, scalable, and responsive.
2. Manufacturing is using AI not just to fix machines, but to build better relationships. Rick Roeske shares how BW Converting uses AI to detect fault anomalies, preserve expert knowledge, and improve customer support—often solving problems before clients even notice. It’s not just about performance; it’s about trust.
3. For startups, AI partnerships can unlock capabilities far beyond their headcount. Alex Tyink explains how Fork Farms built a proprietary AI farm management system with help from the Microsoft AI Co-Innovation Lab—accessing high-level expertise and infrastructure that most early-stage companies could never afford to build in-house.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Matt Kirchner (00:00):
Hi. It's Matt Kirkner. I am the host of the
TechEd podcast, hoping to seeyou at the Midwest manufacturing
AI summit on October 7 inMilwaukee, Wisconsin, at State
Fair Park. You know, one of thethings that we hear from so many
people in manufacturing is, Iknow I should be doing something
in artificial intelligence. I'mjust not sure where to start.
(00:21):
I'm not exactly sure what Ishould be doing. We have people
ask us, are other companies evengoing down an AI journey? The
answer to that question isabsolutely, and that is the
reason that we're having thissummit. There's an opportunity
for everyone, regardless ofwhere they are, on their AI
journey, to come together, tomeet with other like minded,
(00:42):
technology driven manufacturingcompanies if you're just
starting your journey, a greatopportunity to talk to peers and
meet others that are on theirjourney. We are going to have
incredible keynote speakers andpanelists from companies large
and small, telling you how youcan implement AI in your
manufacturing operations, inyour front and back office.
Starting today, we are going tohave a whole collection of
(01:06):
technology partners, people whoare providing goods and services
in the world of industry, 4.0 inthe world of artificial
intelligence, available for youto learn from. We're going to
have a great speed round with somany of those companies. You're
going to come away with atremendous understanding of AI
technology as it exists today.
We also have a whole group ofstrategic partners, educators
(01:26):
and consultants and people inthe business of helping you on
your artificial intelligencejourney. Is one day, a single
day is all we're asking for totransform everything about your
manufacturing operation again.
It's October 7 at State FairPark in Milwaukee, Wisconsin,
the Midwest manufacturing AISummit. Hope to see you there.
(01:53):
Welcome to this week's episodeof The TechEd podcast, the
number one podcast in STEM andtechnical education. This is
Matt Kirkner. I am your host.
You know, as part of our work atthe TechEd podcast and our
parent company, the tech edMedia Group, we have
opportunities to go to all kindsof amazing events, gatherings of
people that are sharing bestpractices, sharing ideas,
talking about STEM education,talking about technology. I have
(02:16):
to tell you that if I was tomake a list of my top five
events, probably so far of 2025at or near the top of that list
would be the event that we didat Titletown tech. Now if you're
not familiar with Title towntech, this is an organization
that is a partnership primarilybetween the green may packers
and Microsoft. And who hasn'theard of those two great
(02:37):
organizations? And they createdthis amazing incubator, this
amazing venture capital fund,helping emerging companies,
helping founders build theirdreams around advancing
technology. In partnership withthat organization, right in the
shadow of Lambeau Field in GreenBay, Wisconsin, we held this
event as part of that event,which was all around artificial
(02:59):
intelligence. We broughttogether thought leaders. We
brought together individualsfrom across every economic
sector to learn about what'shappening in the world of
artificial intelligence. As partof that event, we had an
incredible panel. We wanted thatpanel to include representatives
of K 12 education, of highereducation, a startup company,
(03:20):
maybe a smaller to mid sizecompany that's into their AI
journey, as well as a larger,established company that is
investing in artificialintelligence. We had tremendous
feedback from the over capacitycrowd that showed up for that
event. We thought there was nobetter way to spend this episode
of The TechEd podcast, than toshare that panel with you. So
(03:41):
what follows is our panel fromearlier this year at Titletown
tech, all around artificialintelligence. I hope you enjoy
it. Thanks to Jill. You know,said everybody here at Titletown
tech, what an honor it is to beconnected with these great
brands. So many good thingshappening here, driving
innovation forward as we thinkabout the future of applied
(04:02):
artificial intelligence, it's mypleasure now to introduce our
panelists. First in line thereis my dear friend Mike Bigley.
Mike is the districtadministrator at the white ball
School District, and he is inhis 23rd year as district
administrator and 29th year asWisconsin school superintendent.
So if I mean, what did you startwhen you were 26 Yeah, he
(04:23):
actually did start when he was26 he had his first
superintendent job when he was26 years old. Mike is an
incredible innovator. I'veworked with Mike on a whole
number of efforts andinitiatives around artificial
intelligence industry 4.0 andapplied learning. You're going
to learn about the great thingshappening at the White Hall
School District. Next it lookslike is Doctor Kate burns. And
Kate is the Provost and ViceChancellor of Academic Affairs
(04:46):
at UW Green Bay. She's beenthere since 2006 since she was
formerly a professor ofpsychology and Associate Dean
for the College of Arts,Humanities and Social Sciences.
Her teaching is focused. Basedon statistics, research methods
and social psychology, so youcan understand where she is
going to fit in. Rick reski isnext, and He is senior director
(05:07):
of service and solutions at BWconverting. He's been there 25
years, and has 25 years ofexperience in the equipment
industry leadership roles plantspanning service sales and
digital services. And what Ilike most about Rick, we've met
a couple times over the courseof the last couple of weeks, is
like the examples that Katherinegave us. He's going to talk
(05:28):
about real, applied artificialintelligence and manufacturing.
And finally, Alex tynk is thefounder and CEO of fork farms
and the innovator of its farmingtechnology and methods. I wasn't
going to read the whole bio, butyou have to
Mike Beighley (05:41):
know, we all have our route to artificial
intelligence and to education.
Alex is as an opera singer, sohe has 10 years of leadership
experience following being anopera singer, and he was in the
social service sector beforestarting fork farms eight years
ago. We're gonna learn all aboutthe great things happening at
fork farms as well. Please joinme in welcoming our panelists as
they take a seat. So let's startwith Mike. Since you've got the
(06:05):
microphone there, Mike in 30seconds, how is AI transforming
education? You can fit that allinto 30 seconds, right? I think
education, in our perspective,it is transforming each and
every aspect. What it should bedoing across our state across
our country, is causing us toreally re evaluate what future
ready should look like. We needto reevaluate what kids need to
(06:26):
know. We need to figure out howto get that to them very
quickly, where they are notwhere a bunch of old people
sitting in a in a standardswriting scenario says they
should be.
Matt Kirchner (06:38):
So let's go on to Dr. Kate burns, Kate, how is UW
Green Bay leveraging AI andstudent support? Yeah.
Kate Burns (06:44):
So on the student side, we are looking at it for
student success. So we have atool that we use both as a chat
bot, right? So students are ableto get answers at any time, but
also trying to look at it's away to give out surveys on a
regular basis. So we say thingslike, how are you feeling about
the start of the semester? Areyou worried about being able to
pay for college? And we're ableto use those answers. Students
(07:06):
are much more likely to selfdisclose to this tool than they
would be to raise their hand andtalk to a person about any
problems that they might behaving. We then use their
answers, and we direct them to areal life person, right to be
able to follow up with them, tobe able to use the technology,
but then to be able to partnerwith an actual human, and we
found that these are studentswho are not on our radar. We did
(07:26):
not realize they were havingproblems, and that we were able
to discover that through thistool and using that generative
AI then as that layer to helpsupport awesome.
Matt Kirchner (07:34):
And while you have the microphone there, we
might as well let you ask thesame 32nd question too. Is there
a quick 32nd answer to how AI istransforming
Kate Burns (07:41):
your space? Yeah, I think very similar to Mike
right. I think education just asa whole, right, being disrupted
in terms of what do we wantstudents to gain upon
graduation?
Unknown (07:50):
Very good. Rick, same
Rick Roeske (07:52):
question to you, 30 seconds, how is AI transforming
manufacturing? Yeah, so AIthrough machine performance. You
know, we've done a lot withfault anomalies to help get
quicker reactions for ourcustomer support. So for mainly
for us, it's through customersupport and large language
models that we're using, alongwith tools for marketing and all
different kinds of everydaythings that we're using it
Matt Kirchner (08:12):
for. And I know we're gonna go deeper on that as
the discussion goes on. Alex,same question, how in 30
seconds, is AI transforming
Alex Tyink (08:19):
your work again two ways. So one is on the product
side. We're developing educationfocused AI tools to help the
learning process. So how can weuse generative AI as a more
engaging way for the kids tointeract with our work and our
fields? The second way isinternal workflows. We're
integrating AI across ourbusiness, pretty much in every
(08:40):
way, making sure it's additiveto our team, that it's not
subtracting from the qualitythat human beings still bring to
the work, and making sure thatit's not replacing jobs, but
really using it as a way toincrease efficiency. I think
that's a really, really gooddistinction that we hear from so
many people, that AI is notreplacing jobs, it's changing
the world of work. That's whywe're here today. But but
(09:00):
certainly I think as we did asurvey about a year ago of
employers across Wisconsin, theyactually expect AI to be
accretive to employment. So Ithink you make a really, really
good point. I also know, Alex,you've been incredibly involved
with the Microsoft co InnovationLab. Tell us about the work that
you did with the CO InnovationLab at the University of
Wisconsin, Milwaukee. Yeah, I'dlove to first. Thank you to
jewel and the title Town team,they got us in pretty early on
(09:22):
that, and we had this incredibleexperience where the Microsoft
team comes in and they reallywork hand in hand with you, but
it becomes our company'sproprietary intellectual
property, which is reallyincredible when you think about
the amount of resource that youget. Our use case was, as a
company, we're offering a widervariety of products as we grow
and scale, one of which is anenterprise project. And so when
(09:45):
you think about farming at acommercial scale, if you're a
school district, that'sdaunting. It's not part of the
core competency. But everyschool has a food problem. They
want to grow a lot of food. Theywant to give kids this level of
industry level interaction. Butthe fear is, how. Are we going
to make sure it's successful?
