Can AI Help Make Higher Education More Equitable?

to solve, we're never going to use it right. And so, you know, in that question, you know, how are we going to use AI to help with policy and advocacy, particularly for student success? And for equitable outcomes? In post secondary, I think that we need to like, zoom back a little bit, right, we have to start with the acknowledgement of where higher education is at where the sector is at in terms of equity and in

terms of completion rates. And then what are the policy and advocacy levers that we're supposed to, you know, use to ameliorate the situation? And really only then how, how can generative AI help at least that's how we see that Complete College America. So just really quick, running that through. Higher Ed, in my opinion, is not really in a great place, right. And I don't think a lot of people really know this outside of the sector. And it does, indeed need transformation. So

we'll start with some basic facts, right. So for full time students attending public four year colleges and universities, the number of full time students who graduate in four years from four year colleges is 40%. And that's a number that's buoyed by well resourced public flagship universities. For two year colleges, the two year graduation rate for full time students is about 18%. So that's, you know, less than one in five. And it's roughly the same for part time students at

community colleges who graduate in six years. So these numbers are lower for students who come from less resource communities for students of color. And it's especially true because colleges, you know, this, you know, it's a struggle to work with working learners, the most students who work more than 20 hours per week, rarely graduate, graduate, and when they do, you

know, they, they get saddled with debt. So the students who do go to college, the students who most need to be the beneficiaries of that transformative social mobility that that college can offer, these are the students who are most likely not to graduate and the way the current system is set up. So you know, this is happening, of course, at a time when a college degree has never had a higher premium. So not only do most jobs require a degree, but almost all good

careers do. And by 2031 Georgetown estimates, I want to say it's 72%, or about three and four US residents need to have a post secondary credential of some sort to meet our workforce needs optimally. And yet in the last few decades, you know, what we've set up is, you know, a financial system, where the Pell Grant only has a third of its original purchasing power in

tuition and student loan debt has skyrocketed. So in other words, hold the same Yeah, hold KiKi L'Italien: up like just saying that They think, oh my gosh, I had no idea. First of all, I had no idea that that was the case. But that's some. That's scary. That's really I mean, that's something that people really rely on to be able to go. And just sorry, I was just distracted by that was pretty crazy statistic added to all the other. Well, yeah, and I think that the big problem,

right is I mean, that's a problem in itself. But at the same time, we're also putting forward this message that's very accurate and true, that higher education, you need a credential now more than ever. So the only thing worse than being able to struggle to afford it while attending, which is something that, you know, causes student attrition in a very large amount, but the only thing worse is being shut out of the economy because you don't have the degree or certificate to be able

to participate in the first place. And so like, this is where we get to the those policy and advocacy levers for equitable student success, you know, and, and I, I know, this is a long lineup, but I am going to bring it to AI. But I think you can boil that down to three things, right? I think first, we

need to get out of our own way. And we need to get rid of bad practices and bureaucracy that prevents students from having purpose in their major structure to their academic experience, momentum to timely completion, and really having an enough support to complete college on time. And, you know, we'll we'll hear from, you know, a college president who's doing exactly those things. Second, we need targeted interventions to support students equitably. So not every student needs the same

thing, right, like, that's the whole premise. Some students may need relatively shallow support some deep, some need different individualized attention, depending on what their needs are. And then the third is we can't just efficiency our way

out of this, right. There's been a lot of studies that have gone on in high schools to show that, you know, you add a lot of high touch interventions in a under resourced school, you can see incremental gains in high school completion rates, but it's never going to be as strong as that, well, resource high schools, it's the same thing. In colleges, students success interventions can create incremental change. We've seen it in the last decade in the college completion movement, but

we'll hit a ceiling soon. And we have to fund higher education like we care about it. And so that's where generative AI comes in, not just on that funding piece, of course, but

essentially on those three areas, right. So like, first, we have to have a stance towards generative AI as something that is a power for good, not just a power for plagiarism, like to your point about all the controversy in AI around higher education, you know, it's been so much of the attention has been on this negative side of things, but we have to dive

headlong into its promises. The second is that we have to use it to solve the problems that we see in student success, you know, talking about having equitable interventions, the need to innovate and schedules and credit for prior learning for part time students need to get all students on semester by semester academic plans, and really liberate advising functions, you know, where counselors meet with students, so much of that is manual transactional conversations on

