The Power of AI for Credit Mobility

How can AI improve credit mobility in higher education? In this episode, Jesse Boeding, Co-Founder of Education Assessment System, joins us to share research on AI and credit mobility. She highlights how to think about AI, why it’s more than just a means to efficiency, and why leveraging AI is ultimately in the best interest of learners and institutions.

Transcript

Matt Sterenberg (00:01.356)

All right, I’m here with Jesse Boeding. Jesse, welcome to the podcast.

 

Jesse Boeding (00:06.138)

Thanks for having me. It’s good to see you.

 

Matt Sterenberg (00:08.854)

So Jesse, we’re talking about AI and credit mobility and you’re doing a fair amount of work in this area and have some cool research that’s coming out. How did you get into this work? Give me a little bit of your background.

 

Jesse Boeding (00:21.572)

Yeah, I’ve been in higher ed for the last couple of decades now. you know, as the demographics have changed, started moving slowly into adult learning. And then in the last couple of years, really starting to see the impact of credentials and different types of stacking that support adult learners and, you know, kind of this notion of lifelong learning. So,

 

I think it’s really shown up in my own life because I’ve taken some different paths, tried some different things, did a certification here, did a licensure here, and they all have had significant impact on not only the things that I do, but how I see the world and certainly my financial viability over the course of time. So this notion of how all these things fit together,

 

is really interesting, right? Because we’re so much more interesting than just the degree that we have. And yet the degree or the credential or how we position that within the scope of what we know is often where people go to first, right? It’s the first thing that they look for on your resume. It’s what they look for in your LinkedIn profile. It’s how they introduce you, right? Like you have your degree from here. And it’s definitely a source of connection.

 

But then how does it all fit together? And as we look at a population that is largely changing and a workforce that it constantly is saying, we don’t have the right skilled labor, this notion of credit mobility becomes more and more important. So super sexy topic for sure. But something that we need to figure out and be diligent about and be consistent.

 

Matt Sterenberg (02:17.538)

Well, I think it is a sexy topic. Why not? mean, when you frame it in the current chasm or divide between education and the workforce of, hey, we’re not getting the skills that we need to fill these positions and higher ed largely saying like, okay, how do we create those skills and competencies? How do we stand the right programs? But also we have been maybe,

 

giving students the right skills, but we haven’t been communicating those effectively enough. And so part of this is a communication mechanism. Another part of it is actually changing the nature of our programs, for instance, or thinking about the different way we credential. But like, why is AI an important component of this? tell us how the Genesis story of how you started to think about AI with credit mobility. What’s the Genesis of that?

 

Jesse Boeding (03:09.072)

Yeah. So just as background, when I was, I did my dissertation research and how AI supports staff to support the student experience, right? This whole idea that I had this really talented staff who could, you know, literally change somebody’s entire perception of education just by having some time with them. But they were so busy doing this administrative, these administrative tasks.

 

that they didn’t have time to spend with a student where they had the highest impact. So that’s where it started. And now we’re in a volume game, right? There are over a million micro-credentials in the United States. We have 4,500 institutions. What is it that most learners now are going to five, six, seven different institutions before they actually get their, they finalize that degree. And so we’re gonna have to thread the needle.

 

on this to be able to figure out how we can more quickly get individuals information and then layer on top of that accurate information and then layer on top of that what that actually means. So as an example, maybe to bring it to life, when we think about individuals that have gone to multiple institutions, when or have multiple credentials,

 

when they look at an institution that they might want to get a new credential from, they can submit all of their background, right? I can submit my micro credentials, maybe I have some certificates, badges, maybe a couple of college credits, whatever that might be. And that incoming institution then looks at all of that. But if it hasn’t already been evaluated, somebody needs to do that evaluation.

 

So you have to collect all that and make that decision first. Once that’s done, can you imagine if we could do that in a day or in a week instead of multiple months? Right? I submit my documents today and it takes a couple of months for people to have time to review it and say, okay, this thing that I took over here is worth this thing at my incoming institution. That’s a really long timeline. What is an acceptable timeline? In a world

 

Jesse Boeding (05:33.056)

Like in our consumer world, these timelines are shrinking, right? I returned something to Costco, I expect my refund to be on my Costco receipt in seconds, right? Or in my credit card in seconds. Why won’t we expect the same thing in our education journey, right? So now that I have it, so now it’s been evaluated, the next element is how does it fit, right? So it’s great that you might be able to get

 

a credit in management 101, but how does that fit into the end credential that I’m trying to get? And there is where like you really have to thread the needle because individuals have perceptions about what kind of end credential that they want to get, that there’s what employers want and need, and then how it’s all fitting together, right? So.

