Building the Bridge: Strengthening the Employer-Education Connection for Tomorrow's Workforce transcript

I'm Tom Ap Simon.

I lead Pearson's higher education and virtual learning businesses globally, and I'm delighted to be here with Valerie and Joe from AWS and Joe from University of Pittsburgh.

So Valerie Joe, would you like to both do a quick introduction?

Certainly, I'll go first.

My name is Valerie Singer.

I lead global education at Amazon Web Services focused on higher education, K12, the research space, our Ed tech partners and how they can facilitate skills based learning and education, including Pearson and our skills based programs including cloud innovation centers and skills to jobs.

I'll pass it on to the esteemed Doctor Joseph Hyun, who will introduce himself as well.

Thank you so much, Val.

It's awesome always actually being on any panel with Val.

My name is Joe.

I'm at the University of Pittsburgh.

I have a couple roles.

First, I'm a research professor in electrical computer engineering.

My background was with a lot of these technologies and, and researching a lot of pre generative AI tech.

And then I'm also, I'm actually now I'm the chief AI architect for campus in which I essentially kind of lead the technical strategy for most AI deployments on our campus.

Small, the large, you know, University of Pittsburgh is, is basically a very large Medical Center.

And so there's a lot going on with AI and medicine as well as many other disciplines, but kind of medicine is the big, you know, kind of gorilla in the, in the room with regards to the university.

And then I also started and lead the Amazon AWS AI cloud Innovation Center that sits on our campus where we have students working on real applied AI projects for both university application as well as general public sector application as we work with different organizations.

And then I do also lastly want to mention I I do quite a bit of consulting for upper mid market companies, partially to just understand what companies are really doing with these technologies that then kind of Dr.

insights back into working with our students and and trying to figure out kind of how that whole spectrum works from a workforce development perspective.

It's a pleasure to be here.

Cool, Thanks, Joe.

And let me just say, hopefully by the end of this panel, you'll be thrilled to be on the panel with me.

This is my second go at this panel in this conversation with AWS and and the wonderful Valerie.

So we'll see how we do.

So you know, clearly what we're trying to focus on here is the the research that AWS and Pearson did earlier this year.

It was global research to really understand what sort of being AI ready means for today's graduates.

We've got 2700 learners, higher education leaders and and employers in six countries really trying to understand what AI readiness was all about.

And it became very clear that it was much more than just about technical AI skills.

It's a combination of proficiency, practical experience, strategic thinking, and human judgement.

All of that's rooted in the learning experience at universities, and yet we see some of those capabilities are not being developed consistently, in part because we're seeing six different areas of friction across education and work, pace, governance, capability, connection, experience and skills.

So today we'll jump into sort of understanding a little bit about what students are experiencing and how their institutions are responding, what employers need and how education and industry can work together.

And we'll tag team and make this as fun as we can in the next 40 minutes.

So Joe, I'll kick it over to you first as as you think about institutions and governments, when institutions are working to keep up with the speed of change, policies around responsible AI US can become fragmented and inconsistent.

And if students are kind of figuring this out on their own, what are they missing?

What does the absence of guardrails mean?

And how should we be thinking about this?

Yeah, it's a really good question, Tom.

So I think the University of Pittsburgh is not unique in the greater landscape of higher Ed where, you know, figuring out policies and figuring out governance processes in general has never been a fast kind of process.

Even pre AII think anybody that's been in academia for any period of time will realize that it kind of the system is maybe meant to to move slower than faster with those kind of things for for some good reasons.

With that said, I mean, this technology is obviously moving so fast that it's really testing the ability for probably any hired organization to be able to manage these policy changes that are needed, as well as governance entities that are needed.

As I actually believe that there just needs to be new types of structures on higher campuses from a governance standpoint that just have never existed.

And and so, you know, unfortunately, you know, we are, as every school is, you know, we're dealing with this real time.

I will say that at our school, what I tried to do with my team was, you know, we have some folks here that are pretty knee deep in the technology and really understand kind of how it works and how we can build it out.

