AI Transforms Student Engagement at Indian River State College

Jeff Mosier:

Hi, I'm Jeff Mosier. I'm the marketing content lead for the Infosys Knowledge Institute. I'm here with Michael.

Michael Hageloh:

I am Michael Hageloh, Dr. Michael Hageloh, and I am part of Indian River State College here in Florida.

Jeff Mosier:

Well, tell us a little bit about how y'all use AI and marketing.

Michael Hageloh:

At the college level, AI has been both a nemesis and an advantage. So we use it very specifically in marketing to do the analysis work, do the synthesis work that a large crew would've been needed to do. So we can look over broad industries. We can look over broad customer opportunity. And I use that word very specifically because we in marketing at the college do not view individuals as students. We view them as customers because you can enroll anywhere and get education. To enroll with us, we have to work you like a customer and take you through a full cycle. So our AI use is just that. It is very targeted at understanding who you are, what you want to accomplish, and where we can assist you.

On the flip side, on the academic side, AI is, again, a nemesis for some faculty, but we've turned that around. We've got one of the industry first leading programs at Grammarly, and as opposed to looking at Grammarly as something to cheat the system with, it's a coach that's with you 24/7. And we've just announced a college-wide use of that product for all students. So on the front side, we're looking at who you are and what you want to be when you grow up. On this side, when you join us, we're here to coach you through it so you can become what you want to be.

Jeff Mosier:

What's the greatest value you've gotten from AI so far and what you do?

Michael Hageloh:

We use AI agents to answer preemptive calls to the college about what is this program? How long does it take to become an RN? How long does it take to become a welder? How long does it take to do this, to do that? That would take an army of people. And we do it multilingually. We're a Hispanic serving institution. So the native language isn't just English, it's English, it's Spanish. And to have that talent sitting behind a telephone ready to go would be almost impossible to generate. AI does that for us with a click of a mouse.

Jeff Mosier:

How do you think the benefits are going to change say, in a year from now? Do you think you're going to add a lot of additional value from any AI programs or initiatives?

Michael Hageloh:

I'm in a naturally cautious industry. Higher education has hundreds of years of experience. I think institutions like ours will go rapidly because we must to stay alive, to stay engaged with the customers. My group, as I am the Chief Marketing Officer, we absolutely look at every, that's why we're here today at ANA, we look at every corporate example. We look at everything business is doing. We want to engage at the speed of business. We have to.

Jeff Mosier:

What do you envision as the benefits of the future for AI, things that you're not getting from it now, but you think will be getting from it? What are some use cases?

Michael Hageloh:

I think for us, it is clearly first and foremost, understanding a marketplace that is very young. College decisions are often made if there is a family member that has been to college in the 15, 16 year old range. As an institution that has a primary population of first time in college, those decisions may not be happening at a home. So we have to keep our ear to the ground using tools to understand again, what is a 16-year-old thinking about? What is a 17-year-old thinking about? How do we go from 54% capture of a graduating high school class to 70%? Where is that other 20%?

Well, that 20 is probably in new AI tools that you nor I know about yet. It is this constant evolution to understand our customer base. That for us is primary because once you're in, it's almost as if you naturally stay with the program. You want to see success. And that's where the AI side of us for the academics, the coaching in math, the coaching in English comes to play. Again, we have both functionalities, what I see, both functionalities in AI, both in acquisition mode and a retention mode.

Jeff Mosier:

You mentioned, obviously, there's a lot of AI tools out there, almost unlimited number. How do you go about experimenting and figuring out what works for you and what's not going to work? What's your process?

Michael Hageloh:

So I have a team. A team consists of one person at the moment, that's a team, that I've tasked to fill out that info block on the website, get them to call you, the first sales call to go over. I come from a sales background. I spent 22 years at Apple. I understand how innovation flows into organizations. I understand how one has to process through that front end. And I have someone who's tasked at just looking through the news and signing up and asking questions. And then quite honestly, we go to a next step pretty quickly where we'll all get on a conference call, a Teams call, conference call. Sorry, I'm dating myself. But we'll get on a Teams call or a Zoom and we'll ask some relevant questions. They won't know anything about higher ed. They won't understand. They've been to college, but they don't know how to sell to college.

So we invite in and we try things out. We have a lab that tries things out. We are not afraid of trying. And that is the only way you understand, I believe, is to touch it and feel it, give it a shot. It's easier to drop it and move on than it is to stick with something that doesn't work. We have a proprietary system where we analyze through the different parts of the institution. Remember, I've got a whole institution full of academics, everybody who knows everything, just ask. So we have a very thorough check through, but it goes rapidly. And I will say that faculty has been excellent at wanting to adopt.

