Episode 125: Elliot Felix on AI and the Student Experience
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How are colleges and universities responding to the evolving artificial intelligence landscape, how policy choices truly drive student adoption, and what can institutions learn from each other? How can institutions identify, support, and collaborate with internal "lead users" at the forefront to transform the student experience? How do we scale peer-to-peer learning and engage frontline staff without simply adding administrative complexity? In this episode originally recorded as a webinar for EDUCAUSE, Elliot Felix explores real-world institutional examples, actionable strategies for discovering campus innovators, and frameworks for scaling change through strategic alignment, peer engagement, and intentional collaboration.
Harnessing AI to Drive Student Success in Higher Education
The higher education sector stands at a pivotal crossroads. As institutions navigate demographic shifts, changing workforce expectations, and evolving student needs, artificial intelligence has emerged as both a powerful catalyst and a complex challenge. While many campus leaders recognize the potential of emerging technology, transforming that potential into measurable student success requires more than simply buying new software. It demands a fundamental shift in strategy, culture, and operational models.
To build a truly connected college, higher education leaders must look beyond administrative silos and create ecosystems where faculty, staff, and students collaboratively leverage AI. By studying current adoption trends, drawing inspiration from pioneering institutions, and empowering frontline staff and "lead users," colleges can transition from passive technology adopters to proactive experience architects.
The AI Landscape
Understanding where artificial intelligence currently stands in higher education requires examining both student usage habits and institutional readiness. Recent research from Gallup and Lumina reveals a profound connection between clear institutional policy and actual student engagement. While roughly one-fifth of college students report using AI tools daily under standard conditions, that number jumps to nearly 60% when institutions explicitly encourage its use with thoughtful guidance. Clear policies do not just permit technology use; they actively engage and set constructive boundaries.
Despite this clear correlation between guidance and adoption, institutional confidence remains surprisingly low. Surveys of college presidents indicate that only about 20% believe their campuses are responding adeptly to the rapid growth of AI, and less than a third feel that basic AI literacy is widespread among key stakeholder groups. This gap highlights a significant disconnect: students are ready and eager to engage, but leadership teams are struggling to build the necessary policy frameworks and learning environments.
Furthermore, when faculty and staff consider how they want to build their digital literacy, top-down mandates and formal, one-size-fits-all training sessions rank at the bottom of the list. Instead, higher education professionals overwhelmingly prefer peer-to-peer learning. Trust, shared context, and grassroots knowledge sharing drive meaningful adoption far more effectively than administrative directives.
Inspirational Examples of AI Adoption
Across the country, forward-thinking colleges and universities are already proving that artificial intelligence can meaningfully improve every stage of the student lifecycle—from enrollment and retention to classroom learning and career preparation.
Improving Credit Transfer and Enrollment: The transfer process has historically been a point of massive friction, with transferring students losing significant credit hours on average. To solve this, Arizona State University leveraged its My Path to ASU platform alongside its trusted learner network. By ingesting syllabi and competencies from hundreds of partner institutions, the system provides transparent credit mapping before a student even enrolls, drastically reducing lost credits and saving students both time and tuition.
Boosting Student Retention: Supporting students requires meeting them where they are. California State University, Northridge implemented CSUNny, a personalized text-messaging chatbot powered by AI. Through a rigorous multi-year study, the institution found that students who utilized the chatbot achieved a four-percentage-point increase in retention compared to non-users. The platform provides a low-barrier, judgment-free space for students to ask routine administrative and academic questions.
Scaling Curricular Integration: Rather than restricting AI to computer science departments, the University of Florida implemented a campus-wide initiative ensuring that over 70% of its student body takes at least one course incorporating AI concepts. By offering an accessible, cross-disciplinary AI fundamentals certificate, the university directly enhances graduate employability, as research consistently shows higher job placement rates for students holding verified digital skills.