And so we just kind of started building capabilities over the last three years that was kind of showing the art of the possible of what could be done with these technologies, largely in partnership with AWS.

And then I think what happened was then everybody started realizing, Oh my gosh, like these, these technologies are not just some kind of hype.

This is real.

And so then we recently have had a lot more engagement from, you know, campus leadership, you know, faculty members, all these things trying to figure out, OK, how are we going to actually build policies and governance around this.

And that's happening concurrently while we're still trying to figure out how to move the technology forward.

And I say all of that because the, you know, the one constituent group that is kind of, I wouldn't say fully hurt by this, but they're kind of, you know, trying to figure things out on the per periphery of all this sort of students.

I mean, this at the end of the day, I'm sure it's again true at most other schools, many students over the last 3-4 years, they have probably had a common experience where they'll go into one class and AI will be banned.

Then they'll go into the, the, the next class in the same day.

And then AI will be forced to be used 100%.

And then they'll go to 3rd class.

And then it's just kind of like, do whatever you want with regards to AI as long as it follows a certain level of, you know, adherence to the syllabus.

And so it's super confusing and, and the reality is, you know, as, as I work with companies, all of our students as they graduate, they're going to have to have some level of competency with this technology as they go into the workplace.

And so it's just been a, it's, it's just been a rough transition for higher Ed and rough transition for students.

And again, I'm not, I don't think there's anyone to place blame on.

I I just think that we're in a rapidly changing, you know, kind of evolutionary situation.

Yeah, I think that's beautifully put, Joe.

It's funny, I was, I was talking to a number of leaders earlier this week and they were talking less about the technology and more about the culture and governance and higher education being the biggest barrier to sort of change.

And, and I think you're absolutely right.

You know, you described what I I view as the governance gap, right, which is a disconnect between how the institutions work, where they need to shift, and then the implications for students, right.

And we see that in terms of the uncertainty it's creating for students, for faculty.

We see that in terms of the independent sort of freestyling approach to taking to using AI that many students are doing on their own.

And they're not necessarily sure what some of the institutional policies are that's leading to sort of radical, you know, to your point, sort of almost on the hoof responses by students to different classes, you know, whether how to think about it, what to do, what not to do.

And so I think it's, you know, if 2/3 of students can not say with any certainty that their university has AI policies in place, well, that that points to a communication challenge and a clarity around how to think about this.

And so I think actually creating the right framework for learning just got a lot harder.

And, you know, we're saying that student experience really be 1 of struggle and challenge.

And just to finish off that thought, one of the biggest themes from the report that really struck A chord with me was that only about 16% of faculty feel that they've had appropriate professional development from an AI perspective, which is a staggeringly low number if you think of the, you know, impact of faculty like yourself in terms of making this a success.

And so Valerie, if you sort of shift gears a little bit and it's easy for sort of me or Joe to say that higher education needs to move faster, But but employers also have a, a vital role here.

And the report sort of implies that that industry isn't always communicating their needs consistently to university partners.

So how, how do you think about the role that industry plays in helping institutions to better understand and respond to workforce demand?

I think that's a really great piece of how all this comes together.

So this morning I was reading a WEF report that said the life scan, the lifespan of a skill will be about 2 1/2 years by 2030.

So even all of these really great skills that Joe and his team at University of Pittsburgh are teaching their students will be almost defunct by the time they graduate and they have to apply it into a work setting.

And so when you look at this dichotomy in the in the readiness report, it really did strike me as being critical that 25% of faculty state they don't have any systematic way of being able to take workforce data, while 36% of employers say, you know what, we really need you to actually indicate in your curriculum what we're going to be needing.

And so that dichotomy in that gap really, really says to me something really strongly what I see when I go around the globe, which is that employers are not that active when it comes down to it, you know, universally within the higher education system to both understand what their skills base needs are going to be for for their student, you know, for students who are matriculating into their organizations and to communicate that back to higher education.