And so what we developed was anAI system that basically brings
the core competency of acommercial farm manager to a
(10:06):
digital solution, and it makesit where just by utilizing this
tool, it both teaches the kidshow to do the farming, but it
also makes sure that nothinggoes wrong on the farm itself.
And so what we see this doing isbreaking down barriers to entry
for anybody who wants to getinto our space. And it's like a
first of its kind. So really,really exciting.
Matt Kirchner (10:28):
And so you were basically allowed or able to
just plug into the CO InnovationLab and leverage the expertise
there to help that project goon. Yeah, we
Alex Tyink (10:35):
had to hire an internal development team, a
small development team, toactually put the hands on the
keyboard. That's how you makesure you retain the IP rights.
But other than that, they hadlong standing AI experts from
Microsoft, from their Redmondoffice, coming to Milwaukee to
meet with our team to help usunderstand, first the
architecture of the solution,and then they actually worked
(10:55):
hand in hand with us as wefigured out what actual
mechanically has to happen inthe cloud and with different
plugins that they offered tomake it come to life. And it
was, it's moving so fast as aspace, to have people on the
inside developing all thearchitecture and the back end,
supporting the development wasreally key for us to make sure
it was successful.
Matt Kirchner (11:15):
Excellent.
There's so many great thingshappening at UW M and really,
really excited for theinnovation that's happening
there. Excited to hear about thework you're doing, Rick, in this
whole area of capturingknowledge from seasoned
employees. And that was one ofthe things that impressed me
most about the work that you'redoing, is, how do we take folks
as we know there's there's moreand more people that are getting
deeper into their careers, morepeople are retiring out of the
workforce, in many cases, thatare entering the workforce,
(11:37):
capturing this knowledge frompeople that have been in our
organizations forever and ever,and leveraging AI to do that,
tell us about that work. Yeah,so our company has been around
for over 100 years, and we had alot of technicians that were 30
to 40 years, and we they'd walkout the door and we weren't
capturing any of it, right? Andthat's how we came up with our
large language model, ourchatgpt, right? And so we
(11:58):
started collecting data fromthem and our service reports. We
went back and pulled thosemachine manuals, machine any
machine documentation we had.
Then with our new CRM that weinstituted, we were able to use
knowledge based articles. So weare looking at all our knowledge
base articles. We actually havea little game and incentive for
the team to create knowledgebased articles. So our large
(12:20):
language model is capturing allof that information and bringing
it in, and it's it's been reallygreat for our team. If you have
any new people, and we're tryingto bring them up to speed, it's
increased that rate a lot. Theyfeel comfortable answering
questions. It's been a reallybig game changer for us. And so,
if I understand correctly,you're taking, like, all of your
work instructions, all thearticles, anything that pertains
(12:41):
to a job, creating an LLM out ofthat information, and then
you've got some kind of an agentthat enables people to an AI
agent, or an app that enablesthem to access that tell us
about that. Yeah, exactly. Soit's we work with another
company, a third party companythat it developed the actual
large language model itself. Soit works just like chatgpt. You
ask it a question, and it givesyou the answer. What's nice
(13:01):
about it? What we're workingwith the company is we all know
AI hallucinates, so there'sactually a thumbs up or a thumbs
down. If the information isn'tcorrect, you give a thumbs down,
and then it records all that.
And then we meet with the teamthat we're working with, and
they go through and make changesto our large language model. And
you can give a plug to thecompany too. I'm sure a lot of
people, yes, it's called abacusAI. I'll just say they have a
(13:22):
fantastic service. Anytime wecall them, they answer, I
believe it's 50 PhDs, andthey're always willing to help
at any time. So they've beenremarkable to
Mike Beighley (13:32):
work with.
Absolutely awesome. And Kate,
Matt Kirchner (13:35):
when we think about whether it's leveraging an
LLM, whether it's leveraging AIin a business like Rick's, or we
could think about studentsleveraging AI in their
coursework, and we know that,and I spend a lot of time in
higher education these days,there's perhaps no more greater
disruption happening in anysegment of our economy than is
what is happening in education.
So talk a little bit about howyou think about things like
(13:57):
writing and assessment in highereducation in the age of
artificial intelligence.
Kate Burns (14:03):
Yeah, our writing foundations department, which is
like our English Compositionintro to writing department, has
really used this as anopportunity to rethink about
what does writing look like inthis modern era. So they really,
they start the course with whatthey call extreme transparency,
right? They just say, okay, AIis here. What are your fears?
What are your anxieties? Whatare you excited about? Right?
(14:24):
Let's talk about this and beopen about it. And they partner
that then with zero punitiveaction on their part. So what
happens then is when studentswrite something that they feel
like, you know, this kind ofsounds like it's aI generated
completely they say the tone onthis seems a bit off. It seems a
little stilted here and and, youknow, I think right now I'm
going to give you a placeholderas a zero, but I would love for
(14:46):
you to have a chance to rewritethis and to be able to improve
the tone, and so that kind ofgets out of the litigating and
instead focuses on, this is ateaching moment. We need to be
able to use it. The other thingthat they've really been trying
to do is trying to replicate.
Replicate the world of work. Sothey've been using, you know,
let's have we're all in the labtogether. We're all going to
create publishable professionalwriting during our lab time
together. So they've really beentrying to focus on, how do we
(15:09):
replicate that work environment,so that students are thinking
about, how am I going to usethis tool, and how am I getting
practice in my current time? SoI think it's been a nice way of
really building thatrelationship side, but then also
really having some goodpractice.
Matt Kirchner (15:23):
I heard somebody say not too long ago that in the
past, that school was where wewould go to learn, right? We
would go to school and learn,and then we would go home to
practice. We'd go home to do ourhomework, we'd write essays,
we'd study for exams. And thatthat is being flipped in higher
education, and now with so manyways to learn, whether it's e
learning, whether it's YouTube,whether it's Tiktok, whether
it's videos, we're flipping thatmodel and home, in many ways, is
(15:45):
where we can learn, and thenschool is where we go to
practice, which is almostexactly what you just talked
about, which is, how do wecreate a higher education
environment that mimics the workenvironment that our students
are going to that are they'regoing to see when they move out
of higher Education? Speaking ofthat applied engagement in that
applied environment forlearning. I want to, I want to
ask Mike Bigley the nextquestion. But before I do that,
(16:07):
I want to set this question up alittle bit. So Mike, I mean,
he's a true disrupter. And ifthere's a education model that
is willing there, that can bedisrupted, Mike will find a way
to do it. I've seen, we've beenworking together on projects for
probably going on 10 years,absolutely fascinating the work
that he does. He's a realinnovator. And to put this next
question into perspective, heopened up this emerging
(16:27):
technologies lab he and thedistrict in White Hall last
September, at the beginning ofthe last school year, and we
were kind of hoping and prayingthat we would get 120 students
or so to sign up for thatemerging technologies lab. We
had 400 plus students in thatdistrict sign up for that at the
beginning of last year. So ayear ago, at the beginning of
second semester, I think thatnumber was 500 plus. And I got a
(16:49):
text from Mike a couple of weeksago that now we're into the
quadruple digits, if I'm notmistaken. So if I didn't set
that up well enough for you tellus a little bit about this
incredible project. You're oneof the first districts in the
country, maybe the first toteach applied artificial
intelligence in a K 12 or in asecondary education environment,
and certainly first one to do itin the way that you're doing it.
So talk about how students arelearning about AI in a hands on
(17:11):
way at Whitehall, well, that's a
Mike Beighley (17:12):
tough setup.
Matt, thanks, but I appreciatethat. I think that the emerging
technology lab that we rolledout last fall, with the help of
the folks at Ashley furnitureand the wanick Foundation really
allowed us to really look atwhat was possible for kids. If
you see the equipment, here itis in our Emerging Technology
Lab, and then some okay. And intop of this stuff, we're
(17:33):
teaching computer science andcoding, and we teach SolidWorks
and rest of the automation suitethat revolves around industry
4.0 but what we also set out todo was remove the artificial
limits that we continue to placeon kids. Okay, the folks at
Ashley hauled me all over thecountry, spent some time in
Germany, gonna spend some timein China in a few weeks, really
looking at what we need to do tobetter prepare our kids. And
(17:56):
what was evident very quickly ishow limiting we were to our
kids. We spent a lot of time inthis country telling them what
they can't do, so we set out toreally challenge our kids and
figure out what they could do.
And they have really blownthings away. To do that, though,
one of the most limiting factorsis our schedule. We're a small
rural school, one of everything,okay, I'm a product of that,
(18:19):
trying to figure out how tonavigate through a system where
you can't get the classesbecause you have another class
that butts up against it. So ourfolks got together, once we had
the technology, once we had theequipment, and figured out how
to personalize all of thatstuff. If the kids have an hour
in their day, they can come tothe emerging tech lab. We build
a class for them. We turn themloose. And not only did the
(18:40):
exceed expectations. They wereteaching the instructors how to
use pieces of the equipment.