like, well, what course at what time, and there's not enough time to have those rich conversations where advisors are

able to give actual advice. And finally, and I would say, most importantly, we need to make sure generative AI isn't the sole purview of rich colleges, right, the MIT's the Stanford's the pens, the colleges, whose presidents are important enough to get yelled at by Congress, we need to make sure that community colleges, rural colleges, HBCUs, PDAs, and other minority serving institutions aren't working off hardware infrastructure routers

and switches from the 90s that are woefully inadequate to the horsepower required to optimize generative AI for student success. So, you know, I know that was a bit of a long answer, but I think it's good to like at the outset, just talk about, you know, the problems that we're trying to solve, because that's the only way we switch orientation or generative AI.

KiKi L'Italien: I mean, I think it was the perfect setup, because I think, you know, you provided a really great background and foundation for why we're having this

conversation in the first place. So thank you for doing that. You know, you talked about the promise of AI and the problems with success for many students why they can't be successful or find success and higher ed, and then not leaving all of this to the rich colleges that can afford to be left alone when they're when they're experimenting, and trying some new things. It's fair, it sounds scary, let's say trying new

things. I want to actually bring this over to someone who probably has very interesting perspective to share on it as well. And so so let's talking to you president Michael Basta. And let's talk about this, about from the perspective of a community college president. What are the practical challenges and the opportunities of integrating AI into the curriculum? What are the challenges that you're seeing?

have access to the technology. And so it is not feasible to just simply say, Well, we're going to put in our syllabus, you can't use any AI, because they're going to use grammerly, which is AI, they're going to use all of the other kinds of technology and tools that are going to be helpful, and that actually, the faculty do want them to actually have that

support. So I think it's important for educational institutions to resource and provide professional development for faculty, so that they can best integrate the opportunities

with AI. And particularly at the community college, when we focus heavily on thinking about the career exploration of our students and the kinds of career aspirations that they have, are thinking about preparing those students to go into the world of work for those who will go right into it, you want them to be prepared for the expectations of employers who when they get on in the workplace are going to expect them to work smarter and

not harder to be producing at a higher level. And so you know, even the basic premise of generative at the prompt, sort of the ability to get students to understand prop characteristics and, and how to use that effectively, will actually separate those out in the workplace who don't have

that orientation and background and skill set. And so, you know, we do a disservice to our students, if we are not arming them with everything they need to effectively navigate the realities of a changing landscape, whether it's in education, or in the world of work. And also listen, we have turned it in. So you can get your students to actually look at, you know, the content of Turnitin tells you not only about plagiarism, but it also tells you about AI generated

content. So it is not that faculty members are completely devoid of any kind of ways to ensure originality and authenticity of student work, it is to show students you know, use this for inspiration, use this for feedback, use this to help you sort of, you know, I can't be with you 24/7, you're at three o'clock in the morning, and you're doing your essay, I

can't be there with you to do that essay. But you can use this technology to help you spark ideas, but at the end of the day, you're gonna get credit for your originality, you're gonna get credit for your authenticity, and we shouldn't act like they're not going to have access to what they already

have access to. Right. KiKi L'Italien: I mean, I remember this back when we started looking at things like social media and, and in the workforce, what we saw was this desire to control by pretending that if we suppress this, if we said this technology is not allowed to be used in the air, what happened, you know, everybody's over here doing that, you know, like, you couldn't suppress it, you couldn't outline and when you're talking about something like generative AI, I think the

challenge is that, to your point, people are focusing on on weaponizing it in a way that it can be used for things like creating malicious code, or breaking the, you know, breaking the rules by like plagiarizing and things like that. But you could also be weaponizing it on the other side by not allowing people to learn how to effectively use it in day to day life, when it's such an effective tool. So

And, and sometimes we don't acknowledge that we invent the bad thing. And in this case, I think it's the opposite. It's like we just ran to Oh, no, we invented electrocution, without even bothering to see what sort of power we had in our hands that we could harness. And and I think, you know, to President bassins point, I think it's very telling that certain things that, you know, students with way more resources have been able to do forever, like pay people to write essays for them.

