December 23, 2025 • 46 mins
Watch the episode on Youtube: https://youtu.be/f5gWUVQI0jI
Melissa Martin and Matt Kirchner are back to answer your questions, covering everything from university curriculum design, to AI in the classroom, to what employers actually expect when they invest in education.
This one moves fast, but it’s focused: how do you build programs that truly prepare students for modern work? How do you keep education from falling behind as technology accelerates? Along the way, Matt and Melissa break down what universities need to change, how to raise the bar in the age of generative AI, why ethics can’t be an afterthought, and how to help HR teams understand the value of credentials and new pathways.
Listen to learn:
- What university programs should teach (in one course) to better prepare grads for modern manufacturing work
- How educators can help students identify when AI is wrong and why we need to level-up our homework in the age of AI
- The role of ethics in modern CTE
- The five components of a world-class, modern advanced manufacturing high school program
- How educators can measure program effectiveness and show ROI to industrial partners
- What HR teams need to understand about changing credentials, degrees, and how to evaluate technical candidates
3 Big Takeaways from this Episode:
1. have to teach applied industrial skills, not just theory. Matt argues that a four-year program can cover a lot of “cool stuff in the lab,” but it still needs authentic manufacturing equipment and technology so graduates understand what they will actually see in industry. He frames this as an employer expectation problem: even when budgets are tighter at the four-year level, universities still need to build around the same core technologies students will encounter on day one in manufacturing.
2. AI changes the standard for student work and makes ethics a core requirement. Melissa and Matt point out that AI is designed to produce an answer even when it doesn't know (causing a 'hallucination'), which means students must learn to question outputs and verify accuracy instead of treating AI as a sole source of truth. From there, the conversation moves from classroom integrity into broader ethics: what it means to do original work, and how humans should think and behave as AI becomes more capable and more embedded in decision-making.
3. Industry and HR and educators must understand each other's needs to build a successful partnership. Education and Industry both have a responsibility to do their part in a partnership. HR departments must understand the changing landscape of certifications, 3-year degrees and other credentials that students are gaining to demonstrate their technical competency. Likewise, educators must adopt industry practices of tracking metrics to show employer partners the ROI of their investments in the program.
Access tons of links & resources on the episode page: https://techedpodcast.com/askusanything-122025/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
TechEd Podcast Introductio (00:09):
This is the TechEd podcast, where we
feature leaders who are shaping,innovating and disrupting
technical education and theworkforce. These are the stories
of organizations leading thecharge to change education, to
rethink the workforce and toembrace emerging technology.
You'll find us here everyTuesday on our mission to secure
the American Dream for the nextgeneration of STEM and workforce
(00:32):
talent. And now here's yourhost, Matt Kirchner,
Matt Kirchner (00:38):
welcome into the TechEd podcast. This is your
host. Matt Kirkner, we had suchgreat feedback on our last
episode of Ask us anything. Wehad great questions, first of
all, from our audience, and wehad so much fun answering those
questions. The interest thistime around, absolutely
overwhelming. We put out arequest. We say, hey, there's
another episode coming up. Andthe world of technical
(00:58):
education, TechEd nation, as welike to call it responded in
droves with fantastic questionswe've narrowed down to a couple.
Can't answer every questionevery single episode, but we
picked the best and brightest,and we try to tackle them here
on the TechEd podcast. The otherthing that we do is that it's
not just Matt Kirkner speakingon this particular episode, our
producer, the heart of theTechEd podcast. Ms, Melissa
(01:21):
Martin is joining us to ask thequestions and respond to a few
of the answers. So Melissa,welcome back that side of the
microphone, this side of themicrophone again. Great to have
you with us.
Melissa Martin (01:31):
Thanks, Matt.
Great to be here again. It'salways fun. It is always fun.
All right, so we got a number ofquestions, again, submitted.
Thank you to everyone forsubmitting those. And I'll tell
you this, because we had somany, we have to pair it back so
that we can fit into ourallotted time frame. So if we
didn't get to your question thistime around, we'll make sure to
get to it next time. So again,subscribe. Stay tuned. We'll do
this every single quarter. Andagain, for anybody, you got
(01:54):
ideas, you've got questions forus, you can always throw them in
that, submit your questions spoton our website, and we'll save
them for next time around. ButMatt, are you ready for the
Spanish questions? All right, sothe first question comes to us
from Gustavo Gomez. Gustavo is aprofessor over at Purdue Global
University, and he says, I'mworking on a new course called
(02:15):
production, machine,technologies and tooling. I was
wondering if you have apresentation geared to that
subject and anything aroundautomation and robotics, so I'll
let you rephrase that question.
Matt Kirchner (02:28):
Gustavo, he said, Yes. All right. Gustavo, thank
you for a terrific question.
Thanks for somebody beingsomebody in higher education who
is focusing on this. This reallycool question is about, what
should we be teaching in highereducation when it comes to
automation, robotics, machines,controls and so on. So really,
really awesome that you're thatyou're thinking about it that
way. So let's, let's start withthinking about what's important
(02:52):
in the university program whenit comes to advanced
manufacturing. And you know,we've had so many guests on this
podcast, folks that are workingin manufacturing who have come
to us, and maybe, I don't know,complaining a little bit about
the whole idea that, boy, we getthese engineers and these
technical folks out of four yearprograms. They're smart, wicked
(03:12):
smart. They're really, reallygood with numbers and data and
theory, but then they're scaredto death when they get out on
the shop floor. And so in answerto the first part of the
question, do we have anypresentations and so on,
absolutely, we've had dozens ofepisodes of the podcast talking
more specific terms about whatwe should be teaching at the
university level when it comesto advanced manufacturing. So
(03:34):
we'll link a few of those up inthe show notes. But let's think
about this. First of all, we'vegot employers that are coming to
us and saying, Hey, I've gotengineers, for example, that are
coming out of four yearprograms. And they're, I'm just
quoting them. They're, you know,they're no good to me for the
first two or three years I havethem because they haven't spent
any time on the shop floor. So Iwould say the first thing to
think about is, yes, we have tohave engineering talent that
(03:57):
understands calculus and physicsand has gone deep in math. I
mean, we get that that'simportant. We know a bed has all
kinds of all kinds ofrequirements around that. The
HLC has requirements around thatwe I understand all that stuff,
but the truth of the matter isthat if we're going to produce
students who are job ready toadd value in manufacturing,
they've got to have that handson experience as well, and so
(04:19):
lots of different ways to dothat. I'll point as an example,
because it's fresh in my mind.
What's going on at theUniversity of Wisconsin. Stout
recently announced a hugepartnership with with an
employer in western Wisconsin.
They are putting in all kinds ofhands on experiential learning.
So they're they're adding, inthat case, you know FANUC Robo
(04:40):
drills with with robotic machinetending fanic controls, which we
know are big fan of. FANUC fanshere on the TechEd podcast,
where students are learninghands on technology. They're
doing some things with anenterprise smart factory system,
and literally learning the fine.
Are aspects of data acquisition,smart sensors and smart devices.
(05:04):
What? How you know how a PLCworks, how we acquire data, what
we do with that data on thecloud, how we protect data in a
manufacturing operation, manageswitches, unmanaged switches,
all these kind of things. Whatis an ERP system? How do we pull
data up to an enterprise levelsoftware program to be able to
to work with that data. And howdo we integrate all these
(05:24):
technologies, things likeconveyors, and we mentioned
sensors, programmable logiccontrollers, stepper motors, you
know, all these differenttechnologies you see in
manufacturing. It's that handson integration part. So, so I
think, you know, in any fouryear engineering program, yes,
get to cover the theory. Let thestudents do some fun stuff early
on. Don't save all the coolhands on stuff for junior and
(05:45):
senior year. But then let'sreally embed their some awesome
hands on projects, work basedlearning and a capstone project
that requires them to put allthese, all these technologies
together. There's, there's moreand more four year universities
that are doing this now. Purdueglobal is a great example of a
spot where folks can get good,you know, Polytechnic education,
(06:05):
good hands on learning. Butthose are the kinds of things
that I would be thinking about.
Is that, yeah, we can do a lotof cool stuff in the lab. We can
do a lot of experimental stuffand experiential stuff, but
let's also make sure we'regetting good hands on experience
with authentic manufacturingequipment and technology.
Melissa Martin (06:21):
And I'll just maybe ask a follow up question,
Matt. So it sounds like Gustavohas got, you know, one course
that he's working on. So if youhad to boil it down to like
you've got one course to teachthe most fundamental things,
what would you include in that?
Matt Kirchner (06:35):
Yeah, so I'm gonna make the assumption that
the students outside of thiscourse are getting some of that
fundamental, data analytics anddata acquisition, and what we're
doing now with AI and analysisand those kind of things. I
would say it's a handful ofthings. I would say, first of
all, let's think about thatauthentic manufacturing
technology. So what is discreteIO or a programmable logic
(06:56):
controller doing? That's thecomputer of manufacturing. I
would think specifically aboutthings like ultrasonic sensors,
proximity sensors, temperaturesensors, moisture sensors, force
sensors. So how are we usingsensor technology at the edge? I
would get that in there forsure, you're going to want to
teach some basic machining, orsubtractive manufacturing. So
(07:16):
you think about a machiningcenter where you're removing
metal from a part to createanother part or removing metal
from a substrate. You'lldefinitely want to get that in.
You probably want some basicmetal fabrication. So what is a
punch press? What is a turretpress? What is a press brake?
How are we fabricating metal?
How are we welding metal? You'regoing to want to work in. In
addition to that, like we said,you know, conveyor technology,
(07:37):
electric motors, you're going towant to understand electric
motors, variable frequencydrive. Variable frequency
drives, motor control,mechanical drives, which they're
going to be learning some ofthat outside of that course, but
understanding how to gears, pullthese shafts and so on. Work in
a manufacturing environment.
You're going to want to work insome fluid power, so hydraulics
and pneumatics, that depends alittle bit on, you know, which
(07:58):
of those focuses depends alittle bit on where you are and
what your employers are doing,and then, you know, the
automation and robotic side. Soyou need to have both, you know,
a six axis traditionalindustrial robot. Everybody
looks at collaboratives andsays, Oh, that's what
everybody's using right now. 90%of the robots, probably more,
that are used in manufacturing,are still traditional six axis
(08:21):
robots. And that's not justbecause that's what people have
bought in the past. It'sbecause, based on precision and
payload capacity, a lot of timesa you know, a traditional six
axis robot is the bestapplication. But then we also
have to teach collaborativerobotics as well. So I think
those would be the basicelements that I would place into
that program. Look, I recognizethat the four year level, a lot
(08:42):
of times we're constrained interms of budget, our community
and technical colleges, manytimes for equipment, are better
funded than our four yearuniversities, but those are the
kinds of technologies we have tostart building around, if for no
other reason, that that's whatour students are going to see
when they get to manufacturing,and that's what employers are
expecting students to know.
Melissa Martin (08:59):
100% your employers will get behind you,
100% when they percent when theyhear that. That's the kind of
course you're going to put yourengineering students through,
absolutely all right. The nextquestion comes to us from
Michael McArdle over at WesternTechnical College, and Michael
asks students, use AI, but donot question the results. What
are some strategies to have themsee the futility of that
(09:20):
approach? Yeah.
Matt Kirchner (09:21):
Well, so I guess the first thing is, if you know,
looking back to all of oureducation journeys, don't be
afraid to tell them they'rewrong, right? I mean, if I, if I
answered a question, whether I,you know people long before AI,
whether I did it in theclassroom or outside of the
classroom, regardless of how Idid my research, if that answer
was wrong, my teachers weren'tafraid to say, look, that's seen
a nice try, but you're wrongthere. So let's start with not
(09:43):
being afraid to to, you know, tocall that out. The truth of the
matter is, accuracy isimportant. It's important in the
workforce and in the workplace,and it should be important, and
often is in education as well.