Enhancing Academic Support and Simulation: At the University of Michigan, the development of local tutoring tools like the Maisey Tutor Bot allows instructors to quickly build custom, course-specific AI assistants. In initial pilots, students using the bot demonstrated higher average GPAs, while instructors saved between five to twelve hours per week—time that was immediately redirected into high-impact mentoring and one-on-one relationships. Similarly, the University of Sydney has scaled interactive simulation tools across hundreds of instructors, allowing students to practice complex scenarios in safe, low-risk virtual environments.
Accelerating Career Readiness: Career services offices are also leveraging intelligent tools to provide real-time coaching, resume feedback, and interview practice at scale. The University of Connecticut reports that 86% of its students utilize digital career readiness tools—well above the national average—leading directly to higher starting salaries and better employment outcomes for graduates.
Working with Lead Users at Your Institution
When looking to innovate, university leaders do not always need to invent new strategies from scratch. Instead, they can look to "lead users"—a concept coined by MIT professor Eric von Hippel. Lead users are individuals on the front lines of an organization who encounter needs months or years before the general population and, out of necessity, hack together their own creative solutions.
On a college campus, lead users are easy to spot if you know what to look for:
They make unusual administrative or technical requests that challenge standard operating procedures.
They form unexpected, cross-disciplinary partnerships across campus or with external industry partners.
They act as magnets for ambitious students, outside funding, and pilot projects.
Engaging these innovators requires shifting away from rigid surveys or formal committee meetings. Instead, leaders should spend unstructured time shadowing lead users in their working environments, observing their processes, and asking open-ended questions about where they encounter friction.
Once lead users create successful pilots, institutions must apply a clear scaling strategy. Drawing from Everett Rogers’ classic Diffusion of Innovations framework, leaders must recognize that different groups adopt change differently:
Innovators & Early Adopters: Driven by novelty and a desire to pioneer new methods.
Early Majority: Driven primarily by observing the success of their peers.
Late Majority: Driven by established social norms and proof of institutional safety.
To scale innovation, leaders must explicitly build bridges between early adopters and the early majority, providing visible platforms and peer-led forums where successful experiments can be shared and replicated.
Engaging Front-Line Staff in the Process
Rolled-out technology often fails not because the tool is flawed, but because the human element was ignored. To successfully integrate new digital tools into administrative and academic operations, leadership must bring frontline staff to the table as co-designers rather than passive recipients.
The key to engaging frontline staff lies in establishing what Rogers termed "relative advantage"—clearly answering the question, What’s in it for me? Staff members rarely welcome additional software platforms if they are perceived as added administrative burden. However, when new technology is specifically deployed to eliminate daily operational pain points, save time, or eliminate repetitive manual tasks, enthusiasm replaces resistance.
Additionally, institutions can apply this same collaborative approach to the student experience. While traditional group projects frequently suffer from low student satisfaction due to poor structuring and lack of guidance, redesigning group work around real-world problem solving—such as collaborative AI workshops, hackathons, or industry-sponsored projects—allows students to learn digital tools peer-to-peer while mastering critical teamwork skills.
Why We Must Innovate Differently to Meet the Moment
For decades, the standard playbook for higher education innovation was simple: add. When a new challenge or opportunity arose, campuses added a new center, launched a new institute, hired more staff, or built a new facility. Over time, this additive culture created unsustainable operational complexity and rising institutional costs.
In today's fiscal and demographic reality, institutions can no longer innovate solely by addition. True innovation in the modern era requires subtraction, consolidation, and strategic realignment. To fund and resource critical digital transformations, college leaders must get equally comfortable with sunsetting legacy programs, streamlining administrative layers, and breaking down departmental silos.
Public confidence in higher education fluctuates heavily based on whether institutions are perceived as forward-thinking or stuck in tradition. By moving away from isolated pockets of experimentation and embracing scalable, peer-led, and student-centered innovation, higher education can bridge the experience gap, demonstrate clear value, and build a truly connected college for the future.