Now that everybody can do it, it's democratized. Oh, no, now it's bad. And I think that's a big talent, what our priorities are. KiKi L'Italien: I think that's really I mean, it's a great segue into the next question that I had because I want to reach over to Audrey and give Audrey a chance to speak to this

great play. But the work that you've been doing, you've been thinking about this quite a bit, you've been thinking about this, this topic and the issue at hand, which is, you know, what are we doing to actually take the power of some of this generative AI? And some of the advancements in AI to use it for good, right? And how can we, how can we allow higher ed to actually, you know, make the make use of this in a better

way. So you have a playbook, you outline in this playbook, you outline strategies for leveraging AI to increase college completion in equity. And so, I would love if you could just elaborate on one or two of the key strategies that would be particularly relevant, or association professionals who are listening to this now, or maybe they're listening to it later, for them to pay attention to on this.

space. And so I'm going to go a little bit more broad in terms of the practices or strategies that I suggest, because we're still in the early stages. And I don't think it makes a lot of sense to get too tactical right now. But But kind of building off of Charles and President bassins points, the first suggestion that I have is to think about how you can create learning opportunities for your higher education partners. In whatever industry you're in. I know that today we're speaking

to industries, really from across the the workforce. And so someone actually I saw from Facebook said, higher ed should partner more with industry partners. And I couldn't agree more. And I think that's what's so exciting about this podcast.

You know, I spoke with a college Provost recently who was working with their welding corporate partner to go learn all about how they are using AI in their welding work right now, and to then revamp all of their curriculum to have, you know, AI embedded so that they're preparing their welding students to graduate and with those competencies instead of having to get them out in the field. And as much as higher ed is a

source of learning and knowledge. We're on campus, right, like we're working and doing research and working with our students and community colleges, especially, are a really amazing intersection of the workforce space and the campus environment. And we with such a rapidly changing technology, we can't possibly know exactly how it's impacting your industry and less, the lines of communication are wide

open. And I think that because this is also a cost prohibitive technology, right now, there's a lot of upfront costs with building your own AI infrastructure, getting your

data even ready to the point where you could do that. The other I guess this is kind of a sub strategy, one recommendation would be to work with your industry or your higher education partners to support them in their learning, even if that looks financial, because it's an investment in your future workforce to ensure that they're at an institution that is preparing them for the expectations you're going to have for your future employees. And the second piece that I'll

touch on, is around thinking about things in phases. We kind of talked about this. Charles has a really interesting analogy of electrocution and electricity. But we do tend to think of the ends of the spectrum, right? Like what's the

most AI futuristic vision possible? And what's right now, but there really, in my mind are kind of three steps that we need to be thinking about, which might look different for every institution, depending on if you're an association, or if you're an institution of higher education, which is right now, the middle term, and the future. And I think that we can't miss that middle term, because we're in this process of building

towards that potential future moment. And if we're not kind of planning and thinking about all of those stages, we're going to do ourselves a disservice when we get to that middle stage. We also need to be thinking really creatively and big picture like Charles suggested around what is the ultimate problem we're

trying to solve? Not just how can we efficiency ourselves out of this situation in this exact moment, so keeping all three of them those timeframes as top of mind, I think applies to every industry right now. Because AI is such a ubiquitous tool that can apply to really any element of our lives. KiKi L'Italien: Yeah. And we had a comment from someone who is watching live who said, Remember when electronic calculators weren't allowed in the classroom? I do. I do, actually.

And, you know, we laugh about it now, but it definitely wasn't that long ago, relatively speaking. And, you know, it does feel like that, where, when you have these tools, why wouldn't you figure out how to use them. And almost it's a hindrance if you don't teach how to use them accurately and and in the best possible way to help people to do what they need to do better. Yeah, and if I could just add one piece to that, I think that AI the calculator is a really convenient analogy or

kind of mental reference point. I've heard some interesting pushback on that, because AI is applicable to so much more than just math, which, of course, is not just math. But I think that we need to really not just teach people how to use AI, but also how to decide if AI is necessary to use and you can't really make that distinction if you've never used it. So that's why we need, it might be good to learn your times tables, memorize your

multiplication figures without a calculator. But if you know that there's this thing out there that you've never been allowed to try, you might be very tempted. Instead, we should be I think, convincing people and giving them that kind of ability to judge for themselves when it's appropriate to use and when it isn't, that's a KiKi L'Italien: great point, because my daughter is getting ready to learn how to drive and she doesn't know life without