 

You can use the same ingredients to get a lot of different types of output. Does that make sense?

 

Matt Sterenberg (06:33.492)

And I really, yeah, I really like how you broke it down in the, in the paper too. In the, obviously everyone thinks of AI as an efficiency tool and it can be, but you really highlight that it, the, the phrase that’s stuck out to me and you just highlighted this as meaning making, right? Not just using this as an efficiency tool, but okay. How do we get credits to transfer faster? Or how do we get the evaluation of transfer credits faster? Great.

 

That’s an efficiency that we desperately need. But then what does this communicate to me as a learner? What do I do with this information? How can it color the degree that I pursue or the pathway that I pursue? How can it bring to light new opportunities for me that I didn’t know existed before? And I think that’s really the power, putting it in the hands of the learner and allowing them to navigate or at least have a menu of choices. Because what happens today? I send all my credentials in.

 

And then I’m waiting. And if you’re a learner, none of that makes sense to you. like, you got all my documents. What are you, what are you really considering over there at this new institution? Be like, I took that course at another institution. Don’t you can’t just give me some credit for it. Doesn’t don’t you already have these pathways mapped out or these crosswalks mapped out? It’s a strange process for a student. You’re like, what’s taking so long? Don’t you, don’t you know what I should be given credit for already? And so much of it is still.

 

you know, not transparent. So for a learner to be able to make better sense of time to degree for these different paths, how much is it going to cost me? When can I actually complete these courses, right? Like this only takes a year and a half, but none of those courses are available until 2027. You know, I think it adds so much power back to the learner that they wouldn’t have previously.

 

Jesse Boeding (08:31.792)

And I guess I would just put a pin in the fact that I don’t know that AI yet is smart enough to navigate all of that because every institution does it completely different. There is no model and the beauty of the people on the ground, they know. That advising staff, they know things that nobody else knows. They know where there are exceptions, where there are…

 

where there’s a little cushion in the system that you can push against and make it work, right? But I think that the other element is that AI provides not just power to the learner, but if we can get that initial evaluation done faster, it gives an ecosystem blueprint for the administration to be able to say, if we did…

 

If we offered more of this class, I could actually entice more students into the system, right? It’s a rising tide lifts all boats type of experience. But the reality is to what you’re saying also is that we are living in a world that’s highly consumeristic and we’re operating in an education evaluation credit mobility ecosystem that is a little bit dated.

 

And so we’re going to have to rethink how we navigate.

 

Matt Sterenberg (09:59.34)

And I really like how you broke down in the paper as well, just the different types of AI. These are things that I think are, when you hear it, go, yeah, that makes a lot of sense, but having it broken down that way. if you’re listening to this and you’re, obviously you’ve heard of AI, you’re dipping your toes in it, but I think it does a really good job of explaining all the different iterations of AI. I’ll just highlight them briefly. There’s conversational AI, which is a chat bot, predictive AI.

 

Generative AI, which we think of typically with like chat GPT and then prescriptive AI, like based on what we’ve heard in the past, what do we predict to happen in the future? And so I think that’s a good breakdown for people as well. If they think about the applications of AI, how are we actually, what AI do we want to use for this specific task? What do we think is going to be the most beneficial for learners? But tell us a little bit more about the survey that you did.

 

the landscape of higher ed, credit mobility and AI specifically. Who did you survey? What are the results? And what’d you find out?

 

Jesse Boeding (11:05.744)

So I won’t want to talk about it with a nap because that’s all Wendy, Wendy ran all of that. What I will say.

 

is this, is, you know, in some of the work that I’ve been doing with, with surveying and around credit mobility, and how, how institutions are thinking about doing it, I think that there’s a big disconnect in between what people understand and how they’re using AI in their daily lives and how they’re interacting with AI.

