Kyle Bylin: Hello everyone and welcome to the Infinite Stream Podcast. This episode explores the current state of AI in higher education and the radical future that more powerful AI could create. I'm your host, Kyle Boleyn. Today on the show, I'm joined by Jessica Stansbury, the founding director of the Center for AI Learning and Community Engaged Innovation at the University of Baltimore. She holds a doctorate in instructional technology and a master's degree in experimental psychology. Jessica recently spoke at the conference Teaching with AI, where I saw her presentation on why AI training isn't enough, the hidden gap between faculty awareness and institutional change. I really enjoyed her talk because it gave an executive level perspective on AI literacy initiatives at a major college. And I also love the fact that you included an assessment tool that everyone could use. So Jessica, thank you so much for coming on the show today. Jessica Stansbury: Yeah, thank you so much for having me. Kyle Bylin: So to start out, Jessica, could you tell me a little bit about the Center for AI Learning and Community Engage Innovation? What's kind of like the overall mission in terms of what are you guys hoping to accomplish at the University of Baltimore? Jessica Stansbury: Yeah, so a a little quick origin story. So I also serve as the director of teaching and learning excellence for our Center for Excellence in Learning, Teaching and Technology. And when I took that role, which was 2023, AI kind of became massively public in November of 22 and then it blew up the scenes. So one of the first things I was tasked with was, Hey, can you help us with this AI problem? And I was like, Yeah, sure. and in doing that, started leading a lot of the AI initiatives. But what we found was that it was kind of overpowering the teaching and learning center. And the teaching and learning center only serves faculty. So AI, as we know, impacts more than just faculty. So that really became the birth of the Center for AI Learning and Community Engaged Innovation because we needed a neutral space. That all of the schools within our institution could connect with. So this center sits underneath the provost office rather than with one specific school, which really gives it kind of the neutral context to be able to collaborate, share without it being tied to one specific discipline, which I felt was really important in order for it to grow. But also that it serves not just our internal institution of faculty, staff, and students, but it serves the community. So that portion of the community engaged piece is really central to who we are and our mission. And our mission is to cultivate AI literate community engaged leaders so that they can solve real world problems using AI. And our focus really has been a lot, aside from we will always serve our institution, is how can we really serve the communities around us because higher education is really primed to do that and doesn't do enough of that. Kyle Bylin: Mm-hmm. Mm-hmm. And on top of that, what are a few of the initiatives that you've overseen that in your eyes have been most successful in terms of helping build AI literacy amongst faculty and students? Jessica Stansbury: Yeah, so in the beginning, the first thing that we did was, and this was part of that presentation, was we did a assessment across the landscape. So we we initially became part of initiative at the time called Making Generative AI for higher education research initiative, and that was with Ithaca SNR. And it was essentially a cohort of, I think about 19 institutions, international and national, mostly top-tier institutions. and when I saw this initiative happening. I'm a risk taker myself. So I had reached out to them despite the selection that they already had and said, I know we're not like the other institutions that were not an R1 institution, but I really think we could contribute to this process. So they were like, sure, we'd be happy to have you. And in doing that, we became part of that initiative, which allowed us the space to do that initial assessment. the institution. So we asked faculty, staff, and students where they were, what their perceptions were related to AI, what they knew about it, what they were using. And then from there that became like everything was grounded in data. So that was our data point moving forward. So we knew from there we needed education, right? So the first thing we did was we created two courses, asynchronous courses, one for faculty That was five weeks or five modules and one for students. And then, because I'm also a researcher, we assessed that. We wanted to make sure that those courses weren't just courses, but they were courses that were going to improve or increase confidence and knowledge and understanding related to AI. So we did find significant increases more so in the student side of the house than we did in the faculty side of the house. And then After those two courses, we said, okay, now what do we do? Right? We're educating, but is that enough? So Kyle Bylin: Mm-hmm. Jessica Stansbury: then we created our first AI annual summit, which was back in 24. And we brought together industry, community organizations, we brought together nonprofits, community residents, faculty, staff, students on a three-day summit. And essentially the goal was really to start to bridge the gap that often is between industry and academia. Because Kyle Bylin: Mm-hmm. Jessica Stansbury: we were seeing a lot of curriculum like blowing up all over the place related to AI, but we didn't really have anything that was guiding the curriculum except for maybe computer science expertise related to machine learning. But Kyle Bylin: Mm-hmm. Jessica Stansbury: that doesn't mean that everybody needs to know how to do that, right? And and how does that impact the workforce? So we were really intentional in trying to bring in that perspective so that we could start developing AI curriculum or AI courses that were related to what industry actually what what we were seeing in the industry space. And we've continued to do that now every year. and we did do another assessment in 2025. So we've been doing a lot in that space. Kyle Bylin: As you've gotten to hear more from industry stakeholders and faculty and the like, how has your understanding of AI literacy evolved from kind of this academic concept to what we're hoping to be would be real-world applicable skills and