They learned it on their own. Soall this engagement conversation
that we continue to have, it'sus as adults. We're the
engagement problem. We limitthem. That's what we've done
with the emerging tech lab. Theequipment is cool. It grabs
their attention. It turns themon. What we've done inside it is
(19:01):
more meaningful. We've removedthe limits. Removing limits is
what it's all about. I want topose a question now to Kate, and
it really kind of speaks to
Matt Kirchner (19:12):
this idea of what Mike just talked about, right?
If we think about and wechallenge people in higher
education, when you think aboutwhat students are seeing in
places like the emergingtechnologies lab. They're
working with edge to cloudtechnology, working with drones,
the autonomous vehicles that arebehind me, autonomous mobile
robots, mechatronics industry,4.0 all of these technologies
that many of us wouldn't haveeven dreamed of 10 years ago.
(19:35):
And now you think about a highschool student that comes to the
world of higher education withall of that experience, and is
expecting that same kind ofexperience, because that's the
way they've learned really putsome pressure on folks like Kate
burns right to create this nextgeneration of higher education.
So if we think about five yearsfrom now, as students who are
freshmen or maybe eighth gradersat the White House School
(19:57):
District are coming throughsecondary education. Then
entering higher ed. What ishigher education gonna look like
when a student can just go to anLLM or just go to a chat bot for
an answer to a question?
Kate Burns (20:08):
Yeah, I think the answer is really relationships.
And I think about relationshipsin two ways. So one way right,
in terms of the faculty member,right? So the classic model is
the sage up on the stage, right?
And so that person is supposedto be imparting their knowledge
to you? Well, you know, it'sreally the guide on the side,
right? Like, how do I make surethat I'm supporting you? How do
I make sure that you know whenyou have questions, I'm helpful?
(20:29):
And it's really more aboutmentorship and relationships, as
opposed to just that expertisealone, full stop. The other way
I think about relationshipsreally then, is on the student
side. So thinking about thatcollaboration, that connection
that students need to bebuilding with each other, how do
we make sure that students, thenare building that network while
(20:49):
they are in higher education?
How do we make sure that theyare building the soft skills,
ability to work as a team, sothat they are going to be
equipped for that future world,right? But really that focus has
to be on the humanity side ofhuman connection and
relationships.
Matt Kirchner (21:05):
Absolutely, I know. I read a book Kate not too
long ago called Genesis, and itwas written by and some of you
might have read it. It was HenryKissinger who wrote it in
association with the former VicePresident of Strategy for
Microsoft and the former CEO ofGoogle. And one of the things
that they talk about, there'ssome pretty dystopian stuff in
that book about what happens ifwe let AI just go in and on its
(21:26):
own without any guardrails. Andit can get really, really scary.
I think it starts to speak tothe idea that some of these,
some of this coursework thatmaybe we've gotten away from. I
remember my my journey throughhigher education included nine
credits in theology, ninecredits in philosophy and and
really thinking about what itmeans to be a human in as much
as you've got this incrediblebackground, Kate in in the
(21:47):
humanities, is that what you'regetting at when you talk about
the importance there, and maybean example or two of how that
might look?
Kate Burns (21:53):
Yeah, I think that's a piece of it for sure, right?
So when we think about, youknow, the ethics side of it,
right? So thinking about thingswhere philosophy or close
reading, right? Like, what arethese skills that are still
going to translate into themodern world of humanities, but
making sure that studentsunderstand the why, right? So
that we're not just teachingthis in a bubble of, okay, why
do I have to memorize this? Orwhy do I have to read this?
(22:14):
Well, this is why, and this ishow we're going to see that play
out in the real world, I think,is even more important now,
Matt Kirchner (22:19):
absolutely, way more focused on the meaning of
what we're learning and notjust, not just the facts. So
related to that topic, Rick, Iknow you've been really, really
focusing on the meaning ofbusiness and customer
relationships in your in yourwork at BW, converting so much
so that you also partnered withthe Microsoft co Innovation Lab
at the University of Wisconsin,Milwaukee, in the same way that
Alex did. I know our audiencewould love to hear about that
(22:41):
experience. Yeah. So coming intoTitletown and meeting Katherine
close has been just a greatexperience for us. Working with
the engineering team down at theCO Innovation Lab, was great, as
Alex said, you know that you getto own the proprietary
information when you come out ofit. But what we wanted to do is
create an AI model for faultanomalies, and we chose fault
anomalies because we buildseveral different kinds of
(23:03):
equipment. We're an OEMequipment builder. I didn't
mention that before, but severaldifferent kinds of products all
over the world, and we wantedsomething that could go across
all the different equipmentlines. So fault anomalies
worked, where that all tied intorelationships, though, was we
were surprised not about howmuch we were learning about our
equipment and our customers werelearning about our equipment,
but how much it improved ourrelationships with our
(23:24):
customers. You were reaching outto us more often, having more
meaningful conversations withus, and that was the part that
shocked us the most, becausewhen you think about AI, you're
not really thinking about howit's going to build a customer
relationship, but it was really,really good, well,
Alex Tyink (23:37):
and maybe just a little bit more on making that
connection between the faultanomalies in and improving the
customer experience, and thefeedback you got from customers
was that just because yourperformance as a supplier was
was improving, or how does thatfit together? Yeah. So yes, it
was. It was improving as asupplier, like I said, we were
surprised how much in what welearned about our equipment and
how people used it through thefault anomalies, and they were
(23:59):
teaching us things about ourequipment, but yeah, so as part
of the fault anomalies, we weregetting alerts whenever their
machine had an anomaly. So wewere reaching out to the
customer, and many times beforethey even knew they had an
issue. So the leadership teamsat our customers facilities were
very happy to know that we werejumping on issues before they
even knew they had them. Sort ofput that into perspective from
my 25 years in manufacturing. Imean, the way we troubleshot
(24:22):
equipment was, usually, we havea problem. We'd have a fault,
we'd send the maintenance teamthere. They'd review it, they'd
try to fix it. If they couldn't,then we'd bring another group of
people to review it. Maybe atsome point, we do a whole Kaizen
Event around that problem. Andwhat I'm hearing from you is
that you're actually usingartificial intelligence to help
identify where that problem is,what the root cause of that
problem is, and even suggestthis potential corrective
(24:45):
action, right exactly, and notonly that, but you know, like
you said, how do we servicecustomers? These days, we have a
complete remote service team.
Obviously, these guys areexperts that can go on and
troubleshoot electronic issues,but we also have visualization
of data, so we have what we asour digital. Services branded
accelerate. We created our owndashboards to visualize the
data, so we use that data alsoto troubleshoot the issues.
Awesome, awesome application forartificial intelligence. I know
(25:09):
Alex, one of yours, has been toleverage AI to actually develop
curriculum. You and I are bothhuge believers that you know, if
you're a supplier to education,it's one thing to have a really,
really cool piece of equipmentthat's like 25% of the equation,
and the other 75% is, what arewe trying to teach? What are the
learning outcomes? What do wewant our students to know? What
do we want them to be able to dowhen they're through this
(25:29):
experience? That all speaks tocurriculum and delivery. So talk
about how you've leveraged AI onthe curriculum side, especially
for your horticulture product.
Yeah, great question. So for us,it's really about trying to meet
the kids where they're at andallowing them to engage at their
own pace, and allowing them toengage at whatever level they
feel most comfortable with. Andso we've had curriculum as an
(25:52):
organization for quite a while,but what we learned is that
usage of the curriculum wasreally spotty across different
locations. It was reallydependent on the staffing and
the teacher that was there onthe class, the nature of that
district and school community.
And so one thing we've alwaysbeen seeking to do is, how do we
deepen that, and how do we allowthe flexibility to engage with
our program in whatever way ismost meaningful to them, while
(26:15):
also allowing them to take itmore deeply, more easily. And so
for us, that's taken the form ofgenerative AI and using an LLM
and training it on ourcurriculum, and giving the kids
a digital friend, basically alittle farmer buddy to talk to
and ask questions of and to andto engage with. And the early
results on this have been reallyexceptional. I think the biggest
(26:39):
learning that I've had from itis that there's an active way to
engage with AI, and there's apassive way to engage with AI,
and in my opinion, teaching kidshow to be active with it and to
really understand that, yes, itcan teach you a lot, but you
also have a lot to add that theAI is not going to be able to
bring to the table like this.
(26:59):
Focus on relationships is sokey. Because I think, and in our
organization internally, that'sa big thing that we're
constantly doing, is qualitychecking the AI and quality
checking it for humanism. Andyou know, if you're talking to a
customer, like remembering whentheir kid's birthday is, I mean,
that's the stuff that the AI isnot going to be able to do
anytime soon, and is reallycritical to being effective.