 

and how that translates into the potential that we have on our college campuses. And so it’s going to be really important that we kind of step back for a minute. And to your earlier point, there are lots of different types of AI. And depending on the engine that you use and whether it’s a closed environment or an open environment and the training that we’re going to be doing with that AI, there is a lot of opportunity to get this right.

 

AI is not a time saver. just really, if one thing that people walk away from this is that AI in and of itself is not a time saver. Properly trained AI that has really good data feeding it and is constantly being trained is incredibly powerful and will save people a lot of time. So it is really important to parse that point because

 

AI is like a child. You can train that child to be whatever you want it to be. It’s going to have some affinity, right? But you can really hone that child to be respectful and kind and a good human being. And you can also neglect that child and it can go off the rails. And so we have an obligation to really, really understand that and how what we put in is what is going to be what we

 

Jesse Boeding (13:12.686)

So yes, the lift that we can get from AI is substantive. It is going to take humans, and this is why I don’t worry about AI taking everybody’s jobs, is because it’s going to take humans to really think about how it all fits together.

 

One thing that we didn’t talk about was a genetic AI where it’s basically an independent, right? It’s an independent being and can build and create on its own. And so if we back into that is we want to get, we want to be, we want to keep everything in its lane. And so there is, there is a reason why credit mobility right now is super hard. And that is because every institution has its own rules.

 

We have a lot of volume of not only micro credentials, but people, 140 million people that potentially could be using different types of credit mobility to move and be able to get new jobs or better jobs or completely different types of jobs. But we’re going to have to, we’re going to have to get really, really specific on that to be able to find a little bit of volume. And that is going to start with some.

 

definitions and getting everybody’s nomenclature on the same page to your point. AI is not AI is not AI. There’s a lot of different elements of it. And then really thinking about what we’re trying to do. Are we trying to make it better for the student? Are we trying to make it better for the institution, for the employer? All of those things can fit together, but whoever is the primary, you’re going to build that system a little bit different, I think.

 

Matt Sterenberg (14:58.016)

I love the AI as a child analogy. And it’s clear, Jesse, you fall within the nature versus nurture. It’s nurture. You got to nurture the AI to get the results. like thinking about that analogy, I was like, AI could really be this employee that’s been around a long time, right? When the employee starts, you have to train it, train him or her, make sure that, you know, they…

 

Jesse Boeding (15:18.51)

Yeah.

 

Matt Sterenberg (15:26.292)

have all the right inputs that they know how the system works, all that stuff. It’s not just inherent, you know, to the AI, but then over time has all this institutional knowledge that you’ve got. It doesn’t forget. That’s the beauty. They don’t take another job, you know? Yeah. And you can run a lot more through it. So, I think that’s a really good framing for people of just thinking like we’re onboarding this new employee happens to be AI.

 

Jesse Boeding (15:39.342)

and never gets tired.

 

Matt Sterenberg (15:52.482)

But we have to give it everything that we would give someone that was going to do this position is I think an interesting framing.

 

Jesse Boeding (15:56.698)

Yeah. Yeah. When I work with faculty, I often talk about it as a TA. It’s your teaching assistant, right? That when you first have your teaching assistant, they don’t really know how you like to do grading and how you like to work with the students. But in short order, they understand that this is how you do your grading, but it takes some interaction, right? They’re not just going to be able to read your mind. And it takes coaching. So.

 

That’s why it’s really, really important that people understand that there is a training component to this. And we have a responsibility to be actively engaged when we’re working with new potential products is how do you train your product? Where’s the data coming from? How do we know that it’s our data and not somebody else’s?

 

And it isn’t even about PII, which everyone gets really excited about. It is about this notion that you can train the child on what I want it to know, or we can train it on what my entire neighborhood wants it to know, and there might be a lot of variation.

 

Matt Sterenberg (17:11.47)

One of the differentiations you make in the paper as well as differentiating the difference between credit mobility and the transfer of credits. I think there’s, you if you were to ask me, what’s credit mobility? I would say maybe like, I don’t know how you get credits from one area to the next, but you really highlight the difference between credit mobility and just the transaction of credit transfer. Tell us a little bit more about that.