GPS existing. But we also know that sometimes it doesn't always work. And so Doesn't she need to know how to use an atlas if she needs to. Right. So these are things that I think it's a really good point, you know, when should we use it? When

Shouldn't we? And then actually is a thank you very much, Andre leading me into my next question, which has to do with some of the ethical considerations, when we're thinking about and specifically thinking about associations in this case, but it really could be any organization, when you're thinking about enhancing engagement, enhancing learning? What are the ethical considerations that you should

be mindful of? So this is an open question. I don't know if Michael, if you would like to answer it, or Charles or Audrey, but it's open to anyone who feels particularly passionate in their answer. Clearly,

due diligence. Because, quite frankly, you know, when we don't use this work, and we don't see it as a tool, and we think that it is a solution and not a tool, then when ultimately could be spreading false information, we could be, you know, actually

perpetrating a lot of frauds out there with, with information. So it's very important from the ethical perspective that you as a person who's going to utilize the AI that you actually do your due diligence, so that you're not kind of spreading out misinformation and bad information, quite frankly. KiKi L'Italien: Well, so Charles, were you about to say something?

soccer and I love that and thought, you know, why not? So I did terribly this year, which is another reason for the record, I have one before, but this year was not my year. And I kind of just gave up and I got a season recap. That was chat GPT generated by Yahoo. And chat GPT really just took my team name play like a girl, and the fact that I lost and ran with it. So this summary was absolutely full of suggestions that like a girl, I lost and had a terrible season and like a girl, I don't know

anything about football. And it was honestly, this. I think, Charles, you're misremembering, the Boston Globe to not pick this up. But I did submit, you know, feedback to Yahoo. And I think that, you know, as funny as it is, and generally, people are playing fantasy football as a hobby, and hopefully not in many professional contexts. But people can put kind of all kinds of things in their team names, and in their smack talk and all of that. And the fact that all of that was potentially

informing this use case for an AI bot. Unchecked is a pretty risky application so early in this in the time of generative AI. So hopefully, they learned their lesson, but hopefully also brought some laughs. KiKi L'Italien: Oh, my gosh, that sounds that sounds like it could have been a nightmare, would it? It is such a great example, though. Charles, I'm so glad you brought it up. Because this is it is you can easily see where that could have gone

really, really, really terribly wrong. If that had been at a larger scale. So excellent, excellent example. Well, you know, I was wondering about, for associations, there's an association for everything, as we like to say. And so certainly, there's always this concern about where can things go wrong? And how can we mitigate risk? I'm sure, in higher ed, you're very familiar with this concern about

mitigating risk. So are there ways for associations to advocate for or against certain uses of AI in education, to ensure that it benefits a diverse student body, because we have, we have associations that are looking for its members that are going into its industries, starting all the way back, going through school, very concerned about making sure that this science or this industry is receiving the type of education and protection and concern that it needs needs to have, and

certainly with a new technology, or something that's advancing so quickly? There's a lot of concern about like, Well, how do we how do we make sure it benefits our people, our students, the ones that are coming into our field?

you're trying to represent for your members. Because the problems that we're grappling with in higher education, poor to, you know, real estate, they poor to health care, you know, are you being, you know, problem first, in terms of what this technology can do? And then are you taking a comprehensive look on all the issues that your association is grappling with? Are you tracking those issues in a mutually exclusive manner, such that you know, exactly where AI could plug in, or any

technology for that matter? And what the limitations are? If you just don't do that, in that way, it's going to be, I think, a lost cause. The second piece is, is really getting directly to your question, which is, you know, how can associations interact with higher education on this? You know, I'm curious what others here, you know, Audrey President passed and think about this. But I think that we need to find a way to very quickly get AI learning outcomes into the heads of all

of our students in all disciplines. Because what I fear is going to happen is that a lot of social mobility ladders that exists to really good paying jobs, like really strong careers, like thinking like major accounting firms, major law firms, major consultancies, lots of these things start at