knowledge bases? Jessica Stansbury: Mm-hmm. So that's a great question. And when I what I have learned over the last almost three years, I guess, in this space now is, you know, we we hear a lot about AI literacy, which is fundamental, right? So we need to have a basic, fundamental understanding of ethics related to AI, you know, what it is, what it does, how it works, you know, basic, how do we evaluate the output that we're getting literacy piece? And then we also hear more so recently about the AI fluency piece, which is now how do we use it? Right. And when we think about AI fluency, meaning Cali's perspective on AI fluency, we're not just thinking about use, we're thinking about the what, the where, the why, the how, and the when of use. So is it We are not, and we often, at least I often get placed in this category by others, is like, she's just like all pro AI. And that's not really the case. The case is really to teach people so that they can make informed decisions on is this something I need to outsource to AI? Is this something that AI can help me improve for this reason and why? Or is it something that AI shouldn't be a part of, right? Like, how do we make those kind of decisions? And then from there. Which are decisions that are being made actively in the workforce. And then from there, I now kind of shift us into this AI readiness, right? Like what does it Kyle Bylin: Mm-hmm. Jessica Stansbury: mean to be AI readiness? Is it having a fundamental of AI literacy and then having a fundamental of AI fluency? And then that gets us to AI readiness. But when I started to really think about and define what AI readiness was. To me, it's the ability to take those two things of AI literacy and AI fluency and be able to translate that back out into the communities that you serve and that you are part of, right? And really those communities are and and it doesn't have to be just communities, it could be the workforce, it could be the industry, the organizations, right? But how do you take that and translate it to meet their needs? And then if we can do that, then I think we can. move forward maybe in multiple directions, but at least we'll still end up at the same pathway if that makes Kyle Bylin: Yeah, I can see that. Well, and like during this time period, you know, even from the teaching with AI conference until now, it definitely felt like, in my opinion, that a lot of higher education is still very much in the chatbot era and is still trying to understand what exactly an agent is and how it factors into the larger picture. But when I went to the AI four conference in Las Vegas, you know, they weren't talking about chatbots at all. It was all about agents, all about automation, all about the, you know, the visualization of workflows and the different ways that they could be broken down and taken care of with agents. And it just kind of made me realize that like we're just not there yet in terms of like what we're teaching. But in the same way, it's like almost our students aren't there yet because like while they may be outsourcing some of their thinking and some of their homework, the whole idea that like agents could now perform some level of that homework is like this whole new Rubicon that we've kind of crossed with the MIT study that basically suggests that a lot of undergraduate homework is now automatable. Jessica Stansbury: Mm-hmm. Yeah. So agentic AI is really interesting to me. I get, you know, kind of excited when I start to hear about how people are using it and thinking about it. And, you know, I think what you're what you're saying is also touching on a really important point of when we ask people how, you know, do you use AI in your work in some way, shape, or form? Most people will say yes, right. And there is a study by Ernest and Young there. you know, I think they looked at like 15,000 people and those people, 85% said, yeah, I use it. But then if you ask those people, are you using it in a way that is transforming how you think about work, or are you using it in a way that is transforming the performance of your work, only five percent say yes, right? So I think, you know, what you're touching on is is just so important. And I I think it's hard for higher education to get us over that hump because for a couple reasons. The first reason I think is we're stuck in this constant loop of AI for cheating, outsourcing critical thinking, you know, AI is bad. Let's not, you know, like and and and I'm not saying everybody says that, but that seems to be the loop, right, that's been playing for at least two years. and the lit and it ebbs and flows, right? Like some weeks you don't hear anything about academic integrity and cheating, and then other weeks it's like full speed ahead. especially like this week with all the New York and the now LA schools saying they're gonna ban in K through twelve. But so that's one reason why I I think we're we're struggling to get students there because we're focusing on their inability in a way to want to learn. And I'm not sure Kyle Bylin: Mm-hmm. Jessica Stansbury: that that's necessarily the case, right? I think there's a lot of factors that go into that. I say that and also I'm not saying that it is not real that students are using it to outsource some of their work or that agentic AI isn't able to complete an entire course, right? Those Kyle Bylin: Uh-huh. Jessica Stansbury: are our current realities. But I think what it does do is it shines light on a problem that we have had in higher education. I've been in higher education now for almost two decades, and that is our assessment. We have Kyle Bylin: Mm-hmm. Jessica Stansbury: always said, here, I'm gonna teach you this information, and then you're gonna write a paper or you're gonna take a test and you're gonna show me that you've you've absorbed that knowledge in some way. And I was a faculty for over 10 years, I'm guilty of that myself. But in today's workforce, what students are being asked to do is they need to be asked to come in and they're going to be asked questions of, do you know what AI is? Do you know how to work alongside AI? Right. So there is something called an AI use portfolio that some companies are starting to ask for. And in that portfolio, it's not just show me what you did with AI. It's show me what you did with AI, but show me where you were in that process. Kyle Bylin: Hmm. Jessica Stansbury: Right. Like what was your critical