But I, you know, I go backMelissa too. I had a sixth grade
teacher, and Mr. Schumann, hisname was, and one of the things
that. We were required to doduring sixth grade, and you got
extra points. And this is backin the days of newspapers and
(10:04):
magazines and whatever. Nobodywas, you know, nobody was going
online and looking for anythingback then, you got extra points.
If you could find an error in anews article, really, and bring
it in, you got extra credit forthat. Wow. So when you were in
and, believe it or not, ithappened quite often. I mean,
this has been the days oftypeset. And you didn't have
spell check and so on. Yeah,about once a week, if you read
(10:25):
the paper often enough, you'dfind a mistake in an article,
either a factual mistake or agrammatical mistake or a
spelling mistake, and you bringit in and get extra credit
challenge students to find wheregenerative AI is wrong.
Generative AI isn't perfect. Weknow that it hallucinates it's
getting better. I was justlistening to a podcast. It was a
Joe Rogan podcast, actually,with Jensen, Jensen Wong from
(10:45):
from Nvidia. But they weretalking about how AI and
generative AI is hallucinatingless and less and less, which is
why people are using it more andmore and more. Is just becoming
more and more accurate. But Iwould say, have a student, you
know, just understand that youcan't just take what that
generative AI is telling you andtake that as fact. And I've
(11:06):
gotten myself into into troublewhen I'm doing quick research
for the podcast or something,and you jump on, you know, I
won't embarrass any of theplatforms, because they all do
this, but it gives you a wronganswer. You write it down, and
all of a sudden you use it, andthen, you know, 10 listeners are
commenting on whatever, sayingyou got this wrong. So, so at
any rate, you can't. You can'tjust rely on the generative AI
to give you the right answer. Iwould also so so challenge
(11:30):
students to find it makingmistakes and point out when it's
making mistakes. Also let themknow that they're responsible
for their answers. They can'tjust blame generative AI for
that answer. Use a transformer.
Use a GPT that gives you itssources. Several of them do so
you can actually go through andif it says something, it'll go
back and it'll show you itssource material for whatever it
(11:51):
was telling you. Use that. Butthen the other part of it is
we've got to put the challengeto teachers and educators and
say, Look, in this age ofgenerative artificial
intelligence, you've got to stepup your game, and you've got to
find new ways to challengestudents, whether it's talking
about material, whether it's youknow, rather than asking them to
write a report outside of class,maybe they come in and they
(12:12):
write a report inside of class,or you have them read an
article, and then they have tocome in and tell you a little
bit about what they learnedusing generative AI as as an
assistant, but Not as the as thesource material. And then I
think the other thing is justfor students, it's time for them
to step step up their game, sowe can raise the bar on
students. Doing research is wayeasier than it used to be. I
mean, I don't have to sound likean old guy, but I remember, you
(12:34):
know, we used to have to go tothe library, find a book, and
then, like, if you were writinga term paper, take the book over
to the copy machine, make copiesof the book, or do your research
in the library, and then takeall that home and write your
term. I mean, think about that.
And that's not like 300 yearsago. That was like 20 years ago,
30 years ago. That was
Melissa Martin (12:52):
when I was in college. I mean, hours and hours
and hours of reading all thesereference materials so that you
could write your paper and bewell informed from, you know,
these different authors who haddifferent things to say about
that, that topic, it's a lot ofresearch,
Matt Kirchner (13:05):
absolutely so now, using AI, you can literally
collapse what used to be maybe asemester to write a 20 of
research to write a 20 pageresearch paper to you could do
that in a day. So Okay, step upthe game. Set expectations
higher. We have to finddifferent ways to challenge
students. We have to have themexplain more about what they're
learning, more group projects,all these kind of things, not
just, you know, replacing rotememorization with the use of
(13:27):
gender of artificialintelligence. So it is okay to
expect more of your students,and the best educators and the
best educational institutionsare going to be doing that as
the as the years go on in the inthe age of artificial
intelligence.
Melissa Martin (13:38):
That's absolutely right. And I would
add to that, students need tounderstand these AI tools as
they're they're designed to giveyou an answer, whereas a person,
you ask them a question, theydon't know the answer, they're
going to tell you, hopefully, Idon't know the answer to that.
But let's look it up together.
Here's where you might be ableto find the solution. And AI
isn't made to say, I don't know.
(13:59):
It's May. If it doesn't know theanswer, it will hallucinate, and
that's what it'll make somethingup just to give you an answer.
And so students need tounderstand that when they're
using these AI tools, that theyare designed to give you an
answer, whether or not it'sright, because that's just how
they're programmed. So just haveset that expectation with them
as well, so they understand whatthese tools are for and what
(14:19):
they're not for Absolutely?
Yeah, great points. All right,so that kind of segues into
another question, becausethere's this whole ethical
debate around AI and you knowwhat's right and wrong, and how
much do we use it? And HollyAtha, over at the MBA research
and curriculum center, asks aquestion. She says, what role do
you feel ethics education shouldplay in CTE, today and tomorrow?
Matt Kirchner (14:40):
Yeah, I love that question. And and a huge role is
the short answer for a whole,whole collection of reasons. I
mean, let's start with our lastquestion about, you know, how
students use AI for their theircoursework and so on,
recognizing you know that youdon't, you've got to be, you've
got to be doing your own work,that you don't take credit for
somebody else's work, that. Kindof things really, really
(15:01):
important, ethical questions.
But it gets a lot deeper thanthat, you know, you start
thinking about, you know, in theage of artificial intelligence,
where computers are able to, atleast on the technical side, you
know, think as fast as humanscan, and faster and no more and
access more data and so on. Youknow, what does it mean to be
truly human? Is a really, reallystrong question. When we start
(15:25):
having agentic AI making all ourdecisions for us, and we can
easily get to that point, whatdoes it mean to be human? What
does it mean to be human in anage where we don't have to work
as many hours, because we canuse AI robotics and automation
to do a lot of the things thatwe used to have to do,
physically, to where, literally,where, you know, anybody today
can pretty much have withinreach is the is the lifestyle of
(15:47):
the wealthiest person 100 yearsago, right? Like anybody today
can have that can have thelifestyle of somebody that was
like the wealthiest person onearth 100 years ago. And we're
just gonna, that's just gonnacontinue. So we'll have all
these questions about, what doesit mean to have a soul? What
does it mean to be human? Howdoes that mean in terms of how
we treat each other? How doesthat mean in terms of how we use
(16:08):
artificial intelligence? We goback, oftentimes, on this
podcast, to the book Genesis,which was Henry Kissinger, Eric
Schmidt, as you know, and CraigMundy, and they published that
book about a year ago, give ortake, and it's just an
outstanding book, but they godeep into the exploration of
ethics in the age of artificialintelligence and technology. So
I think there, I think ethicsplays a huge role. Well, you can
(16:31):
weave it into weave it into thecoursework. You can force
students to think about ethicalquestions. I go back a lot of
times. I was a business schoolmajor, but I went to a Jesuit
university, and we were requiredto study nine credits in
theology and nine credits inphilosophy. And you really, you
know, it didn't really, peoplewould look at that and say,
well, that has nothing to dowith being a business person.
(16:51):
Well, maybe. But what you learnin terms of how to think about
humanity, how you communicatereally, really important stuff.
And so we're going to have toolsat our disposal. In the future
that can be used for all kindsof purposes. And people like,
aren't you worried about AIbeing used for, you know, for
really, really bad purposes? Andthe answer is, well, as long as
people have good conscience areusing it, and as smart, or
(17:13):
smarter than the people that arebad actors, the better off we're
going to be. But we need toweave that, that element of
ethics into the into thecoursework.
Melissa Martin (17:22):
Yeah, all the more reason to include it now
with AI and everything,absolutely so next question
comes from Bob Manning, over atStillwater area high school, and
Bob has a fantastic question.
It's a little bit long, so staylong for the ride. He says, Hi,
Matt, many schools, many schoolswould love to have the
manufacturing programming thatyou highlight in your podcast.
However, we all have differentstarting points and assets
(17:44):
elements of quality programmingthat come to mind include
visionary and strategicleadership, physical space, with
modern equipment andinfrastructure, genuine industry
partners, teacher capabilitiesand buy in, funding options,
etc. What do you think are themost important components a high
school must have when attemptingto build a robust manufacturing
pathway for the sustainablefuture. Can you also rank them
(18:05):
in order of importance? Or wouldit make the most sense to list
what needs to come first, secondand so on? All right? Great
question. It's great question.
Matt Kirchner (18:14):
Yeah, absolutely.
You know, and I spend a lot oftime with educators, and I think
kind of the default playbook alot of times, is they all go to
money, right? And I say all, Imean, I use that figuratively,
but, but, but everybody comesback and says, Well, until
somebody walks in the door witha million dollars, until we have
a referendum that gives me, youknow, $5 million to spend, until
I have a school board, until Ihave a superintendent, a
(18:35):
district administrator, youknow, the list goes on that
provides me the resources to beable to expand my program until
I have those things. You know, Ireally don't have anything to do
here because I couldn't affordto do it anyway. And you know,
the first thing we always, andthis would be number one on the
list, is, before anybody's goingto give you a penny, you have to
have a dream. And you know,we've been involved in so many
(18:57):
of these amazing K 12 projectsand at other levels of education
as well, but lots of high schoolprograms where we're integrating
all kinds of fascinating,amazing technology, autonomous
vehicles, drones, flying drones,unmanned ground vehicles, 3d
design and fabrication, coding,programming. I mean, all these
really, really cool things. Ithink when Bob says the kind of
programs we highlight on thepodcast. Think that's what he's
(19:20):
pointing to. All this reallycool technology, robotics,
automation, mechatronic systems,all those things cost money.
There's no question about it.
But if we start by saying,we'll, we'll, we'll work on this
when we have the money, thenthat that never happens, right?
What we have to do is have adream that is so big that
somebody's willing to fund it.
And every one of these projectsthat I've seen where whether
it's a community or an employeror a wealthy individual, or some
(19:43):
combination, usually of allthree of those, stand up and
say, we're going to do this.
It's because someone had a dreamso big that they couldn't say no
to it. So first thing I wouldsay is, start with a dream.
Think about what could be if youcould have the coolest lab, the
best program, regardless of whatthat. Costs, what would that
look like? Yeah, and then createthem. Create a message so
(20:03):
compelling that nobody can sayno. From there, I think I'm a
huge believer in meeting everylearner where they are, and I
think this is really important,especially as we move into the
future of education, is that weall have different learning
modalities. We talk about thatall the time on the podcast. I
take sometimes, take this backto my days of selling
(20:24):
manufacturing technology and soon. It's like you'd always say,
Well, some people are auditorylearners, or in some people are
kinesthetic and some people arevisual. So in other words, some
people learn with their ears,some with their you know, with
their eyes, some by doing, notright, wrong, good, bad. It's
just that we're all different.
So how do you meet every singlelearner, where you where they
are? You have to have variousmodalities of learning, whether
(20:44):
that's a lecture, whether it'shands on, learning, whether it's
e learning, whether it's workbased learning, all those things
are great, great, great ways tolearn. We have to have that.
Number three, I you know, I'm abeliever, as part of that, that
you create some opportunity forasynchronous learning. I think
we're getting away from the ageof a teacher standing in front
of a class and lecturing for,you know, for an hour, and then
(21:08):
moving the class on to the nextclassroom and lecturing for an
hour. I just that that thatversion of education was never
for me. And so havingopportunities where we take our
high flyers and if they can movefaster, let them go, and if a
student, for whatever reason,needs a little bit more time on
something that's awesome, justtake the time that you need to
be able to gain the competency.