the associate level. And we're beginning to see the Inklings that a lot of those jobs that start these career ladders could go away, because AI can sweep these knowledge bases and do these things that end up in forming, you know, the the lawyers or the consultants of the, you know, engagement managers. And so, and yet, if if we the way At higher ed works, it's kind of slow, right? Like, we have to, you know, to get new programs approved and all this, you have to go through this

accreditation process. And you know, sometimes these are multi year things. And so I think, you know, there's, there's players in the field of post secondary, that associations, you know, and I think complete college, America is one of them. And I encourage folks to, you know, if they want to reach out after this, maybe they could have my contact info. But that can look at ways to make sure that the course by course level, and at the certificate level, that these competencies as the field

is just starting, that they get swept in right away. So then that way, students are ready in their existing programs of study. So we don't need to wait, the several years that higher ed always takes to catch up to these things, if that makes

sense. You know, I think for people who are not in higher ed, it, you know, so often, we think of the 7% of colleges that are selective enrollment that get hauled in front of the Senate to get yelled at and stuff, because that's where like, most of the media went to college, but like 93% of students are at, you know, non selective colleges, and most of them are in a resource, and lots of them through no fault of their own

have to move a little slower than they could otherwise. And so we need to find ways to fund that, but also disrupt it so that way, students are ready, not just in terms of the AI that is used to deliver higher education, which has been our discussion so far, but also being a successful lung, which is the point of higher ed is including being very competent in AI.

KiKi L'Italien: I mean, well said, and actually, I want to give a shout out to a friend of ours in the association space six degrees of associations that says love this discussion, so many aha moments in this one. And I have to say, first of all, check out their podcast to also can't can't help but agree. I mean, I think that there are a lot of aha moments I've certainly heard in this one. And and you know, Charles, when you were talking about it, I was thinking, absolutely.

Associations are always thinking about like, what's the future for the industry? What's the future for our members? How do we best prepare them, like higher ed, higher ed is also

thinking about this. And when you want to talk about some really great partnerships, all you know, higher ed, and the and the associations that are working to represent some of those industries, that we're all concerned about the students that are going through the individuals who are going through, and then forming the workforce and and really the society that's around us. So that all leads me to this next question that has to do with what are some examples where

it's already beginning to work? Or we're starting to see, okay, we're using AI in a meaningful way that is successful. That's something that we would look at, and we would say, okay, AI, your we have you in this higher educational setting? What lessons can we learn from some of the early success stories that we're hearing, we

can't unilaterally disarm opportunity for students. The fact is, if we want graduates who are going to effectively navigate the workplace environment, and this technology is now an expectation in that environment, if we don't look at our learning objectives, and now reassess them, and actually invigorates our syllabi, in ways that allow them to develop the skill sets are, they're not going to be as successful and we're not going to be as effective in the work that we

do. So there are a lot of folks all around the country who are in higher education spaces that are already starting to really examine this work. You look at Maricopa, that, that system actually has degrees now in a brand new degree. So So while it often takes a long time for some in higher ed to move, not in a non degree space, you can run certificate programs are pretty flexibly, you can build those stackable credentials into existing programs. So so it's not that we are completely

handcuffed from being innovative. What we have to do is understand the importance of balancing AI with really the human interaction because it's not gonna replace all of the humans as well, but we've got to learn how to best integrate the efforts together. KiKi L'Italien: Right, that balanced approach, I think is is

it's full of nuances. And as with anything that has nuances, it's very difficult when when something's not black or white, it's very difficult To know what the right thing is, and the best way to navigate it. But we have to we have to figure out the best way forward. Andre, do you have examples? What are you hearing? I know you're pulling together stories and and talking with everyone.