 

Jesse Boeding (17:41.104)

We often conflate the two, right? That to your point that it is, well, I took this class here and I get credit here. So A, P, I, B, CLEP, I went to a community college, I went to another university or college, I’m transferring it to this institution. Yeah? And so there’s a notion of like, these things match.

 

I took financial accounting here, you’re offering financial accounting here, these two things should be a fit. That’s not always the case, right? Because there is a notion that as you are moving from institution to institution, that different things matter, right? So one institution, they’ll take your certificate, they’ll take your life experience.

 

they will do an apprenticeship program or a internship and they give you credit.

 

If we’re not able to move that currency, right, if you think of that learning as currency, it makes it really difficult for learners to move from one institution to the other. So if I was in the military for a couple of years and I was at five different bases, and each of those bases I took classes through different institutions, plus everything that I was learning in the military,

 

How does this new school that I’m thinking about going to take all of that?

 

Jesse Boeding (19:18.84)

That is credit mobility, right? And being able to move and be able to know that it has value. If you think of the old days, and there will be some people listening to this that will remember doing a study abroad perhaps in Europe or going traveling in Europe, when every time you got to a new country, you had to change and do a currency exchange, right? And they would always take a little bit off the top, right? So you had $100.

 

Matt Sterenberg (19:47.31)

Thanks

 

Jesse Boeding (19:48.624)

and then you got 100 pounds and then you went you got to the next thing or you got 98 pounds and then you went to the next place and then you got some francs and then you went to some place you get some deutsch marks right and every time somebody was taking a little bit off the top to take that extra thing in an ideal world for credit mobility we would say you already know this you’ve demonstrated knowing you can go to any of these places and they will accept it as that

 

Matt Sterenberg (20:17.314)

Yeah, like a zero loss currency exchange. The other side of this that’s challenging is, I went to a community college, and then I went to a four year, you know, I’m jumping from institution to institution. The community college took a certificate I had earned and they, got community college credit. Now will the four year take the community college credit that didn’t come from them, but they accept it. Like there’s the downstream.

 

impacts of, just because your last institution accepted it for this. It’s like, what was the originating credit that you got for this? think is interesting. Like, because you have some institutions that will say, I know you got this accepted at all these other institutions. We will not accept what they necessarily accept. So now you’re back at square one, but going back to your earlier point, like we have to understand all this. Like, why haven’t I been given credit for this? Like is a re re evaluation necessary?

 

Jesse Boeding (21:16.228)

Nothing.

 

Matt Sterenberg (21:16.68)

And even like a rules of the road, which, which institutions are communicating whether or not they’re going to do a reevaluation. And I think that’s an important process for the learner where so often it’s like anything medical where you’re like, how much do I need to advocate for myself in the doctor’s office or just trust that they know what’s best for me? You know, like, actually, I don’t think you’re understanding my symptoms as well, or like, how much does a student need to push back? And I think a lot of that is missing in our ecosystem.

 

today.

 

Jesse Boeding (21:47.726)

Yeah, well, there’s a huge power differential, right? If people are committed to access, a lot of people will say that they’re committed to access, but are they committed to access? This becomes a really, really critical question when we’re talking about credit vulnerability. I’d like to highlight, you know, I think that the work that Northern Virginia Community College has done with George Mason University, right? There is an agreement between the two schools. you take…

 

this class and these classes here, we will absolutely accept it here. The crazy part is that’s the exception, not the rule. That is highlighted as like this magnificent high impact practice, but yet it’s so rare. We have states that have actually put in policy that says that you need to accept the credit, but the way that it’s written, the school is actually the destination school actually has the final

 

the final power, the say about whether they’re gonna accept it or not. And then it gets truncated into like, well, it depends on which degree program you go into. So if you go into this engineering program, they’re not gonna take it. But if you do the BA in liberal studies, we’ll take it. It becomes.

 

Matt Sterenberg (23:03.48)

and then they change the course description. It’s slightly different now. So is it still? Yeah.

 

Jesse Boeding (23:07.438)

That’s right. That’s right. And so you can imagine now you’re going through the stress not only of moving to a new institution, you’re super excited because you’re saying, okay, well, if I, you know, I’m going to be in this great two plus two program. And then you get there and you’re like, Whoa, wait a second, I have my community and my associate’s degree done. Why? What do mean you’re not taking it? Like the faculty said no, right? Or our policy doesn’t require it.