(27:21):
What have you heard fromstudents as they've engaged with
the curriculum? It's just beenmore excitement. It's been more
engagement. It's been, you know,I think, a little bit faster for
us to get traction in theclassroom. And those are the
things that we like to see. Itjust feels like a more seamless
integration, if that makessense, it does
Matt Kirchner (27:40):
indeed, Rick, I know you've done, obviously, a
ton on the whole use of AI forfault anomalies and recognizing
faults. You're also starting tothink, if I'm not mistaken,
about how we use it forforecasting. And you know, I
tell people quite often, youknow, in the past, we used to
look over our shoulder. You getyour financial statements on the
15th of the month to see how youdid in June, for example, and
(28:01):
then really good companies gotgood at figuring out where are
we today, and if I don't likewhat I'm seeing today, I fix it
now, rather than fixing aproblem I had a month ago. Now
you're starting to use AI tolook forward for forecasting and
fix problems before they everhappen. Tell us about that.
Yeah, so we haven't jumped inyet full board, but definitely
my leader and I have beentalking a lot about how we do
(28:22):
better forecasting for serviceservices, so up and down, and it
can be extremely busy one monthand light the next month. So how
do we look at trends, and how dowe use that data to better
forecast for our company? Soforecasting financials, but also
inventory control for our partsdepartment. I actually heard
this morning from one of ouroperational leaders, how they're
using it for inventory control,just using copilot, and it's
(28:44):
actually been working reallywell for them. So that we can
improve our on time delivery,awesome. So obviously, improving
that on time delivery, customersare getting products more
quickly. We're not stringingpeople out. We're not straining
capacity in manufacturing. Sosuper, super important and a
great application for artificialintelligence. Kate, I'm going to
put you on the spot here for amoment and ask you a question
(29:04):
about what you're starting tohear from your students. I know,
as we look as whether we'reeducators or people that work in
and around education, we're allstarting to think about the
future of education in higher edand in that environment. What
are you starting to hear fromstudents about both their
concerns and opportunities asthey look to the future of
higher education for in this ageof artificial
Kate Burns (29:23):
intelligence, yeah, I think it's a real mix. So I
think, you know, I'm reallyexcited by what Mike's doing. I
think that for some K through 12districts, the approach is just
to ban it, right? So they kindof, that comes in when they when
they enter college, right? Of,I'm still not supposed to do
this. This is kind of illegal,you know. And so they don't
really have any kind of baselineknowledge, because it's just
been prohibited full stop. Somepeople are over reliant, right?
(29:46):
And they've kind of used it,they don't necessarily
understand the limitations, andso they maybe don't realize that
I need to think about how I'mgoing to kind of pare down a
little bit. And I think somepeople are just very much at an
anxious stage, right? Sothinking about what does the
future. Mean, for me, am I goingto be able to get a job? Is AI
going to take my job? What doesthis look like as a person who's
going to be graduating in thismany years? So it is really,
(30:09):
it's that emotional that you'rehaving to work with as opposed
to just just pure knowledge andtrying to work with students.
Mike Beighley (30:15):
So like anything else in education, it sounds
like it's meeting students wherethey are. They're all coming to
this from a different angle,making sure that we're
personalizing this experience asmuch as possible. Which Mike, I
know, is something that you'redoing a ton of work on. I know
you were in San Diego just a fewshort weeks ago doing a
presentation and talking aboutsome of the applications for
personalized learning throughartificial intelligence. So take
(30:37):
some time to talk about theinnovation that's happening on
that side at White Hall, andwhat you've learned from some of
your early work when we startedin on the Emerging Technology
Lab and talked about removingthose artificial limits, it
didn't take us long, for thoseof you that haven't seen it, it
is across the street from ourexisting K 12 building, and we
literally, we were sitting inthe lab one day, and we thought,
(30:57):
if this works over here, whydoesn't it work over there? So
we set out to personalize ourenvironment across the board.
Meet the kids where they are.
Much of our time is spent inminimal compliance, okay, as
opposed to human capacity. Weuse that phrase a lot. What's
the minimum Mike Bigley could doto be eligible to play football
each fall? Was what mattered,right? Because what I was
(31:19):
learning was determined bysomeone else, and it really
didn't have a lot of effect onmy future. Okay, that's flipped.
Now the personalized learningenvironment has put kids in
charge of their learning westarted, in fact, when we get
done with this, our teacher thatstarted our first pilot is here.
I just happened to be married toher, so that's phenomenal, too,
but she jumped in with eighthgrade math. Okay, took all of
(31:43):
our eighth graders, not a selectgroup. We took all of our eighth
graders, started them in acompletely personalized learning
environment. She flipped herentire world upside down. We got
huge results right away. Theengagement level went up, the
behaviors went down, theperformance went through the
roof. By Christmas time, abouthalf the kids, not quite half
(32:04):
the kids, had completed eighthgrade math to mastery, not
minimal compliance. They hadcompleted eighth grade math and
wanted more. They were askingfor more help, more assignments,
more more to do. I wanted tomake sure that I've learned this
stuff, as opposed to regurgitatesomething on a test. That's how
we've leveraged the AI tools,and I think it's important to
(32:25):
remember they are as poor or aslimited as they are ever going
to be right now we're gettingbetter and better every day. Now
we have the ability to do thosethings, but the but the
relationships matter. Okay? Wegot great results with eighth
grade math, but Shannon alsochanged her entire learning
environment. Talked aboutworking with kids, teaching them
(32:45):
how to learn, holding themaccountable, teaching them
empathy, teaching them how tocommunicate. And they did it
without her making it happen.
They just did it naturally. So Ithink that's the biggest thing
that we have to learn again. Imentioned the limits. I will say
that until I'm dead, we are thelimits. We as the adults, are
(33:06):
the problem, not the kids. Ithink as we continue to go down
that road, it's going to getbetter and better. We've done
the same thing now.
Kindergarteners watched a groupof kindergarteners this summer
or this winter. Excuse me, thespring, I think some of you were
there, had an opportunity 20kindergarteners in a room,
teacher with a small group offour kids up at the board, doing
a little mini lesson. The otherkids were involved with their
(33:27):
iPads, okay, reading into theiPad, which was recording their
reading, which wasdifferentiating what they were
having trouble with, giving thatfeedback to the teacher,
suggesting what they should dotomorrow. She just had to sign
off on it, and every one of the20 kids was engaged. There's
never a kindergarten class with20 kids engaged. Every one of
them was engaged with me walkingaround the room. That's the
(33:50):
power, and that really is thefuture of education. Mike, I'm
going to pose one last question,both to you and Kate. But the
question is this, you talkedabout we as adults being the
limiting factor. I've got tobelieve, I mean, we've got a lot
of educators in the room today.
I've got to believe that you'reseeing every version of, let's
go into this with everythingguns a blazing, and we've got
(34:10):
people that are saying, oh mygoodness, this isn't for me. How
are you managing through some ofthe some of the changes, and
what kind of feedback are yougetting from your instructional
staff as you're asking them tomake changes in the way they're
delivering education. I thinkthat the important thing is,
we've spent the last two, twoand a half years explaining the
why of this as well, talkingabout the need to really re
evaluate what kids need so theyare successful in their future,
(34:33):
not the past we lived in in it'sbeen mixed. I will tell you, our
staff is exceptional, though.
Everybody says that I was reallyis okay. There's been, there
have been a few that have been alittle resistant. We've spent a
lot of time explaining the whyand the right up until the point
where we say, this is wherewe're going, because this is
good for kids. If you don't likeit, find someplace else to be.
That's the bottom line. Theseare kids. This is our collective
(34:54):
future. We don't say that in athreatening manner, though. You.
Really don't Okay. We just tellthe truth. This is what we need
to do. I think that the PD side,we've done a lot to free our
folks up to try things, to givethem the opportunity. If it
doesn't work, re evaluate it,figure out what to do next, and
we hold accountability in termsof pushing the limit, as opposed
(35:18):
to conforming or or hitting thatminimal compliance piece,
terrific.
Matt Kirchner (35:25):
And when we put the student first, it's amazing
what can happen. It sounds likeyou're doing exactly that. Kate,
any last thoughts on on managingthrough some of the challenges
or some of the feedback you'regetting from faculty in this age
of huge innovation andeducation?
Kate Burns (35:38):
Yeah. I mean, I think it is a mix, for sure. Of
some folks are very excited.
Some folks, I think, lessexcited, to put it terribly, and
so it is tricky to manage. Ithink we've really been trying
to lean into teaching withtechnology. Is really about good
teaching, right? When you'retalking about, Why are students
learning? Why am I teachingthis? Right? You need to be able
to explain that, and that showswhat the role of technology is,
(36:00):
but that also shows therelevance, right? And I think
that sometimes folks aren't usedto focusing on that relevance in
that same way, but that is core,right? And that's just even more
important now than it was in thepast, importance
Matt Kirchner (36:14):
for students to understand the relevance, and,
for that matter, for faculty aswell. So great advice from Kate
burns. Let me thank ourpanelists, Mike Bigley, Kate
burns, Rick rusky and Alex tyingplease give them a big round of
applause. Wow. What anincredible panel we had, and an
incredible discussion aroundartificial intelligence. Lessons
for everyone, regardless ofwhether you're in education,
(36:37):
you're in industry, you're inpublic, policy, doesn't matter.
There was takeaways from everysingle aspect of the AI economy
in that panel. So glad youjoined us. And of course, there
were a number of references thatfolks made over the course of
that discussion. As always, wewill link them all up in the
show notes, which you will findat TechEd podcast.com/titletown
(37:00):
AI, that's TechEd podcast.com/t.