person for the first time, which is really exciting. And so we hope that that will really help us kind of gather and collect these practices, because I couldn't agree more President Bastien that this is happening in incredible scope and scale, unfortunately, what I'm seeing is that it's happening in a really decentralized way, which is not bad for innovation, don't get me wrong, but makes it really hard to learn from what others are doing and replicate without starting from scratch in

every pocket. And so you have institutions that are maybe have some pockets of enthusiasm, and some pockets of terror, or however you want to call it, whatever you want to call it. And then you have institutions themselves, where you have some institutions that are just totally on board and some that

are not. So I think that that that is, it's really interesting, because there's a lot of great examples of AI broadly, right, and what it's how it's been used in AI, in higher education, you know, Georgia State University, as in a lot of retention work for years, and kind of initially started a lot of that focus. Recently, John J. Work partnered with a startup that was, you know, a nonprofit startup to

figure out how to increase their retention. But these are our old, not older, but less, kind of groundbreaking types of AI, because AI is a wide swath of technology. What we're really interested right now, and everyone is is generative AI, chat, GPT, things like that, and how that can then inform

everything else, even other types of AI. And that's where I think we still haven't really been able to put a pin on exactly what institutions are doing necessarily, or anything beyond pilots, because there hasn't been enough time, we need

to see how this goes. But we also can't wait necessarily, until they're done or until the academic peer review process finalizes so that, you know, a journal article can get published to talk about it, because I can guarantee that other institutions are thinking about it or attempting it themselves, and would benefit greatly from hearing kind of

more intermediate updates on how things are going. So that's why we're really trying to facilitate that more rapid lesson sharing practices through the playbook through the council, so that we're not, you know, hurry up, then wait, hurry up and wait type of timeline and process because AI is not waiting. It is just like full speed ahead, whether or not we get on its timeline? Well.

be shared in the chat that that Audrey is referring to. Because even though it's highly specific, I think it's organized into things that all organizations end up benefiting from, right, like we split it by, like, you know, teaching and learning and student success and organizational effectiveness, and, and data. And sure some of these won't be one to one, right, especially like the teaching and learning session, although most companies have professional development, right?

Most companies have a knowledge base. And I think that the big thing to get those use cases going at such an embryonic stage for the industry is to get the sample prompts into more and more hands. And and so whether it's porting these over yourself, or even asking a generative AI to like do that work for you? How can this document work for my industry, I think that'd be like a very worthwhile task and could save hundreds of hours of time.

KiKi L'Italien: I mean, it really could. And what I was gonna say is, and I love I love when somebody can share their prompt, and I know that it's going to get me closer to more usable information that I can then apply and customize for myself. So excellent, excellent advice there. And for anyone who's who's listening to this now or listening to it later. What I'm going to do is I'm gonna grab the link to that

playbook. You want to check out the playbook. I'll put it in the show notes from the edited version of the show, and then you'll be able to download that there and go check it out. You know, one thing you said earlier, Audrey was about you talked about, you can't wait necessarily for the peer reviewed everything to come out when there's something advancing so quickly right in the case of AI, we can't wait until the end of the day. It's not like it's going to just stop and say,

Okay, now catch up. Let's wait for all the peer reviewed research to be done. We have to be sort of developing, as they say, you know, while the planes in the air, we have to be building the plane, right, so. So my point to that, is this quantifying success? How can we look at and this is forever, the thing that I know, my friends in association land are looking for. I know, like any professional, really any anyone in higher ed, you're looking for? How can I quantify that

this is something that's successful? So what are we looking at? What are some measures of success that we might be able to point to to say, I yeah, here's where it's working. Here's where we know that either the efficiency, or the new ideas and innovation that's a result of it, the new products, etc, etc, it can come into play? Does that for anybody? KiKi L'Italien: This for anyone? And? You go, Charles, I love it. I love it answers. Well, I'll just say that I think that there's

probably two different types of metrics, right. And I'm sure that this ports over into other sectors for higher ed, there's like your students success metrics. And then there's like the operational metrics that you use to get that done. Right. And so, you know, when we look at things like, you know, are we increasing our graduation rates? are we increasing retention, there's usually going to be leading indicators of that,

like, how many students are on education plans? And can we track that like week to week, because we know that the proofs out there, that's mastery by semester education plans that actually registered towards them end up graduating at a higher rate? I'm sure there's like analogous outcomes, metrics versus process metrics for all types of sectors on this call. And so I think that you probably get pretty deep into some weedy AI metrics, right. So it's like, what percent of students are in

education plans, you go one layer down? To what extent were they created by this technology of that technology and tracking that like day to week? I think that we're very nascent here. So we haven't like made that full metrics tree as a secretary at AI, or at least I haven't seen it, because we need to, like start getting more of these use cases. I would also say that, again, I don't know how this ports outside of higher ed, but I'm curious if Audrey and President Bassett agree with