Episode 125 Transcript
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Elliot Felix: I gave a webinar for EDUCAUSE on AI and the student experience and I'm excited to share the audio with you in this special episode where you'll hear my 20 minute talk and then about 10 minutes of Q&A moderated by Ben Bongers from EDUCAUSE. I talk about the landscape of AI adoption, share inspiring examples from a variety of institutions, and then walk through the idea of finding and innovating with the "lead users" at your institution. There were also great questions about involving frontline staff, enabling peer-to-peer learning, and what makes this moment unique for colleges and universities. Big thanks to EDUCAUSE for inviting me to their great AI Leadership series and generously sharing the audio so that the conversation can continue! Let's dive in.
Elliot Felix: Welcome to the Connected College podcast. I'm your host, Elliot Felix. I've helped more than a hundred colleges and universities change what they offer, how they operate, and the way they're organized to enable student success. Join me for insightful interviews with higher ed innovators, sharing the stories, stats, and strategies to create better connected colleges and universities.
Elliot Felix: I'm really excited for our conversation today, and I think we're gonna have a great one from all the different presenters and from all of you learning and leading together as we think about AI. And I'm gonna really compliment other folks who are going in depth from an institutional perspective and offer maybe a broader one from my consulting work and my research and my writing. So I'm Elliot Felix. I lead the higher ed advisory practice at Buro Happold, and I've been lucky enough to work with more than 100 colleges and universities on strategic plans that provide focus and org designs that create alignment and facility plans that foster collaboration and operating models that enable change. And I put what I've learned from all that work, from my research, from my interviews with dozens of higher ed innovators into The Connected College, which is an encouraging evidence-based playbook for breaking down silos so that students succeed. And I'm really excited to share some of the insights with that with you today from that on AI and on student success as they relate and intersect because we're all here to help students succeed and understand and improve and innovate when it comes to the student experience. My work-- So much of my work, and certainly today's presentation, has been guided by this idea that the future is already here, it's just not very evenly distributed. And I'm forever grateful to science fiction author William Gibson for putting this into words because so much of what you do as a strategy consultant is help people see the future and prepare for it, right? Figure out where they are, where they wanna go, how to get there. And one of the best ways I know to see the future is to find where it's already here, and then think about how you can learn from that, how you can adopt it, adapt it as you adopt it and then scale it to success at your institution, on your campus, within your consortium, among your partners. So this is the guiding idea, and we're gonna talk about it in three parts. First, I'll talk a little bit about the broader AI landscape referencing-- there's so many studies out there, but there are three I wanna highlight that I think make some interesting points about where we are and where we're headed. Then I'll share some examples from the book about people doing in- interesting things, innovative things with AI as it relates to the student experience. And then the last piece will be kind of a methodology or an approach to learn from folks as you're innovating, as you're improving, as you're integrating AI into the student experience. So- To start off with the AI landscape. And this is a study from Gallup and Lumina that I found fascinating for so many different reasons. AI in higher education, widespread use, unclear rules. And by the way, all the references I'm going through are listed in as resources in the Canvas site, so you can refer back to these. You don't have to madly take screenshots or-- but you can do that too if you want. But I think the-- among all the interesting charts and graphs in this study, the one I found most interesting is this difference in how AI is positioned and the policy of an institution. Because while the average is about 21 percentage of students using it on a daily basis, when students are encouraged to use it, it's actually 59%, right? And if they're encouraged but with limits, it drops to 24% daily use. If they're discouraged, 15. So that means that when institutions make it a priority, when they position AI as something for students to use, and they create the right policies and structures around it, the AI use is nearly three times as much. So policy can really move the needle. But then when you ask, and in fact, Inside Higher Ed did, college presidents, how are we doing relative to that positioning, relative to those policies, relative to those processes? Pretty low report card, right? Only 19% think we're responding adeptly and appropriately. Only 31% of presidents think that AI literacy is widespread across the key groups. Only one in five college presidents thinks their institution is responding well to AI. So we know that policy can really move the needle But we're not really harnessing it, and so the question is how? And Ithaka did a really interesting study engaging faculty in particular, but staff and university leaders as well on where they're at, and even folks that haven't adopted feel like they will soon. And then when you ask them how they wanna learn, it's not from top-down mandates. It's rarely from formal training. It is really they wanna learn from peers who they trust, which is not surprising 'cause this is how all ideas spread really. And so it's really thinking about how we create opportunities for people to learn from peers in consortia, in cohorts, in conferences like this one. So I'm just delighted that you're all here. I'm delighted to be here to maybe jumpstart or spark something as you're trying to learn, and maybe give you some tools and some ideas to keep that learning going as you learn from others and from those at your institution.