I, T, L, E, T, O, W, n, a, i,that's where you'll find the
show notes. When you're donethere, as always, check us out
on social media, Instagram,Facebook, tick, tock, LinkedIn,
wherever you go. To consume yoursocial media, you will find the
TechEd podcast when you'rethere, reach out and say hello
and don't forget to join usagain next week for another
(37:22):
episode of The TechEd podcast.
Until then, I am your host. MattKirkner, thanks for being with
us.
Unknown (37:34):
You.
Hi. It's Matt Kirkner. I am the host of the
TechEd podcast, hoping to seeyou at the Midwest manufacturing
AI summit on October 7 inMilwaukee, Wisconsin, at State
Fair Park. You know, one of thethings that we hear from so many
people in manufacturing is, Iknow I should be doing something
in artificial intelligence. I'mjust not sure where to start.
(00:21):
I'm not exactly sure what Ishould be doing. We have people
ask us, are other companies evengoing down an AI journey? The
answer to that question isabsolutely, and that is the
reason that we're having thissummit. There's an opportunity
for everyone, regardless ofwhere they are, on their AI
journey, to come together, tomeet with other like minded,
(00:42):
technology driven manufacturingcompanies if you're just
starting your journey, a greatopportunity to talk to peers and
meet others that are on theirjourney. We are going to have
incredible keynote speakers andpanelists from companies large
and small, telling you how youcan implement AI in your
manufacturing operations, inyour front and back office.
Starting today, we are going tohave a whole collection of
(01:06):
technology partners, people whoare providing goods and services
in the world of industry, 4.0 inthe world of artificial
intelligence, available for youto learn from. We're going to
have a great speed round with somany of those companies. You're
going to come away with atremendous understanding of AI
technology as it exists today.
We also have a whole group ofstrategic partners, educators
(01:26):
and consultants and people inthe business of helping you on
your artificial intelligencejourney. Is one day, a single
day is all we're asking for totransform everything about your
manufacturing operation again.
It's October 7 at State FairPark in Milwaukee, Wisconsin,
the Midwest manufacturing AISummit. Hope to see you there.
(01:53):
Welcome to this week's episodeof The TechEd podcast, the
number one podcast in STEM andtechnical education. This is
Matt Kirkner. I am your host.
You know, as part of our work atthe TechEd podcast and our
parent company, the tech edMedia Group, we have
opportunities to go to all kindsof amazing events, gatherings of
people that are sharing bestpractices, sharing ideas,
talking about STEM education,talking about technology. I have
(02:16):
to tell you that if I was tomake a list of my top five
events, probably so far of 2025at or near the top of that list
would be the event that we didat Titletown tech. Now if you're
not familiar with Title towntech, this is an organization
that is a partnership primarilybetween the green may packers
and Microsoft. And who hasn'theard of those two great
(02:37):
organizations? And they createdthis amazing incubator, this
amazing venture capital fund,helping emerging companies,
helping founders build theirdreams around advancing
technology. In partnership withthat organization, right in the
shadow of Lambeau Field in GreenBay, Wisconsin, we held this
event as part of that event,which was all around artificial
(02:59):
intelligence. We broughttogether thought leaders. We
brought together individualsfrom across every economic
sector to learn about what'shappening in the world of
artificial intelligence. As partof that event, we had an
incredible panel. We wanted thatpanel to include representatives
of K 12 education, of highereducation, a startup company,
(03:20):
maybe a smaller to mid sizecompany that's into their AI
journey, as well as a larger,established company that is
investing in artificialintelligence. We had tremendous
feedback from the over capacitycrowd that showed up for that
event. We thought there was nobetter way to spend this episode
of The TechEd podcast, than toshare that panel with you. So
(03:41):
what follows is our panel fromearlier this year at Titletown
tech, all around artificialintelligence. I hope you enjoy
it. Thanks to Jill. You know,said everybody here at Titletown
tech, what an honor it is to beconnected with these great
brands. So many good thingshappening here, driving
innovation forward as we thinkabout the future of applied
(04:02):
artificial intelligence, it's mypleasure now to introduce our
panelists. First in line thereis my dear friend Mike Bigley.
Mike is the districtadministrator at the white ball
School District, and he is inhis 23rd year as district
administrator and 29th year asWisconsin school superintendent.
So if I mean, what did you startwhen you were 26 Yeah, he
(04:23):
actually did start when he was26 he had his first
superintendent job when he was26 years old. Mike is an
incredible innovator. I'veworked with Mike on a whole
number of efforts andinitiatives around artificial
intelligence industry 4.0 andapplied learning. You're going
to learn about the great thingshappening at the White Hall
School District. Next it lookslike is Doctor Kate burns. And
Kate is the Provost and ViceChancellor of Academic Affairs
(04:46):
at UW Green Bay. She's beenthere since 2006 since she was
formerly a professor ofpsychology and Associate Dean
for the College of Arts,Humanities and Social Sciences.
Her teaching is focused. Basedon statistics, research methods
and social psychology, so youcan understand where she is
going to fit in. Rick reski isnext, and He is senior director
(05:07):
of service and solutions at BWconverting. He's been there 25
years, and has 25 years ofexperience in the equipment
industry leadership roles plantspanning service sales and
digital services. And what Ilike most about Rick, we've met
a couple times over the courseof the last couple of weeks, is
like the examples that Katherinegave us. He's going to talk
(05:28):
about real, applied artificialintelligence and manufacturing.
And finally, Alex tynk is thefounder and CEO of fork farms
and the innovator of its farmingtechnology and methods. I wasn't
going to read the whole bio, butyou have to
Mike Beighley (05:41):
know, we all have our route to artificial
intelligence and to education.
Alex is as an opera singer, sohe has 10 years of leadership
experience following being anopera singer, and he was in the
social service sector beforestarting fork farms eight years
ago. We're gonna learn all aboutthe great things happening at
fork farms as well. Please joinme in welcoming our panelists as
they take a seat. So let's startwith Mike. Since you've got the
(06:05):
microphone there, Mike in 30seconds, how is AI transforming
education? You can fit that allinto 30 seconds, right? I think
education, in our perspective,it is transforming each and
every aspect. What it should bedoing across our state across
our country, is causing us toreally re evaluate what future
ready should look like. We needto reevaluate what kids need to
(06:26):
know. We need to figure out howto get that to them very
quickly, where they are notwhere a bunch of old people
sitting in a in a standardswriting scenario says they
should be.
Matt Kirchner (06:38):
So let's go on to Dr. Kate burns, Kate, how is UW
Green Bay leveraging AI andstudent support? Yeah.
Kate Burns (06:44):
So on the student side, we are looking at it for
student success. So we have atool that we use both as a chat
bot, right? So students are ableto get answers at any time, but
also trying to look at it's away to give out surveys on a
regular basis. So we say thingslike, how are you feeling about
the start of the semester? Areyou worried about being able to
pay for college? And we're ableto use those answers. Students
(07:06):
are much more likely to selfdisclose to this tool than they
would be to raise their hand andtalk to a person about any
problems that they might behaving. We then use their
answers, and we direct them to areal life person, right to be
able to follow up with them, tobe able to use the technology,
but then to be able to partnerwith an actual human, and we
found that these are studentswho are not on our radar. We did
(07:26):
not realize they were havingproblems, and that we were able
to discover that through thistool and using that generative
AI then as that layer to helpsupport awesome.
Matt Kirchner (07:34):
And while you have the microphone there, we
might as well let you ask thesame 32nd question too. Is there
a quick 32nd answer to how AI istransforming
Kate Burns (07:41):
your space? Yeah, I think very similar to Mike
right. I think education just asa whole, right, being disrupted
in terms of what do we wantstudents to gain upon
graduation?
Unknown (07:50):
Very good. Rick, same
Rick Roeske (07:52):
question to you, 30 seconds, how is AI transforming
manufacturing? Yeah, so AIthrough machine performance. You
know, we've done a lot withfault anomalies to help get
quicker reactions for ourcustomer support. So for mainly
for us, it's through customersupport and large language
models that we're using, alongwith tools for marketing and all
different kinds of everydaythings that we're using it
Matt Kirchner (08:12):
for. And I know we're gonna go deeper on that as
the discussion goes on. Alex,same question, how in 30
seconds, is AI transforming
Alex Tyink (08:19):
your work again two ways. So one is on the product
side. We're developing educationfocused AI tools to help the
learning process. So how can weuse generative AI as a more
engaging way for the kids tointeract with our work and our
fields? The second way isinternal workflows. We're
integrating AI across ourbusiness, pretty much in every
(08:40):
way, making sure it's additiveto our team, that it's not
subtracting from the qualitythat human beings still bring to
the work, and making sure thatit's not replacing jobs, but
really using it as a way toincrease efficiency. I think
that's a really, really gooddistinction that we hear from so
many people, that AI is notreplacing jobs, it's changing
the world of work. That's whywe're here today. But but
(09:00):
certainly I think as we did asurvey about a year ago of
employers across Wisconsin, theyactually expect AI to be
accretive to employment. So Ithink you make a really, really
good point. I also know, Alex,you've been incredibly involved
with the Microsoft co InnovationLab. Tell us about the work that
you did with the CO InnovationLab at the University of
Wisconsin, Milwaukee. Yeah, I'dlove to first. Thank you to
jewel and the title Town team,they got us in pretty early on
(09:22):
that, and we had this incredibleexperience where the Microsoft
team comes in and they reallywork hand in hand with you, but
it becomes our company'sproprietary intellectual
property, which is reallyincredible when you think about
the amount of resource that youget. Our use case was, as a
company, we're offering a widervariety of products as we grow
and scale, one of which is anenterprise project. And so when
(09:45):
you think about farming at acommercial scale, if you're a
school district, that'sdaunting. It's not part of the
core competency. But everyschool has a food problem. They
want to grow a lot of food. Theywant to give kids this level of
industry level interaction. Butthe fear is, how. Are we going
to make sure it's successful?