this. But I think that we can also like fetishize the causation versus correlation thing a little too much. Because in higher ed, you know, I think about the things that you know, Tracy is doing in other colleges where you're doing a lot of things at once, and you're not waiting to say, Oh, it was the eggplants. Oh, it was making sure every student's on advisor,

and I think AI is just going to be in that mix in that way. And we're never going to be able to, you know, if you make an academic scholarly article about it, that would be helpful for the field if somebody wants to do a randomized controlled trial, but the problem is, if it's a common sense implementation solution, that you hate to have somebody be the control group. So I hope that makes sense. It wasn't too like nerdy. KiKi L'Italien: I don't think it was, too.

which maybe sounds really broad and lofty. But I think that one, one of the things that we might, that might be as a hypothesis, I guess, at the root of some of the kind of panic around AI is that AI is a threat to kind of how we value knowledge, how we think it can be assessed, or it should be assessed how we think it should be measured. And I think that we're gonna see a lot of conversation here in the coming months and years ahead, because as the purveyors of knowledge and higher education.

I think there's a feeling that kind of, we know, we're the experts on that. But this is really going to shake up and shift potentially, how we view all of that and how we do assess

that. And so one leading indicator that I'm looking for, in on the equity space, in particular on equity conversation, in particular, is for new types of knowledge and voices and perspectives from from groups of people who have historically not had their value, their knowledge valued at the same level as past maybe folks who can make it all the way through an academic journey all the way to that point of

peer reviewed work. I want to see new voices and I want I think AI will be working if it's helping bubble up and surface, different perspectives that In the past, because of kind of the structure of, of higher education, those perspectives might have been kept out. So that's more on like a societal systems level. But I think that will be a really great signifier that we're doing something right if we're making new paths for people to contribute to our way of thinking through AI.

quality information. And we can actually use ways to actually streamline processes and actually accelerate the ability to people to navigate complex systems that don't need to be complex. You know, when we think about the bureaucratic structure of higher education, it was supposed to actually not help the student, but actually help the bureaucracy of how the the administration of higher education Well, if we now don't need all of that bureaucracy that supports the actual

structures, but not the students. And we actually can utilize some technology that gets at minimizing all of that bloat. Wow, what a wonderful breakthrough opportunity for students to not have to, you know, at three o'clock in the morning, when they're up, and they need somebody that they actually can go to a knowledge base that can resolve issues without talking to somebody who's going to send them someplace else. And then they play the chutes and ladders game

until they drop out of school. So here's an opportunity if we think about it, and we utilize it. Well, I KiKi L'Italien: mean, the people who are listening to this as an audio podcast later, they cannot see me right now, but I'm just like furiously shaking my head, my hands, anything that can move in the camera, just in furious agreement. Because it's so true. Can you imagine not having all of the bureaucracy that's

pressing back and making things so difficult? It's hard. It's hard for me to imagine, but you're painting a picture that I really want to see painted. So you're the let's go there. And actually, to that end, my my last question for you, as I'm, as I'm wrapping things up, which by the way, this has been a phenomenal conversation today. So thank you so much ahead of time, but where do you see this work that you're doing going? Now all of you know each other, you're you're paying attention

to what's happening with the development of AI. And as it pertains to higher ed, where do you see what do you see for the work that you're doing right now to sort of help shape this conversation around AI and higher education as you're moving forward? What is to come? All right, and I see you, Adrienne, go, I'm looking for you.

talking about? And who was included in the survey? And what groups do they represent. And so this is really interesting report, lots of great insights that I think can relate to lots of industries. A lot of things we covered today, strategic planning for AI prepare your data for aI think about ethical

and equitable use cases. But what I'm still seeing is, you know, 13% of the participants were from the community college space, and about 21% of the participants were from minority serving institutions, which is not on which is not does not represent parity, if you look at the survey, you know, the survey

population and our larger post secondary space. And so what I hope will become an emerging trend is that we can push the edges of our data collection and our conversations, our sharing of knowledge and resources, right, like funding and legal support and technical support to these institutions who right now aren't even participating in surveys or and therefore aren't even being considered part of the landscape when they are because we're not making true truly representative statements

like most colleges are doing this if we really aren't even including You know, so many community colleges that educate a vast majority of our students in the country that are in the post secondary space, and probably your future workers at, you know, all of the associations and the industries you represent. So that's one space where I'd really like us to see the conversation pushed and, you know, supported further. KiKi L'Italien: Absolutely.