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Elliot Felix: So in that spirit of learning from others, but of course not, cutting and pasting because as you adopt new ideas, you also need to adapt them to your campus, your curriculum, your culture. I wanted to offer some of the interesting examples of AI innovation and improvement in the student experience from the book and beyond. And I think the first thing students need to do is enroll, right? And the transfer process is one of the areas ripe for improvement and reinvention. A GAO report found that the average student loses 43% of their credits when they transfer, which is just awful. Right? Fun maybe to learn something new as you repeat a class, but maybe not the best use of time and resources. And this is an area where AI can really help. ASU's My Path to ASU, which leverages their trusted learning, learner network Is a great example of this, ingesting syllabi competencies from 800, I think they're up to 800 now, plus institutions, so that instead of in- students deciding to enroll in the dark, they actually know how things will will match up because that, that dropdown box contains an awful lot of insight about how these things these things line up. Once students are there through the recruitment, we really need to think about retention, right? And that retention relies on ongoing support. It may be about ha- hard questions, it may be about easy questions, it may be about questions that are s- easy or matter of fact. Students are way more likely, as we found with, Georgia Tech and Pounce, or sorry, Georgia State and Pounce, a lot more comfortable asking the easy questions to a chatbot than they are to a person. And Cal State Northridge with CSUNY created, I think, a really interesting way of rolling this out, which is a random control trial over three years, and they found that peop- holding everything else constant, students who were using it got a four percentage point retention gain. So we can better enroll them, we can better support them. We can also enhance the learning experience by integrating AI within the curriculum, and of course, everyone is working on this in different ways, so there's so many great people to highlight here. I thought I'd highlight University of Florida just because of the scale at which it's being rolled out and adopted. We have s- they have 71% of students taking at least one course in AI, and they've rolled out an AI fundamentals certificate, which is great. It's a pretty low bar. It's, three classes, nine credits. And independently, Research.com is finding 20% higher job placement for those with an AI credential. A way to integrate it within the curriculum at scale. I think in addition to that integration, there's also the support during it, and I'm a big fan of University of Michigan's Maisey Tutor Bot, right? Which takes minutes to ingest a course and prepare a local instance of the Tutor Bot, and they've been doing a great job rolling this out. And when they piloted it there was about a f- c- compared to holding other things equal, compared to other sections, 5% higher GPA, and saved the instructor, instructors a range of five to 12 hours per week. Which is, imagine what that time frees up. You can do more office hours, you can do more engagement you can build relationships, you can mentor, you can do lots of great things. And in addition to providing the support AI can also as, as many of and there are probably examples of just the folks on the on the Zoom today that are using it for simulation. And University of Sydney is doing a great job with this leveraging s-simulation more than 800 instructors and it's a great way to, I think, make some of these kinds of experiences come alive in a way that scales pretty and that, that support can extend not only through coursework, but also into careers. And there's, of course, there's, reviewing resumes, s- cover letters, but there's also real-time coaching, another kind of simulation interview practice, and the career applications are really limitless. And it's interesting, UConn has really embraced this, and they have 86% of their students using digital tools for career readiness compared to about a 60% national average. And they're finding those students who do engage with these tools are getting higher salaries, perhaps not surprisingly because they're, better prepared, better coached getting better feedback. And of course, to make this happen in addition to the informal peer-to-peer, you also need the formal structures and systems and situations that develop faculty and staff. And Johns Hopkins Applied Physics Lab is a great example of this. Their Level Up program has has re- has won lots of awards and has more than 1,500 folks participating in more than 10,000 sessions. So there's lots of great examples that institutions can learn from each other and, of course, adapt as you adopt. It's not cut and paste. It's information, it's inspiration.