So this getting back to what just comment was, we don't want to put any blame on anything, but this is a huge opportunity for us to, you know, to build that alignment a lot closer than it has been ever before, to make sure that those signals are being shared between employers and education and that education can respond in kind.

Otherwise we're just kind of guessing and we end up with that, you know, 17% gap or whatever it is between what I need and what you're teaching.

And I think it does students a disservice.

So how do we get to that point?

We have a couple of programs that do that and one Joe mentioned just the cloud Innovation Center.

The cloud Innovation Center is an experiential Learning Center of activity for students who come together with peer and with faculty mentors.

They're getting a lot of inputs from employers about what's relevant and the students actually create open source assets that are durable skills that they can, you know, skills that they can point to that they've created something that are open sourced and that are available through GitHub repository.

And so by doing that, they get a few things.

One is they're they're actually identifying the kinds of projects that have real world applications.

They are actually developing those those those projects against a set of rubrics and against mentorship.

They are being able to developed as a monsterble skills and they have this this internal feedback loop with employers that I think which is really valuable.

We need to build that at scale.

And so that's not just AWS that, you know, that is taking this on board, but AWS and many other companies should be taking this kind of approach where we give students a more active learning cycle so that they can then build that kind of flywheel with employers in education.

I think that's beautifully put, Valerie.

And you know, it's it's a clearly a very well thought through partnership with you and Joe and the team at the University of Pittsburgh.

If turning to Joe a little bit, if you think that, you know, one of the things the report uncovers is the huge perception gap, which is 4 out of five higher Ed leaders believe graduates are meeting employer expectations, but employers are saying somewhat the opposite.

So as you think about sort of faculty AI capability, why do you think that disconnect exists and how do we think about educated capability in terms of closing that gap?

Yeah, it's a really good question, Tom.

I mean, you know, the way I think about it is if you actually think about the path of somebody becoming a faculty member, going through grad school, getting their PhD, you know, again, obviously everybody's different, everybody has different personalities, but the route to get a PhD is very similar across all disciplines, right?

There's like a process and that process filters out certain types of people and, and kind of keeps other types of people.

And so the, you know, if I, if I was to put it kind of just in a summary form, folks that are, you know, really interested in, you know, focusing and diving deep into some topic and then just spending, you know, a good amount of concentrated time playing around, experimenting, innovating, you know, with that a specific focus like this is a very typical kind of path to getting a PhD and then becoming a faculty member.

And so on one hand, you have a group of people as faculty members that if they were kind of given space and time to work on figuring out AI and figuring out how it applies to their field.

I mean, it, it, this is a very, it's a very good group of people to allow to do that.

And I think that they would excel.

We would excel in, in being able to gain those kind of skills.

But again, the reality is that this it's just a lack of time and the lack of ability to kind of catch up at this point because things are moving so fast.

And so, you know, it's just one of those things where I have been one of my good colleagues at Pitt.

Her name's Annette.

She's an English professor.

She's deep in the humanities.

And she had a sabbatical, you know, last year.

And so she went deep into AI.

She was already, you know, studying it on our own and, and, and gaining skills, but especially during her sabbatical, she just kept going.

And now just seeing how she's applying it to her classes, how she's applying it to her own, you know, thought of her life, her career, etcetera, etcetera.

I mean, it's pretty notable.

And again, she's in the humanities where maybe some people would think, OK, folks in the humanities, you know, they're not as technical and so they're not going to be able to pick up these skills as well.

It's just absolutely untrue.

I just believe it is a lack of time and a lack of ability to put focused effort into this.

And again, I'm not trying to make excuses for anybody.

I'm just saying that I've thought a lot about this and I and I've just thought, man, if if we were able to say pause and everybody just kind of take some time and, and, you know, do deeper dives into this technology, then I think we would have a lot more progress.

Obviously we can't do that.

The real world doesn't work like that.

And you know, there are some people that are saying we never want to learn this technology.

I'm I'm sure they're out there, but I think that's a, that's a minority position.

I think there's a lot of people that if they had the resources and the time, they would, you know, gladly dive deep into this.