wasn't like, you know, what's going to happen? This, and then it was like, even remotely correct, like 10 years later. And so I think that's what we're seeing now. So I think like to Adrienne's point about, like, how are we going to like hacked right now, I would consider two or three things, right? Like one is that we've got to like stay vigilantly ahead of where AI is going and not be, you know, doing too much punditry or

something. The second thing is like we have to shift. I don't know the best way to put this but like shift the moral urgency around things like plagiarism to the problems that we need to solve. So instead of moral panics for things, like, you know, oh my gosh, somebody's gonna write their essay using Chechi. Btw, how about we have the moral panic about a one in

five graduation rate? How about we have a moral panic about countries, leapfrogging us in higher education attainment of having an innovation gap during climate change and COVID and threats to democracy, we need to be a beacon to the world again. And that's what Higher Ed was supposed to be in the post war era. And that's how they talked about it. And I think we need to have that orientation again, and bring generative AI in that

orientation. And then I guess the last thing is, this is just building off of what President passen said, but like, I think the lowest hanging fruit is that bureaucracy is that bloat. You know, I think that that's even true with just writing, right? I mean, you wouldn't trust AI so much to write you a novel. But in terms of making some anodyne policy that gets the job done like it can we find all the ways to make life hard for students. And I'm sure that's true in health care for patients and

just pick your sector pick the association here. And I think you start with the low hanging fruit and exactly what President Bastian said about killing the Bloat killing the bureaucracy, this could really be a bureaucracy killer in the best way possible. KiKi L'Italien: Oh, gosh, where is it? The words that speak right to my heart.

they kept their radios too. So we have to understand that you can actually keep two thoughts in your mind at the same time, you actually can continue to advance and be built on the foundations of really understanding how the world

works. It's not either or, and we've got to be able to expand capacity, we've got to be able to give people the resources are particularly under resourced institutions, the opportunity to engage and to learn and to grow, and to get with it, because if the country is going to move forward, we we really don't have any educational institution to leave. KiKi L'Italien: Yes, I mean, oh, my gosh, this is this has been such a great, great show. I want to end on a high note, and I see

this great question. Actually, that comes from Peggy Lambertson. So if you have the time, just answer one question, folks. This is Audrey, do you think the pool of knowledge feeding AI is at risk for cyber attack? There might be pollution of online knowledge. Okay, so I get this. This is about talking about, you know, where is the data coming from? Can we trust where it's the pool of knowledge that that generative AI is is pulling from for the information that's giving us?

garbage out. That's exactly how I'm thinking about this. And so really important for us to be thinking about any the quality of any data that we're using to train these systems, and asking those critical questions as consumers have, what data are you using to? You know, give me this product or service? And also, what data are you collecting for me? So really

important question. And I think, you know, we might get to a point you might have noticed on Google Now, you search and there's like an AI generated sub summary, before you even you know, get to the results. And for all, you know, we're gonna end up at a time where AI is training AI is training AI. And

so like, I still think we're really early in this space. And we need to think about, again, how are we shaping it, and also whose voices stories perspectives, knowledge is even included, for better or for worse, looking at the history of kind of many disparate use cases of tech and equity. But there's a lot of a lot of information and knowledge to come out here. And I will just sound the equity piece. There's a great book, unmasking AI, if you're interested in learning more

about equity and AI, especially in terms of biometric data. So like facial recognition, things like that. Highly recommend,

taking the time to talk with us today. Audrey, thank you. I first talked, I first spoke with Audrey, she was able to bring Charles and bring Michael into this discussion. So I'm very thankful for that. I think it was a really meaningful conversation. And I hope that you all will be open to maybe me reaching out for a follow up conversation one of these days to find out as we go along. What's the story now? What's happened since then?

something different because of this. Whether it's something that's going to help you to explore further ask more questions. I hope that you will stay curious and keep asking questions every day, especially when you're scared because as Joseph Campbell once said, The cave you fear to enter holds the treasure you seek. Have a great rest of the week, everyone