So I think, I think that'sreally important, is that is
(21:30):
delivering asynchronouslearning, and then making sure
we've got a significant portionof that that's hands on, and
that It's project based. Andthat's, I think, where the
future of education is going andthat puts a challenge on a
teacher, right? Can you know,managing a classroom full of
students working on projects allday might be better for the
student. It's probably a lotharder for the teacher. Thank
God for teachers that arewilling to be patient enough to
(21:52):
work through something likethat. We need to have hands on
learning, and the more technicaland the more technology driven,
the better. So that would benumber three, and then the other
fourth one is and all these tietogether. But, you know, you got
to have skills and competenciesthat lead to whatever comes
next. And in a lot of times, Ithink that's, you know, that's
(22:13):
career based stuff, right? So,you know, manufacturers who walk
into a high school that wantsto, you know, spend a bunch of
money on a new lab that can'tsee a direct line from whatever
that is, and, you know, a vinylcutter sitting in the corner to
a job at that employer. It'sreally, really hard to get them
excited about that. So, and I'mall for, I mean, there's a lot
(22:35):
of different age appropriate andgreat, appropriate ways to
deliver learning, and we're nottaking sixth graders and
teaching them complex ladderlogic on a programmable logic
controller. Right? That comeslater, but, but teaching
students at the right level, youknow, here, here's how to
program a robot, here's how tooperate a robot, here's how to
program a programmable logiccontroller. Here's how you get
(22:56):
data into and out of smartsensors. Here's what you do with
the data set. Here's how youweld the part. Here's how you
fabricate a part. Here's how youmachine apart. Here's how you
program a CNC machine. Havingthose kinds of programs in a
high school, so that studentsare learning those competencies
along the way, and then have jobready skills tied to and this
would be number fivecertifications wherever we can
(23:16):
so third party certificationsthat students can earn, that
they can take to an employer andsay, I know how to do this. This
is what I learned in highschool, creating a competency
portfolio in addition to that,that high school diploma would
be and I could keep going, Icould come up with another
probably list of 10 for Bob,but, but those would be my top
five.
Melissa Martin (23:35):
All right. So, so to recap, for Bob and
everyone else in our audience,and to make sure that I was
listening so our five were havea dream so big that people can't
say no to it. Two would be meetevery learner where they are.
Three would be asynchronous,hands on, project based
learning. Four is make sure thatit's career relevant and ties to
something that they're going tosee after school. And number
(23:55):
five, ties to certifications andthat they're earning something
along the way. You could be apodcast.
Matt Kirchner (24:01):
Have to segue into the next question that's
perfectly smooth.
Melissa Martin (24:03):
So my segue is that the next question has
nothing to do with anythingwe're talking about. So we're
gonna take a quick break fromthe technical education and
workforce topics and go to kindof a an unrelated topic. If
you've been around the TechEdpodcast for a while, you'll know
that sometimes we have reallyunique, fun episodes that kind
of dive into topics outside ofthe classroom, but really, we
always find a way to make itwork within the greater
(24:25):
technical education, data,skills, science, all that kind
of fun stuff.
Matt Kirchner (24:29):
TechEd stem podcast about whatever we think
is interesting.
Melissa Martin (24:33):
And so you did a really fun episode over the
summer previewing the Tour deFrance, yeah. So we actually had
somebody ask a question aboutthat. So Bruce, Bruce Anthony
reaches out and he says, Do youthink today will win the 2026
Tour de France? Follow upquestion, who will be the next
rider to win the yellow jerseycompetition not named today?
(24:56):
Wow.
Matt Kirchner (24:57):
Yeah. That's an awesome, awesome question from
Bruce the. Answer his firstquestion is, yes, there's, I
mean, so the thing to know aboutcycling, like any sport, there's
a lot of wild cards, right?
Yeah, but if you stay healthyand you don't crash, right,
those are kind of the two bigthings. Then I think, you know,
if today makes it to the tourwithout a significant crash, if
he if he's as healthy next yearas he was this year, no reason
to believe he won't be. I don'tknow that there's anybody that
(25:19):
can touch him. He ran away withthe 2025, Tour de France, you
know, I think, I think Jonasfinger go still has few years
left in him, and he was today's,you know, and has been his, his
kind of most aggressive rivalover the course of the last
several years, five, six yearsor so, you know. I think Jonas
has a chance, and it reallydepends on what his conditioning
(25:39):
is coming into the tour nextyear, but if he rides at the
highest level that Jonas canride at, I think he's got a
chance to at least make it acompetitive tour. My bet would
still be on today. Pa, gotchanext one not named today to win
the Tour. If it's not Jonas,there's a lot of people I'll
highlight too. Remco venopol hasbeen one that we had our eye on
(26:02):
last year and and I would saythat he is still, I mean, if you
look at the rankings, the top 10or 20 riders in the world, Remco
is still right there. And theother one I'll add is a little
bit of a wild card, because Ihave a soft spot my heart for
him. Would be Mateo Jorgensen,American writer, okay, he's
probably not top 10, but he'sprobably top 15 right now. And
as we talked about on thepodcast, actually, our son, who
(26:24):
raced for the junior developmentteam for track bicycles for a
whole bunch of years when he wasgrowing up, actually raced
against Mateo at juniornationals a couple times. So and
again, got beat pretty handilyby Mateo, I should say, but, but
would to be, you know, be at thestarting line with a guy that
eventually is competitive forthe tour was a really, really
cool thing to look back on andand think about so, so Matteo
(26:46):
Jorgensen, Bruce. Bruce wouldalso, also be on that, on that
list, and hard to believe.
We're, you know, almost sixmonths away, a little bit more
than that, from the next year'sTour. So it won't be long before
we're, you know, before we'rehandicapping bike racing. Maybe
Jason will come back again thisyear, and if it's not Jason,
I'll guarantee some high profilecycling expert that will will
(27:07):
have joined us, because that wasa
Melissa Martin (27:08):
really fun episode. Yeah, that was a fun
one. And did did some greattying back into science, the
nutrition science data, all thedata that goes into preparing
these teams so
Matt Kirchner (27:18):
it drives technology shifting, all that,
getting excited already.
Melissa Martin (27:23):
All right. So back to our education topic. So
the next question comes fromMichael ezeki, and he asks,
educational institutions areless tuned into dashboards? Can
you suggest some good dashboardmetrics for work towards
development programs ineducation?
Matt Kirchner (27:38):
Oh, that's a good one metrics. And he's right.
That premise less tuned intodashboard. So we think about,
Yeah, we love metrics. You thinkabout data and manufacturing?
What's what gets measuredimproves? Everything's about
data. Everything's aboutmetrics. And I would just say to
kind of introduce the answer tothat question, having been
involved in a lot of theseprojects where we've got
manufacturing companies, privateemployers that are funding work,
(28:00):
workforce, or or technicalskills training programs at
their high schools, for example,technical community colleges as
well. That's one of thequestions they'll ask. Is
they're writing that check is,what are we going to measure?
How do we know we're successful?
And so you can make a list.
We'll talk about it here in amoment. The thing that's
interesting, though, is sixmonths later, a year later, when
(28:23):
that employer comes back andsays, you know, we wrote that
check for a million dollars. Howwe doing a lot of times, in
fact, most times the educatorsare surprised that they're
asking. And I, you know, I don'tknow. I won't make any judgments
about that education versusversus industry. People can draw
their own conclusions, but Iwill tell you that if you are
partnering almost to that early,earlier question from Bob, if
(28:45):
you're partnering with a privateemployer, and they're writing a
check, and then part of theexpectation is that we're going
to create the next generation ofworkforce, that we're going to
get young people excited aboutcareers in manufacturing, that
we're going to have peoplegraduating from those programs
and going on to related Programsin Higher Education, or coming
to workforce and working inmanufacturing, and those are the
expectations that have been laidout. Yeah, those employers are
(29:07):
going to expect you to hit them,and so expect that conversation,
and it's they won't let you offthe hook that way. They don't do
that in manufacturing. If yousay, in manufacturing, we're
going to increase throughput,increase throughput to this, or
we're going to get our revenuenumber to that, or we're going
to get our gross profit marginto this. And that's a goal. You
set that goal, and you come backand maniacally measure that if a
if a manufacturing employer or aprivate employer sets a goal for
(29:30):
a school, and you jointly agreeto that, expect them to come
back and ask about whether ornot we got there, because
they're going to So measure thatall the time. Here are some of
the metrics now that we've gotthat topic introduced. You know,
the first one is, how manystudents are participating in
the program, right? So if we puttogether a manufacturing or Work
(29:51):
Based Learning program or a workbased skills program, I should
say in a high school, how manystudents are signing up for that
course, how many students arecompleting the. Course, that's
going to be a key metric. Almostevery one of these now has third
party credentials tied to it. Soyes, the students are earning
credit in the school. They maybe earning dual credit at a
technical college or auniversity related to the
(30:13):
coursework that they're doing,and they're earning a third
party certification. You know,there's lots of them out there.
We talk a lot here aboutmanufacturing skills Standards
Council, the smart automationcertification Alliance, the two
are my favorite, nocti, this wasright up there as well. So those
are just some examples. But ifwe're tying third party
credentials, how many studentsare earning those credentials?
(30:34):
What credentials are theyearning? And then it's going to
be about All right, how many ofthem are working in
manufacturing during the summer,in between the school year, how
many of them are choosingcareers in manufacturing or
choosing manufacturing relatedcoursework beyond secondary if
they're going on to a technicalcommunity college or university?
So those would be the ones thatwould be on my list. I would
(30:55):
certainly think about how manystudents are participating and
what certifications are theyearning? And you know, where are
they going after after thatprogram, after high school?
Those are probably the three,the three biggest that employers
are going to want to look atperfect.
Melissa Martin (31:10):
So expect to have those metrics. If you're
getting money from a privatedonor, it's the same thing, like
you have to report back on yourgrants. You know, you get money
from the government or fromsomething, you have to report
back on how that grant money isbeing used, right? It's the same
thing here.
Matt Kirchner (31:23):
Yep, and you'll get follow ups too. So it's not
enough to just answer thequestion. Or, you know,
sometimes you get a little bitof the bobbing and weaving. You
know, I'm not, and I'm nottrying to paint these employers
as mean spirited, or I mean, orwhat have you. It's just that
they're metric driven. That'swhy they're I mean,
Melissa Martin (31:36):
if you think about if you put money, if you
put some of your money into aninvestment portfolio, and then
you don't find out how it'sperforming, you're going to take
that money right back out.
You're not interested incontinuing to invest. And it's
kind of they're looking for areturn on their investment in
terms of the skilled workforcecoming out, students interested
in manufacturing careers, as oneexample. And if you do give them
really great metrics and showthem the benefit of their
(31:58):
investment, the upside isthey're going to continue to
invest got
Matt Kirchner (32:02):
it? Yep, that's a great analogy. Awesome.
Melissa Martin (32:04):
Okay, so I'm going to keep on this kind of
concept of employers, workforceand certifications with a
question from Brian badura andand Brian says higher education
is at a tipping point with moreflexibility than ever before to
complete degree and sort ofcertificate programs. How do we
help employers understand thevalue of these new educational
(32:25):
choices when many HR teams andexecutives may not understand
how to integrate educationalprograms like three year degrees
or advanced certifications intotheir job descriptions and the
workforce?
Matt Kirchner (32:35):
Yeah, so awesome question from Brian. Let's see.
We'll take half a step back andreally, you know, he has some
really good examples, like, youknow, you know, three year
degrees and certifications andso on. You know, first, a little
in defense of employers. Youknow, partnering with education
as important as it is, is never,this is never at the top of
(32:57):
their list. And the reason forthat is that they're trying to
manage a workforce. They'retrying to keep customers happy.
So think about somebody in humanresources, the stuff they're
dealing with all day might be adiscipline issue. One day it
might be an attendance issue.
Another day it might be somebodyquit their job and you need to
fill that job, or you got a bigproject and you need to staff
that up. Or, you know, you'vegot a regulatory audit or you I
(33:19):
mean, their days are filled withall this kind of stuff. People
in manufacturing are trying tocrank orders out. They're trying
to meet lead times. They'retrying to improve quality.