thinking? Where was the ethics and the reason and the human judgment that was in that loop? Right. So you're you're going back to the process, which is the learning process. You can equate it or make it parallel Kyle Bylin: Mm-hmm. Jessica Stansbury: to the learning process. And you're making that visible. You're making your humanness visible working alongside the AI. And then they're saying, okay, now what was your return on what was your ROI? And Kyle Bylin: Mm-hmm. Jessica Stansbury: So that means now the student also has to think, or the employee that's going for that job also has to say, did I need to outsource this? Or was this like Kyle Bylin: Mm. Jessica Stansbury: a grant, for example, that would have taken me six months to write, but now I was able to do it in a month. And that Kyle Bylin: Yeah. Jessica Stansbury: is that's a good return on investment, right? But that's not always the case. So I think the same thing applies in the classroom. If we can get to the point where we start to understand that what The workforce is doing through automating these workflows is not replacing people. It's acting Kyle Bylin: Mm-hmm. Jessica Stansbury: you still need that human judgment in the loop, like whether it's you're using agentic AI for some type of advising or tutor or like, you know, workflow in HR so that if I want to understand the benefits better, I could have, you know, it it's gonna provide me that information. But somebody still has to be on the back end. Kyle Bylin: Mm-hmm. Jessica Stansbury: And I I think we miss that piece. And because we miss it, it's a struggle, right, to get it into the higher education space. Kyle Bylin: I I think you're touching on so many different aspects of of Jessica Stansbury: Okay. Kyle Bylin: the challenge here. The the first one I wanted to touch on is the notion of a of a portfolio. I mean, I think that's becoming more and more of a thing for for my previous career, which was in user experience design and research. It was very common to have different design assets or research assets that you would have to basically walk a hiring committee through in terms of your thinking, your methodology. your outcomes, your impacts, and all of those things. And now thinking about how those workflows have been transformed by AI in the in the sense of like, well, now it really can write surveys pretty well. It can do the the data analysis pretty well. And increasingly even put together like the final slideshow presentation and analysis. And you really do have to like be mindful of like, well, where are your fingerprints in terms of how you're directing this? And not just solely relying on like whatever you get out the other side. But then there's this this other factor of like almost like the dupe factor nowadays of like, well, how long did that even take you to do? You know, like we we showed off student projects of different websites and applications that they had built over the a course of the semester, but we kind of knew in the back of our minds that like, you know, that could have taken them five hours or five minutes. Like sometimes it's not really sure exactly what work went into things and like where they actually struggled. And to Jessica Stansbury: Mm-hmm. Kyle Bylin: like how then you you show your work and show your thinking and show your ethics and show your literacy. Cause I suppose like in this age where anyone can kind of fake anything, you're really now looking for the people who actually can think through their own work, that understand the implications of it, and that aren't just using the tools, but like actually understand the outcomes and what they're doing, what they're being judged by. And that people that have become disconnected from that are probably people they they're not interested in hiring. Jessica Stansbury: Right. Yeah, and I think you know, to speak to that, transparency is really important in in these kind of conversations Kyle Bylin: Mm-hmm. Jessica Stansbury: and processes. And I I, you know, the transparency piece works both ways, right? So you hear a lot of faculty say, you know, I want my students to be transparent, and then that you hear the students actually say, I want my faculty to be transparent, right? And then I you know I speak to a lot of faculty who I know are using AI, but would they admit to their colleagues that they're using AI? Maybe not. Kyle Bylin: Mm-hmm. Jessica Stansbury: Right? And why not? Well, because we've built this kind of you know, I I hate to call it like a culture of shame, but I feel like we've we've started to build this this culture where you know you I'm gonna use it. I'm just not gonna tell you about it. Cause if I tell you about it, then you might think that I'm not smart or you might think that, you know, I just didn't care and I was trying to take the easy way out when really maybe I was just using it because it was more ROI for me, right? Like I Kyle Bylin: Mm-hmm. Jessica Stansbury: could do it in a shorter amount of time. But by taking, you know, I I've written a couple working papers recently and in the bottom of at the bottom of each of those working papers, I put a transparency statement related to the use of AI and then put like where the human judgment was within the loop, which was me essentially taking accountability of saying like, yes, I use AI, but I'm still responsible for what's in this paper. Right. Kyle Bylin: Mm-hmm. Jessica Stansbury: And that's part of the literacy piece, but of you know the ethical judgment and you know being able to say like just because you're using AI doesn't mean AI is responsible for the AI that it puts out, right? And there I was listening to I was in a kind of a webinar with Allie Miller, who I think is amazing, and she was talking to Mark Cuban, and Mark Cuban had said this at when he said it, I was like, yeah, it just seems so simple and we know this, but at the time that he said it, I was like, Well, that's really profound. But what he said was AI doesn't know the consequences of its actions. And then like you think about that, you're like, yeah, I mean, we know this, but just to have somebody put it in like that very kind of simplistic way, I it was just kind of a moment for me to like stop and say, Yeah, so why are we right like taking ourselves it almost feels like sometimes we're taking ourselves out of the loop, right? And and Kyle Bylin: Mm-hmm. Jessica Stansbury: then we're putting it on this machine that