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Elliot Felix: But the last thing I wanna talk about is another way to learn, and that is from the lead users at your institution. And the concept of the lead user was coined and created by MIT professor Eric von Hippel. He wrote this book Democratizing Innovation. This is a-- It's actually, it's a free download, and it's linked in the resources as well. But von Hippel's big innovation or his big insight was that a lot of the new products and services that we think of as really game changers, as really innovative, don't often come from companies themselves. They come from their users. They come from someone who's at the forefront of their practice, where the exis- the existing tools and services don't quite cut it because they are, like, at the edge. They're at the bleeding edge. They're at the frontier. They're pushing things. And maybe it's a windsurfer that needs a new kinds of equipment, and they're hacking something together or they're tweaking what they've got, and all of a sudden a new a new idea is born. And a g- a great example of this is the CamelBak water bottle The CamelBak was not created by a bunch of people sitting in a room brainstorming at a whiteboard saying "What's the next generation water bottle?" It was created actually by a distance cyclist named Michael Edison, who was preparing for a hundred mile road race through Texas, which I think is called the Hotter'N Hell race. And E- Edison was also an EMT, so he hacked together an IV bag a- and that became the inspiration, the impetus for the CamelBak. So Lots of ideas work this way, right? Where your lead users that are at the forefront are hacking things together, they're creating things, they need support and then you can find a way to scale it. And I wrote an article, which is also posted for your for your reference in planning for higher education about how you can lead-- how you can learn from lead users and use them to predict the future and use them to innovate together, and then find ways to scale up those those successes. And it starts with identifying them, then engaging them and then turning that into a scaling strategy. You all may already have a sense of who your lead users may be, or maybe you're already working with them. But in the article, and here I'll offer a few ideas for finding them. They're usually the folks that are making unusual requests like, "Oh, no one's ever asked for that," or, "I'm not sure if we can do that. Let me look into that." Or maybe you're already collaborating them, they've been in one of your spotlight sessions, but they're pushing the boundaries. They usually have unusual collaborators, maybe with industry, maybe beyond the walls of your campus. And they're usually so energetic and so innovative, they're kind of magnets for students and partners and funding. They are proof that, if you want something done well, give it to a busy person because they're often busy, they're doing so many different things. And the way to engage these lead users to learn from and with them is not a survey or bombarding them with 17 structured questions. It's much more about spending unstructured time together, open-ended time observing them as they go about their day, shadowing them asking some questions here and there, asking lots of open-en-ended questions. "Tell me about a time when..." "Tell me about a time in your research you got stuck," or, "Tell me about a time when you adopted something new in your teaching and learning practice," and doing that in context so you can get the clues. And finally, once you have those lead users, you understand the landscape you learn from other institutions, you learn from within your institution, you need a scaling strategy. And often this is the step that people miss, right? They've got innovation at their institution happening in pockets, but it never quite catches on. And a way to make it catch is actually thinking through the adoption curve that Everett Rogers developed in the '50s. Actually, he was looking at how ideas spread using corn seed I think in Iowa. But it's-- even though this is, 80 years old it's every bit as relevant today. If you've ever heard the term early adopter, Rogers was the social scientist who coined that, and he really understood that different people adopt different ideas for different reasons, right? So you have the innovators that are creating things like your lead users. Then you have early adopters who are risk-takers. They wanna be seen as doing new things. They're influenced by the media, and those are the first people to those are probably the first people to dive into AI on your campus. And then quite importantly, and this is the s-step that people often miss, you have the early majority, and they're influenced almost entirely by their peers. So you need to create the forums, the ways for the early majority to meet the early adopters and learn from them and follow in their footsteps. Then the late majority, they take less risks, and they kinda jump on when the perception is everyone's doing it. And so then y- for that, you really need a, an intentional communication strategy so that the late majority understands, like, "Okay it's safe for me to jump on the bandwagon." And then the laggards are the people that are clutching rotary phones and basically they're only gonna, they're gon- only gonna start using AI if they have to. But you can, you can focus on the other 80 some 80 some percent. I hope that's helpful as you think about the AI landscape and how you get inspired by other institutions and adapt and adopt, but also how you can learn from the innovators at your institution. And I look forward to your questions. I look forward to keeping in touch. You can connect with me on LinkedIn. You can join 711 institutions and counting getting the "Connected College" newsletter. You can also listen to the podcast, and I'll throw the links in the chat a-as appropriate. Thanks very much for your time and attention. Hopefully I was not on mute.