 

So where does that leave you as a student, right? And then you have to make a decision. Do you take on more debt? Right? This is driving debt. You know, a major reason. If I’ve already taken financial accounting, why are you asking me to take it again? Is there a way for me to test out? Is there a way for me to document that I have it? Can I show you my assignments? But you’re all that burden is falling on the learner. It’s not falling on the institution.

 

And so what we know, mean, the Kale statistics are pretty strong. People who have their former learning recognized or their former experiences recognized are three times more likely to graduate. They’re more likely to take more classes. They’re more likely to be confident. And so, you you’ve got access, you’ve got confidence, you’ve got this notion that you want people to be learning in whatever element it can be.

 

But then you also think about the ecosystem costs, right? Where the individuals are sitting in classes and maybe dual enrollment is another example. So if you took dual enrollment in the high school versus dual enrollment on the college campus, some colleges will accept it both, some will only do it some ways depending on how it’s written on the transcript. You have all of these like kind of weird, weird, strange rules. How does this happen though?

 

because we don’t have a unified language and so we all have different nomenclature. So there are so many layers to this whole thing to peel back, right, of what happens. So at the end of the day, who loses at the end of the day? Actually, the society loses because everybody’s contributing in ideally into the system where we’re having an educated society.

 

Jesse Boeding (25:32.666)

But you’re taking credits, they don’t count. How does it work? What does it mean? And that’s, know, these pathways now are showing really great opportunities. you do something in high school, then you move into an internship program, then you move into the community college or the college and everybody has an agreement. That’s pretty great. It’s also kind of a luxury. Not everybody is lucky enough to be able to stay in the same region, to be able to sit within the same system.

 

and to be able to kind of progress just nicely along a structured path. So we have a lot of opportunity and a lot of upside for sure.

 

Matt Sterenberg (26:14.69)

Yeah, you highlighted the challenges today, is motivation is a big one, right? Just the longer it takes to just get answers back to a learner, they’re gonna be demotivated to re-enroll or whatever it may be. Added costs to degree, added time to degree, wasted money. You’re like, spent time on that degree and you’re not taking it into account, right? And then you obviously highlighted…

 

Jesse Boeding (26:37.081)

Right?

 

Matt Sterenberg (26:41.76)

early loss credits, and then just the new types of credentials only make this that much more important because there are new credentials, new skills and competencies, new data related to them. How do we think about doing this? And if more human intervention is necessary, that means we’ve got to save time somewhere, right? We’ve got to have a model that adds to this. Yeah, I think the credit mobility dream is, you you upload everything and you can get a snapshot into.

 

All right, if I attended this college, how long would it take me to get an engineering degree? How long would it take me to get a philosophy degree? You’d has had, you can really shop. And I think a lot of people maybe recoil at that. They think it’s commoditizing education. I think that’s the subtext of a lot of how people feel about making it really efficient. And I think almost sometimes like we need the human element, which of course we do, but there’s an element where I think it’s a challenge to the

 

way we’ve traditionally done things, right? Where it’s, we get to be the deciding factor, you know, we ultimately get the judgment. And I think there’s a little bit of a pushback against that at times, because in your research, 94 % of people recognize the power of AI. Only 15 % have implemented a solution, which isn’t surprising. AI is still pretty new. Okay. Let’s pump the brakes. We don’t need to, you know, involve it in every single thing right away. And we want to test it out, but

 

I do think there’s maybe even a subconscious, like we don’t want it to be too, like there’s an art. It’s not just a science. And I think if you talk to people in credit evaluation, they would tell you that it’s not just inputs, outputs. There is an element where you need human intervention. There is an art to this as well.

 

Jesse Boeding (28:33.479)

But can you imagine like you book a flight and you want to go to Hawaii, right? And you get on the flight and you’ve got your swimsuit and you’re ready to go. And then somebody is like, well, actually, this flight is going to go via Maine.

 

Matt Sterenberg (28:52.46)

Yeah, or we need 50 bucks from each of you to take off. Okay. It’s gonna, and you know, it’s gonna take us two hours longer. We’re gonna take the long way, you know? Yeah.