And so what we developed was anAI system that basically brings
the core competency of acommercial farm manager to a
(10:06):
digital solution, and it makesit where just by utilizing this
tool, it both teaches the kidshow to do the farming, but it
also makes sure that nothinggoes wrong on the farm itself.
And so what we see this doing isbreaking down barriers to entry
for anybody who wants to getinto our space. And it's like a
first of its kind. So really,really exciting.
Matt Kirchner (10:28):
And so you were basically allowed or able to
just plug into the CO InnovationLab and leverage the expertise
there to help that project goon. Yeah, we
Alex Tyink (10:35):
had to hire an internal development team, a
small development team, toactually put the hands on the
keyboard. That's how you makesure you retain the IP rights.
But other than that, they hadlong standing AI experts from
Microsoft, from their Redmondoffice, coming to Milwaukee to
meet with our team to help usunderstand, first the
architecture of the solution,and then they actually worked
(10:55):
hand in hand with us as wefigured out what actual
mechanically has to happen inthe cloud and with different
plugins that they offered tomake it come to life. And it
was, it's moving so fast as aspace, to have people on the
inside developing all thearchitecture and the back end,
supporting the development wasreally key for us to make sure
it was successful.
Matt Kirchner (11:15):
Excellent.
There's so many great thingshappening at UW M and really,
really excited for theinnovation that's happening
there. Excited to hear about thework you're doing, Rick, in this
whole area of capturingknowledge from seasoned
employees. And that was one ofthe things that impressed me
most about the work that you'redoing, is, how do we take folks
as we know there's there's moreand more people that are getting
deeper into their careers, morepeople are retiring out of the
workforce, in many cases, thatare entering the workforce,
(11:37):
capturing this knowledge frompeople that have been in our
organizations forever and ever,and leveraging AI to do that,
tell us about that work. Yeah,so our company has been around
for over 100 years, and we had alot of technicians that were 30
to 40 years, and we they'd walkout the door and we weren't
capturing any of it, right? Andthat's how we came up with our
large language model, ourchatgpt, right? And so we
(11:58):
started collecting data fromthem and our service reports. We
went back and pulled thosemachine manuals, machine any
machine documentation we had.
Then with our new CRM that weinstituted, we were able to use
knowledge based articles. So weare looking at all our knowledge
base articles. We actually havea little game and incentive for
the team to create knowledgebased articles. So our large
(12:20):
language model is capturing allof that information and bringing
it in, and it's it's been reallygreat for our team. If you have
any new people, and we're tryingto bring them up to speed, it's
increased that rate a lot. Theyfeel comfortable answering
questions. It's been a reallybig game changer for us. And so,
if I understand correctly,you're taking, like, all of your
work instructions, all thearticles, anything that pertains
(12:41):
to a job, creating an LLM out ofthat information, and then
you've got some kind of an agentthat enables people to an AI
agent, or an app that enablesthem to access that tell us
about that. Yeah, exactly. Soit's we work with another
company, a third party companythat it developed the actual
large language model itself. Soit works just like chatgpt. You
ask it a question, and it givesyou the answer. What's nice
(13:01):
about it? What we're workingwith the company is we all know
AI hallucinates, so there'sactually a thumbs up or a thumbs
down. If the information isn'tcorrect, you give a thumbs down,
and then it records all that.
And then we meet with the teamthat we're working with, and
they go through and make changesto our large language model. And
you can give a plug to thecompany too. I'm sure a lot of
people, yes, it's called abacusAI. I'll just say they have a
(13:22):
fantastic service. Anytime wecall them, they answer, I
believe it's 50 PhDs, andthey're always willing to help
at any time. So they've beenremarkable to
Mike Beighley (13:32):
work with.
Absolutely awesome. And Kate,
Matt Kirchner (13:35):
when we think about whether it's leveraging an
LLM, whether it's leveraging AIin a business like Rick's, or we
could think about studentsleveraging AI in their
coursework, and we know that,and I spend a lot of time in
higher education these days,there's perhaps no more greater
disruption happening in anysegment of our economy than is
what is happening in education.
So talk a little bit about howyou think about things like
(13:57):
writing and assessment in highereducation in the age of
artificial intelligence.
Kate Burns (14:03):
Yeah, our writing foundations department, which is
like our English Compositionintro to writing department, has
really used this as anopportunity to rethink about
what does writing look like inthis modern era. So they really,
they start the course with whatthey call extreme transparency,
right? They just say, okay, AIis here. What are your fears?
What are your anxieties? Whatare you excited about? Right?
(14:24):
Let's talk about this and beopen about it. And they partner
that then with zero punitiveaction on their part. So what
happens then is when studentswrite something that they feel
like, you know, this kind ofsounds like it's aI generated
completely they say the tone onthis seems a bit off. It seems a
little stilted here and and, youknow, I think right now I'm
going to give you a placeholderas a zero, but I would love for
(14:46):
you to have a chance to rewritethis and to be able to improve
the tone, and so that kind ofgets out of the litigating and
instead focuses on, this is ateaching moment. We need to be
able to use it. The other thingthat they've really been trying
to do is trying to replicate.
Replicate the world of work. Sothey've been using, you know,
let's have we're all in the labtogether. We're all going to
create publishable professionalwriting during our lab time
together. So they've really beentrying to focus on, how do we
(15:09):
replicate that work environment,so that students are thinking
about, how am I going to usethis tool, and how am I getting
practice in my current time? SoI think it's been a nice way of
really building thatrelationship side, but then also
really having some goodpractice.
Matt Kirchner (15:23):
I heard somebody say not too long ago that in the
past, that school was where wewould go to learn, right? We
would go to school and learn,and then we would go home to
practice. We'd go home to do ourhomework, we'd write essays,
we'd study for exams. And thatthat is being flipped in higher
education, and now with so manyways to learn, whether it's e
learning, whether it's YouTube,whether it's Tiktok, whether
it's videos, we're flipping thatmodel and home, in many ways, is
(15:45):
where we can learn, and thenschool is where we go to
practice, which is almostexactly what you just talked
about, which is, how do wecreate a higher education
environment that mimics the workenvironment that our students
are going to that are they'regoing to see when they move out
of higher Education? Speaking ofthat applied engagement in that
applied environment forlearning. I want to, I want to
ask Mike Bigley the nextquestion. But before I do that,
(16:07):
I want to set this question up alittle bit. So Mike, I mean,
he's a true disrupter. And ifthere's a education model that
is willing there, that can bedisrupted, Mike will find a way
to do it. I've seen, we've beenworking together on projects for
probably going on 10 years,absolutely fascinating the work
that he does. He's a realinnovator. And to put this next
question into perspective, heopened up this emerging
(16:27):
technologies lab he and thedistrict in White Hall last
September, at the beginning ofthe last school year, and we
were kind of hoping and prayingthat we would get 120 students
or so to sign up for thatemerging technologies lab. We
had 400 plus students in thatdistrict sign up for that at the
beginning of last year. So ayear ago, at the beginning of
second semester, I think thatnumber was 500 plus. And I got a
(16:49):
text from Mike a couple of weeksago that now we're into the
quadruple digits, if I'm notmistaken. So if I didn't set
that up well enough for you tellus a little bit about this
incredible project. You're oneof the first districts in the
country, maybe the first toteach applied artificial
intelligence in a K 12 or in asecondary education environment,
and certainly first one to do itin the way that you're doing it.
So talk about how students arelearning about AI in a hands on
(17:11):
way at Whitehall, well, that's a
Mike Beighley (17:12):
tough setup.
Matt, thanks, but I appreciatethat. I think that the emerging
technology lab that we rolledout last fall, with the help of
the folks at Ashley furnitureand the wanick Foundation really
allowed us to really look atwhat was possible for kids. If
you see the equipment, here itis in our Emerging Technology
Lab, and then some okay. And intop of this stuff, we're
(17:33):
teaching computer science andcoding, and we teach SolidWorks
and rest of the automation suitethat revolves around industry
4.0 but what we also set out todo was remove the artificial
limits that we continue to placeon kids. Okay, the folks at
Ashley hauled me all over thecountry, spent some time in
Germany, gonna spend some timein China in a few weeks, really
looking at what we need to do tobetter prepare our kids. And
(17:56):
what was evident very quickly ishow limiting we were to our
kids. We spent a lot of time inthis country telling them what
they can't do, so we set out toreally challenge our kids and
figure out what they could do.
And they have really blownthings away. To do that, though,
one of the most limiting factorsis our schedule. We're a small
rural school, one of everything,okay, I'm a product of that,
(18:19):
trying to figure out how tonavigate through a system where
you can't get the classesbecause you have another class
that butts up against it. So ourfolks got together, once we had
the technology, once we had theequipment, and figured out how
to personalize all of thatstuff. If the kids have an hour
in their day, they can come tothe emerging tech lab. We build
a class for them. We turn themloose. And not only did the
(18:40):
exceed expectations. They wereteaching the instructors how to
use pieces of the equipment.