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Ben Bongers: Shoot, we just missed all of that, Elliot. That would be great if you could repeat. No, thank you so much. Elliot's been a friend of Educause for a long time and has contributed in a lot of ways, so we're really glad to have his perspective.. We wanna dive into some conversation and continued questions. So we'll start with this first one how do you effectively bring in frontline staff as partners rather than just rolling out tools to them?
Elliot Felix: I agree with these ideas about getting folks to the table. And the book I talked about earlier, Everett Rogers' Diffusion of Innovations, even though it's a classic, let's call it, in addition to coining the early adopter term and creating that adoption curve, Rogers also identified the five key considerations that people make when they're adopting a new idea: compatibility, complexity, trialability, observability, and the fifth one that I'm gonna focus on, which is called relative advantage. What's in it for me? So I think in addition to getting staff and frontline folks at the table, you need to articulate how this is gonna benefit them, right? Is it gonna save them time? Is it going to help them deliver more impact, right? Live the mission. That's generally why they're there. What are the benefits for them going to be? And I a while back did a project with a university, and it was so interesting because, like, how many of you wanna sit in a all-day training session? Probably zero if we raised our hands. But we got staff to participate in several all-day training sessions because they were specifically designed to fix the pain points that staff were feeling, and it was positioned not as "Hey, come learn this stuff you may never use." It was like, "Hey, you know that stuff that's driving you nuts? We're actually gonna figure that out together tomorrow. See you there." And so I think if you really double down on relative advantage, not only will you have them at the table, but they'll be excited to be there, and then they'll reap the benefits, and so will everyone else.
Ben Bongers: Let's keep cruising. I'm still hanging on to the idea that we all prefer to learn about AI with and from our peers. Surveys at our institution indicate this too. How are we applying this preference to student use and learning?
Elliot Felix: When you're short on time and funds, you have to get the most of both. And every other year we do a national student experience survey, and it asks, I think, across 48 different dimensions of student experience. And can anybody guess what's the lowest c- every year, what's the lowest satisfaction- Anybody wanna take a guess? Parking? Par- parking's pretty low, but even below it is group projects group projects I think is 44% satisfaction. And it's often because we're just throwing students together in groups. We're not forming those groups correctly. We may not be guiding or mentoring those groups. But it seems to me that we have an opportunity to make group work not suck and have students learn AI from and with their peers at the same time, and that could be a class project for company, an experiential learning opportunity. It could be through co-curriculars, an AI workshop at the library. It could be a a hackathon, in the business school. As we dig into learning design, looking for ways students can learn from and with each other in a way that solves the AI problem but also solves the group work problem would be delicious.