They're trying to expediteorders solve a quality issue
that already got out in thefield. I mean, that's the day of
a manufacturing person. So, soin defense of them, you know,
they've got their own prioritiesthat they're working through,
but the short answer to Brian'squestion is seven times seven
(33:41):
different ways we talk aboutthat all the time here on the
podcast. It's not enough to tellsomebody something once I can
tell you, boy, somebody shouldsometimes ask me a list of my
greatest frustrations in workingin education and manufacturing.
Near the top of the list Melissais the number of people in HR
functions that don't understandthe value of third party
(34:03):
credentials. And you know, theythey obsess about wanting more
people to come intomanufacturing. They obsess about
wanting more people to filltheir workforce, to enroll in
technical college programs orcommunity college programs that
are that are related, but thenthey they don't take the time to
value and understand what'savailable to them. And I'll just
(34:26):
give them not a little bit of acalm rant right now. You know,
you hear a lot of you know, alot of folks say we want, we
need more skilled people. I justhad a meeting like this. I was
in, I won't say the state, I wasin another state within the last
few weeks talking to an owner ofa major manufacturing company,
yeah? And they were just like,Yeah, we don't, we're not seeing
(34:48):
the people coming out of thetechnical college program that's
related to the work that we do,it's in manufacturing. And
they're like, where they're justnot producing enough, enough
people. You. And then I've satin the same room with that same
company as they're telling theiremployers, well, all we really
want is people that will show upto work every day, stay off of
drugs and take direction. AndI'm like, and they're telling
(35:10):
their educators that. So if Isaid employers, I might say,
educators. You can't have itboth ways, right? You can't. And
this whole idea, I mean, I'm thebiggest believer in soft skills.
Yes, we need people thatunderstand professional
behavior, workplace behavior.
Just wrote a magazine column onthat for Gardner Business Media
get published in January of 2026yes, we need that, but we can't
(35:32):
simultaneously say that that'sall we're looking for and then
complain when we're not gettingskilled talent from our from our
educational institution. Sothere's just a lot of things
that I think are happening ineducation, a lot of trends, like
third party credentials, likethree year baccalaureate
degrees. And that's, you know,that's something that's
definitely on the way it's, it'sa hot topic in my home state of
(35:52):
Wisconsin, and it'll be acontroversial one. So the you
know that that's not going to bea heavy lift, especially for
public institutions, but it'scoming and but what we need to
tell those HR folks, everyopportunity we have them, what's
available in education, what'shappening in third party
credentialing, what's happeningin innovation, what's happening
(36:13):
in hands on learning. I mean,there's all of these great
things that are happening ineducation. What's happening with
teaching authentic industrialskills. So if I'm a machining
company and any machiningtalent, I need to be teaching
that technology, like we saidbefore, in the classroom, in the
lab, in the high school, gettingstudents excited about it,
credentialing them, sending themdirect to workforce, sending
(36:34):
them to a Technical CommunityCollege program where they can
build those skills. And that'show we create the next
generation of the workforce. Sothere is no easy answer to
Brian's question other than likeanything when we say seven
different ways, you can tellsomebody something once, and it
may not sink in. In fact, itusually doesn't once they hear
it seven times through sevendifferent modes. You know, we
talk about email, social media,traditional media, having a
(36:58):
conversation, doing a sitevisit, sitting in a meeting,
same message over and over andover again. Sometimes people say
it seems like sometimes on thepodcast, you make the same
points week in and week out.
That's on purpose. You rememberthose points exactly you got.
Yeah, thank you for listening.
And it obviously worked, becausehe caught that. Yeah, it's seven
times, seven different ways.
Melissa Martin (37:16):
Awesome. Yes, I don't have anything to add that.
That's just absolutely true.
Okay, so we've got time for onemore question, all right, and
this is actually with a futureguest of the TechEd podcast, and
we're going to end on an AIquestion. I love AI questions.
Get so many AI questions. Welove it. It's such a great
topic. Okay, so Peter mera, hesays AI is about to out think us
(37:36):
in almost every technicaldomain, the real advantage will
be in what makes us human, suchas EQ, adaptability, creativity,
courage, judgment, wisdom,ethics and so on. So what's
going to take to blow up the oldmodel of technical education and
rebuild around the skillsmachines can't copy so our
workforce doesn't just survivethe AI era, but thrives in it?
Matt Kirchner (38:01):
Yeah, it was a great question. And I think the
key, there's a key word in thefirst sentence which is
technical, right? Just rereadthat first. I want to make sure
that sinks in with the audience,yeah, because reread that
question,
Melissa Martin (38:10):
AI is about to out think us in almost every
technical domain.
Matt Kirchner (38:14):
So when we say technical domain, it's not going
to out think us in every domain.
It's going to think out, thinkus in every technical domain.
And there's actually some debateabout how soon that happens,
right and and we'll see. I mean,some people would tell you it's
going to happen in 18 months.
Others are saying it might bemore like 10 years. Nobody knows
for sure, but certainly, ifanybody who's watched the
advancement, it's not justgenerative AI and what we're
(38:37):
capable of doing withperplexity, cloud, meta, chat,
GPT and so on, Gemini, andseeing just how that's advanced
in the last year, year and ahalf. And it's incredible,
right? So, so it's anybody'sguess how quickly this is going
to extend itself to physical AI,in the rest of the world. I
think it's a really, really goodquestion. You know? What happens
in an age where a computer cananswer any question, what
(39:01):
happens when a computer can, orAI can program a CNC machine?
What happens in an age where,where AI can program a program,
a logic controller can write anycode? Can, you know, fill in the
blank we're already seeing now,where some of the you know, some
of the opportunities forcomputer science majors,
especially ones new to theworkforce, and data science
(39:21):
majors and so on, struggling alittle bit to find jobs, or at
least jobs that pay what theywant them to pay, because
there's so much of this can bedone with AI every single day.
There's another article in thein the Wall Street Journal about
what AI is doing the workforce.
Another one just this morning,as matter of fact. So so all. I
mean, I think Pete's, I thinkhis premise is a good one, which
is, we're going to get to thispoint before too long, before
too long, before AI can do evenmore than it's doing now, and
(39:44):
that is going to have a hugedisrupting impact on the
workforce. And so what you know?
What do we need to do to ourtechnical programs, technical
education and all that STEMprograms to make sure that we
are future ready in thoseprograms? I think a lot of
things. That we need to do we'realready doing in education. Not
enough educators doing it. We'vetalked on this podcast a number
(40:06):
of times how AI education ismandatory in China. It's not in
the US, and even when it is,when we're teaching it, a lot of
times it's just teaching gptsand prompt engineering, which is
fine, but there's so much moreto AI when it comes to applied
artificial intelligence,humanoid robots, quadropods,
drone technology, 3d design andfabrication, some of the coding,
(40:29):
programming, video gamedevelopment, autonomous
vehicles, applications forbattery technology, electrical
vehicles, biomimicry. I mean,all of these things are
changing. The entire way that weengineer, the way that we design
the way that we innovate, and sowe have to prepare students with
the kinds of skills that will berelevant in that age. There's
schools doing this right. Wetalked not too long ago with the
(40:51):
CEO of Ashley Furniture, Toddwanick, and not long before
that, with the superintendent ofthe White House School District,
Mike Bigley, about appliedartificial intelligence learning
about how we can put studentsthrough an E Learning course
that helps them understandapplied AI and what we call the
edge to cloud continuum. Andthis is where I think we're
getting to the core of Pete'squestion is you have to teach
(41:15):
how. And this is nothing new toour audience, but sensors at the
edge communicate with controlsystems communicate with
regional data centers, datacollectors, computer networks
communicate with the cloud. Andwhat is happening at every
single level of what we call theedge to cloud continue, because
AI isn't about just what thealgorithm is doing and what's
happening at the cloud level.
What's happening, you know,behind the iPhone, if you will,
(41:35):
it's how are we, how we'regathering data, extracting data,
analyzing data, discerning data,storing data, what we're doing
with it, and then how we usethat to improve literally, every
aspect of the economy, whetherit's energy, defense,
hospitality, retail,manufacturing, smartphones,
every single aspect of our ofour economy, precision
agriculture. It's changingeverything. So we have to turn
(41:57):
our students into systemsthinkers when it comes to AI and
how they're analyzing everysingle thing they look at, as
far as that edge to cloudcontinuum that's happening
already in in technicalcolleges, we're doing a number
of projects where we're, youknow, doing cause cloud based
analysis on data that's beenpulled off of manufacturing
equipment across an entiretechnical college. We have
(42:18):
things like that going on atGateway Technical College in
Kenosha, Wisconsin. And there'sa couple other great examples of
how we're doing that at thetechnical college level, at the
high school level, the whole,you know, the whole idea of
discover AI and full disclosure,I think people know we have a
financial interest in thateffort, but discovering how
artificial intelligencemanifests itself across the
entire economy, that's what thisis about. That's how we blow up
(42:39):
education. I mean, if Pete wantsto see some really cool stuff,
go to an emerging technologylab, go to a discovery I lab.
See where you walk in the doorand you've got, you know, three
students working on drones andanother three working on
autonomous vehicles, anotherthree working on, you know, 3d
design and 3d scanning. Threemore are, you know, are building
their own, their own video game.
(43:01):
They're earning competencycredentials and creating that
competency portfolio the wholeway through kind of stringing to
a bunch through a bunch ofthemes we've had on this episode
of The TechEd podcast. That isthe future of education, and we
can do it at every level. It'sasynchronous, it's student
driven, meets every studentwhere they are, and it prefer,
prepares the workforce of thefuture. I think, I think pizza
(43:22):
asked a really, really goodquestion, and it was one I was
happy to answer.
Melissa Martin (43:25):
So that's future of technical education. That's
going to be a really, reallyexciting time. And I think a
great way to end this episode.
As we wrap up, 2025 look forwardto 2026 think about what the
year ahead holds for technicaleducation. I think that this is
there's a lot. It can feeloverwhelming to educators, but I
think that there's a once in alifetime opportunity for us in
the world of education, if wecan embrace the change and
(43:49):
really do it for our studentsand for the right reason,
because if we don't, we're doingour students a disservice for
their future. So I'm excitedabout 2026
Matt Kirchner (44:00):
How about you.
It's gonna be amazing. Yeah. Infact, speaking of being excited
about 2026 we've got some reallycool episodes teed up, right? A
predictions episode, which we doevery year.
Melissa Martin (44:08):
Yeah, that's coming out next week. And you
guys love our predictionsepisode. And in case you don't
know, every single year, Mattmakes predictions about what the
future will hold for that year,and he always ranks himself on
how well he did the previousyear, and you You're pretty
good, right? Reading for years?
Matt Kirchner (44:23):
Yeah, we'll see how we did this year. I have an
inkling that we were prettysolid.
Melissa Martin (44:26):
I have an inkling as well. So, yeah,
excited to release that next oneto our audience. So if you
aren't subscribed, make sure yousubscribe. That one comes out
next week, Tuesday.
Matt Kirchner (44:37):
Wow, awesome.
Yeah, super, super excited forthat. Any other episodes coming
up, the audience should bethinking about
Melissa Martin (44:42):
there are. But I I'll just say you have to say
subscribe, because we got somereally high profile guests
coming out in early January. Somake sure that you're
subscribed. We're on Apple,Spotify, any other podcast
platform that you might belistening to, if you're only
listening to this, by the way,we're on YouTube and so. If you
like watching rather thanlistening, you can go find us on
(45:03):
YouTube. Watch those episodesthere. If you're watching us on
YouTube, hi, it's great to seeyou, and you can always catch us
again on Apple Spotify orwherever else. Tell your friends
awesome.
Matt Kirchner (45:13):
Please. Tell your friends glad to be here with my
friend Melissa Martin. We had agreat conversation. She does
such a wonderful job of pitchingthose questions up and
responding to the answers aswell. Always a lot of fun. We'll
keep teeing up these episodes ofAsk us anything, so be sure to
keep sending your questions ourway. We'll get through as many
of them as we possibly can. Cannever hit on all of them, but
love to, love to pull the bestand brightest and kick them
(45:35):
around here on the TechEdpodcast at least once every
three months or so. So thank youto our audience for being with
us. It's been a wonderfulepisode of The TechEd podcast.