So let me, if you don't mind, Tom, if I could just jump in a little bit just to point out a couple of programs that pit that I think are worth pointing out.

And to this point, Joe, I mean, one is Pasta, which is PIT artificial intelligence scholar, elder teacher alliance, right.

And then yeah, no, it's great.

It's easy to remember, except for if I can remember all the words.

And then the other one is AI cross disciplines, which is exactly what you were talking about.

And it's really kind of peer-to-peer faculty sharing in a way that is more like luncheon learns, right?

Like.

And so it's it's a much more casual way of faculty learning from each other and sharing best practices.

So why don't you talk a little bit about that?

Because I think it's really important that we don't just anchor on the thought that every faculty has got to go in addition to all of the other workload that they're managing into structured training.

But you can get to the answer around capabilities in a lot of different ways.

Yeah, thank you for pointing that out, Val.

So, so yes, there actually just is no time right now and there isn't a lot of structured learning opportunities as Val's mentioning.

And even if there were just not that much time, So what we decided to do was that we had a law professor that actually created the idea of pasta.

And pasta was the kind of the first step that started 3-4 years ago where we said, OK, we'll just get together.

I think it's like every third Tuesday of the month, we'll have food, you know, And from my grad school days, I still have that kind of reaction.

If there's free food, I'm there, right?

And so, you know, everybody as faculty members, I think they kind of resonate with that from grad school.

And so we provide food.

And then we just started talking about AI.

How do you it was almost like a therapy session, honestly, in the beginning.

But then people started sharing, here's what I'm doing.

And then and then people started presenting here.

Here's a whole curriculum I just created.

What do you think about it?

And it just became this kind of safe place where everybody could say, OK, like, let's learn from one another.

And then my colleague Annette that I mentioned, she created this separate program, but she's heavily in pasta as well, called AI in the disciplines.

And that program was, she was just saying, OK, let's like, let's make even more of a support group where we'll have various professors that are in totally different disciplines and, and we'll have kind of assignments that, that we are, that, you know, we all work on and then we'll gather back together and discuss it.

I, I haven't been in these cohorts, but apparently, I mean, it's been extremely successful.

And now we have the second generation of AI in the disciplines where now the folks that went through that first cohort, they're now doing their own cohorts.

And so it's kind of like this network effect where it's where it's spreading out and, and again, it's all just organically led by faculty members and, and kind of grown by faculty members.

And I think obviously there's a different way to do this, but this is just one way that I do think, again, giving time and space.

And it's kind of like a, a safe space to say, man, we really don't know what we're doing, but let's help each other.

It's been really successful for us.

And, and so, yeah, I think Val bringing that up is a great point.

I think there's that's one way that I think higher educators can, can gain greater skills in AI.

Yeah, no, it's, it's a beautiful example.

And I'm sorry, I love the acronym pastor.

It's, it's one of the especially since lunchtime on the East Coast.

But what you're really getting at is the is the human need to create safe spaces for faculty to sort of have the space to learn and try and do something differently.

And what I thought was interesting there, if you were going through this was sort of the point you made around the second generation of AI, because I think what we're clearly seeing was like Part 1 was, oh, it's a tool.

I've got to learn how to use the tool.

But actually it requires faculty to actually get comfortable with it, as you beautifully gave the examples about.

And then actually, once that happens, it moves away from being a tool, but being much more incorporated into the fabric of teaching and learning.

And I think when that happens, you get to a much richer, more nuanced, more discipline specific approach to actually how you should be using AI, what the implications of it are.

And you actually then get students much more comfortable with using it because you as the faculty.

In question is actually, you know, really jumping in and leading by example and, and I love the example of your colleague Annette, because actually, I think, you know, one of the things that's been very interesting is sort of the resurgence of the humanities and, and liberal arts degrees, right?

Because, you know, a few years ago it was all STEM or bass, right?

You know, I, I exaggerate for effect, but actually, I think the, the, the fundamental skills around, you know, philosophical understanding around judgement about how to apply this is a real boon for many, you know, universities around the country.

So I'd love your thoughts on that.

