August 13, 2025 • 34 mins
"We're in the between times – not fully in the future, but no longer in the past." These words from Ben Tasker, Dean of AI at a leading university, capture the transformative moment we're experiencing as artificial intelligence reshapes education and work.
In this eye-opening conversation with host Anika, Ben shares his remarkable journey from healthcare administration to data science to educational leadership. Through compelling stories about using data to improve cancer screening in rural Maine and developing algorithms to boost student success, Ben illustrates how AI can solve human problems while emphasizing human connection.
The traditional educational model faces unprecedented disruption as information once disseminated in classrooms becomes instantly accessible through AI tools. This reality forces a fundamental shift from time-based degrees toward skills-based learning focused on what you can do rather than how long you spent studying. According to the World Economic Forum, one billion people will need upskill or reskill by 2030, developing both technical AI capabilities and uniquely human qualities like empathy and leadership.
For institutions, this means creating flexible learning pathways through micro-credentials, badges, and shorter courses that stack into larger qualifications. For individuals, success requires developing a personal learning plan and building your brand. The good news? Getting started with AI doesn't require complex mathematics – simple experiences using free LLM tools can build familiarity and confidence.
Whether you're a student, educator, or professional navigating these "between times," this episode offers practical guidance for thriving in an AI-transformed world. Take your first step today by creating your learning plan – when written down, goals become 76% more likely to happen – and begin your AI journey with simple experiments that demonstrate the technology's potential.
This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Welcome to Mediascape insights from digital
changemakers, a speaker seriesand podcast brought to you by
USC Annenberg's Digital MediaManagement Program.
Join us as we unlock thesecrets to success in an
increasingly digital world.
Speaker 2 (00:22):
I'm super thrilled to have this conversation because
this is a topic I'm superpassionate about.
Ben Tasker, you are focused onAI-driven education and, as an
educator, this is something Isee a huge need for right now.
We have professors who havestudents writing papers using
chat, which I think is the worstuse case for AI.
(00:44):
We have teachers, such asmyself, who go AI first and I'm
teaching tools to my studentsand best applications, and we
have teachers who just don'tcare and they've been teaching
for so long they don't want tolearn new technology.
So thank you, first andforemost, for being here to go
deep dive into this conversation.
Speaker 3 (01:05):
Well, thanks for having me, Anika.
Like you said, my name is BenTasker.
I'm an AI expert.
I've worn many hats I've beenan AI engineer, a data scientist
, a team lead and now I'm a deanof AI at the United States top
university and or justuniversity.
I know many listeners might befeeling scared and uncertain,
and I understand that.
I consider this moment as thebetween times.
(01:26):
We're not fully into the future.
Speaker 2 (01:28):
Exactly.
Speaker 3 (01:29):
But we're not fully out of the past yet either, and
that may make people feelanxious or uncertain, but it's
also a great time to learn howto upskill and reskill and
revolutionize and evolve with AI.
Yeah, and evolve with AI.
Speaker 2 (01:42):
Yeah, well, let's talk about your trajectory going
from being, you know, dataanalyst scientist and moving
into education.
What sparked that interest foryou?
Speaker 3 (01:54):
So my upskilling and reskilling journey actually
comes from a lack of dataliteracy.
My undergraduate degree is inhealthcare administration.
I was really passionate aboutrunning a hospital.
One of my first tasks as soonas I was employed in a hospital
was to project patient volumesat doctor's offices.
And maybe I was a little naiveI was younger at the time.
(02:14):
I thought it'd be the latest,greatest technology, the data
would be available, it will be arelatively non-complex project.
And it was the total opposite.
Some of the data was stillwritten down by hand.
I had to go to the doctor'soffices in my car to figure out
patient volumes.
Sometimes I had to meet withphysicians to get accurate
numbers.
The data was in different typesof Excel sheets or some other
(02:37):
data file types, so nothing wasconsistent.
And that's where I realized thereal power of data and why I
should focus on that.
And data is the oil of any AIalgorithm.
But data science was around,but not AI.
People weren't talking about itas much as they were today.
During that same time period,data science programs and data
(02:57):
analytics programs started toemerge.
So I decided to get my master'sin data science.
I learned how to code.
That helped me load the datamore quickly.
It made me understand the datamore accurately.
It also taught me the theorybehind all these AI algorithms,
all the math computations, andthat's where I really saw how
(03:19):
data modeling and all thesealgorithms that we talk about in
math class are really importantbecause, yes, they seem
abstract but you can really haveimpact on the people.
The physician volume predictionmight seem like it's a low-task
, low-bar project, but it'sreally impactful for humans.
If they can't go to a doctor'soffice when they need to go,
(03:40):
that could impact patient care.
So you want to be as accurateas possible or I want it to be
as accurate as possible and datascience is a unique domain
because it encompasses, I think,every other domain.
So in this program I waslearning management,
communication, math, projectmanagement.
All those aspects then pluginto AI.
(04:03):
So I graduated with my master'sdegree.
I went into academics In thatbackground.
I had to create an algorithm tohelp predict students' success
and persistence.
So persistence is making surestudents move from one course to
the next.
Success is when they actuallyget there to a certain amount.
Both are incredibly important.
It was for a community collegesystem, so these individuals may
(04:25):
have been out of school for along time.
They may have been upscalingand reskilling themselves.
Each program is a little bitdifferent, but we took some data
features like distance from thecommunity college that they
were enrolled at the program.
They're in specific sections ofcourses, the time of day.
These courses were running evendown to the faculty number to
create this algorithm to predictsuccess and persistence.
(04:47):
If it was below a certainthreshold, we deployed resources
like tutoring to make sure thatthese individuals could cross
the finish line.
And in that case I really didsee the impact of how AI AI
science can also be a humaninteraction.
I helped many more than oneperson, but just helping one
(05:07):
person complete their degreefelt very powerful, and during
that time I was working withsome faculty members and those
faculty members were starting ahealthcare project.
So I transitioned fromacademics into healthcare again,
this time as a data scientist.
In that role again, it became apublic health campaign, so very
(05:27):
human-centered.
But we had to figure out whichcancer and which types of
patients to focus on for thehighest impact.
So pre-screening,post-screening are there
abnormal levels of cancer in aspecific population?
To do this I had to use a datascience technique and data
visualization, so I took thepatients that were above a
(05:48):
certain threshold.
I plotted them onto a map.
When you look at the map, thiswas in Maine.
A lot of more patients thatwere coming in for cancer
screenings and ultimately, withthe cancer diagnosis, were
coming from rural areas.
This was in.
Yeah, exactly.
This is not the data sciencepart, this was just intuition.
But, looking at the map, what'sin rural Maine Well water?
(06:10):
So if you don't clean itregularly, if you don't get your
pumps flushed regularly, if youdon't get the water tested, you
could end up with stomachcancer.
And so we saw that.
And then we looked at thestomach cancer rates and they
were 1.5 times the standarddeviation compared to the nation
(06:32):
and compared to other parts ofthe same state.
So what that means is that itwas significantly above average
where it should be.
So the public health campaignwas making sure we had to go out
to the community, get thesepeople in for screening.
We had to go to churches, getministers involved, go to
schools, get principals involved, hand out all these brochures.
(06:52):
As a data scientist, yes, wetalk about communication and the
importance of that, but whenyou're actually doing it, it
becomes super, super human.
There's a lot of connectioninvolved in that.
Just because you tell someonethey need to come in for
pre-screening or they need toget their well cleaned, you know
you might think that that wouldget people in there, but that's
not the case.
(07:13):
I had to build relationshipsand that relationship building
skill is what then brought meback into academics.