we are also in the same breath claiming is a machine. that doesn't have Kyle Bylin: Mm-hmm. Jessica Stansbury: feelings and empathy and emotions and a brain. So, Kyle Bylin: Mm-hmm. Jessica Stansbury: you know, it's this very interesting space I I think to be in. I think it's fascinating. Kyle Bylin: It it absolutely is. And I think another thing you kind of touched on there is like I feel like in the day-to-day of just like doing work, a lot of the ways that AI might be utilized to like refine a couple of sentences, do a little basic research and stuff like that, just doesn't really seem all that revolutionary. But then when you start handing it large amounts of work that used to take dozens of hours. And have it done in a matter of minutes, you you realize just how transformative it's becoming. And that like the end goal, or perhaps the near-term goal, is that eventually they do want to automate some amount of work and that you know their hopeful goal is to have some type of a digital automated agent that could act like a coworker or stuff like that. And What I've increasingly started to realize is that like I'm the bottleneck in anything that I'm trying to get done. And like if I haven't imagined what's possible by breaking down the tasks in a project, in a workflow, and started to reconfigure how I could do that with AI, then I'm not thinking on the scale that you know the 10,000 people in Las Vegas were thinking when they, you know, invented all these agent companies. So I think it's it's it's an interesting challenge to be sitting on such transformative technology that is moving so quickly. And in one sense, you're using it to rewrite your email, but in the other sense, it's fundamentally reworking how you work, but only if you push it that far and to continue to see where's the boundary moved in the last three to four months, not the past two years, the past Jessica Stansbury: Mm-hmm. Kyle Bylin: three to four months, because that's how it changes. Jessica Stansbury: Yeah, and it's you know, I was I'm amazed by the agent workforce and like having a a chief of staff agent and I'm on the I've been like teetering, right? Toying, learning, like I'm gonna make myself a chief of staff AI. and for someone like me that's a center of one with our AI center, right? That could be Kyle Bylin: Mm-hmm. Jessica Stansbury: really helpful for someone like me. and it still doesn't, you know, I I It comes down to I I call it the AI ecosystem, right? So some of the research Kyle Bylin: Mm-hmm. Jessica Stansbury: that we done, we kind of build out, we did some persona work as well. and we kind of we assessed the landscape across Baltimore and we came up with five user personas of AI. And you know, to me, that was a really insightful study because it showed me how much our lived experiences shape our interaction with technology, right? Kyle Bylin: Mm-hmm. Jessica Stansbury: And underneath all of those personas, those five personas, what actually sits is what I call the AI ecosystem of understanding, access, trust, participation, and agency. So Kyle Bylin: Mm-hmm. Jessica Stansbury: the things that keep us, like you said, from like that transformative shifting, right? To that, I'm just using this, right? To like how can this transform what I do? in a meaningful way is comes down to me to that ecosystem because I still struggle myself with trust, right? Like if I'm building this AI chief of staff and I'm like, hmm, do I want to connect it to that email? Or do I want to like build out a test email until I'm more familiar with it? Right. There's a trust aspect. And that same trust aspect that I feel, I'm sure, is what and what the data shows communities are feeling. when it comes to Kyle Bylin: Mm-hmm. Jessica Stansbury: a larger AI conversation and the students and the faculty, right? And that that along with trust is that participation. So where are they? Like where are their voices within that conversation that's happening? Right. Rather than feeling Kyle Bylin: Mm-hmm. Jessica Stansbury: like, you know, AI is doing this thing to them. So, you know, in regards to the New York schools and LA schools, I s I've been, you know, kind of following some of that. And what I see is a little bit of the par parental backlash of Kyle Bylin: Mm-hmm. Jessica Stansbury: parents being upset that now they feel like their kids are going to be left behind in the workforce because now they're not teaching or talking about this anymore. We're just pausing it. And that goes back to the participation piece, right? Like and having a a voice in that conversation. And sometimes it really it also comes down to relevance. Right, like people need to understand or see the relevance in order on on how AI is relevant to them or the work that they do, right? Before they even engage in that conversation. Kyle Bylin: Mm-hmm. Jessica Stansbury: So in the presentation that you were part of, that we were talking about institutional strategy, like that ecosystem is clear within that institutional Kyle Bylin: Mm-hmm. Jessica Stansbury: strategy, right? And and AI readiness. Can you get through all of those things? To build that ecosystem to be able to move forward. And a lot of the misses that I see are the lack of relevance, the lack of participation or agency, and the lack of trust. The understanding part we can do, right? Like we can Kyle Bylin: Mm-hmm. Jessica Stansbury: teach that, that's easy. The access part, I don't want to say that's easy, but it's still there. Like we can we can help with that. But those other three things are really human-centered things. And I I think that's where we need to spend a little more time in these larger conversations so that we can move forward. Cause you know, it is growing, it's growing fast, it's not going away, despite what some people might want to believe. Like I, you know, I think Kyle Bylin: Right. Jessica Stansbury: it's here. I I don't think we're gonna have a bubble, like people keep saying. And if we do, it's not gonna be for another five years at least. Kyle Bylin: Right. And by that point in time, even more powerful AI is already here. And even if it was a bubble, even if all progress stopped, we still haven't figured out how to leverage current AI to meaningfully transform the the work that's already there. Like there's a an institutional lag. Kind of piggybacking on this a little bit, Clay Shirke recently had a column in the Chronicle of Higher Education about