 

Jesse Boeding (29:01.006)

Yeah, yeah, yeah. Like you’re kind of going, not only are going the wrong way, but it’s costing you more. And you kind of aren’t actually sure if what you’re like contributing to is actually going to get you, get you to go there. And you know, we have, we have this really weird inability to think beyond ourselves, I think. Where…

 

In my life, when I go to the grocery store, when I go shopping or traveling or whatever it is, I’m engaging with technology and AI and I’m sharing my information all of the time. That is good for many reasons, but it usually helps bring some clarity and transparency. This might be new to in the education world.

 

from a credit mobility standpoint, but it’s already been proven out in all of these other environments.

 

I can move my money from one bank to the next bank in seconds. There might be policies where if it’s a certain amount, I have to wait a couple days until I get access to it, but I certainly can move it. I don’t need to go and get a check and pick it up at one bank and drive it over to the other bank and deposit it and show my ID and everything else. You don’t need to do that. And arguably our money is like our most precious commodity.

 

Matt Sterenberg (30:36.372)

is an actual currency. Yeah.

 

Jesse Boeding (30:37.91)

It is an actual currency. So we already have demonstration that these things work.

 

The question is, can we get out of the way of it and say, you know what, this is working in these other elements, we’ve got to figure out a way to do this. There are some common things we can get on board with. We can get on board with nomenclature. We can get on board with timelines. We can get on board with how much it costs and having full transparency, right? Why is it that we have to wait?

 

Matt Sterenberg (31:11.562)

And I think these models can also like they’re learning. so, this state calls it this, this state calls it this. We see that you’ve accepted 99 % of credits when it’s called this, but you call it this. that looks like that’s a good pathway or that, that we think you’re, mean the same thing, even though you’re using different words, like the power of that I think is, is massive.

 

Jesse Boeding (31:29.423)

Mm-hmm.

 

Jesse Boeding (31:41.498)

Yeah, absolutely. And let’s just be super clear, higher education in general is commoditized. It is, that’s why people pay over $100,000 to go to a particular brand, right? We’re already in a fully commoditized industry. People will give up all of their dual enrollments, all their APs to go to a certain type of school, right?

 

Matt Sterenberg (31:44.097)

Is the-

 

Matt Sterenberg (31:53.932)

Mmm. Yeah.

 

Matt Sterenberg (32:07.574)

Yeah, it’s like a commodity, but also everyone’s trying not to be a commodity. Like we’re different than everyone else. So yeah, it’s interesting. We can do a whole nother podcast about that, but Jesse, is there anything that you wanted to highlight that we didn’t get into anything from the paper or your work with credit mobility that you want to make sure the people listening take away?

 

Jesse Boeding (32:16.496)

Yeah.

 

Jesse Boeding (32:32.24)

Well, I think that we should have a call out for all the institutions who are trying really hard to get this right. And that there’s a lot of people that are invested in making it work. The reality is that we all actually have to get on this train and move it in the same direction. And so I think it’s more of a call to action of being able to say AI is here to stay.

 

We need to figure out how to make it work within our own context. AI has incredible value for credit mobility. And there’s no excuse that we don’t look at that. And that credit mobility needs to be more than course to course, right? Credit mobility is everything that the individual knows that they’re bringing to bear to bring it into the institution, to reduce the time and cost of completion.

 

whatever that credential is and it benefits everyone. Credit mobility is a rising tide and everyone can win and so this isn’t really about quality and it’s not about losing losing dollars. It’s really about accepting the fact that this is good for everyone and it’s a huge opportunity maker.

 

Matt Sterenberg (33:58.424)

Jesse, really appreciate you joining me. And I think what you’re saying too is, now is the time where we can craft what this means for higher ed, what it means for institutions. like, let’s take control rather than sitting back and waiting for it to, this is gonna sound more dystopian before AI controls us. But there is an element of, no, like let’s get in there and let’s dictate to it.

 

what we want it to be and how it can help us serve learners and serve institutions as well. Thanks for joining me.

 

Jesse Boeding (34:31.056)

Absolutely. Thank you. It was great seeing you.

 

There’s always more to learn.