I was brought into an onlineuniversity.
I was asked to help createtheir data science program and
then, in 2022, data scienceevolved rapidly into AI.
It's potatoes, potatoes.
To me it's very similar, butwith AI, I saw the impact that
(07:37):
it will have on education.
Typically, education's alaggard, so if some innovation
happens, usually we'll preachabout it later and we'll make
sure we work.
Great the technology.
But with these LLMs, all theinformation that we would
disseminate to the students isnow at your fingertips.
So you can interact with theseLLMs and it can teach you things
if you're moderately skilledwith using an L on them and you
(08:01):
know kind of what's the value ofschool at that point on them
and kind of what's the value ofschool at that point.
So I made a playbook, I broughtit to the president, the
president disseminated it to theC-suite and we started building
our AI strategy and plan Overthe next couple of years I then
transitioned into my dean roleand that's kind of how I got in
with Slot.
I landed in now, but it wasn'tall of it was with intent, but I
(08:23):
wouldn't.
I planned out to be a dean ofAI, it was more.
I saw that I understood it fromstudying it, enabling it, and
then I said, well, if LLNs canproduce everything that's in a
course, 15 minutes how are wegoing to develop our courses?
Speaker 2 (08:39):
Yeah, no, that makes so much sense and I love that
you shared your full trajectory.
I've worked on campaigns forpublic health as well, just
getting kids back in for routineassessments and vaccinations.
We didn't use a lot of data,except for the focus groups we
did with different constituentsand the main focus areas, but it
(08:59):
was very much centered aroundwhat's the information we need
to provide, who are thestakeholders in the community,
as you mentioned.
Centered around what's theinformation we need to provide,
who are the stakeholders in thecommunity, as you mentioned.
So we had, whether it wastribal leaders, whether it was,
you know, pastors, ministers,educators same kind of concept.
And then we built out a publichealth campaign that was
primarily social media.
You know, PSAs over radio,things like that.
(09:19):
We didn't have that othercomponent and this is also
before people really understood.
I mean because artificialintelligence and machine
learning has been there for avery long time, but before we
started using these specificnames for it and changing the
vernacular and then having itbecome something that was more
mainstream.
So, to your point, it does takea long time to get things to
(09:41):
shift.
I teach in programs that werecreated before Gen AI and now
you know, even though some ofthe material for the courses is
older.
I have to figure out how am Igoing to teach my students new
approaches and new ways, becausethis is the information they
really need to be able to besuccessful in the outside world.
So I went to get back and I'mfinishing up my MBA with a
(10:02):
specialty in AIML right now, sothat I could do a deeper dive
and really learn how to.
And that's why I have such aninterest now in AI first
pedagogy, because I'm seeing itas a student, as a creator of
curriculum for high schoolstudents and for at the
collegiate graduate level.
So I think what you're doing isso vitally important and
thinking about how we help thenext generations and actually
(10:25):
even adults, adult learnersupskill so that they have jobs
in the future, because we knowthat there are a lot of jobs
that are task oriented that willbe replaced.
Speaker 3 (10:35):
Absolutely, and replacement is a vague word.
I think jobs are going to shift.
Ai touches all jobs.
Even if you don't think ittouches your job, it will
eventually.
It might take years, but itwill be there, I promise you.
But 1 billion people, accordingto the World Economic Forum,
need to upskill and reskill by2030.
So what is upskilling andreskilling?
(10:55):
So upskilling is if you're adata scientist, maybe you learn
deeper strategies or you learnabout algorithms that you might
not know.
If you're marketing, maybe youlearn more about AI algorithms
that you might not know.
If you're marketing, maybe youlearn more about AI.
Reskilling is if I'm a datascientist, maybe I become a
clinician or somethingcompletely different than the
field I'm in learning those newskills to maintain market
(11:17):
relevancy.
But I want to express that AIdoesn't need to be complex to
learn.
It can be very fun.
So, just by developing alearning plan which is short,
objectives that can be long-termor short-term, creating an
account on any free LLM website,learning how to basic prompt,
maybe making a dinner plan,maybe making a fitness routine,
(11:38):
making a simple image.
It doesn't have to be complex.
But individuals can get into AIanytime.
You don't need you eventuallymay need the deep theory, but to
get into it you don't need allthe math and theory behind.
Speaker 2 (11:50):
Yeah, and what's interesting is I'm finding even
at the graduate level, there area lot of students who've been
told not to use AI as undergrads.
There are students who have beenin the workforce for a long you
know, many, many years and alsothey have been trained in
certain methodologies fordigital media management, and so
now they're learning how tointegrate more tools into their
(12:12):
toolbox and create better,easier workflows.
So it's all levels of students,and a lot of people still think
of AI as the scary, you know,ephemeral thing that's just out
there and that's going to belike Terminator.
Instead of thinking no, we justhave to take baby steps, learn
one thing at a time and by doingthat you can get comfortable,
(12:34):
you can understand the ethics,the biases, all of that part of
this tool as well, so thatyou're going in with open eyes
into the AI world and figuringout how to make sure that you
are using your ethics, thatyou're considering what kind of
data you're getting Is it thecorrect data?
And these are things that Ithink of when I'm thinking about
(12:54):
the AI journey and just makingsure people are coming in,
learning the basics, but thenalso looking at it through that
lens of ethics.
Speaker 3 (13:03):
Yeah, responsible AI and AI ethics is increasingly
important.
I think as more individuals gointo AI, like AI, the ethical
risk also increases with thatusage.
They go hand in hand.
Unfortunately, I thinkresponsible AI and AI ethics are
laggards compared to the LLMsthat were released.
So they were released.
(13:24):
Impact happens, more impactscould happen.
I consider it uh-ohs or what todo moments.
We're still in the between time, so we haven't seen a major
catastrophe yet, but we'reprobably pretty close to that.
Once one of those aspectshappens, then I think we'll see
a huge focus on AI ethics andresponsibility, maybe even a
dial back on the adoption andrevolution rate.
(13:47):
But unfortunately, I think it'skind of like playing with fire.
You have to get to that pointbefore you realize the harm
before implementing frameworksand solutions to help mitigate
that risk and bias.
Speaker 2 (14:00):
Yeah, so how are you bringing AI into the classroom
as the dean of AI?
What does that entail?
What does that look like?
Speaker 3 (14:09):
I consider myself an individual that's helping
individuals navigate betweentime.
So how are we navigating thisambiguous time?
To make it more known?
And really that comes down tothree different domains and this
concept called skills-basedlearning.
So I'll talk about the threedomains and then I'll go into
(14:31):
skills-based learning.
But the three domains and thisis the World Economic Forum's
classification, so folkslistening might call it
something different but there'strainers.
The trainers are the individualsprogramming the AI AI engineers
, prompt engineers, datascientists the more technical AI
(14:51):
roles.
Then we have explainers, soindividuals that understand the
theory behind these tools, knowhow to use the tools.
They teach organizations how touse the tools so they can be AI
coaches, strategy individuals.
They could even be AIenablement folks.
There's a bunch of differentjob descriptions and job titles
(15:13):
for those individuals, butthey're becoming more and more
popular.
And then there's sustainersindividuals that just use the
tools, like content creators,marketers, lists, any other job
project managers and they usethe tools.
They're not as experienced inthe training, but they keep
consuming and sustaining thetechnology.
So that the trainers and theexplainers have to keep current
(15:37):
and make sure that, to youroriginal point, that the ethics
and responsible ai behind thetools is being maintained and
that you can, that they're usingai for them, like what they're
supposed to do, is you'refollowing a framework and the
outputs make sense, attachingthis all to what I consider
skills.