Don't forget to check out theshow notes. We referenced a few
things we promised to link upfor folks that asked those great
questions and the folks thatlisten to the answers as well.
So check out the show notes.
Those will be at TechEdpodcast.com/ask us anything
that's TechEd podcast.com/ask usanything where you will find
(46:01):
loaded up, not just thisepisode, but every episode of
Ask us anything. So you can goback, if you like, this format,
and check those out when you'redone checking those out. Check
us out as well. On social media,you'll find us on Instagram. We
are on LinkedIn. We are onTiktok, on Facebook, anywhere
you go for your social media,and on YouTube, by the way, as
well. You can check us outthere. Make your comments there
as well, but please comment,reach out, let us know you're
(46:24):
out there. We would love to hearfrom you. Thanks for being with
us on this episode of The TechEdpodcast. For our producer,
Melissa Martin. My name is MattKirkner. I'm your host. We'll
see you next week. You.
This is the TechEd podcast, where we
feature leaders who are shaping,innovating and disrupting
technical education and theworkforce. These are the stories
of organizations leading thecharge to change education, to
rethink the workforce and toembrace emerging technology.
You'll find us here everyTuesday on our mission to secure
the American Dream for the nextgeneration of STEM and workforce
(00:32):
talent. And now here's yourhost, Matt Kirchner,
Matt Kirchner (00:38):
welcome into the TechEd podcast. This is your
host. Matt Kirkner, we had suchgreat feedback on our last
episode of Ask us anything. Wehad great questions, first of
all, from our audience, and wehad so much fun answering those
questions. The interest thistime around, absolutely
overwhelming. We put out arequest. We say, hey, there's
another episode coming up. Andthe world of technical
(00:58):
education, TechEd nation, as welike to call it responded in
droves with fantastic questionswe've narrowed down to a couple.
Can't answer every questionevery single episode, but we
picked the best and brightest,and we try to tackle them here
on the TechEd podcast. The otherthing that we do is that it's
not just Matt Kirkner speakingon this particular episode, our
producer, the heart of theTechEd podcast. Ms, Melissa
(01:21):
Martin is joining us to ask thequestions and respond to a few
of the answers. So Melissa,welcome back that side of the
microphone, this side of themicrophone again. Great to have
you with us.
Melissa Martin (01:31):
Thanks, Matt.
Great to be here again. It'salways fun. It is always fun.
All right, so we got a number ofquestions, again, submitted.
Thank you to everyone forsubmitting those. And I'll tell
you this, because we had somany, we have to pair it back so
that we can fit into ourallotted time frame. So if we
didn't get to your question thistime around, we'll make sure to
get to it next time. So again,subscribe. Stay tuned. We'll do
this every single quarter. Andagain, for anybody, you got
(01:54):
ideas, you've got questions forus, you can always throw them in
that, submit your questions spoton our website, and we'll save
them for next time around. ButMatt, are you ready for the
Spanish questions? All right, sothe first question comes to us
from Gustavo Gomez. Gustavo is aprofessor over at Purdue Global
University, and he says, I'mworking on a new course called
(02:15):
production, machine,technologies and tooling. I was
wondering if you have apresentation geared to that
subject and anything aroundautomation and robotics, so I'll
let you rephrase that question.
Matt Kirchner (02:28):
Gustavo, he said, Yes. All right. Gustavo, thank
you for a terrific question.
Thanks for somebody beingsomebody in higher education who
is focusing on this. This reallycool question is about, what
should we be teaching in highereducation when it comes to
automation, robotics, machines,controls and so on. So really,
really awesome that you're thatyou're thinking about it that
way. So let's, let's start withthinking about what's important
(02:52):
in the university program whenit comes to advanced
manufacturing. And you know,we've had so many guests on this
podcast, folks that are workingin manufacturing who have come
to us, and maybe, I don't know,complaining a little bit about
the whole idea that, boy, we getthese engineers and these
technical folks out of four yearprograms. They're smart, wicked
(03:12):
smart. They're really, reallygood with numbers and data and
theory, but then they're scaredto death when they get out on
the shop floor. And so in answerto the first part of the
question, do we have anypresentations and so on,
absolutely, we've had dozens ofepisodes of the podcast talking
more specific terms about whatwe should be teaching at the
university level when it comesto advanced manufacturing. So
(03:34):
we'll link a few of those up inthe show notes. But let's think
about this. First of all, we'vegot employers that are coming to
us and saying, Hey, I've gotengineers, for example, that are
coming out of four yearprograms. And they're, I'm just
quoting them. They're, you know,they're no good to me for the
first two or three years I havethem because they haven't spent
any time on the shop floor. So Iwould say the first thing to
think about is, yes, we have tohave engineering talent that
(03:57):
understands calculus and physicsand has gone deep in math. I
mean, we get that that'simportant. We know a bed has all
kinds of all kinds ofrequirements around that. The
HLC has requirements around thatwe I understand all that stuff,
but the truth of the matter isthat if we're going to produce
students who are job ready toadd value in manufacturing,
they've got to have that handson experience as well, and so
(04:19):
lots of different ways to dothat. I'll point as an example,
because it's fresh in my mind.
What's going on at theUniversity of Wisconsin. Stout
recently announced a hugepartnership with with an
employer in western Wisconsin.
They are putting in all kinds ofhands on experiential learning.
So they're they're adding, inthat case, you know FANUC Robo
(04:40):
drills with with robotic machinetending fanic controls, which we
know are big fan of. FANUC fanshere on the TechEd podcast,
where students are learninghands on technology. They're
doing some things with anenterprise smart factory system,
and literally learning the fine.
Are aspects of data acquisition,smart sensors and smart devices.
(05:04):
What? How you know how a PLCworks, how we acquire data, what
we do with that data on thecloud, how we protect data in a
manufacturing operation, manageswitches, unmanaged switches,
all these kind of things. Whatis an ERP system? How do we pull
data up to an enterprise levelsoftware program to be able to
to work with that data. And howdo we integrate all these
(05:24):
technologies, things likeconveyors, and we mentioned
sensors, programmable logiccontrollers, stepper motors, you
know, all these differenttechnologies you see in
manufacturing. It's that handson integration part. So, so I
think, you know, in any fouryear engineering program, yes,
get to cover the theory. Let thestudents do some fun stuff early
on. Don't save all the coolhands on stuff for junior and
(05:45):
senior year. But then let'sreally embed their some awesome
hands on projects, work basedlearning and a capstone project
that requires them to put allthese, all these technologies
together. There's, there's moreand more four year universities
that are doing this now. Purdueglobal is a great example of a
spot where folks can get good,you know, Polytechnic education,
(06:05):
good hands on learning. Butthose are the kinds of things
that I would be thinking about.
Is that, yeah, we can do a lotof cool stuff in the lab. We can
do a lot of experimental stuffand experiential stuff, but
let's also make sure we'regetting good hands on experience
with authentic manufacturingequipment and technology.
Melissa Martin (06:21):
And I'll just maybe ask a follow up question,
Matt. So it sounds like Gustavohas got, you know, one course
that he's working on. So if youhad to boil it down to like
you've got one course to teachthe most fundamental things,
what would you include in that?
Matt Kirchner (06:35):
Yeah, so I'm gonna make the assumption that
the students outside of thiscourse are getting some of that
fundamental, data analytics anddata acquisition, and what we're
doing now with AI and analysisand those kind of things. I
would say it's a handful ofthings. I would say, first of
all, let's think about thatauthentic manufacturing
technology. So what is discreteIO or a programmable logic
(06:56):
controller doing? That's thecomputer of manufacturing. I
would think specifically aboutthings like ultrasonic sensors,
proximity sensors, temperaturesensors, moisture sensors, force
sensors. So how are we usingsensor technology at the edge? I
would get that in there forsure, you're going to want to
teach some basic machining, orsubtractive manufacturing. So
(07:16):
you think about a machiningcenter where you're removing
metal from a part to createanother part or removing metal
from a substrate. You'lldefinitely want to get that in.
You probably want some basicmetal fabrication. So what is a
punch press? What is a turretpress? What is a press brake?
How are we fabricating metal?
How are we welding metal? You'regoing to want to work in. In
addition to that, like we said,you know, conveyor technology,
(07:37):
electric motors, you're going towant to understand electric
motors, variable frequencydrive. Variable frequency
drives, motor control,mechanical drives, which they're
going to be learning some ofthat outside of that course, but
understanding how to gears, pullthese shafts and so on. Work in
a manufacturing environment.
You're going to want to work insome fluid power, so hydraulics
and pneumatics, that depends alittle bit on, you know, which
(07:58):
of those focuses depends alittle bit on where you are and
what your employers are doing,and then, you know, the
automation and robotic side. Soyou need to have both, you know,
a six axis traditionalindustrial robot. Everybody
looks at collaboratives andsays, Oh, that's what
everybody's using right now. 90%of the robots, probably more,
that are used in manufacturing,are still traditional six axis
(08:21):
robots. And that's not justbecause that's what people have
bought in the past. It'sbecause, based on precision and
payload capacity, a lot of timesa you know, a traditional six
axis robot is the bestapplication. But then we also
have to teach collaborativerobotics as well. So I think
those would be the basicelements that I would place into
that program. Look, I recognizethat the four year level, a lot
(08:42):
of times we're constrained interms of budget, our community
and technical colleges, manytimes for equipment, are better
funded than our four yearuniversities, but those are the
kinds of technologies we have tostart building around, if for no
other reason, that that's whatour students are going to see
when they get to manufacturing,and that's what employers are
expecting students to know.
Melissa Martin (08:59):
100% your employers will get behind you,
100% when they percent when theyhear that. That's the kind of
course you're going to put yourengineering students through,
absolutely all right. The nextquestion comes to us from
Michael McArdle over at WesternTechnical College, and Michael
asks students, use AI, but donot question the results. What
are some strategies to have themsee the futility of that
(09:20):
approach? Yeah.
Matt Kirchner (09:21):
Well, so I guess the first thing is, if you know,
looking back to all of oureducation journeys, don't be
afraid to tell them they'rewrong, right? I mean, if I, if I
answered a question, whether I,you know people long before AI,
whether I did it in theclassroom or outside of the
classroom, regardless of how Idid my research, if that answer
was wrong, my teachers weren'tafraid to say, look, that's seen
a nice try, but you're wrongthere. So let's start with not
(09:43):
being afraid to to, you know, tocall that out. The truth of the
matter is, accuracy isimportant. It's important in the
workforce and in the workplace,and it should be important, and
often is in education as well.
But I, you know, I go backMelissa too. I had a sixth grade
teacher, and Mr. Schumann, hisname was, and one of the things
that. We were required to doduring sixth grade, and you got
extra points. And this is backin the days of newspapers and
(10:04):
magazines and whatever. Nobodywas, you know, nobody was going
online and looking for anythingback then, you got extra points.
If you could find an error in anews article, really, and bring
it in, you got extra credit forthat. Wow. So when you were in
and, believe it or not, ithappened quite often. I mean,
this has been the days oftypeset. And you didn't have
spell check and so on. Yeah,about once a week, if you read
(10:25):
the paper often enough, you'dfind a mistake in an article,
either a factual mistake or agrammatical mistake or a
spelling mistake, and you bringit in and get extra credit
challenge students to find wheregenerative AI is wrong.