Oh my goodness.

I mean, I have so many thoughts on that.

But I will just say that I think this AI kind of, you know, zeitgeist thing has really pushed us minimally in higher Ed to think about what is higher Ed, You know, what is the university?

And I think there's part of it that obviously it's a skills part of it that we need to train up our students.

And I, I'd love to talk about the cloud Innovation Center more just because of just unbelievable transformation I'm seeing in students lives.

But then the other side of it is just that universities are not just for, you know, taking exams and getting grades and and so on and so forth.

It's it's also about the development of the human being.

And I think this whole realizing that the humanities are not just a nice to have, but it's kind of the core of, you know, who we are as humans, I think that's a really positive development through this process.

You know, we have a very strong philosophy department at the University of Pittsburgh.

And so we have a lot of philosophy professors and that's an English professor, as I mentioned, heavily, not only participating, but leading a lot of these AI initiatives on campus.

And you know, we don't talk about the technology that much.

I mean, everybody kind of knows that myself and others, like we'll help the the technology be figured out, right?

But let's figure out what our business goals are, what our, you know, philosophical goals are.

And that has been incredibly positive with regards to just asking ourselves, what are we really doing here, right?

Well, what is the university and, and how should we think about making sure that we don't lose our identity and don't lose the core of what we are supposed to be?

I think part of workforce development and, and I think we've heard about this with regards to Gen.

Z and I think Gen.

Alpha is the next generation that, you know, sometimes critical thinking or just kind of, you know, specific kind of social skills might not be as strong.

And, and part of that might be because we're losing a bit of what the university was supposed to be, which is kind of partially the building up of the human being.

And so I'm seeing a lot more of that on our campus.

And I'm sure that's happening across campuses, you know, globally.

So I think it's a good development.

Yeah, I agree.

And, you know, on the employer side, if you don't mind my jumping in there too, Tom, because I think this has a lot of applicability.

And it was actually pointed out in the study too.

When you take the STEM learning, you know, STEM for everybody, and then you you put it together with human critical thinking skills, you get actually what employers really want.

So, you know, we clearly want a functional representation and proficiency.

Can you use the tools?

Can you demonstrate that you actually know what the application is from a workflow perspective, understand how the technology works, and then have the strategic intelligence to know when to apply it and when to offload onto your own skills, right.

So we're seeing a lot of this where we're now people are starting to use AI more as drafting or more as coding, but you know, with a suit with a human in the middle.

There's also this really important piece that you talked about on this philosophy side, and I've found it really interesting that that technology companies are starting to actually recruit philosophy majors.

So, you know, don't give up on humanities because, you know, we need this kind of skill, but that's the ethical stewardship, right?

So how do we really think about the ethics behind what we're what we're applying and what are those guardrails?

What is the governance criteria?

What's responsible and ethical AI and what does that mean?

And then lastly, that critical thinking, you know, so that's the adaptability, that's communicating with others, solving complex problems and understanding how the tool is actually deriving all of the benefits behind that.

And so when you look at that package and then you combine, and then you combine it with what you're thinking about, Joe, in terms of this holistic view of student where you're, you know, giving students the critical thinking skills, the functional skills, and you're giving them the demonstrable hands on skills.

I mean, that's really what employers are looking for.

And I think that's where we start closing the gap between what is being taught and what we're delivering in terms of, of student capability and then how they're going to play in the workforce and, you know, and start their career journey at the outset.

So I think all of that comes together in a really nice package when you have all of these different systems in place that Pitt's doing.

Yeah, no, I, I, I completely agree with you, Valerie.

And what I think is interesting going back to Joe's point is that point of like renewal for universities.

And actually, I think this becomes a very exciting time for universities to really rethink how they're helping students get ready in this very different, very changing, very challenging era because actually their jobs got a lot harder, right, Given, you know, some of the challenges that Joe articulated earlier.

And so figuring that out and working out how, how to really be clear as to sort of how to bring the AI into the course design, how to help students understand this.