So a skill is something that isattainable, that you're good at
.
So, annika, you're good atdoing podcasts.
(15:59):
That's a skill.
It's communication, that's theboot skill.
And other skills the WorldEconomic Forum describes is AI
skills and human skills.
So AI skills those technical,traditional hard skills systems
thinking, coding, analyticalthinking, prompt engineering.
What's interesting why theycall them AI skills is because
(16:22):
AI is going to get really goodat these things.
But it's going to get reallygood at those skills which could
cause the softening in themarket which leads to human
skills, human skills, uniquelyhuman.
That's interesting to me.
They could have just calledthem soft skills, but human
skills what humans are reallygood at Empathy, communication,
(16:45):
leadership, management AI isgoing to get good at those
things Probably not as good as ahuman.
So it's important to focus onboth with your learning plan.
So I'm making sure that ourtraining programs are
skills-based, so they'reteaching individuals relevant
skills Communication, forexample.
(17:06):
The medium might change.
Maybe we're interacting with achatbot in the future, so you
have to teach the communicationand the prompt engineering.
But we're not changing, we'renot calling that something
different, we're not callingcommunication prompt engineering
.
It's distinctly its own currency, so to speak.
So you can collect the skillsand the currency.
They're like coins.
(17:26):
You can fill up a piggy bankand when you have to go and
break the piggy bank, you havethose collection skills, money
right to be able to change inthis AI economy which?
Trickling this all back toeducation in a traditional sense
, education is based on time.
So in the United States, ittakes four years to get a
(17:46):
bachelor's degree.
Typically, on average, it takesfive to six years to get a
master's degree and it takeseven longer than that to get a
doctorate's degree.
All of those are great concepts, but with AI, the time it takes
to do things, the skill value,diminishes.
So typically, when you get adegree, it's relevant for around
(18:07):
five years on average.
With AI, since it's at yourfingertips this is just my
opinion, but it's probablyskills are probably going to be
relevant for two years.
So you have to do things morequickly, more relevant.
You have to explore new areasthat you might not have explored
before, just to keep up witheconomic changes.
That means colleges anduniversities are going to have
(18:27):
to change with that.
Yes, we'll still have degrees.
Yes, we'll still have programs.
Yes, those things are stillvery important.
But with shorter form learningmicro-credentials, badges,
six-week courses, two-weekcourses, live on-demand training
you can acquire these skills,practice them and then adapt
(18:47):
them to your job and quickly notonly upskill and reskill but
move up in your company, whichdecreases your value, shows the
impact of not only responsibleAI, generative AI, whatever the
AI is but also shows theimportance of the other skills
of communication, leadership,systems, thinking.
If you can create that returnon investment, organizations are
(19:11):
going to not only invest moreinto individuals, but I think
they're going to value theseshorter-form learning aspects.
And then those shorter-formlearning aspects and credentials
can stack into a program and adegree and it's a little bit of
an ecosystem change, butfundamentally it's no longer
(19:32):
based on time.
It's based on what you can doand what you can showcase how
you can do those things.
Speaker 2 (19:37):
Yeah, that's such a good point because one of the
programs I teach in is digitalmedia management and for that
program we have 12 courses andthey're outlined and you can do
a one-year really fast trackprogram or two-year program.
I get my students for 90 minutesonce a week, you know for about
so they're eight weeks, sothey're very shortened programs,
(19:59):
but in that time I can teachthem.
You know, we have the conceptsof whatever the topic is, but
then that class time is when Ican bring in other applications
and bring in AI tools.
So it is fast track, butthere's not enough time to fully
embrace.
So that's why I alwaysrecommend here's a great
training or here's a webinarabout this topic so you can get
(20:20):
more in depth.
Or if you're interested inlearning about this, here's
where to go so that they can getthat.
So it's not necessarily part ofthe degree that they're going
to get from USC, but it is stillimportant to their learning to
get that extra knowledge.
And, as you said,certifications, right, short
form programming, skill stacking, so things that will, with the
(20:43):
foundation of all of thedifferent you know digital media
areas and aspects and knowingthe foundational information,
then they can use these othercertifications to just really
level up.
Speaker 3 (20:55):
Absolutely, and the leveling up is important.
But it's also that continuouslearning and it's a mindset
change.
So continuous learning doesn'thave to be completely academic.
You might not even have toenroll into a university or a
college.
You could go on your own andtry to acquire some of these
skills.
So what might that look like inyour own organization?
(21:16):
Can you sign up for anadditional project?
So if I'm a data scientist, canI take on some project
management tasks?
Gain organization skills.
Gain managing people skills.
Communication.
Gain organization skills.
Gain managing people skills,communication those are relevant
and important skills that couldalso position you for more of a
leadership role.
So it's kind of thinkingoutside of the box a little bit
to really focusing, you know,linking this all back to a
(21:38):
learning plane so that you knowyou plan the work and then you
work the plan Without that plan.
Even writing it down mightsound silly to some folks
listening, but when you writesomething down, it's 76% more
likely to occur.
So write down where you want togo and the steps it's going to
take for you to get there.
Spend some time on it.
Speaker 2 (21:58):
Yeah, yeah.
So being dean of AI, but alsotalking about the future of
education, not necessarily beingin the classroom traditionally
what do you think your role isgoing to lead into and have you
seen any shifts yet?
I mean, I know we're in thatin-between phase, we're at the
early stages.
Still Some people think they'reway behind.
(22:18):
They really aren't necessarily.
So what do you see as the nextstep, the next vision of
education, and will your rolestill be relevant?
Speaker 3 (22:30):
So education has been time-based.
I talked a lot about that today, so I'm not going to repeat all
that, but I still.
That probably will change.
It will still require theamount of credits and you'll
still need to put time in, butif this more adaptable ecosystem
approach occurs, it won't befour years in a seat.
(22:51):
You'll be out in the ecosystem,so to speak, learning and can
transfer this currency back andforth.
We're going to bring in theworkforce, so the workforce is
usually adjacent to education,but I think those two worlds are
now merging.
More and more employees need tobe upskilled and reskilled.
These individuals also need tounderstand not only the impacts
(23:13):
of AI but how to use AI fortheir roles.
I think academics is good atteaching some concepts of that,
but a traditional university orcollege can't teach you AI for
every single role.
It's just not scalable and it'sconfusing as an offering.
So, like those two worldsmerging together, you get that
workforce aspect of it.
(23:34):
So maybe there's mentors, maybesome of the courses are offered
through an HR training program.
Once you complete those courses, then you learn more theory
back at the university.
It becomes more of a flippedclassroom scenario, so to speak.
Then a traditional student, ifthey haven't had a job before
and they're going to college forthe first time, those students
(23:58):
can be matched with individualsin the workforce.
So they get a mentor, a mentee.
You're more likely to get a job.
It's probably funner to learnbecause you're learning from an
expert, right?
So you're learning from aprofessor and you're learning
from an expert in industryprobably two completely
different mindsets, but now thecontent's relevant, it's more
current, it's more fun.
(24:19):
I think learning can be fun.
But then what happens with AI?
How does that work with all ofthis?
Right, and I think that's someof the main question.
I think with the AI, we need toreally understand that the
world's still changing and thatthere needs to be some
flexibility.
Linking this back to everythingI just said, I think the system
(24:39):
needs to become more flexible,but new roles are going to exist
Not every single role we knowyet.
So I don't think we need tofocus on roles.
We need to focus on skills.
Skills is a little bit differentof a concept than a way.
A university creates programsnow, so universities can start
tagging their courses to skills.
(25:00):
They can figure out what skillstaxonomy they want to use to
the world Economic farms one,for example, but there's many
which ones work for youruniversity.