how Jessica Stansbury: about now. Kyle Bylin: AI is this. Wicked problem where, you know, most people don't even really agree of like what the problem is. And meanwhile, there's a hundred different ways to go across solving it, but it's really a process that's going to be ongoing because it's never going to be 100% solved, especially not at an institution or like a university. I'd be curious, does this framing of a wicked problem kind of align with? what you're seeing in terms of AI on campus and trying to do all these efforts. Jessica Stansbury: I don't know that I would say. I I agree with that and I see it probably or hear it more in industry, maybe. Yeah, probably hear it in in higher ed circles that what is the technology trying to solve? What problem is that trying to solve? Like what are we, you know, what are we doing? Why do we need to have it? but I think it that gets back to that relevance point of like what Kyle Bylin: Mm-hmm. Jessica Stansbury: is the relevance and where is the need. I I you know, I don't know whether I agree or disagree. I think I'm really neutral on that because I feel Kyle Bylin: Mm-hmm. Jessica Stansbury: that higher ed is really good at making problems out of nothing. And then I feel like higher ed is really good at addressing really critical problems. And what Kyle Bylin: Mm-hmm. Jessica Stansbury: I really see lacking right now, what I would love to see more of is instead of the latest. potential scandal of cheating, I would like to see more about where is higher education in the thought leadership conversations that need to be happening around AI. Like we're not having them at a senior level that I've known Kyle Bylin: Right. Jessica Stansbury: or that I've seen. So until we start having conversations that actually, you know, like higher education, that's what we're primed to do, right? Is to be able to have intellectual discourse. And for Kyle Bylin: Mm-hmm. Jessica Stansbury: some reason in this AI, and you know, I have my speculations of why that is, we just miss that. Like we've we missed it and we focused on the wrong thing. And again, Kyle Bylin: Mm-hmm. Jessica Stansbury: not saying that academic integrity is not important, but in a lot of the cases that I read, there are always factors underlining those cases or news stories that aren't mentioned. Kyle Bylin: Mm-hmm. Jessica Stansbury: And you know, to There and you know, I studied my master's is experimental psych with the concentration in social. So I am all about, you know, how our different social factors influence what we do, especially in a teaching and learning environment. And it's never as simple as just one thing, right? It's never Kyle Bylin: Mm-hmm. Jessica Stansbury: as simple as just, well, the student wanted a quick way out, so they cheated. Or, you know, it's just never just one factor or they, you know. The test scores dropped dramatically when we brought them into the classroom. Well, what else was going on that day? you know, like so there's just all of these things that I don't think we're not having conversations about. We're just, Kyle Bylin: Mm-hmm. Jessica Stansbury: we're kind of like we get stuck. And I would really like to see higher education become more involved in the conversation of true intellectual discourse about just what you asked me. What is the problem right now? Like, Kyle Bylin: Right. Jessica Stansbury: why are we, why are we so resistant in some circles and we're not resistant in others? What is the workforce Kyle Bylin: Mm-hmm. Jessica Stansbury: doing and where is higher education in that conversation? Are we are we communicating with the workforce? If we know that industry is going to make these requirements of our students, what does that mean for us at a curriculum level? Do we want to at least give every student a basic AI literacy understanding? And there are some institutions that are doing this. Kyle Bylin: Mm-hmm. Jessica Stansbury: our institution is not taking a mandatory stance on, you know, putting students through AI literacy courses, but there are Purdue is, for example, Kyle Bylin: Mm-hmm. Jessica Stansbury: and other institutions. So I I would like to see more of those conversations happening to answer the. Kyle Bylin: Yeah. Jessica Stansbury: That question because I I couldn't answer it and I don't think one person can answer it, but I think if you get all of the people with the various different subject matter expertise sitting in the same room doing what Kyle Bylin: Mm-hmm. Jessica Stansbury: they do best, which is talking, then let's talk about something that's meaningful and and and like propel us forward and be higher education again. Like people are asking Kyle Bylin: Yeah. Jessica Stansbury: what is the value in higher education? Well, to me, it's in thought leadership. But I haven't seen Kyle Bylin: Mm-hmm. Jessica Stansbury: us focus on that in the last year. Kyle Bylin: Right, right, right. I think what what resonated me with with Clay Shirke's post was just this kind of like broader idea of like the wicked problem in general, which is Jessica Stansbury: Mm-hmm. Kyle Bylin: like one of the the takeaways that I took from your talk was this idea that you guys had a a bunch of different initiatives that you've been doing over the course of two years, but once you actually tried to assess your efforts, One could argue that it was kind of like two steps forward, one step back, just because, you know, some of the engagement went down amongst the faculty and students. And I saw that in other people's presentations where, you know, they had five different workshops that they did, peaked at the middle, and then the last two no one attended. And I just kind of realized like, we're gonna be working on this forever. Like this is a problem that's never gonna be solved. No one's gonna 100% agree on the way to solve it. And even in our best efforts to address it, like the situation is just gonna keep evolving in terms of like faculty perspective, student perspective, which is obviously now centering more and more on the environment of data centers, of of water usage, like the ethics of it and stuff like that. And You can you can hope and seem like you're bringing this like wonderful knowledge to them that everyone wants. And increasingly it's kind of like hard to even gauge who beyond like the optimists or the the AI accelerationists are. And there's really this like messy middle process of now trying to like