So it becomes, it doesn't feel disjointed, but it becomes coherent and it becomes focused on helping these humans, as Joe put it beautifully, actually be ready to to jump into the workforce and, and to be in a really strong position.

And, and Valerie, as you think about sort of the, the disconnect between students having access to the AI and the real experience that, that, that, that you're seeing from an employer perspective, we all know that employers aren't just asking for graduates to have those technical skills.

What do you think is the real disconnect between sort of the AI that sort of universities are providing today in some cases and AI ready graduates from your perspective?

And what are the biggest things we could do to move the dial there?

You know, I, I think it really does get into the two things that I think I've been hammering on in this conversation, right?

One is the, is the, the connection of communication between the university and the employers, because we're changing really fast too, right?

We've adapted within the last six months to a very new and changing way of using AI.

And that usage of AI has continued to evolve.

Some of those we have also, you know, given a lot of access to our higher education, our higher education partnerships worldwide, including free access to Kira, which is our vibe coding platform that we've allowed students have access to.

And so we're starting to incorporate a lot of these tools much faster if we don't communicate at the higher education level, what our expectations are not just of the tools development, but where they apply, whether that's coding, whether that is in marketing, whether that is even in disciplines that, you know, in our in our medical device, you know, medical teams.

We are at a disconnect with higher education.

And so that communications through a way of how we're adapting to technology and how we're expecting our students to learn and to assembly that in the workforce has to be something that lives in a, in, in an organic and systemic or systematic way of communicating between higher education and employers.

How that works in practical terms is putting together councils and bodies where we actually Co develop curriculum, where we Co develop a perspective on applicability of technology moving forward and where we continue to build out programs like cloud innovation centers and cloud innovation labs.

And the differences between those two is a center that that can take on a lot of different projects among different disciplines and labs tend to be very focused.

We have programs like skills to jobs tack Alliance that actually bring together employers, education, government and collaborating bodies to build to do build a thongs hackathons, all kinds of experiential learning in, you know, on a massive scale for students to have experience and exposure to employers so that they have access to jobs and where we build these Co connections.

That's where I think we're really going to see this gap shift.

I will say that you did bring up, I think you brought up kind of the faculty challenges here of whether faculty is actually able to translate their understanding of AI into job ready skills.

And that's another thing that I think employers really need to help with.

And so programs like PASTA and and other kind of applied programs like like Pitt's working on really do help because I think it brings faculty into a very active conversation.

Once I adapt to my own way, way of thinking about teaching and learning.

How does that apply back to what students are going to need when they graduate, where they leave my classroom?

And so, you know, this is, you know, we're at the beginning stages, as you know, you guys pointed out and we were talking about over 2000 year old system of education that we've had to adapt very, very quickly to over the last, you know, two years.

And I think that higher education institutions are doing it, you know, boldly for the most part and really trying to figure this out.

And this interconnect between employers and and the higher education institutions I think is really paramount.

Yeah, I, I love what you said there about the Co creation of a curriculum from a design perspective and also the role that employers have in some of the stuff you've been doing with you, Pip, in terms of getting that ready.

Joe, you talked about pasta being a really good example of what's worked well.

What are some of the challenges?

What are some of the other things that have gone well for you and not so well?

I think not so well.

Well, I don't know if it's not so.

Well, I think at the end of the day, academia has always been proud that it has quite a bit of diverse thoughts, diverse opinions.

That's kind of the essence of, I guess the concept of a university is that you have folks that come from different discipline backgrounds, just come from different backgrounds and you know, you kind of stitch them together.

I mean, a true university, I mean, it's really like a research 1 university is a bunch of colleges that are just stuck on the same plot of land, but they kind of operate totally separately, right?

And so the thought of something like AI that is so disruptive and, and I'm listening only in a bad way.

I mean, it's just, it's a, it's a, it's an agent of change for like Val said, an institution that has been around for 2000 plus years.

I would say that again, I don't know if it's a bad, but it's just very difficult to find common ground on really anything regarding this technology.

And that doesn't mean that things aren't moving forward.