How can you start changing thelanguage so, when you interact
with your partners and workforce, you're making sure that you're
teaching the relevant skills,and then how can you create that
ecosystem of a back and forthenvironment?
(25:21):
I honestly think that with AI,education is going to be more
accessible.
It will ultimately lower costsif more people are going to
universities right Quality costaccess.
So access increases, qualitywill increase, volume should
increase.
Speaker 2 (25:39):
Yeah, fantastic.
I love that vision of the worldwhere things are more
accessible to people and thatyou're not just hearing the
theoretical perspective butyou're also getting practical
application and inroads, Becausesometimes students can graduate
from any university and theythink the job's going to be
waiting for them, but it mightnot be if they don't know how to
(26:02):
apply those skills andinterview appropriately based on
those skills.
Speaker 3 (26:09):
Yeah, absolutely so.
Degrees traditionally I knowpeople upskill on the side now,
but traditionally a degree isone and done, so you go and get
your bachelor's, then you go andget your master's, and maybe
you go and get your bachelor's,then you can only get the
master's and maybe you will getyour doctorate or or some
combination of those.
Right, that's not super.
I'm hoping to get a job.
I'm not sure.
With the skills-based economy,in an ai economy, we're shifting
(26:32):
our mindsets to curiosity,learning, agility.
We're always trying new things.
Learning doesn't always have tobe in the workforce or in an
institution.
For example, I'm doing morepodcasts to learn more about the
communication skill.
It's something I wanted to do.
I'm interested in doing it.
There's no credit for that.
I'm just experimenting with itright?
Speaker 2 (26:55):
Well, you're doing a great job.
So how has being dean of AI andputting so much into AI first
pedagogy for your universityhelped shape and transform the
university?
Are you seeing more studentswho are interested, engaged,
more press?
Speaker 3 (27:13):
So well.
First, you asked the questiondo you think a dean of AI is
market relevant and do you thinkthat that job is going to
continue to grow?
I think that it might not becalled a dean of AI.
It could be called dean of AIenablement, a director of AI
enablement, but I think thatyou're going to see it in
workforce and I think you'regoing to see it in universities.
(27:34):
All the worlds are merging.
It's extremely market relevant.
I don't think AI is going away.
Linking this back to what youjust asked with what skills and
how are the universitiestransforming to this?
I think some institutions werein a lot of trouble before AI.
Covid, for example, I think,changed a lot of the playing
(27:56):
field.
A lot of institutions wentonline At that time.
Ai was kind of released.
We were kind of getting out ofCOVID but still in it, and that
kind of turned up the heat.
You're going to need revenue tosuccessfully implement AI and a
lot of the revenue.
A lot of colleges don't haveaccess to that, so you're going
(28:17):
to see mergers.
I think there's probably goingto be less colleges.
That doesn't necessarily meanit's a bad thing, but it could
also change the dynamics.
So right, it can make thingsless expensive or more expensive
.
I think less expensive, butwith this focus on AI and AI
enablement, it's just going tochange the nature of the game.
So if you're not thinking aboutyour AI strategy or your AI
(28:40):
implementation planner how SEOis kind of it's a thing, but
we're moving away from that.
Search engine optimization andLLM is a recommendation engine.
So does your university collegeshow up inside ChatGPT Gemini
Plot?
Is it accurate?
Does it list the propersequence of courses?
(29:02):
You have to redesign yourwebsites to make it accurate.
You have to have good searchengine optimization to feed to
these LLMs.
Are people thinking about that?
I know I'm thinking about that,but I'm a simple one, and some
universities are ignoring itcompletely.
Some are playing in the middleand some are being more
(29:24):
innovative.
I think the middle and the moreinnovative are going to get
closer together and then there'sgoing to be the colleges and
universities that work againstit and I guess we'll see how
that plays out.
Speaker 2 (29:35):
It's so interesting that you say this because I
teach this to my students interms of personal brand, because
the whole thing is right.
Google not as much now, butGoogle and your LLMs are the
basis for how people understandus.
Now, as more and more peopleuse LLMs for search, we need to
make sure that, as individuals,we have our own website, which
(29:58):
you do have.
That kind of is the hub for allof the information we want
people to know, so that the LLMsaren't just out there scraping
the internet guessing at who weare, or taking information about
somebody with the same name whomight be doing something very
different, or taking really oldinformation that isn't relevant
to who we are now applying it tothe university level and how
(30:22):
businesses in general need to bedoing the same thing is, so I
would say that's one of thebiggest changes that's needed
right now.
Speaker 3 (30:27):
Exactly and it goes all the way down to the course
level.
So if someone Googles youruniversity, the course and the
course number, and it gives anaccurate description of that
course, an accurate coursecontent, because it will pull up
everything that it finds, yeah,People may be less or more
interested to you know thecourse, the program, the program
(30:47):
links to enrollment.
They might go elsewhere.
Speaker 2 (30:51):
Yeah, what is I mean?
We've talked about a lot.
I think that we have a clearvision for where you see the
world going.
I agree with you on all of itthat you would leave for one of
our students or one of ourfaculty, or even some of the
directors who listen to theprogram, for them to continue
(31:17):
this change in mindset and thismove towards being really AI
first and making sure we'reimplementing everything really
effectively for our students.
Speaker 3 (31:22):
I would focus on a learning plan.
So organizations can focus on alearning plan.
Individuals can focus on alearning plan.
A professor can have her classfocus on a learning plan, so
organizations can focus on alearning plan.
Individuals can focus on alearning plan.
A professor can have theirclass focus on a learning plan
that they want to learn in thatclass.
So you could do a classroomscenario.
But my point is by thinking andgetting those thoughts out there
.
If it's at the organizationlevel, then employees know what
(31:42):
to attach to and what theyexactly need to learn.
And then at the individuallevel, yes, I need to attach to
these things, but you can takeany type of force or do I want a
mentee, or do I want to pick upa project within the
organization?
You can solidify it more.
So really start investing inyour personal brand and your
learning plan.
Those are the two biggesttakeaways.
(32:04):
Because your personal brand,even though you don't think it
links to a learning plan, itdoes, because if you're trying
to go get a mentee or you'retrying to upskill, reskill and
then switch into a neworganization, you're going to
need a portfolio to fall back on.
All these things interconnect.
So really focus on thoseaspects.
I know skills and upskilling andreskilling might be scary.
(32:25):
To some folks it's probablyjust as scary as AI.
But it's nothing that'snon-attainable.
And you can start small, loginto an LLM, have it, make a
three-day vacation plan or getaway for you See if it's
accurate, it's summer, I meanpeople are having fun right now.
Have it plan.
If you paddle for it, have itplan a route along a river or a
(32:45):
lake.
It's completely plausible.
And if it's something you doall the time, you can understand
the trade-offs and benefits.
And then, right, you check offone aspect of your learning.
Speaker 2 (32:55):
Yeah, fantastic.
And, ben, thank you so much forcoming on the show and bringing
your perspective Again.
We're so aligned and this issomething I've really been
thinking about how to create newplans for student learning and
also for teaching teachers howto teach effectively at every
level, whether it's upskillingthe adult workforce, whether it
(33:15):
is the university level or evenK-12 education.
So I think we need to have moreconversations like this out in
the world, and thank you fortrusting me with this
conversation and, of course,bentaskeraicom is you for
trusting me with thisconversation and, of course,
spentaskeraicom is the websitewe'll lead everybody to.
So I appreciate your time and Iwant to thank everybody in the
audience as well for listeningto this episode.