do this work with what we're given and trying to grow Jessica Stansbury: Mm-hmm. Kyle Bylin: these efforts without necessarily knowing what success even looks like. Or what sustainable Jessica Stansbury: Yeah. Kyle Bylin: success looks like. Jessica Stansbury: Yeah, a hundred percent. we see you know, f like you mentioned from the the talk, and you know, it's nice to hear that other institutions are seeing similar data. And you know, I wonder, and I was just talking about this this morning with somebody else, and we are so fragmented and siloed in higher education, right? And we're we're not just fragmented and siloed. across like institutions, we're also fragmented in if you just take one institution and let's say you take R1 or an R2, a very large institution. And then you have, you know, you're siloed at the administrative level compared to the faculty level. So you have silos, right? Faculty administration, separate half staff is considered completely separate, despite the fact that they might have the same degrees as the faculty, right? Like it's this dichotomy. And then you have schools or colleges. If you're at an oral, you probably have a lot of colleges, they're siloed from each other. And then you have schools within the colleges, those are siloed, and then you have departments within the colleges, those are siloed. And then even within a department, within a psych department, which I used to be part of the psych department, there's silos based on your discipline, right? So now imagine that amount of silos. And then you throw in something like AI that impacts all of those silos, right? But the silos don't know how to communicate with one another, right? Because that's the point of being siloed. So in turn, you have all of these fragmented efforts. You might have business doing webinars, you might have a teaching and learning center doing webinars, you might have arts and sciences doing workshops. But they're not communicating with each other. So I wonder if some of what we see in the decline is that people are just burnt out because there's so many initiatives. And then it's Kyle Bylin: Uh-huh. Jessica Stansbury: kind of like, well, where do I go? Because if I go to this and this one, I'm learning, you know, here's how we think about AI readiness. If I go to this one, I'm I'm learning about AI literacy, but that AI literacy might not match this school's AI literacy. So now I'm just confused again. And I don't really Kyle Bylin: Mm-hmm. Jessica Stansbury: know where to start. And then you put that, you imagine that at a faculty staff level. And then you bring in the students. So now imagine all those students are seeing all these fragmented efforts, right? And different views. And part of that is good because it's intellectual discourse of higher education to be like, okay, we have different views, but none of those silos are bringing people from Each of them together to share and have a conversation about their views related to AI or their perception. Kyle Bylin: Mm-hmm. Jessica Stansbury: Which, if you did that, right, like if you think about it from a systems thinking approach, it would reduce some of the confusion that is out there among the students because now they can have a broader understanding because they're seeing the interaction. Between why arts and sciences might be thinking about environmental factors and cybersecurity is over here saying, well, we need to think about this from governance and guardrails. And then we have engineering who's like, let's build the next robot, right? So, but if you bring all those people together in the same space and have that conversation, then I think you might see different data in the end because there's a shared responsibility. And I think, and I I said this recently. To someone, it's fascinating to me that everybody wants to own a piece of AI. But the Kyle Bylin: Mm-hmm. Jessica Stansbury: truth is nobody actually owns AI in the regards Kyle Bylin: Right. Jessica Stansbury: of like higher education, right? It's a shared, it needs to become a shared responsibility. And there needs to be people, senior level leadership, pulling people together across all of these silos and saying, hey, how do we make this work? How, you know, help me understand. Tell me your story, your lived experience. And then how do we translate that back out to the community so that they're prepared for the workforce, to the students so that they're prepared for the workforce and to the faculty so they're prepared to teach the students, right? So like how do we do all of that? And I would love to see that happen, but it's just, I mean, again, this morning I was I was talking to a library and even then there was fragmented efforts. And it's like, why are we not all working together? Kyle Bylin: Yeah, and like what what does it actually take to to do that? And you know, it's tempting to fall upon a a trusty task force or something like that, but Jessica Stansbury: yeah. Kyle Bylin: even in that, in its end, is not truly capable of managing the the wider project. Jessica Stansbury: Yeah. Kyle Bylin: and I I think what you're saying about the the silos and everything, not just within our own institutions, but higher education more broadly. Is that it's it's really hard to know like what is actually going on at our own institution, let alone at other places. You see the the fancy press releases that you know everyone at OSU, Ohio State University is gonna be AI fluent by 2028 or whatever it is, but it's it's hard to know what that means. And to zoom out a little bit to add to this kind of rhetoric. Open AI, as Greg Brockman recently said at the end of the call with the release of the Astra model, welcome to the AGI era. And Jessica Stansbury: Mm-hmm. Kyle Bylin: basically, what they're trying to signpost is this idea that whether you think it's this model or the next model, this agent or that agent, we firmly believe that we've reached a level of of generality with the intelligence of the models that we believe is is matches this like milestone. And I think it's interesting that it's coming right at a time when MIT is suggesting that like undergraduate homework across the board is now basically susceptible to automation. Cause I mean that would be like what artificial student intelligence. And you Jessica Stansbury: Mm-hmm. Kyle Bylin: know, arguably we've we've we've built something beyond that. So I guess like Zooming out, this would