Generative AI isn't perfect. Weknow that it hallucinates it's
getting better. I was justlistening to a podcast. It was a
Joe Rogan podcast, actually,with Jensen, Jensen Wong from
(10:45):
from Nvidia. But they weretalking about how AI and
generative AI is hallucinatingless and less and less, which is
why people are using it more andmore and more. Is just becoming
more and more accurate. But Iwould say, have a student, you
know, just understand that youcan't just take what that
generative AI is telling you andtake that as fact. And I've
(11:06):
gotten myself into into troublewhen I'm doing quick research
for the podcast or something,and you jump on, you know, I
won't embarrass any of theplatforms, because they all do
this, but it gives you a wronganswer. You write it down, and
all of a sudden you use it, andthen, you know, 10 listeners are
commenting on whatever, sayingyou got this wrong. So, so at
any rate, you can't. You can'tjust rely on the generative AI
to give you the right answer. Iwould also so so challenge
(11:30):
students to find it makingmistakes and point out when it's
making mistakes. Also let themknow that they're responsible
for their answers. They can'tjust blame generative AI for
that answer. Use a transformer.
Use a GPT that gives you itssources. Several of them do so
you can actually go through andif it says something, it'll go
back and it'll show you itssource material for whatever it
(11:51):
was telling you. Use that. Butthen the other part of it is
we've got to put the challengeto teachers and educators and
say, Look, in this age ofgenerative artificial
intelligence, you've got to stepup your game, and you've got to
find new ways to challengestudents, whether it's talking
about material, whether it's youknow, rather than asking them to
write a report outside of class,maybe they come in and they
(12:12):
write a report inside of class,or you have them read an
article, and then they have tocome in and tell you a little
bit about what they learnedusing generative AI as as an
assistant, but Not as the as thesource material. And then I
think the other thing is justfor students, it's time for them
to step step up their game, sowe can raise the bar on
students. Doing research is wayeasier than it used to be. I
mean, I don't have to sound likean old guy, but I remember, you
(12:34):
know, we used to have to go tothe library, find a book, and
then, like, if you were writinga term paper, take the book over
to the copy machine, make copiesof the book, or do your research
in the library, and then takeall that home and write your
term. I mean, think about that.
And that's not like 300 yearsago. That was like 20 years ago,
30 years ago. That was
Melissa Martin (12:52):
when I was in college. I mean, hours and hours
and hours of reading all thesereference materials so that you
could write your paper and bewell informed from, you know,
these different authors who haddifferent things to say about
that, that topic, it's a lot ofresearch,
Matt Kirchner (13:05):
absolutely so now, using AI, you can literally
collapse what used to be maybe asemester to write a 20 of
research to write a 20 pageresearch paper to you could do
that in a day. So Okay, step upthe game. Set expectations
higher. We have to finddifferent ways to challenge
students. We have to have themexplain more about what they're
learning, more group projects,all these kind of things, not
just, you know, replacing rotememorization with the use of
(13:27):
gender of artificialintelligence. So it is okay to
expect more of your students,and the best educators and the
best educational institutionsare going to be doing that as
the as the years go on in the inthe age of artificial
intelligence.
Melissa Martin (13:38):
That's absolutely right. And I would
add to that, students need tounderstand these AI tools as
they're they're designed to giveyou an answer, whereas a person,
you ask them a question, theydon't know the answer, they're
going to tell you, hopefully, Idon't know the answer to that.
But let's look it up together.
Here's where you might be ableto find the solution. And AI
isn't made to say, I don't know.
(13:59):
It's May. If it doesn't know theanswer, it will hallucinate, and
that's what it'll make somethingup just to give you an answer.
And so students need tounderstand that when they're
using these AI tools, that theyare designed to give you an
answer, whether or not it'sright, because that's just how
they're programmed. So just haveset that expectation with them
as well, so they understand whatthese tools are for and what
(14:19):
they're not for Absolutely?
Yeah, great points. All right,so that kind of segues into
another question, becausethere's this whole ethical
debate around AI and you knowwhat's right and wrong, and how
much do we use it? And HollyAtha, over at the MBA research
and curriculum center, asks aquestion. She says, what role do
you feel ethics education shouldplay in CTE, today and tomorrow?
Matt Kirchner (14:40):
Yeah, I love that question. And and a huge role is
the short answer for a whole,whole collection of reasons. I
mean, let's start with our lastquestion about, you know, how
students use AI for their theircoursework and so on,
recognizing you know that youdon't, you've got to be, you've
got to be doing your own work,that you don't take credit for
somebody else's work, that. Kindof things really, really
(15:01):
important, ethical questions.
But it gets a lot deeper thanthat, you know, you start
thinking about, you know, in theage of artificial intelligence,
where computers are able to, atleast on the technical side, you
know, think as fast as humanscan, and faster and no more and
access more data and so on. Youknow, what does it mean to be
truly human? Is a really, reallystrong question. When we start
(15:25):
having agentic AI making all ourdecisions for us, and we can
easily get to that point, whatdoes it mean to be human? What
does it mean to be human in anage where we don't have to work
as many hours, because we canuse AI robotics and automation
to do a lot of the things thatwe used to have to do,
physically, to where, literally,where, you know, anybody today
can pretty much have withinreach is the is the lifestyle of
(15:47):
the wealthiest person 100 yearsago, right? Like anybody today
can have that can have thelifestyle of somebody that was
like the wealthiest person onearth 100 years ago. And we're
just gonna, that's just gonnacontinue. So we'll have all
these questions about, what doesit mean to have a soul? What
does it mean to be human? Howdoes that mean in terms of how
we treat each other? How doesthat mean in terms of how we use
(16:08):
artificial intelligence? We goback, oftentimes, on this
podcast, to the book Genesis,which was Henry Kissinger, Eric
Schmidt, as you know, and CraigMundy, and they published that
book about a year ago, give ortake, and it's just an
outstanding book, but they godeep into the exploration of
ethics in the age of artificialintelligence and technology. So
I think there, I think ethicsplays a huge role. Well, you can
(16:31):
weave it into weave it into thecoursework. You can force
students to think about ethicalquestions. I go back a lot of
times. I was a business schoolmajor, but I went to a Jesuit
university, and we were requiredto study nine credits in
theology and nine credits inphilosophy. And you really, you
know, it didn't really, peoplewould look at that and say,
well, that has nothing to dowith being a business person.
(16:51):
Well, maybe. But what you learnin terms of how to think about
humanity, how you communicatereally, really important stuff.
And so we're going to have toolsat our disposal. In the future
that can be used for all kindsof purposes. And people like,
aren't you worried about AIbeing used for, you know, for
really, really bad purposes? Andthe answer is, well, as long as
people have good conscience areusing it, and as smart, or
(17:13):
smarter than the people that arebad actors, the better off we're
going to be. But we need toweave that, that element of
ethics into the into thecoursework.
Melissa Martin (17:22):
Yeah, all the more reason to include it now
with AI and everything,absolutely so next question
comes from Bob Manning, over atStillwater area high school, and
Bob has a fantastic question.
It's a little bit long, so staylong for the ride. He says, Hi,
Matt, many schools, many schoolswould love to have the
manufacturing programming thatyou highlight in your podcast.
However, we all have differentstarting points and assets
(17:44):
elements of quality programmingthat come to mind include
visionary and strategicleadership, physical space, with
modern equipment andinfrastructure, genuine industry
partners, teacher capabilitiesand buy in, funding options,
etc. What do you think are themost important components a high
school must have when attemptingto build a robust manufacturing
pathway for the sustainablefuture. Can you also rank them
(18:05):
in order of importance? Or wouldit make the most sense to list
what needs to come first, secondand so on? All right? Great
question. It's great question.
Matt Kirchner (18:14):
Yeah, absolutely.
You know, and I spend a lot oftime with educators, and I think
kind of the default playbook alot of times, is they all go to
money, right? And I say all, Imean, I use that figuratively,
but, but, but everybody comesback and says, Well, until
somebody walks in the door witha million dollars, until we have
a referendum that gives me, youknow, $5 million to spend, until
I have a school board, until Ihave a superintendent, a
(18:35):
district administrator, youknow, the list goes on that
provides me the resources to beable to expand my program until
I have those things. You know, Ireally don't have anything to do
here because I couldn't affordto do it anyway. And you know,
the first thing we always, andthis would be number one on the
list, is, before anybody's goingto give you a penny, you have to
have a dream. And you know,we've been involved in so many
(18:57):
of these amazing K 12 projectsand at other levels of education
as well, but lots of high schoolprograms where we're integrating
all kinds of fascinating,amazing technology, autonomous
vehicles, drones, flying drones,unmanned ground vehicles, 3d
design and fabrication, coding,programming. I mean, all these
really, really cool things. Ithink when Bob says the kind of
programs we highlight on thepodcast. Think that's what he's
(19:20):
pointing to. All this reallycool technology, robotics,
automation, mechatronic systems,all those things cost money.
There's no question about it.
But if we start by saying,we'll, we'll, we'll work on this
when we have the money, thenthat that never happens, right?
What we have to do is have adream that is so big that
somebody's willing to fund it.
And every one of these projectsthat I've seen where whether
it's a community or an employeror a wealthy individual, or some
(19:43):
combination, usually of allthree of those, stand up and
say, we're going to do this.
It's because someone had a dreamso big that they couldn't say no
to it. So first thing I wouldsay is, start with a dream.
Think about what could be if youcould have the coolest lab, the
best program, regardless of whatthat. Costs, what would that
look like? Yeah, and then createthem. Create a message so
(20:03):
compelling that nobody can sayno. From there, I think I'm a
huge believer in meeting everylearner where they are, and I
think this is really important,especially as we move into the
future of education, is that weall have different learning
modalities. We talk about thatall the time on the podcast. I
take sometimes, take this backto my days of selling
(20:24):
manufacturing technology and soon. It's like you'd always say,
Well, some people are auditorylearners, or in some people are
kinesthetic and some people arevisual. So in other words, some
people learn with their ears,some with their you know, with
their eyes, some by doing, notright, wrong, good, bad. It's
just that we're all different.
So how do you meet every singlelearner, where you where they
are? You have to have variousmodalities of learning, whether
(20:44):
that's a lecture, whether it'shands on, learning, whether it's
e learning, whether it's workbased learning, all those things
are great, great, great ways tolearn. We have to have that.
Number three, I you know, I'm abeliever, as part of that, that
you create some opportunity forasynchronous learning. I think
we're getting away from the ageof a teacher standing in front
of a class and lecturing for,you know, for an hour, and then
(21:08):
moving the class on to the nextclassroom and lecturing for an
hour. I just that that thatversion of education was never
for me. And so havingopportunities where we take our
high flyers and if they can movefaster, let them go, and if a
student, for whatever reason,needs a little bit more time on
something that's awesome, justtake the time that you need to
be able to gain the competency.
So I think, I think that'sreally important, is that is
(21:30):
delivering asynchronouslearning, and then making sure
we've got a significant portionof that that's hands on, and
that It's project based. Andthat's, I think, where the
future of education is going andthat puts a challenge on a
teacher, right? Can you know,managing a classroom full of
students working on projects allday might be better for the
student. It's probably a lotharder for the teacher. Thank
God for teachers that arewilling to be patient enough to
(21:52):
work through something likethat. We need to have hands on
learning, and the more technicaland the more technology driven,
the better. So that would benumber three, and then the other
fourth one is and all these tietogether. But, you know, you got
to have skills and competenciesthat lead to whatever comes
next. And in a lot of times, Ithink that's, you know, that's
(22:13):
career based stuff, right? So,you know, manufacturers who walk
into a high school that wantsto, you know, spend a bunch of
money on a new lab that can'tsee a direct line from whatever
that is, and, you know, a vinylcutter sitting in the corner to
a job at that employer. It'sreally, really hard to get them
excited about that. So, and I'mall for, I mean, there's a lot
(22:35):
of different age appropriate andgreat, appropriate ways to
deliver learning, and we're nottaking sixth graders and
teaching them complex ladderlogic on a programmable logic
controller. Right? That comeslater, but, but teaching
students at the right level, youknow, here, here's how to
program a robot, here's how tooperate a robot, here's how to
program a programmable logiccontroller. Here's how you get
(22:56):
data into and out of smartsensors. Here's what you do with
the data set. Here's how youweld the part. Here's how you
fabricate a part. Here's how youmachine apart. Here's how you
program a CNC machine. Havingthose kinds of programs in a
high school, so that studentsare learning those competencies
along the way, and then have jobready skills tied to and this
would be number fivecertifications wherever we can
(23:16):
so third party certificationsthat students can earn, that
they can take to an employer andsay, I know how to do this. This
is what I learned in highschool, creating a competency
portfolio in addition to that,that high school diploma would
be and I could keep going, Icould come up with another
probably list of 10 for Bob,but, but those would be my top
five.