(33:37):
Start thinking about the waysthat you can incorporate more AI
strategies and tools into yourlives, even if it's learning,
prompt engineering, proofplanning, vacations, meal
planning, looking atuniversities for your child, you
know and figuring out, based ontheir criteria, what the good
fits are, which is somethingI've been doing as well for mine
(33:58):
.
So, with that, thank you forjoining us today to the audience
and thank you, ben, as well.
Speaker 3 (34:05):
Thank you so much for having me.
This was a lot of fun.
I am very passionate about this, and thank you, ben, as well.
Thank you so much for having me.
This was a lot of fun.
I am very passionate about thisand, once again, I hope
everybody starts their AIlearning quick.
Speaker 1 (34:11):
To learn more about the Master of Science in Digital
Media Management program, visitus on the web at dmmuscedu.
Welcome to Mediascape insights from digital
changemakers, a speaker seriesand podcast brought to you by
USC Annenberg's Digital MediaManagement Program.
Join us as we unlock thesecrets to success in an
increasingly digital world.
Speaker 2 (00:22):
I'm super thrilled to have this conversation because
this is a topic I'm superpassionate about.
Ben Tasker, you are focused onAI-driven education and, as an
educator, this is something Isee a huge need for right now.
We have professors who havestudents writing papers using
chat, which I think is the worstuse case for AI.
(00:44):
We have teachers, such asmyself, who go AI first and I'm
teaching tools to my studentsand best applications, and we
have teachers who just don'tcare and they've been teaching
for so long they don't want tolearn new technology.
So thank you, first andforemost, for being here to go
deep dive into this conversation.
Speaker 3 (01:05):
Well, thanks for having me, Anika.
Like you said, my name is BenTasker.
I'm an AI expert.
I've worn many hats I've beenan AI engineer, a data scientist
, a team lead and now I'm a deanof AI at the United States top
university and or justuniversity.
I know many listeners might befeeling scared and uncertain,
and I understand that.
I consider this moment as thebetween times.
(01:26):
We're not fully into the future.
Speaker 2 (01:28):
Exactly.
Speaker 3 (01:29):
But we're not fully out of the past yet either, and
that may make people feelanxious or uncertain, but it's
also a great time to learn howto upskill and reskill and
revolutionize and evolve with AI.
Yeah, and evolve with AI.
Speaker 2 (01:42):
Yeah, well, let's talk about your trajectory going
from being, you know, dataanalyst scientist and moving
into education.
What sparked that interest foryou?
Speaker 3 (01:54):
So my upskilling and reskilling journey actually
comes from a lack of dataliteracy.
My undergraduate degree is inhealthcare administration.
I was really passionate aboutrunning a hospital.
One of my first tasks as soonas I was employed in a hospital
was to project patient volumesat doctor's offices.
And maybe I was a little naiveI was younger at the time.
(02:14):
I thought it'd be the latest,greatest technology, the data
would be available, it will be arelatively non-complex project.
And it was the total opposite.
Some of the data was stillwritten down by hand.
I had to go to the doctor'soffices in my car to figure out
patient volumes.
Sometimes I had to meet withphysicians to get accurate
numbers.
The data was in different typesof Excel sheets or some other
(02:37):
data file types, so nothing wasconsistent.
And that's where I realized thereal power of data and why I
should focus on that.
And data is the oil of any AIalgorithm.
But data science was around,but not AI.
People weren't talking about itas much as they were today.
During that same time period,data science programs and data
(02:57):
analytics programs started toemerge.
So I decided to get my master'sin data science.
I learned how to code.
That helped me load the datamore quickly.
It made me understand the datamore accurately.
It also taught me the theorybehind all these AI algorithms,
all the math computations, andthat's where I really saw how
(03:19):
data modeling and all thesealgorithms that we talk about in
math class are really importantbecause, yes, they seem
abstract but you can really haveimpact on the people.
The physician volume predictionmight seem like it's a low-task
, low-bar project, but it'sreally impactful for humans.
If they can't go to a doctor'soffice when they need to go,
(03:40):
that could impact patient care.
So you want to be as accurateas possible or I want it to be
as accurate as possible and datascience is a unique domain
because it encompasses, I think,every other domain.
So in this program I waslearning management,
communication, math, projectmanagement.
All those aspects then pluginto AI.
(04:03):
So I graduated with my master'sdegree.
I went into academics In thatbackground.
I had to create an algorithm tohelp predict students' success
and persistence.
So persistence is making surestudents move from one course to
the next.
Success is when they actuallyget there to a certain amount.
Both are incredibly important.
It was for a community collegesystem, so these individuals may
(04:25):
have been out of school for along time.
They may have been upscalingand reskilling themselves.
Each program is a little bitdifferent, but we took some data
features like distance from thecommunity college that they
were enrolled at the program.
They're in specific sections ofcourses, the time of day.
These courses were running evendown to the faculty number to
create this algorithm to predictsuccess and persistence.
(04:47):
If it was below a certainthreshold, we deployed resources
like tutoring to make sure thatthese individuals could cross
the finish line.
And in that case I really didsee the impact of how AI AI
science can also be a humaninteraction.
I helped many more than oneperson, but just helping one
(05:07):
person complete their degreefelt very powerful, and during
that time I was working withsome faculty members and those
faculty members were starting ahealthcare project.
So I transitioned fromacademics into healthcare again,
this time as a data scientist.
In that role again, it became apublic health campaign, so very
(05:27):
human-centered.
But we had to figure out whichcancer and which types of
patients to focus on for thehighest impact.
So pre-screening,post-screening are there
abnormal levels of cancer in aspecific population?
To do this I had to use a datascience technique and data
visualization, so I took thepatients that were above a
(05:48):
certain threshold.
I plotted them onto a map.
When you look at the map, thiswas in Maine.
A lot of more patients thatwere coming in for cancer
screenings and ultimately, withthe cancer diagnosis, were
coming from rural areas.
This was in.
Yeah, exactly.
This is not the data sciencepart, this was just intuition.
But, looking at the map, what'sin rural Maine Well water?
(06:10):
So if you don't clean itregularly, if you don't get your
pumps flushed regularly, if youdon't get the water tested, you
could end up with stomachcancer.
And so we saw that.
And then we looked at thestomach cancer rates and they
were 1.5 times the standarddeviation compared to the nation
(06:32):
and compared to other parts ofthe same state.
So what that means is that itwas significantly above average
where it should be.
So the public health campaignwas making sure we had to go out
to the community, get thesepeople in for screening.
We had to go to churches, getministers involved, go to
schools, get principals involved, hand out all these brochures.
(06:52):
As a data scientist, yes, wetalk about communication and the
importance of that, but whenyou're actually doing it, it
becomes super, super human.
There's a lot of connectioninvolved in that.
Just because you tell someonethey need to come in for
pre-screening or they need toget their well cleaned, you know
you might think that that wouldget people in there, but that's
not the case.
(07:13):
I had to build relationshipsand that relationship building
skill is what then brought meback into academics.
I was brought into an onlineuniversity.
I was asked to help createtheir data science program and
then, in 2022, data scienceevolved rapidly into AI.
It's potatoes, potatoes.
To me it's very similar, butwith AI, I saw the impact that
(07:37):
it will have on education.
Typically, education's alaggard, so if some innovation
happens, usually we'll preachabout it later and we'll make
sure we work.
Great the technology.
But with these LLMs, all theinformation that we would
disseminate to the students isnow at your fingertips.
So you can interact with theseLLMs and it can teach you things
if you're moderately skilledwith using an L on them and you
(08:01):
know kind of what's the value ofschool at that point on them
and kind of what's the value ofschool at that point.