They learned it on their own. Soall this engagement conversation
that we continue to have, it'sus as adults. We're the
engagement problem. We limitthem. That's what we've done
with the emerging tech lab. Theequipment is cool. It grabs
their attention. It turns themon. What we've done inside it is
(19:01):
more meaningful. We've removedthe limits. Removing limits is
what it's all about. I want topose a question now to Kate, and
it really kind of speaks to
Matt Kirchner (19:12):
this idea of what Mike just talked about, right?
If we think about and wechallenge people in higher
education, when you think aboutwhat students are seeing in
places like the emergingtechnologies lab. They're
working with edge to cloudtechnology, working with drones,
the autonomous vehicles that arebehind me, autonomous mobile
robots, mechatronics industry,4.0 all of these technologies
that many of us wouldn't haveeven dreamed of 10 years ago.
(19:35):
And now you think about a highschool student that comes to the
world of higher education withall of that experience, and is
expecting that same kind ofexperience, because that's the
way they've learned really putsome pressure on folks like Kate
burns right to create this nextgeneration of higher education.
So if we think about five yearsfrom now, as students who are
freshmen or maybe eighth gradersat the White House School
(19:57):
District are coming throughsecondary education. Then
entering higher ed. What ishigher education gonna look like
when a student can just go to anLLM or just go to a chat bot for
an answer to a question?
Kate Burns (20:08):
Yeah, I think the answer is really relationships.
And I think about relationshipsin two ways. So one way right,
in terms of the faculty member,right? So the classic model is
the sage up on the stage, right?
And so that person is supposedto be imparting their knowledge
to you? Well, you know, it'sreally the guide on the side,
right? Like, how do I make surethat I'm supporting you? How do
I make sure that you know whenyou have questions, I'm helpful?
(20:29):
And it's really more aboutmentorship and relationships, as
opposed to just that expertisealone, full stop. The other way
I think about relationshipsreally then, is on the student
side. So thinking about thatcollaboration, that connection
that students need to bebuilding with each other, how do
we make sure that students, thenare building that network while
(20:49):
they are in higher education?
How do we make sure that theyare building the soft skills,
ability to work as a team, sothat they are going to be
equipped for that future world,right? But really that focus has
to be on the humanity side ofhuman connection and
relationships.
Matt Kirchner (21:05):
Absolutely, I know. I read a book Kate not too
long ago called Genesis, and itwas written by and some of you
might have read it. It was HenryKissinger who wrote it in
association with the former VicePresident of Strategy for
Microsoft and the former CEO ofGoogle. And one of the things
that they talk about, there'ssome pretty dystopian stuff in
that book about what happens ifwe let AI just go in and on its
(21:26):
own without any guardrails. Andit can get really, really scary.
I think it starts to speak tothe idea that some of these,
some of this coursework thatmaybe we've gotten away from. I
remember my my journey throughhigher education included nine
credits in theology, ninecredits in philosophy and and
really thinking about what itmeans to be a human in as much
as you've got this incrediblebackground, Kate in in the
(21:47):
humanities, is that what you'regetting at when you talk about
the importance there, and maybean example or two of how that
might look?
Kate Burns (21:53):
Yeah, I think that's a piece of it for sure, right?
So when we think about, youknow, the ethics side of it,
right? So thinking about thingswhere philosophy or close
reading, right? Like, what arethese skills that are still
going to translate into themodern world of humanities, but
making sure that studentsunderstand the why, right? So
that we're not just teachingthis in a bubble of, okay, why
do I have to memorize this? Orwhy do I have to read this?
(22:14):
Well, this is why, and this ishow we're going to see that play
out in the real world, I think,is even more important now,
Matt Kirchner (22:19):
absolutely, way more focused on the meaning of
what we're learning and notjust, not just the facts. So
related to that topic, Rick, Iknow you've been really, really
focusing on the meaning ofbusiness and customer
relationships in your in yourwork at BW, converting so much
so that you also partnered withthe Microsoft co Innovation Lab
at the University of Wisconsin,Milwaukee, in the same way that
Alex did. I know our audiencewould love to hear about that
(22:41):
experience. Yeah. So coming intoTitletown and meeting Katherine
close has been just a greatexperience for us. Working with
the engineering team down at theCO Innovation Lab, was great, as
Alex said, you know that you getto own the proprietary
information when you come out ofit. But what we wanted to do is
create an AI model for faultanomalies, and we chose fault
anomalies because we buildseveral different kinds of
(23:03):
equipment. We're an OEMequipment builder. I didn't
mention that before, but severaldifferent kinds of products all
over the world, and we wantedsomething that could go across
all the different equipmentlines. So fault anomalies
worked, where that all tied intorelationships, though, was we
were surprised not about howmuch we were learning about our
equipment and our customers werelearning about our equipment,
but how much it improved ourrelationships with our
(23:24):
customers. You were reaching outto us more often, having more
meaningful conversations withus, and that was the part that
shocked us the most, becausewhen you think about AI, you're
not really thinking about howit's going to build a customer
relationship, but it was really,really good, well,
Alex Tyink (23:37):
and maybe just a little bit more on making that
connection between the faultanomalies in and improving the
customer experience, and thefeedback you got from customers
was that just because yourperformance as a supplier was
was improving, or how does thatfit together? Yeah. So yes, it
was. It was improving as asupplier, like I said, we were
surprised how much in what welearned about our equipment and
how people used it through thefault anomalies, and they were
(23:59):
teaching us things about ourequipment, but yeah, so as part
of the fault anomalies, we weregetting alerts whenever their
machine had an anomaly. So wewere reaching out to the
customer, and many times beforethey even knew they had an
issue. So the leadership teamsat our customers facilities were
very happy to know that we werejumping on issues before they
even knew they had them. Sort ofput that into perspective from
my 25 years in manufacturing. Imean, the way we troubleshot
(24:22):
equipment was, usually, we havea problem. We'd have a fault,
we'd send the maintenance teamthere. They'd review it, they'd
try to fix it. If they couldn't,then we'd bring another group of
people to review it. Maybe atsome point, we do a whole Kaizen
Event around that problem. Andwhat I'm hearing from you is
that you're actually usingartificial intelligence to help
identify where that problem is,what the root cause of that
problem is, and even suggestthis potential corrective
(24:45):
action, right exactly, and notonly that, but you know, like
you said, how do we servicecustomers? These days, we have a
complete remote service team.
Obviously, these guys areexperts that can go on and
troubleshoot electronic issues,but we also have visualization
of data, so we have what we asour digital. Services branded
accelerate. We created our owndashboards to visualize the
data, so we use that data alsoto troubleshoot the issues.
Awesome, awesome application forartificial intelligence. I know
(25:09):
Alex, one of yours, has been toleverage AI to actually develop
curriculum. You and I are bothhuge believers that you know, if
you're a supplier to education,it's one thing to have a really,
really cool piece of equipmentthat's like 25% of the equation,
and the other 75% is, what arewe trying to teach? What are the
learning outcomes? What do wewant our students to know? What
do we want them to be able to dowhen they're through this
(25:29):
experience? That all speaks tocurriculum and delivery. So talk
about how you've leveraged AI onthe curriculum side, especially
for your horticulture product.
Yeah, great question. So for us,it's really about trying to meet
the kids where they're at andallowing them to engage at their
own pace, and allowing them toengage at whatever level they
feel most comfortable with. Andso we've had curriculum as an
(25:52):
organization for quite a while,but what we learned is that
usage of the curriculum wasreally spotty across different
locations. It was reallydependent on the staffing and
the teacher that was there onthe class, the nature of that
district and school community.
And so one thing we've alwaysbeen seeking to do is, how do we
deepen that, and how do we allowthe flexibility to engage with
our program in whatever way ismost meaningful to them, while
(26:15):
also allowing them to take itmore deeply, more easily. And so
for us, that's taken the form ofgenerative AI and using an LLM
and training it on ourcurriculum, and giving the kids
a digital friend, basically alittle farmer buddy to talk to
and ask questions of and to andto engage with. And the early
results on this have been reallyexceptional. I think the biggest
(26:39):
learning that I've had from itis that there's an active way to
engage with AI, and there's apassive way to engage with AI,
and in my opinion, teaching kidshow to be active with it and to
really understand that, yes, itcan teach you a lot, but you
also have a lot to add that theAI is not going to be able to
bring to the table like this.
(26:59):
Focus on relationships is sokey. Because I think, and in our
organization internally, that'sa big thing that we're
constantly doing, is qualitychecking the AI and quality
checking it for humanism. Andyou know, if you're talking to a
customer, like remembering whentheir kid's birthday is, I mean,
that's the stuff that the AI isnot going to be able to do
anytime soon, and is reallycritical to being effective.
(27:21):
What have you heard fromstudents as they've engaged with
the curriculum? It's just beenmore excitement. It's been more
engagement. It's been, you know,I think, a little bit faster for
us to get traction in theclassroom. And those are the
things that we like to see. Itjust feels like a more seamless
integration, if that makessense, it does
Matt Kirchner (27:40):
indeed, Rick, I know you've done, obviously, a
ton on the whole use of AI forfault anomalies and recognizing
faults. You're also starting tothink, if I'm not mistaken,
about how we use it forforecasting. And you know, I
tell people quite often, youknow, in the past, we used to
look over our shoulder. You getyour financial statements on the
15th of the month to see how youdid in June, for example, and
(28:01):
then really good companies gotgood at figuring out where are
we today, and if I don't likewhat I'm seeing today, I fix it
now, rather than fixing aproblem I had a month ago. Now
you're starting to use AI tolook forward for forecasting and
fix problems before they everhappen. Tell us about that.