Melissa Martin (23:35):
All right. So, so to recap, for Bob and
everyone else in our audience,and to make sure that I was
listening so our five were havea dream so big that people can't
say no to it. Two would be meetevery learner where they are.
Three would be asynchronous,hands on, project based
learning. Four is make sure thatit's career relevant and ties to
something that they're going tosee after school. And number
(23:55):
five, ties to certifications andthat they're earning something
along the way. You could be apodcast.
Matt Kirchner (24:01):
Have to segue into the next question that's
perfectly smooth.
Melissa Martin (24:03):
So my segue is that the next question has
nothing to do with anythingwe're talking about. So we're
gonna take a quick break fromthe technical education and
workforce topics and go to kindof a an unrelated topic. If
you've been around the TechEdpodcast for a while, you'll know
that sometimes we have reallyunique, fun episodes that kind
of dive into topics outside ofthe classroom, but really, we
always find a way to make itwork within the greater
(24:25):
technical education, data,skills, science, all that kind
of fun stuff.
Matt Kirchner (24:29):
TechEd stem podcast about whatever we think
is interesting.
Melissa Martin (24:33):
And so you did a really fun episode over the
summer previewing the Tour deFrance, yeah. So we actually had
somebody ask a question aboutthat. So Bruce, Bruce Anthony
reaches out and he says, Do youthink today will win the 2026
Tour de France? Follow upquestion, who will be the next
rider to win the yellow jerseycompetition not named today?
(24:56):
Wow.
Matt Kirchner (24:57):
Yeah. That's an awesome, awesome question from
Bruce the. Answer his firstquestion is, yes, there's, I
mean, so the thing to know aboutcycling, like any sport, there's
a lot of wild cards, right?
Yeah, but if you stay healthyand you don't crash, right,
those are kind of the two bigthings. Then I think, you know,
if today makes it to the tourwithout a significant crash, if
he if he's as healthy next yearas he was this year, no reason
to believe he won't be. I don'tknow that there's anybody that
(25:19):
can touch him. He ran away withthe 2025, Tour de France, you
know, I think, I think Jonasfinger go still has few years
left in him, and he was today's,you know, and has been his, his
kind of most aggressive rivalover the course of the last
several years, five, six yearsor so, you know. I think Jonas
has a chance, and it reallydepends on what his conditioning
(25:39):
is coming into the tour nextyear, but if he rides at the
highest level that Jonas canride at, I think he's got a
chance to at least make it acompetitive tour. My bet would
still be on today. Pa, gotchanext one not named today to win
the Tour. If it's not Jonas,there's a lot of people I'll
highlight too. Remco venopol hasbeen one that we had our eye on
(26:02):
last year and and I would saythat he is still, I mean, if you
look at the rankings, the top 10or 20 riders in the world, Remco
is still right there. And theother one I'll add is a little
bit of a wild card, because Ihave a soft spot my heart for
him. Would be Mateo Jorgensen,American writer, okay, he's
probably not top 10, but he'sprobably top 15 right now. And
as we talked about on thepodcast, actually, our son, who
(26:24):
raced for the junior developmentteam for track bicycles for a
whole bunch of years when he wasgrowing up, actually raced
against Mateo at juniornationals a couple times. So and
again, got beat pretty handilyby Mateo, I should say, but, but
would to be, you know, be at thestarting line with a guy that
eventually is competitive forthe tour was a really, really
cool thing to look back on andand think about so, so Matteo
(26:46):
Jorgensen, Bruce. Bruce wouldalso, also be on that, on that
list, and hard to believe.
We're, you know, almost sixmonths away, a little bit more
than that, from the next year'sTour. So it won't be long before
we're, you know, before we'rehandicapping bike racing. Maybe
Jason will come back again thisyear, and if it's not Jason,
I'll guarantee some high profilecycling expert that will will
(27:07):
have joined us, because that wasa
Melissa Martin (27:08):
really fun episode. Yeah, that was a fun
one. And did did some greattying back into science, the
nutrition science data, all thedata that goes into preparing
these teams so
Matt Kirchner (27:18):
it drives technology shifting, all that,
getting excited already.
Melissa Martin (27:23):
All right. So back to our education topic. So
the next question comes fromMichael ezeki, and he asks,
educational institutions areless tuned into dashboards? Can
you suggest some good dashboardmetrics for work towards
development programs ineducation?
Matt Kirchner (27:38):
Oh, that's a good one metrics. And he's right.
That premise less tuned intodashboard. So we think about,
Yeah, we love metrics. You thinkabout data and manufacturing?
What's what gets measuredimproves? Everything's about
data. Everything's aboutmetrics. And I would just say to
kind of introduce the answer tothat question, having been
involved in a lot of theseprojects where we've got
manufacturing companies, privateemployers that are funding work,
(28:00):
workforce, or or technicalskills training programs at
their high schools, for example,technical community colleges as
well. That's one of thequestions they'll ask. Is
they're writing that check is,what are we going to measure?
How do we know we're successful?
And so you can make a list.
We'll talk about it here in amoment. The thing that's
interesting, though, is sixmonths later, a year later, when
(28:23):
that employer comes back andsays, you know, we wrote that
check for a million dollars. Howwe doing a lot of times, in
fact, most times the educatorsare surprised that they're
asking. And I, you know, I don'tknow. I won't make any judgments
about that education versusversus industry. People can draw
their own conclusions, but Iwill tell you that if you are
partnering almost to that early,earlier question from Bob, if
(28:45):
you're partnering with a privateemployer, and they're writing a
check, and then part of theexpectation is that we're going
to create the next generation ofworkforce, that we're going to
get young people excited aboutcareers in manufacturing, that
we're going to have peoplegraduating from those programs
and going on to related Programsin Higher Education, or coming
to workforce and working inmanufacturing, and those are the
expectations that have been laidout. Yeah, those employers are
(29:07):
going to expect you to hit them,and so expect that conversation,
and it's they won't let you offthe hook that way. They don't do
that in manufacturing. If yousay, in manufacturing, we're
going to increase throughput,increase throughput to this, or
we're going to get our revenuenumber to that, or we're going
to get our gross profit marginto this. And that's a goal. You
set that goal, and you come backand maniacally measure that if a
if a manufacturing employer or aprivate employer sets a goal for
(29:30):
a school, and you jointly agreeto that, expect them to come
back and ask about whether ornot we got there, because
they're going to So measure thatall the time. Here are some of
the metrics now that we've gotthat topic introduced. You know,
the first one is, how manystudents are participating in
the program, right? So if we puttogether a manufacturing or Work
(29:51):
Based Learning program or a workbased skills program, I should
say in a high school, how manystudents are signing up for that
course, how many students arecompleting the. Course, that's
going to be a key metric. Almostevery one of these now has third
party credentials tied to it. Soyes, the students are earning
credit in the school. They maybe earning dual credit at a
technical college or auniversity related to the
(30:13):
coursework that they're doing,and they're earning a third
party certification. You know,there's lots of them out there.
We talk a lot here aboutmanufacturing skills Standards
Council, the smart automationcertification Alliance, the two
are my favorite, nocti, this wasright up there as well. So those
are just some examples. But ifwe're tying third party
credentials, how many studentsare earning those credentials?
(30:34):
What credentials are theyearning? And then it's going to
be about All right, how many ofthem are working in
manufacturing during the summer,in between the school year, how
many of them are choosingcareers in manufacturing or
choosing manufacturing relatedcoursework beyond secondary if
they're going on to a technicalcommunity college or university?
So those would be the ones thatwould be on my list. I would
(30:55):
certainly think about how manystudents are participating and
what certifications are theyearning? And you know, where are
they going after after thatprogram, after high school?
Those are probably the three,the three biggest that employers
are going to want to look atperfect.
Melissa Martin (31:10):
So expect to have those metrics. If you're
getting money from a privatedonor, it's the same thing, like
you have to report back on yourgrants. You know, you get money
from the government or fromsomething, you have to report
back on how that grant money isbeing used, right? It's the same
thing here.
Matt Kirchner (31:23):
Yep, and you'll get follow ups too. So it's not
enough to just answer thequestion. Or, you know,
sometimes you get a little bitof the bobbing and weaving. You
know, I'm not, and I'm nottrying to paint these employers
as mean spirited, or I mean, orwhat have you. It's just that
they're metric driven. That'swhy they're I mean,
Melissa Martin (31:36):
if you think about if you put money, if you
put some of your money into aninvestment portfolio, and then
you don't find out how it'sperforming, you're going to take
that money right back out.
You're not interested incontinuing to invest. And it's
kind of they're looking for areturn on their investment in
terms of the skilled workforcecoming out, students interested
in manufacturing careers, as oneexample. And if you do give them
really great metrics and showthem the benefit of their
(31:58):
investment, the upside isthey're going to continue to
invest got
Matt Kirchner (32:02):
it? Yep, that's a great analogy. Awesome.
Melissa Martin (32:04):
Okay, so I'm going to keep on this kind of
concept of employers, workforceand certifications with a
question from Brian badura andand Brian says higher education
is at a tipping point with moreflexibility than ever before to
complete degree and sort ofcertificate programs. How do we
help employers understand thevalue of these new educational
(32:25):
choices when many HR teams andexecutives may not understand
how to integrate educationalprograms like three year degrees
or advanced certifications intotheir job descriptions and the
workforce?
Matt Kirchner (32:35):
Yeah, so awesome question from Brian. Let's see.
We'll take half a step back andreally, you know, he has some
really good examples, like, youknow, you know, three year
degrees and certifications andso on. You know, first, a little
in defense of employers. Youknow, partnering with education
as important as it is, is never,this is never at the top of
(32:57):
their list. And the reason forthat is that they're trying to
manage a workforce. They'retrying to keep customers happy.
So think about somebody in humanresources, the stuff they're
dealing with all day might be adiscipline issue. One day it
might be an attendance issue.
Another day it might be somebodyquit their job and you need to
fill that job, or you got a bigproject and you need to staff
that up. Or, you know, you'vegot a regulatory audit or you I
(33:19):
mean, their days are filled withall this kind of stuff. People
in manufacturing are trying tocrank orders out. They're trying
to meet lead times. They'retrying to improve quality.
They're trying to expediteorders solve a quality issue
that already got out in thefield. I mean, that's the day of
a manufacturing person. So, soin defense of them, you know,
they've got their own prioritiesthat they're working through,
but the short answer to Brian'squestion is seven times seven
(33:41):
different ways we talk aboutthat all the time here on the
podcast. It's not enough to tellsomebody something once I can
tell you, boy, somebody shouldsometimes ask me a list of my
greatest frustrations in workingin education and manufacturing.
Near the top of the list Melissais the number of people in HR
functions that don't understandthe value of third party
(34:03):
credentials. And you know, theythey obsess about wanting more
people to come intomanufacturing. They obsess about
wanting more people to filltheir workforce, to enroll in
technical college programs orcommunity college programs that
are that are related, but thenthey they don't take the time to
value and understand what'savailable to them. And I'll just
(34:26):
give them not a little bit of acalm rant right now. You know,
you hear a lot of you know, alot of folks say we want, we
need more skilled people. I justhad a meeting like this. I was
in, I won't say the state, I wasin another state within the last
few weeks talking to an owner ofa major manufacturing company,
yeah? And they were just like,Yeah, we don't, we're not seeing
(34:48):
the people coming out of thetechnical college program that's
related to the work that we do,it's in manufacturing. And
they're like, where they're justnot producing enough, enough
people. You. And then I've satin the same room with that same
company as they're telling theiremployers, well, all we really
want is people that will show upto work every day, stay off of
drugs and take direction. AndI'm like, and they're telling
(35:10):
their educators that. So if Isaid employers, I might say,
educators. You can't have itboth ways, right? You can't. And
this whole idea, I mean, I'm thebiggest believer in soft skills.