So I made a playbook, I broughtit to the president, the
president disseminated it to theC-suite and we started building
our AI strategy and plan Overthe next couple of years I then
transitioned into my dean roleand that's kind of how I got in
with Slot.
I landed in now, but it wasn'tall of it was with intent, but I
(08:23):
wouldn't.
I planned out to be a dean ofAI, it was more.
I saw that I understood it fromstudying it, enabling it, and
then I said, well, if LLNs canproduce everything that's in a
course, 15 minutes how are wegoing to develop our courses?
Speaker 2 (08:39):
Yeah, no, that makes so much sense and I love that
you shared your full trajectory.
I've worked on campaigns forpublic health as well, just
getting kids back in for routineassessments and vaccinations.
We didn't use a lot of data,except for the focus groups we
did with different constituentsand the main focus areas, but it
(08:59):
was very much centered aroundwhat's the information we need
to provide, who are thestakeholders in the community,
as you mentioned.
Centered around what's theinformation we need to provide,
who are the stakeholders in thecommunity, as you mentioned.
So we had, whether it wastribal leaders, whether it was,
you know, pastors, ministers,educators same kind of concept.
And then we built out a publichealth campaign that was
primarily social media.
You know, PSAs over radio,things like that.
(09:19):
We didn't have that othercomponent and this is also
before people really understood.
I mean because artificialintelligence and machine
learning has been there for avery long time, but before we
started using these specificnames for it and changing the
vernacular and then having itbecome something that was more
mainstream.
So, to your point, it does takea long time to get things to
(09:41):
shift.
I teach in programs that werecreated before Gen AI and now
you know, even though some ofthe material for the courses is
older.
I have to figure out how am Igoing to teach my students new
approaches and new ways, becausethis is the information they
really need to be able to besuccessful in the outside world.
So I went to get back and I'mfinishing up my MBA with a
(10:02):
specialty in AIML right now, sothat I could do a deeper dive
and really learn how to.
And that's why I have such aninterest now in AI first
pedagogy, because I'm seeing itas a student, as a creator of
curriculum for high schoolstudents and for at the
collegiate graduate level.
So I think what you're doing isso vitally important and
thinking about how we help thenext generations and actually
(10:25):
even adults, adult learnersupskill so that they have jobs
in the future, because we knowthat there are a lot of jobs
that are task oriented that willbe replaced.
Speaker 3 (10:35):
Absolutely, and replacement is a vague word.
I think jobs are going to shift.
Ai touches all jobs.
Even if you don't think ittouches your job, it will
eventually.
It might take years, but itwill be there, I promise you.
But 1 billion people, accordingto the World Economic Forum,
need to upskill and reskill by2030.
So what is upskilling andreskilling?
(10:55):
So upskilling is if you're adata scientist, maybe you learn
deeper strategies or you learnabout algorithms that you might
not know.
If you're marketing, maybe youlearn more about AI algorithms
that you might not know.
If you're marketing, maybe youlearn more about AI.
Reskilling is if I'm a datascientist, maybe I become a
clinician or somethingcompletely different than the
field I'm in learning those newskills to maintain market
(11:17):
relevancy.
But I want to express that AIdoesn't need to be complex to
learn.
It can be very fun.
So, just by developing alearning plan which is short,
objectives that can be long-termor short-term, creating an
account on any free LLM website,learning how to basic prompt,
maybe making a dinner plan,maybe making a fitness routine,
(11:38):
making a simple image.
It doesn't have to be complex.
But individuals can get into AIanytime.
You don't need you eventuallymay need the deep theory, but to
get into it you don't need allthe math and theory behind.
Speaker 2 (11:50):
Yeah, and what's interesting is I'm finding even
at the graduate level, there area lot of students who've been
told not to use AI as undergrads.
There are students who have beenin the workforce for a long you
know, many, many years and alsothey have been trained in
certain methodologies fordigital media management, and so
now they're learning how tointegrate more tools into their
(12:12):
toolbox and create better,easier workflows.
So it's all levels of students,and a lot of people still think
of AI as the scary, you know,ephemeral thing that's just out
there and that's going to belike Terminator.
Instead of thinking no, we justhave to take baby steps, learn
one thing at a time and by doingthat you can get comfortable,
(12:34):
you can understand the ethics,the biases, all of that part of
this tool as well, so thatyou're going in with open eyes
into the AI world and figuringout how to make sure that you
are using your ethics, thatyou're considering what kind of
data you're getting Is it thecorrect data?
And these are things that Ithink of when I'm thinking about
(12:54):
the AI journey and just makingsure people are coming in,
learning the basics, but thenalso looking at it through that
lens of ethics.
Speaker 3 (13:03):
Yeah, responsible AI and AI ethics is increasingly
important.
I think as more individuals gointo AI, like AI, the ethical
risk also increases with thatusage.
They go hand in hand.
Unfortunately, I thinkresponsible AI and AI ethics are
laggards compared to the LLMsthat were released.
So they were released.
(13:24):
Impact happens, more impactscould happen.
I consider it uh-ohs or what todo moments.
We're still in the between time, so we haven't seen a major
catastrophe yet, but we'reprobably pretty close to that.
Once one of those aspectshappens, then I think we'll see
a huge focus on AI ethics andresponsibility, maybe even a
dial back on the adoption andrevolution rate.
(13:47):
But unfortunately, I think it'skind of like playing with fire.
You have to get to that pointbefore you realize the harm
before implementing frameworksand solutions to help mitigate
that risk and bias.
Speaker 2 (14:00):
Yeah, so how are you bringing AI into the classroom
as the dean of AI?
What does that entail?
What does that look like?
Speaker 3 (14:09):
I consider myself an individual that's helping
individuals navigate betweentime.
So how are we navigating thisambiguous time?
To make it more known?
And really that comes down tothree different domains and this
concept called skills-basedlearning.
So I'll talk about the threedomains and then I'll go into
(14:31):
skills-based learning.
But the three domains and thisis the World Economic Forum's
classification, so folkslistening might call it
something different but there'strainers.
The trainers are the individualsprogramming the AI AI engineers
, prompt engineers, datascientists the more technical AI
(14:51):
roles.
Then we have explainers, soindividuals that understand the
theory behind these tools, knowhow to use the tools.
They teach organizations how touse the tools so they can be AI
coaches, strategy individuals.
They could even be AIenablement folks.
There's a bunch of differentjob descriptions and job titles
(15:13):
for those individuals, butthey're becoming more and more
popular.
And then there's sustainersindividuals that just use the
tools, like content creators,marketers, lists, any other job
project managers and they usethe tools.
They're not as experienced inthe training, but they keep
consuming and sustaining thetechnology.
So that the trainers and theexplainers have to keep current
(15:37):
and make sure that, to youroriginal point, that the ethics
and responsible ai behind thetools is being maintained and
that you can, that they're usingai for them, like what they're
supposed to do, is you'refollowing a framework and the
outputs make sense, attachingthis all to what I consider
skills.
So a skill is something that isattainable, that you're good at
.
So, annika, you're good atdoing podcasts.
(15:59):
That's a skill.
It's communication, that's theboot skill.
And other skills the WorldEconomic Forum describes is AI
skills and human skills.
So AI skills those technical,traditional hard skills systems
thinking, coding, analyticalthinking, prompt engineering.
What's interesting why theycall them AI skills is because
(16:22):
AI is going to get really goodat these things.
But it's going to get reallygood at those skills which could
cause the softening in themarket which leads to human
skills, human skills, uniquelyhuman.
That's interesting to me.
They could have just calledthem soft skills, but human
skills what humans are reallygood at Empathy, communication,
(16:45):
leadership, management AI isgoing to get good at those
things Probably not as good as ahuman.