But something I've realized is that when we say policies, I almost feel that actual written down policies are potentially going to come last, right?

As it's, it's almost like everybody has to kind of try to do their own thing and try to figure things out.

Colleges each will do things differently from other colleges.

And then at some point everybody will agree, all right, it doesn't make any sense because we've tried everything for us to move forward in a different way with regards to this thing.

And so for this thing, and maybe, you know, I'm just making something up like we're going to have standardized syllabuses because all of us are creating different syllabuses.

But at the end of the day, they're very similar.

Let's just have a policy, see that all syllabi should look like this, right?

And it might seem silly to folks on the outside to say, well, you, you took, you know, you took eight months to figure out how to write that one piece of paper, right?

But it, it's usually not just, well, it was doing nothing for eight months.

It was a lot of differing opinions, a lot of people trying different things.

And then you finally get to that, that one, you know, agreed upon kind of a standard.

And I don't know, I mean, is that a bad thing?

I, I think it's not good or bad.

It's just kind of how academia has functioned.

Is it going to hold up to the speed in which all of this is moving?

I don't know, to be honest.

And I, I don't think anybody knows.

So I think there's going to be there already has been, but there's going to be a lot of difficulty on college campuses just because of the speed and and the magnitude of change.

And that's not something that any of us can kind of pretend like it's going to go away.

It's only going to accelerate.

Yeah.

And I think, you know, you kind of touched on the tension, right?

So the tension is I want to change really quickly, but we're still operating in an environment where pedagogy changes take a very, very long time.

And you know, we've been, I think dancing around the idea of micro credentialing and badging for a decade, right?

And I do believe that we are at the point where that needs to be the completer, that needs to be the kind of the the curriculum in the middle that we still introduce like short courses to continue the education of our students in a way that, you know, facilitates the speed and get to the point where that's built into the muscle that higher education delivers to students.

Rather than a four year degree that follows a very linear path.

There needs to be start jumping off points where technology's changing.

We're going to go over here and, and take advantage of curriculum that's developing much faster and then measuring against that, right?

That's something Pearson does really well.

Pearson view and, and with, you know, their other certification products.

But the, you know, so this has to be a team sport, right?

Like not one institution's going to answer this and answer it with, you know, with its own authority.

It's different by discipline.

So as you pointed out, computer science and engineering and the capability development against that's going to be very different than how AI is incorporated into a classroom for English or for other social sciences.

And so, you know, we we all need to be to be in this together, both as companies like Pearson, who's highly influential in the academic setting, as well as institutions like University of Pittsburgh, as well as companies like AWS, right?

We all are in this together to inform how the the student create, we can create a fully formed student who can then, you know, help to progress this technology moving forward.

Yeah, I, I think that's a beautiful point, Val, because actually, I think what, what Jay was highlighting there was is really hard.

And so therefore, for for all of us, it is a shared responsibility to figure out how we can each play a different role to, you know, provide, you know, all of the students in universities an incredible, enriching and wonderful experience.

And that's, you know, a lot easier said than done, right, given some of the changes we're seeing and the pace of those changes.

And, you know, to the point somebody made a few minutes ago, if university structures and governance processes are 2000 years long, Well, you know, as Jay pointed out a few a while ago, we need brand new governance.

Well, what does that actually look like?

Well, that's, that probably can't take, you know, two years or three years or five years to figure out.

But I think that this is a a fascinating challenge and one that we really do need to get right.

Because I think one thing that we're all seeing is that employers around the globe are placing more value on the degree in this time of increased uncertainty.

And so therefore, it's incumbent upon each and every one of us who's working with higher education to actually make that degree worth it.

And that is an incredibly powerful and thought provoking challenge for the next couple of years.

So thank you both very much for joining.

It's been a thoroughly engaging and thought provoking discussion.

I've I've enjoyed myself and learned a lot and thank you for a great panel discussion.

Thank you and thank you for all the wonderful comments too in the webinar chat.

Yes, a lot of comments.

Thank you so much everyone.

Yes, from around the world.

So thank you.