Yeah, so we haven't jumped inyet full board, but definitely
my leader and I have beentalking a lot about how we do
(28:22):
better forecasting for serviceservices, so up and down, and it
can be extremely busy one monthand light the next month. So how
do we look at trends, and how dowe use that data to better
forecast for our company? Soforecasting financials, but also
inventory control for our partsdepartment. I actually heard
this morning from one of ouroperational leaders, how they're
using it for inventory control,just using copilot, and it's
(28:44):
actually been working reallywell for them. So that we can
improve our on time delivery,awesome. So obviously, improving
that on time delivery, customersare getting products more
quickly. We're not stringingpeople out. We're not straining
capacity in manufacturing. Sosuper, super important and a
great application for artificialintelligence. Kate, I'm going to
put you on the spot here for amoment and ask you a question
(29:04):
about what you're starting tohear from your students. I know,
as we look as whether we'reeducators or people that work in
and around education, we're allstarting to think about the
future of education in higher edand in that environment. What
are you starting to hear fromstudents about both their
concerns and opportunities asthey look to the future of
higher education for in this ageof artificial
Kate Burns (29:23):
intelligence, yeah, I think it's a real mix. So I
think, you know, I'm reallyexcited by what Mike's doing. I
think that for some K through 12districts, the approach is just
to ban it, right? So they kindof, that comes in when they when
they enter college, right? Of,I'm still not supposed to do
this. This is kind of illegal,you know. And so they don't
really have any kind of baselineknowledge, because it's just
been prohibited full stop. Somepeople are over reliant, right?
(29:46):
And they've kind of used it,they don't necessarily
understand the limitations, andso they maybe don't realize that
I need to think about how I'mgoing to kind of pare down a
little bit. And I think somepeople are just very much at an
anxious stage, right? Sothinking about what does the
future. Mean, for me, am I goingto be able to get a job? Is AI
going to take my job? What doesthis look like as a person who's
going to be graduating in thismany years? So it is really,
(30:09):
it's that emotional that you'rehaving to work with as opposed
to just just pure knowledge andtrying to work with students.
Mike Beighley (30:15):
So like anything else in education, it sounds
like it's meeting students wherethey are. They're all coming to
this from a different angle,making sure that we're
personalizing this experience asmuch as possible. Which Mike, I
know, is something that you'redoing a ton of work on. I know
you were in San Diego just a fewshort weeks ago doing a
presentation and talking aboutsome of the applications for
personalized learning throughartificial intelligence. So take
(30:37):
some time to talk about theinnovation that's happening on
that side at White Hall, andwhat you've learned from some of
your early work when we startedin on the Emerging Technology
Lab and talked about removingthose artificial limits, it
didn't take us long, for thoseof you that haven't seen it, it
is across the street from ourexisting K 12 building, and we
literally, we were sitting inthe lab one day, and we thought,
(30:57):
if this works over here, whydoesn't it work over there? So
we set out to personalize ourenvironment across the board.
Meet the kids where they are.
Much of our time is spent inminimal compliance, okay, as
opposed to human capacity. Weuse that phrase a lot. What's
the minimum Mike Bigley could doto be eligible to play football
each fall? Was what mattered,right? Because what I was
(31:19):
learning was determined bysomeone else, and it really
didn't have a lot of effect onmy future. Okay, that's flipped.
Now the personalized learningenvironment has put kids in
charge of their learning westarted, in fact, when we get
done with this, our teacher thatstarted our first pilot is here.
I just happened to be married toher, so that's phenomenal, too,
but she jumped in with eighthgrade math. Okay, took all of
(31:43):
our eighth graders, not a selectgroup. We took all of our eighth
graders, started them in acompletely personalized learning
environment. She flipped herentire world upside down. We got
huge results right away. Theengagement level went up, the
behaviors went down, theperformance went through the
roof. By Christmas time, abouthalf the kids, not quite half
(32:04):
the kids, had completed eighthgrade math to mastery, not
minimal compliance. They hadcompleted eighth grade math and
wanted more. They were askingfor more help, more assignments,
more more to do. I wanted tomake sure that I've learned this
stuff, as opposed to regurgitatesomething on a test. That's how
we've leveraged the AI tools,and I think it's important to
(32:25):
remember they are as poor or aslimited as they are ever going
to be right now we're gettingbetter and better every day. Now
we have the ability to do thosethings, but the but the
relationships matter. Okay? Wegot great results with eighth
grade math, but Shannon alsochanged her entire learning
environment. Talked aboutworking with kids, teaching them
(32:45):
how to learn, holding themaccountable, teaching them
empathy, teaching them how tocommunicate. And they did it
without her making it happen.
They just did it naturally. So Ithink that's the biggest thing
that we have to learn again. Imentioned the limits. I will say
that until I'm dead, we are thelimits. We as the adults, are
(33:06):
the problem, not the kids. Ithink as we continue to go down
that road, it's going to getbetter and better. We've done
the same thing now.
Kindergarteners watched a groupof kindergarteners this summer
or this winter. Excuse me, thespring, I think some of you were
there, had an opportunity 20kindergarteners in a room,
teacher with a small group offour kids up at the board, doing
a little mini lesson. The otherkids were involved with their
(33:27):
iPads, okay, reading into theiPad, which was recording their
reading, which wasdifferentiating what they were
having trouble with, giving thatfeedback to the teacher,
suggesting what they should dotomorrow. She just had to sign
off on it, and every one of the20 kids was engaged. There's
never a kindergarten class with20 kids engaged. Every one of
them was engaged with me walkingaround the room. That's the
(33:50):
power, and that really is thefuture of education. Mike, I'm
going to pose one last question,both to you and Kate. But the
question is this, you talkedabout we as adults being the
limiting factor. I've got tobelieve, I mean, we've got a lot
of educators in the room today.
I've got to believe that you'reseeing every version of, let's
go into this with everythingguns a blazing, and we've got
(34:10):
people that are saying, oh mygoodness, this isn't for me. How
are you managing through some ofthe some of the changes, and
what kind of feedback are yougetting from your instructional
staff as you're asking them tomake changes in the way they're
delivering education. I thinkthat the important thing is,
we've spent the last two, twoand a half years explaining the
why of this as well, talkingabout the need to really re
evaluate what kids need so theyare successful in their future,
(34:33):
not the past we lived in in it'sbeen mixed. I will tell you, our
staff is exceptional, though.
Everybody says that I was reallyis okay. There's been, there
have been a few that have been alittle resistant. We've spent a
lot of time explaining the whyand the right up until the point
where we say, this is wherewe're going, because this is
good for kids. If you don't likeit, find someplace else to be.
That's the bottom line. Theseare kids. This is our collective
(34:54):
future. We don't say that in athreatening manner, though. You.
Really don't Okay. We just tellthe truth. This is what we need
to do. I think that the PD side,we've done a lot to free our
folks up to try things, to givethem the opportunity. If it
doesn't work, re evaluate it,figure out what to do next, and
we hold accountability in termsof pushing the limit, as opposed
(35:18):
to conforming or or hitting thatminimal compliance piece,
terrific.
Matt Kirchner (35:25):
And when we put the student first, it's amazing
what can happen. It sounds likeyou're doing exactly that. Kate,
any last thoughts on on managingthrough some of the challenges
or some of the feedback you'regetting from faculty in this age
of huge innovation andeducation?
Kate Burns (35:38):
Yeah. I mean, I think it is a mix, for sure. Of
some folks are very excited.
Some folks, I think, lessexcited, to put it terribly, and
so it is tricky to manage. Ithink we've really been trying
to lean into teaching withtechnology. Is really about good
teaching, right? When you'retalking about, Why are students
learning? Why am I teachingthis? Right? You need to be able
to explain that, and that showswhat the role of technology is,
(36:00):
but that also shows therelevance, right? And I think
that sometimes folks aren't usedto focusing on that relevance in
that same way, but that is core,right? And that's just even more
important now than it was in thepast, importance
Matt Kirchner (36:14):
for students to understand the relevance, and,
for that matter, for faculty aswell. So great advice from Kate
burns. Let me thank ourpanelists, Mike Bigley, Kate
burns, Rick rusky and Alex tyingplease give them a big round of
applause. Wow. What anincredible panel we had, and an
incredible discussion aroundartificial intelligence. Lessons
for everyone, regardless ofwhether you're in education,
(36:37):
you're in industry, you're inpublic, policy, doesn't matter.
There was takeaways from everysingle aspect of the AI economy
in that panel. So glad youjoined us. And of course, there
were a number of references thatfolks made over the course of
that discussion. As always, wewill link them all up in the
show notes, which you will findat TechEd podcast.com/titletown
(37:00):
AI, that's TechEd podcast.com/t.
I, T, L, E, T, O, W, n, a, i,that's where you'll find the
show notes. When you're donethere, as always, check us out
on social media, Instagram,Facebook, tick, tock, LinkedIn,
wherever you go. To consume yoursocial media, you will find the
TechEd podcast when you'rethere, reach out and say hello
and don't forget to join usagain next week for another
(37:22):
episode of The TechEd podcast.
Until then, I am your host. MattKirkner, thanks for being with
us.
Unknown (37:34):
You.
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