So it's important to focus onboth with your learning plan.
So I'm making sure that ourtraining programs are
skills-based, so they'reteaching individuals relevant
skills Communication, forexample.
(17:06):
The medium might change.
Maybe we're interacting with achatbot in the future, so you
have to teach the communicationand the prompt engineering.
But we're not changing, we'renot calling that something
different, we're not callingcommunication prompt engineering
.
It's distinctly its own currency, so to speak.
So you can collect the skillsand the currency.
They're like coins.
(17:26):
You can fill up a piggy bankand when you have to go and
break the piggy bank, you havethose collection skills, money
right to be able to change inthis AI economy which?
Trickling this all back toeducation in a traditional sense
, education is based on time.
So in the United States, ittakes four years to get a
(17:46):
bachelor's degree.
Typically, on average, it takesfive to six years to get a
master's degree and it takeseven longer than that to get a
doctorate's degree.
All of those are great concepts, but with AI, the time it takes
to do things, the skill value,diminishes.
So typically, when you get adegree, it's relevant for around
(18:07):
five years on average.
With AI, since it's at yourfingertips this is just my
opinion, but it's probablyskills are probably going to be
relevant for two years.
So you have to do things morequickly, more relevant.
You have to explore new areasthat you might not have explored
before, just to keep up witheconomic changes.
That means colleges anduniversities are going to have
(18:27):
to change with that.
Yes, we'll still have degrees.
Yes, we'll still have programs.
Yes, those things are stillvery important.
But with shorter form learningmicro-credentials, badges,
six-week courses, two-weekcourses, live on-demand training
you can acquire these skills,practice them and then adapt
(18:47):
them to your job and quickly notonly upskill and reskill but
move up in your company, whichdecreases your value, shows the
impact of not only responsibleAI, generative AI, whatever the
AI is but also shows theimportance of the other skills
of communication, leadership,systems, thinking.
If you can create that returnon investment, organizations are
(19:11):
going to not only invest moreinto individuals, but I think
they're going to value theseshorter-form learning aspects.
And then those shorter-formlearning aspects and credentials
can stack into a program and adegree and it's a little bit of
an ecosystem change, butfundamentally it's no longer
(19:32):
based on time.
It's based on what you can doand what you can showcase how
you can do those things.
Speaker 2 (19:37):
Yeah, that's such a good point because one of the
programs I teach in is digitalmedia management and for that
program we have 12 courses andthey're outlined and you can do
a one-year really fast trackprogram or two-year program.
I get my students for 90 minutesonce a week, you know for about
so they're eight weeks, sothey're very shortened programs,
(19:59):
but in that time I can teachthem.
You know, we have the conceptsof whatever the topic is, but
then that class time is when Ican bring in other applications
and bring in AI tools.
So it is fast track, butthere's not enough time to fully
embrace.
So that's why I alwaysrecommend here's a great
training or here's a webinarabout this topic so you can get
(20:20):
more in depth.
Or if you're interested inlearning about this, here's
where to go so that they can getthat.
So it's not necessarily part ofthe degree that they're going
to get from USC, but it is stillimportant to their learning to
get that extra knowledge.
And, as you said,certifications, right, short
form programming, skill stacking, so things that will, with the
(20:43):
foundation of all of thedifferent you know digital media
areas and aspects and knowingthe foundational information,
then they can use these othercertifications to just really
level up.
Speaker 3 (20:55):
Absolutely, and the leveling up is important.
But it's also that continuouslearning and it's a mindset
change.
So continuous learning doesn'thave to be completely academic.
You might not even have toenroll into a university or a
college.
You could go on your own andtry to acquire some of these
skills.
So what might that look like inyour own organization?
(21:16):
Can you sign up for anadditional project?
So if I'm a data scientist, canI take on some project
management tasks?
Gain organization skills.
Gain managing people skills.
Communication.
Gain organization skills.
Gain managing people skills,communication those are relevant
and important skills that couldalso position you for more of a
leadership role.
So it's kind of thinkingoutside of the box a little bit
to really focusing, you know,linking this all back to a
(21:38):
learning plane so that you knowyou plan the work and then you
work the plan Without that plan.
Even writing it down mightsound silly to some folks
listening, but when you writesomething down, it's 76% more
likely to occur.
So write down where you want togo and the steps it's going to
take for you to get there.
Spend some time on it.
Speaker 2 (21:58):
Yeah, yeah.
So being dean of AI, but alsotalking about the future of
education, not necessarily beingin the classroom traditionally
what do you think your role isgoing to lead into and have you
seen any shifts yet?
I mean, I know we're in thatin-between phase, we're at the
early stages.
Still Some people think they'reway behind.
(22:18):
They really aren't necessarily.
So what do you see as the nextstep, the next vision of
education, and will your rolestill be relevant?
Speaker 3 (22:30):
So education has been time-based.
I talked a lot about that today, so I'm not going to repeat all
that, but I still.
That probably will change.
It will still require theamount of credits and you'll
still need to put time in, butif this more adaptable ecosystem
approach occurs, it won't befour years in a seat.
(22:51):
You'll be out in the ecosystem,so to speak, learning and can
transfer this currency back andforth.
We're going to bring in theworkforce, so the workforce is
usually adjacent to education,but I think those two worlds are
now merging.
More and more employees need tobe upskilled and reskilled.
These individuals also need tounderstand not only the impacts
(23:13):
of AI but how to use AI fortheir roles.
I think academics is good atteaching some concepts of that,
but a traditional university orcollege can't teach you AI for
every single role.
It's just not scalable and it'sconfusing as an offering.
So, like those two worldsmerging together, you get that
workforce aspect of it.
(23:34):
So maybe there's mentors, maybesome of the courses are offered
through an HR training program.
Once you complete those courses, then you learn more theory
back at the university.
It becomes more of a flippedclassroom scenario, so to speak.
Then a traditional student, ifthey haven't had a job before
and they're going to college forthe first time, those students
(23:58):
can be matched with individualsin the workforce.
So they get a mentor, a mentee.
You're more likely to get a job.
It's probably funner to learnbecause you're learning from an
expert, right?
So you're learning from aprofessor and you're learning
from an expert in industryprobably two completely
different mindsets, but now thecontent's relevant, it's more
current, it's more fun.
(24:19):
I think learning can be fun.
But then what happens with AI?
How does that work with all ofthis?
Right, and I think that's someof the main question.
I think with the AI, we need toreally understand that the
world's still changing and thatthere needs to be some
flexibility.
Linking this back to everythingI just said, I think the system
(24:39):
needs to become more flexible,but new roles are going to exist
Not every single role we knowyet.
So I don't think we need tofocus on roles.
We need to focus on skills.
Skills is a little bit differentof a concept than a way.
A university creates programsnow, so universities can start
tagging their courses to skills.
(25:00):
They can figure out what skillstaxonomy they want to use to
the world Economic farms one,for example, but there's many
which ones work for youruniversity.
How can you start changing thelanguage so, when you interact
with your partners and workforce, you're making sure that you're
teaching the relevant skills,and then how can you create that
ecosystem of a back and forthenvironment?
(25:21):
I honestly think that with AI,education is going to be more
accessible.
It will ultimately lower costsif more people are going to
universities right Quality costaccess.
So access increases, qualitywill increase, volume should
increase.
Speaker 2 (25:39):
Yeah, fantastic.
I love that vision of the worldwhere things are more
accessible to people and thatyou're not just hearing the
theoretical perspective butyou're also getting practical
application and inroads, Becausesometimes students can graduate
from any university and theythink the job's going to be
waiting for them, but it mightnot be if they don't know how to
(26:02):
apply those skills andinterview appropriately based on
those skills.