Ben Bongers: Let's keep moving forward. There's a lot of questions on here. Elliot, you can keep the mic. This one's for you. You talked about lead users. How do you find them? Can you say a little bit more about how you find lead users? Are they people who are obviously doing something innovative, or is it more about spotting potential and giving them room to run with it?
Elliot Felix: It's great to find emerging leaders and people with potential. Lead users are generally the people that are already doing really interesting stuff. They're already pushing the boundaries. They're making the odd requests. They're taking risks. They're doing things that others might worry would get them shunned by their peers. They have unusual partners. And a good example of that, we led a study for Georgia Tech a while back that was looking at the future of learning and research on their campus, and e- engaging lead users with ethnography was a, a key part of that. And one of them was a literature professor named Hugh Crawford, and how do you get a bunch of engineers to engage in literature? No easy task, potentially. But what he did was he actually had them build Thoreau's Walden cabin in front of the library. And so while not every literature professor is gonna have engineers building cabins, there's some really interesting stuff embedded in that, right? Like primary sources putting things in context, working in groups, hands-on activities partnerships across disciplines. When you spot a literature professor that has engineers building a cabin, that sort of gives you a sense, These are the people that are really at the forefront and going to the library and saying, "Hey, could we build a cabin in your courtyard?" So that's what I would be on the lookout for. And I'll upload the article 'cause that'll help too.
Ben Bongers: How long before we should expect anything to actually show up in outcomes like retention? Does anybody have a clear sense of that? How long before we should expect any of these interventions or use cases to actually show up in re- outcomes like retention?
Elliot Felix: I can just say quickly several of the examples I shared, they were already measuring against retention Cal State Northridge and ASU using RCT data The future is already here, right? Yeah. It's just not very evenly distributed.
Ben Bongers: Let's keep moving through some of these questions. This one is specifically for you, Elliot, but I think it applied for all of you. "Seems like an especially unusual time for innovation with AI capabilities moving so quickly along with mixed sentiment about use, and the future is full of unknowns. Will future costs of using AI tools become prohibitive, like free access versus pay per token models, safety and ethics, et cetera? Are we really in an unusual time, or is this normal, and how is innovation impacted by all of this?"
Elliot Felix: I can start just by saying I think higher ed is always innovating, right? There's always some, some change. There's a new technology, there's societal needs, there's social shifts. I think now might be a moment when it's needed more acutely, and I think in different ways because a lot of that innovation in the past has happened through addition. The higher ed playbook for responding to change and to innovating is generally to add something. We add a center, we add an institute, we add a program, we add a department. As a result of all that adding we add cost and complexity, and the adding was maybe sustainable over the last 50 years when we were in growth mode but now it's not. So we have to innovate without necessarily adding, right? So you have to solve a few problems at once. You have to create new courses and integrate AI into the curriculum while sunsetting other programs so that you have the time and resources to invest in the new ones. And I think that's what makes this moment really hard because you can't build on that tradition of innovation in ex- exactly the same way because you also have to find lots of things that you can stop doing so that you can start doing the new things and do them not just in isolated pockets, which is very resource intensive, but actually do them at scale so that you get some economy of scale. And I think that's what makes this moment different, but I think it's very much needed. And in fact, while you can't govern based on a poll or lead based on a poll, we've probably all been watching the Gallup confidence in higher ed poll, and it declined. Last year it actually went up, and when they asked people why it went up, the biggest reason it went up was a perceived sense of innovation within the sector. So I think we need to keep going. Now, this year it went down again- And we're all getting whiplash, but now's the time to innovate. We just have to do it not by purely adding stuff. We also have to sunset stuff, consolidate, collaborate to free up the time and the resources to make that happen.
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Elliot Felix: Thanks for listening to the Connected College podcast. Go to elliotfelix.com for more information about my book, the Connected College articles I've written and talks I've given. There's also tools you can download information on upcoming events and information on booking me to speak at your institution or organization. Please support the podcast by rating it and reviewing it wherever you're listening. Let's create connected colleges where all students succeed.