be my more direct question, is like when when you hear the the proclamations that we're now in this like wholly new era of AI progress and that it's going to keep going faster, what what kind of goes through your head when you hear this stuff? Jessica Stansbury: Buckle up. No. no. I well it, yeah. So there is there's freaking humor. yeah. I I when I hear and see the progress, the first thing I think of is what are we doing in higher education? Like I I mean, I think about this a lot. I spend a lot of time thinking about it. I recently developed a framework to kind of help think about AI from an institutional strategy lens. I call it the Align framework, which is, you know, advancing, you know, asking these kind of things. Does AI advance the mission of whatever organization you have, right? Or are we just doing it for the sake of doing it? Right. Are we bringing in the AGI because we think it's the cool next thing then we we have to have a piece of it? Or are we doing it because it legit like it moves us forward? the L is leading through partnership. Again, that goes back to me saying that no one owns it. We have to do this together. we also need to innovate responsibly. So there's there's your I, which is we need governance and we need guardrails, and we need policy. And the the issue with the policy is that higher education policy takes forever. I mean, by the time you get it through all the structures you need to get it through. faculty senates, the individual faculty senate, university faculty senate, then it has to go to the AG and then it becomes policy. it's obsolete if you're talking about AI space, right? So how do we what does that look like for higher education now? Like what what Kyle Bylin: Mm-hmm. Jessica Stansbury: is adaptable, flexible policy that is a living policy, right? And I Kyle Bylin: Mm-hmm. Jessica Stansbury: don't think we've been presented with that kind of challenge. And now we are. The G is to grow institutional readiness, right? Like from an instant, I think a lot of organizations and and higher education are focused on the fragmented kind of initiatives rather than what does it look like for an institution to be AI ready and how do we make that happen, right? And if Kyle Bylin: Mm-hmm. Mm-hmm. Jessica Stansbury: you looked at it from that lens, the institutional readiness lens, all the other stuff could fall into place. And it would align nicely in doing that. And then the last Kyle Bylin: Mm-hmm. Jessica Stansbury: one, the end, is navigating with evidence. We need to continue collecting data on everything that we're Kyle Bylin: Mm-hmm. Jessica Stansbury: doing. And we need to, you know, for example, we have enterprise access. We use BootleBox AI at our institution. And we also use Copilot. And it's interesting because even in that, we use BootleBox really for faculty and students because that's collaboration and the transparency. And our staff use it too for some bot. making stuff and really like sensitive data. And then we also have Copilot, which is more user friendly on the staff side because it, you Kyle Bylin: Mm-hmm. Jessica Stansbury: know, meshes with Office 365. But how do we track that usage? And how do we know what if the usage, you know, is is it just are we seeing engaged use of AI or are we actually seeing AI adoption? And how are those Kyle Bylin: Mm-hmm. Jessica Stansbury: defined, right? And they're different actually. but sometimes we just lump them all in and say, like, yeah, we have this really high adoption rate, but we actually don't. We just have some really high usage rates within what's being adopted, right? Kyle Bylin: Mm-hmm. Jessica Stansbury: So I would like to see more of kind of that leadership in in really being intentional about those things. And with the technology growing as fast as it is, and you know. Going back to your homework with the MIT report and saying, you know, AI can do all of the homework. Well, the reality is that AI, it's not just AI that could do the homework, right? Like there's other before we had AI, there were still other ways that people were getting their homework done. That wasn't them doing their homework, right? Kyle Bylin: Mm-hmm. Jessica Stansbury: So that takes you back to assessment. What are we doing? Why are we assessing in very traditional methods like multiple choice and essay tests still after decades of learning and the learning sciences and showing that you know we have tons of data that says, you know, let's focus on process over product and the workforce Kyle Bylin: Mm-hmm. Jessica Stansbury: focusing on process and product. But we tend to only do the latter. So You know, that's what I think about when I hear all these advances is I just kind of like put my head down and give it a shake and say, here we go again. Like, and then I also say to myself, we're about to have another freak out. It's coming. Right. Kyle Bylin: Right. Right. Jessica Stansbury: But I don't think we need to be, I would love to see a culture that is not a culture of fear. And I Kyle Bylin: Mm-hmm. Jessica Stansbury: think we still see a lot of fear in like replacement and identity, and you know, I get it. I understand it as an academic and you know, working most of my career towards, you know, having a a doctorate degree. You your identity is in that, right? Like a piece of you. And if there is some machine that can come and now do it without you, what did I do it for? But there's still Kyle Bylin: Right. Jessica Stansbury: I think we're we're we are as humans, we are the ones that are removing the value from that. I don't Kyle Bylin: Mm-hmm. Jessica Stansbury: really necessarily the AI can't do it. Right. It's Kyle Bylin: Right. Jessica Stansbury: not a a person or a thing. It can't make it can make decisions. I shouldn't say it can't, but it only can with us in the loop. So Kyle Bylin: Mm-hmm. Jessica Stansbury: who's someone that's really saying we're not valued as much with AI there? Kyle Bylin: Right, right, exactly. kind of building up on the the framework discussion and zooming out a little bit, let's imagine a student arriving at University of Baltimore in twenty thirty. If the next few years are are utilized well, what would you hope their education experience looks like? Jessica Stansbury: Wow, that's a great question. So I can use my imagination here. but what I would love to see, so that especially for the University of Baltimore, the University of Baltimore is a