Yes, we need people thatunderstand professional
behavior, workplace behavior.
Just wrote a magazine column onthat for Gardner Business Media
get published in January of 2026yes, we need that, but we can't
(35:32):
simultaneously say that that'sall we're looking for and then
complain when we're not gettingskilled talent from our from our
educational institution. Sothere's just a lot of things
that I think are happening ineducation, a lot of trends, like
third party credentials, likethree year baccalaureate
degrees. And that's, you know,that's something that's
definitely on the way it's, it'sa hot topic in my home state of
(35:52):
Wisconsin, and it'll be acontroversial one. So the you
know that that's not going to bea heavy lift, especially for
public institutions, but it'scoming and but what we need to
tell those HR folks, everyopportunity we have them, what's
available in education, what'shappening in third party
credentialing, what's happeningin innovation, what's happening
(36:13):
in hands on learning. I mean,there's all of these great
things that are happening ineducation. What's happening with
teaching authentic industrialskills. So if I'm a machining
company and any machiningtalent, I need to be teaching
that technology, like we saidbefore, in the classroom, in the
lab, in the high school, gettingstudents excited about it,
credentialing them, sending themdirect to workforce, sending
(36:34):
them to a Technical CommunityCollege program where they can
build those skills. And that'show we create the next
generation of the workforce. Sothere is no easy answer to
Brian's question other than likeanything when we say seven
different ways, you can tellsomebody something once, and it
may not sink in. In fact, itusually doesn't once they hear
it seven times through sevendifferent modes. You know, we
talk about email, social media,traditional media, having a
(36:58):
conversation, doing a sitevisit, sitting in a meeting,
same message over and over andover again. Sometimes people say
it seems like sometimes on thepodcast, you make the same
points week in and week out.
That's on purpose. You rememberthose points exactly you got.
Yeah, thank you for listening.
And it obviously worked, becausehe caught that. Yeah, it's seven
times, seven different ways.
Melissa Martin (37:16):
Awesome. Yes, I don't have anything to add that.
That's just absolutely true.
Okay, so we've got time for onemore question, all right, and
this is actually with a futureguest of the TechEd podcast, and
we're going to end on an AIquestion. I love AI questions.
Get so many AI questions. Welove it. It's such a great
topic. Okay, so Peter mera, hesays AI is about to out think us
(37:36):
in almost every technicaldomain, the real advantage will
be in what makes us human, suchas EQ, adaptability, creativity,
courage, judgment, wisdom,ethics and so on. So what's
going to take to blow up the oldmodel of technical education and
rebuild around the skillsmachines can't copy so our
workforce doesn't just survivethe AI era, but thrives in it?
Matt Kirchner (38:01):
Yeah, it was a great question. And I think the
key, there's a key word in thefirst sentence which is
technical, right? Just rereadthat first. I want to make sure
that sinks in with the audience,yeah, because reread that
question,
Melissa Martin (38:10):
AI is about to out think us in almost every
technical domain.
Matt Kirchner (38:14):
So when we say technical domain, it's not going
to out think us in every domain.
It's going to think out, thinkus in every technical domain.
And there's actually some debateabout how soon that happens,
right and and we'll see. I mean,some people would tell you it's
going to happen in 18 months.
Others are saying it might bemore like 10 years. Nobody knows
for sure, but certainly, ifanybody who's watched the
advancement, it's not justgenerative AI and what we're
(38:37):
capable of doing withperplexity, cloud, meta, chat,
GPT and so on, Gemini, andseeing just how that's advanced
in the last year, year and ahalf. And it's incredible,
right? So, so it's anybody'sguess how quickly this is going
to extend itself to physical AI,in the rest of the world. I
think it's a really, really goodquestion. You know? What happens
in an age where a computer cananswer any question, what
(39:01):
happens when a computer can, orAI can program a CNC machine?
What happens in an age where,where AI can program a program,
a logic controller can write anycode? Can, you know, fill in the
blank we're already seeing now,where some of the you know, some
of the opportunities forcomputer science majors,
especially ones new to theworkforce, and data science
(39:21):
majors and so on, struggling alittle bit to find jobs, or at
least jobs that pay what theywant them to pay, because
there's so much of this can bedone with AI every single day.
There's another article in thein the Wall Street Journal about
what AI is doing the workforce.
Another one just this morning,as matter of fact. So so all. I
mean, I think Pete's, I thinkhis premise is a good one, which
is, we're going to get to thispoint before too long, before
too long, before AI can do evenmore than it's doing now, and
(39:44):
that is going to have a hugedisrupting impact on the
workforce. And so what you know?
What do we need to do to ourtechnical programs, technical
education and all that STEMprograms to make sure that we
are future ready in thoseprograms? I think a lot of
things. That we need to do we'realready doing in education. Not
enough educators doing it. We'vetalked on this podcast a number
(40:06):
of times how AI education ismandatory in China. It's not in
the US, and even when it is,when we're teaching it, a lot of
times it's just teaching gptsand prompt engineering, which is
fine, but there's so much moreto AI when it comes to applied
artificial intelligence,humanoid robots, quadropods,
drone technology, 3d design andfabrication, some of the coding,
(40:29):
programming, video gamedevelopment, autonomous
vehicles, applications forbattery technology, electrical
vehicles, biomimicry. I mean,all of these things are
changing. The entire way that weengineer, the way that we design
the way that we innovate, and sowe have to prepare students with
the kinds of skills that will berelevant in that age. There's
schools doing this right. Wetalked not too long ago with the
(40:51):
CEO of Ashley Furniture, Toddwanick, and not long before
that, with the superintendent ofthe White House School District,
Mike Bigley, about appliedartificial intelligence learning
about how we can put studentsthrough an E Learning course
that helps them understandapplied AI and what we call the
edge to cloud continuum. Andthis is where I think we're
getting to the core of Pete'squestion is you have to teach
(41:15):
how. And this is nothing new toour audience, but sensors at the
edge communicate with controlsystems communicate with
regional data centers, datacollectors, computer networks
communicate with the cloud. Andwhat is happening at every
single level of what we call theedge to cloud continue, because
AI isn't about just what thealgorithm is doing and what's
happening at the cloud level.
What's happening, you know,behind the iPhone, if you will,
(41:35):
it's how are we, how we'regathering data, extracting data,
analyzing data, discerning data,storing data, what we're doing
with it, and then how we usethat to improve literally, every
aspect of the economy, whetherit's energy, defense,
hospitality, retail,manufacturing, smartphones,
every single aspect of our ofour economy, precision
agriculture. It's changingeverything. So we have to turn
(41:57):
our students into systemsthinkers when it comes to AI and
how they're analyzing everysingle thing they look at, as
far as that edge to cloudcontinuum that's happening
already in in technicalcolleges, we're doing a number
of projects where we're, youknow, doing cause cloud based
analysis on data that's beenpulled off of manufacturing
equipment across an entiretechnical college. We have
(42:18):
things like that going on atGateway Technical College in
Kenosha, Wisconsin. And there'sa couple other great examples of
how we're doing that at thetechnical college level, at the
high school level, the whole,you know, the whole idea of
discover AI and full disclosure,I think people know we have a
financial interest in thateffort, but discovering how
artificial intelligencemanifests itself across the
entire economy, that's what thisis about. That's how we blow up
(42:39):
education. I mean, if Pete wantsto see some really cool stuff,
go to an emerging technologylab, go to a discovery I lab.
See where you walk in the doorand you've got, you know, three
students working on drones andanother three working on
autonomous vehicles, anotherthree working on, you know, 3d
design and 3d scanning. Threemore are, you know, are building
their own, their own video game.
(43:01):
They're earning competencycredentials and creating that
competency portfolio the wholeway through kind of stringing to
a bunch through a bunch ofthemes we've had on this episode
of The TechEd podcast. That isthe future of education, and we
can do it at every level. It'sasynchronous, it's student
driven, meets every studentwhere they are, and it prefer,
prepares the workforce of thefuture. I think, I think pizza
(43:22):
asked a really, really goodquestion, and it was one I was
happy to answer.
Melissa Martin (43:25):
So that's future of technical education. That's
going to be a really, reallyexciting time. And I think a
great way to end this episode.
As we wrap up, 2025 look forwardto 2026 think about what the
year ahead holds for technicaleducation. I think that this is
there's a lot. It can feeloverwhelming to educators, but I
think that there's a once in alifetime opportunity for us in
the world of education, if wecan embrace the change and
(43:49):
really do it for our studentsand for the right reason,
because if we don't, we're doingour students a disservice for
their future. So I'm excitedabout 2026
Matt Kirchner (44:00):
How about you.
It's gonna be amazing. Yeah. Infact, speaking of being excited
about 2026 we've got some reallycool episodes teed up, right? A
predictions episode, which we doevery year.
Melissa Martin (44:08):
Yeah, that's coming out next week. And you
guys love our predictionsepisode. And in case you don't
know, every single year, Mattmakes predictions about what the
future will hold for that year,and he always ranks himself on
how well he did the previousyear, and you You're pretty
good, right? Reading for years?
Matt Kirchner (44:23):
Yeah, we'll see how we did this year. I have an
inkling that we were prettysolid.
Melissa Martin (44:26):
I have an inkling as well. So, yeah,
excited to release that next oneto our audience. So if you
aren't subscribed, make sure yousubscribe. That one comes out
next week, Tuesday.
Matt Kirchner (44:37):
Wow, awesome.
Yeah, super, super excited forthat. Any other episodes coming
up, the audience should bethinking about
Melissa Martin (44:42):
there are. But I I'll just say you have to say
subscribe, because we got somereally high profile guests
coming out in early January. Somake sure that you're
subscribed. We're on Apple,Spotify, any other podcast
platform that you might belistening to, if you're only
listening to this, by the way,we're on YouTube and so. If you
like watching rather thanlistening, you can go find us on
(45:03):
YouTube. Watch those episodesthere. If you're watching us on
YouTube, hi, it's great to seeyou, and you can always catch us
again on Apple Spotify orwherever else. Tell your friends
awesome.
Matt Kirchner (45:13):
Please. Tell your friends glad to be here with my
friend Melissa Martin. We had agreat conversation. She does
such a wonderful job of pitchingthose questions up and
responding to the answers aswell. Always a lot of fun. We'll
keep teeing up these episodes ofAsk us anything, so be sure to
keep sending your questions ourway. We'll get through as many
of them as we possibly can. Cannever hit on all of them, but
love to, love to pull the bestand brightest and kick them
(45:35):
around here on the TechEdpodcast at least once every
three months or so. So thank youto our audience for being with
us. It's been a wonderfulepisode of The TechEd podcast.
Don't forget to check out theshow notes. We referenced a few
things we promised to link upfor folks that asked those great
questions and the folks thatlisten to the answers as well.
So check out the show notes.
Those will be at TechEdpodcast.com/ask us anything
that's TechEd podcast.com/ask usanything where you will find
(46:01):
loaded up, not just thisepisode, but every episode of
Ask us anything. So you can goback, if you like, this format,
and check those out when you'redone checking those out. Check
us out as well. On social media,you'll find us on Instagram. We
are on LinkedIn. We are onTiktok, on Facebook, anywhere
you go for your social media,and on YouTube, by the way, as
well. You can check us outthere. Make your comments there
as well, but please comment,reach out, let us know you're
(46:24):
out there. We would love to hearfrom you. Thanks for being with
us on this episode of The TechEdpodcast. For our producer,
Melissa Martin. My name is MattKirkner. I'm your host. We'll
see you next week. You.
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