Speaker 3 (26:09):
Yeah, absolutely so.
Degrees traditionally I knowpeople upskill on the side now,
but traditionally a degree isone and done, so you go and get
your bachelor's, then you go andget your master's, and maybe
you go and get your bachelor's,then you can only get the
master's and maybe you will getyour doctorate or or some
combination of those.
Right, that's not super.
I'm hoping to get a job.
I'm not sure.
With the skills-based economy,in an ai economy, we're shifting
(26:32):
our mindsets to curiosity,learning, agility.
We're always trying new things.
Learning doesn't always have tobe in the workforce or in an
institution.
For example, I'm doing morepodcasts to learn more about the
communication skill.
It's something I wanted to do.
I'm interested in doing it.
There's no credit for that.
I'm just experimenting with itright?
Speaker 2 (26:55):
Well, you're doing a great job.
So how has being dean of AI andputting so much into AI first
pedagogy for your universityhelped shape and transform the
university?
Are you seeing more studentswho are interested, engaged,
more press?
Speaker 3 (27:13):
So well.
First, you asked the questiondo you think a dean of AI is
market relevant and do you thinkthat that job is going to
continue to grow?
I think that it might not becalled a dean of AI.
It could be called dean of AIenablement, a director of AI
enablement, but I think thatyou're going to see it in
workforce and I think you'regoing to see it in universities.
(27:34):
All the worlds are merging.
It's extremely market relevant.
I don't think AI is going away.
Linking this back to what youjust asked with what skills and
how are the universitiestransforming to this?
I think some institutions werein a lot of trouble before AI.
Covid, for example, I think,changed a lot of the playing
(27:56):
field.
A lot of institutions wentonline At that time.
Ai was kind of released.
We were kind of getting out ofCOVID but still in it, and that
kind of turned up the heat.
You're going to need revenue tosuccessfully implement AI and a
lot of the revenue.
A lot of colleges don't haveaccess to that, so you're going
(28:17):
to see mergers.
I think there's probably goingto be less colleges.
That doesn't necessarily meanit's a bad thing, but it could
also change the dynamics.
So right, it can make thingsless expensive or more expensive
.
I think less expensive, butwith this focus on AI and AI
enablement, it's just going tochange the nature of the game.
So if you're not thinking aboutyour AI strategy or your AI
(28:40):
implementation planner how SEOis kind of it's a thing, but
we're moving away from that.
Search engine optimization andLLM is a recommendation engine.
So does your university collegeshow up inside ChatGPT Gemini
Plot?
Is it accurate?
Does it list the propersequence of courses?
(29:02):
You have to redesign yourwebsites to make it accurate.
You have to have good searchengine optimization to feed to
these LLMs.
Are people thinking about that?
I know I'm thinking about that,but I'm a simple one, and some
universities are ignoring itcompletely.
Some are playing in the middleand some are being more
(29:24):
innovative.
I think the middle and the moreinnovative are going to get
closer together and then there'sgoing to be the colleges and
universities that work againstit and I guess we'll see how
that plays out.
Speaker 2 (29:35):
It's so interesting that you say this because I
teach this to my students interms of personal brand, because
the whole thing is right.
Google not as much now, butGoogle and your LLMs are the
basis for how people understandus.
Now, as more and more peopleuse LLMs for search, we need to
make sure that, as individuals,we have our own website, which
(29:58):
you do have.
That kind of is the hub for allof the information we want
people to know, so that the LLMsaren't just out there scraping
the internet guessing at who weare, or taking information about
somebody with the same name whomight be doing something very
different, or taking really oldinformation that isn't relevant
to who we are now applying it tothe university level and how
(30:22):
businesses in general need to bedoing the same thing is, so I
would say that's one of thebiggest changes that's needed
right now.
Speaker 3 (30:27):
Exactly and it goes all the way down to the course
level.
So if someone Googles youruniversity, the course and the
course number, and it gives anaccurate description of that
course, an accurate coursecontent, because it will pull up
everything that it finds, yeah,People may be less or more
interested to you know thecourse, the program, the program
(30:47):
links to enrollment.
They might go elsewhere.
Speaker 2 (30:51):
Yeah, what is I mean?
We've talked about a lot.
I think that we have a clearvision for where you see the
world going.
I agree with you on all of itthat you would leave for one of
our students or one of ourfaculty, or even some of the
directors who listen to theprogram, for them to continue
(31:17):
this change in mindset and thismove towards being really AI
first and making sure we'reimplementing everything really
effectively for our students.
Speaker 3 (31:22):
I would focus on a learning plan.
So organizations can focus on alearning plan.
Individuals can focus on alearning plan.
A professor can have her classfocus on a learning plan, so
organizations can focus on alearning plan.
Individuals can focus on alearning plan.
A professor can have theirclass focus on a learning plan
that they want to learn in thatclass.
So you could do a classroomscenario.
But my point is by thinking andgetting those thoughts out there
.
If it's at the organizationlevel, then employees know what
(31:42):
to attach to and what theyexactly need to learn.
And then at the individuallevel, yes, I need to attach to
these things, but you can takeany type of force or do I want a
mentee, or do I want to pick upa project within the
organization?
You can solidify it more.
So really start investing inyour personal brand and your
learning plan.
Those are the two biggesttakeaways.
(32:04):
Because your personal brand,even though you don't think it
links to a learning plan, itdoes, because if you're trying
to go get a mentee or you'retrying to upskill, reskill and
then switch into a neworganization, you're going to
need a portfolio to fall back on.
All these things interconnect.
So really focus on thoseaspects.
I know skills and upskilling andreskilling might be scary.
(32:25):
To some folks it's probablyjust as scary as AI.
But it's nothing that'snon-attainable.
And you can start small, loginto an LLM, have it, make a
three-day vacation plan or getaway for you See if it's
accurate, it's summer, I meanpeople are having fun right now.
Have it plan.
If you paddle for it, have itplan a route along a river or a
(32:45):
lake.
It's completely plausible.
And if it's something you doall the time, you can understand
the trade-offs and benefits.
And then, right, you check offone aspect of your learning.
Speaker 2 (32:55):
Yeah, fantastic.
And, ben, thank you so much forcoming on the show and bringing
your perspective Again.
We're so aligned and this issomething I've really been
thinking about how to create newplans for student learning and
also for teaching teachers howto teach effectively at every
level, whether it's upskillingthe adult workforce, whether it
(33:15):
is the university level or evenK-12 education.
So I think we need to have moreconversations like this out in
the world, and thank you fortrusting me with this
conversation and, of course,bentaskeraicom is you for
trusting me with thisconversation and, of course,
spentaskeraicom is the websitewe'll lead everybody to.
So I appreciate your time and Iwant to thank everybody in the
audience as well for listeningto this episode.
(33:37):
Start thinking about the waysthat you can incorporate more AI
strategies and tools into yourlives, even if it's learning,
prompt engineering, proofplanning, vacations, meal
planning, looking atuniversities for your child, you
know and figuring out, based ontheir criteria, what the good
fits are, which is somethingI've been doing as well for mine
(33:58):
.
So, with that, thank you forjoining us today to the audience
and thank you, ben, as well.
Speaker 3 (34:05):
Thank you so much for having me.
This was a lot of fun.
I am very passionate about this, and thank you, ben, as well.
Thank you so much for having me.
This was a lot of fun.
I am very passionate about thisand, once again, I hope
everybody starts their AIlearning quick.
Speaker 1 (34:11):
To learn more about the Master of Science in Digital
Media Management program, visitus on the web at dmmuscedu.
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