community-engaged institution. It is an anchor institution in Baltimore City. we have, you know, very applied schools. you know, we have four schools, we have law school, business school, we have arts and sciences, and we have college of public affairs. And all of them are very vested in the community, right? And work with the community. We work the College of Public Affairs, works with the government as well. And you know, we just we do a lot with the city. We have a lot of centers at our institution. We probably have over 15 centers at our institution. And all of them, especially within our law school, I think our law school has a little more than 10 centers and they're heavily invested in working with the community. So in 2030, I would like to see those in an ideal world, if was up to me and it's not, but in an ideal world, those centers become what education looks like. It would be a student enroll the University of Baltimore. Maybe agentic AI helps that process become a little smoother in the enrollment piece, hooks them with an immediate, you know, advisor who then says, You know, we're gonna create, you know, what is a cross-disciplinary degree that mimics the real workforce, right? Because it's no longer just one discipline. It's not, you know, someone who goes into law might also need to have a psych background these days or a business background. And that person, based on whatever it is they are interested in, could sign up to one of these cross enroll in one of these cross-disciplinary programs. That goes through the centers that gives them real world hands-on experiences with a faculty mentor or guidance within the center working on real projects with industry partners, law partners, business, you know, government entities. And then because it's 2030, I would even venture to say that they would be working along some agentic AI teams in Kyle Bylin: Mm-hmm. Jessica Stansbury: those. I won't say necessarily on the U-Bault side, although that would be really cool, but it would be on the industry side. Because by 2030, Kyle Bylin: Mm-hmm. Jessica Stansbury: there I I can, you know, there's a lot of predictions out there, but I would imagine that there will be a lot more agentic AI that Kyle Bylin: Mm-hmm. Jessica Stansbury: you will have to know how to work with. But I think that bringing enrolling them, putting them in a cross-disciplinary program that mimics the more skills you need in a real career. putting them through a center, getting that one-to-one kind of faculty mentorship and expertise while working on real projects with an industry or community organization or a law group, to me, that would be innovation at its best. Kyle Bylin: I I absolutely love that. I think that's great. I think a lot of the thesises that I've come up with are not totally different. basically the library becomes something akin to an imagination lab. And alongside guides, student teams work on wicked problems and basically see based on the momentum of AI, based on you know their intuitions or whatever. How much progress they can make on any kind of wicked challenges that all of our communities are facing. And you know, in the end, some of those challenges might even be caused by AI itself. But like having those teams together, doing real-world work, learning to work alongside agent teams, and like having faculty mentors that are also engaged in the process. would be fantastic. I think that's a future worth building. Jessica Stansbury: Yeah, yeah, absolutely. I I am I was a drug and alcohol therapist for ten years and I'm very passionate about the the substance use field. And one of the you know, I always say in my ideal world of AI, like we have all this AI technology. Why are we not using it for public good and trying to, you know create a wearable that can detect an overdose that can send real time intervention and then help someone right at that that moment get treatment and services, reduce the death. Now they're hooked up to services and treatment. And then from there you've also probably impacted homelessness, you know, family life and children, if they have children, you know, getting them out of the system. So there's like by taking care of one problem. at that level can systemically like help all the other problems right and AI we have the technology to do it like we could do it if we were thinking about AI on that level and that's not a chat GPT kind of AI right Kyle Bylin: Right. Jessica Stansbury: so that's the other thing like when we talk about it we always like the first thought for most is chat GPT or like Gemini or or Claw not Kyle Bylin: Mm-hmm, mm-hmm. Jessica Stansbury: the type of AI that could really solve these types of problems. And I'm not even an expert to throw something out there and tell you what it's called. but Kyle Bylin: Right, right. Jessica Stansbury: I know it's there. Kyle Bylin: Right. We're we're essentially using the research prototype from a lab right now, but like they have other ambitions, to be sure. Jessica Stansbury: Mm-hmm. Yeah. Kyle Bylin: well this has been a really great conversation, Jessica. Any other final thoughts you want to share? Jessica Stansbury: No, I've really enjoyed talking with you though. It I know some of it can get philosophical and but I just I really wish and I hope to see higher ed just kind of do better in this space in these kind of conversations rather than you know, I think we missed the mark when it when it came to things like social media and higher education could have done. We could have intervened in that and like educated Kyle Bylin: Mm-hmm. Jessica Stansbury: about it and and But we did it. We didn't really do much of anything about it and look at all the harm that social media has brought to, you know, body image and you know and depression and and you know Kyle Bylin: Mm-hmm. Jessica Stansbury: isolation. So I would I would like to see higher education just really get back into thought leadership and you know public good. Kyle Bylin: Absolutely. Well, thank you so much for coming on the show today. if anyone wants to look you up online or find any of your resources, where can they do that? Jessica Stansbury: yeah, they can check out University of Baltimore and they can just type my name in the search and I'll pop up. They could also check out the Center for AI Learning and Community Engaged Innovation. Also, short is Cali, C A I L I, and they can also reach me by email. Kyle Bylin: Sounds great.