Jeff Mosier:

How confident are you in the ROI and the metrics that you're using, that it is actually capturing what the AI is doing, that that's actually the AI is contributing to that retention, that new student enrollment.

Michael Hageloh:

We ask. We just finished our first survey with our AI agent. It was in the 80s that the student engagement, the then customer engagement to student engagement was directly related to the answers they were receiving through the agent. Here's an interesting part, they were as comfortable with the agent, AI agent as they are with an advisor, a human. It's a generational thing, potentially, but again, we have to meet our customer where they're at and that includes multilingual.

Jeff Mosier:

But also the agents have to be able to answer the questions that they want to ask. If you ask a couple of questions and you don't get a relevant answer, then people are going to peel off and lose interest.

Michael Hageloh:

It took us a while to find an industry specific AI model that worked. It took us a while. We went through a few commercial ones that did not have the large language models necessary to speak our world. And I think that's the important part about AI is that synthesis, and that synthesis can be very vertical. And when you find that, it is the same as 40 agents, human agents on the phone. But that's the beauty of AI synthesis is we can have generational models without the cost of individuals. That's not to say that my IT staff has to beef up a little. My costs have shifted a little, but my customer product is much, much better. We had an issue with a number of Haitian students. Creole is a unique language, but the model spoke it. It spoke it well enough to bring those individuals in to a human that spoke it to finish the process. We'd have never seen those students.

Jeff Mosier:

What's holding you back with AI?

Michael Hageloh:

Well, I would say budget, but that really isn't it. It is the adaptation of the workforce to understand how to use the tool. So I use this example all the time. Originally, we had a wrench, then we had a socket set, then we had a pneumatic driver. Those are three tools that will take a nut off of a bolt, but they will do it very differently. One, if you're not careful, won't break your wrist. This will take you faster. This will take you very slow. They will all take it off, and that's the same with AI. Just to use a tool, large language model now to write the assignment you've been given for the marketing of a program isn't appropriate. You need to understand how to use that tool. And that, for me, as a leader of a team, it's my job, is to get them skilled to understand how to use the tool.

Jeff Mosier:

What risk do you see with AI? There's a lot of concern about how do you incorporate responsible AI into your organization, into your processes, into your structures? How do you approach that?

Michael Hageloh:

So we probably have, as an industry, the most practice, because what happened first? Students using it to write the paper to get the grade.

Jeff Mosier:

Yes.

Michael Hageloh:

So we are three or four years into that process already. I think that when you transition, again, we've incorporated the tool on the learning side. We're not fighting it. We are using it to be a 24-hour tutor for students. On the administrative side, we're still struggling. We're still trying to figure out where it fits because there is risk. You don't want to use an improper language construct in a multilingual piece. There's still some human interaction there to make sure that it's appropriate. We have to be careful. We do a lot of writing.

Jeff Mosier:

How do you approach that on kind of a overall bigger picture scale? How do you think about how to incorporate that AI risk in it?

Michael Hageloh:

We're just a really early time. It comes back to my time at Apple when I saw Steve with the first iPhone and no one had... Well, I'll go back even further. I go back further to the first iPod and the gentleman next to me who I knew very well said, "Why would you want to carry 2,000 songs in your pocket?" Well, the answer to that was, why wouldn't you? The rest is history. So I think we're at that inflection point. Enough real value is coming out of AI, at least from us in the academic side, that the administrative side will come along. At the end of the day, customers still drive the business.

Jeff Mosier:

What would you like to do with AI that you can't right now, either because you can't afford it, don't know how to do it, or maybe the tools don't exist yet?

Michael Hageloh:

For me, in my corporate world and now in the academic world, what I'd love to see is AI filtering out its own noise. What do I mean by that? There's, again, this solution is everywhere. It's everything. It's this, it's that. And then just the solution was sold to me the other day and, "Oh, it's the hottest thing going. It's got 22,000 views on YouTube." That's not even noise in a noise to signal ratio. Good solutions solve problems, whether they're business problems or learning problems or such. And if we are moving individuals forward in a business cycle or in a learning cycle, that's where I want to focus my AI time. The noise, the signal-to-noise ratio is way off kilter with AI. There are very good solutions for real problems that we face, that business faces, and get the noise sooner out of the way so you can just focus on the signal.

Jeff Mosier:

Fantastic. Well, thank you so much. This has been a pleasure.

Michael Hageloh:

Yeah, thank you. Yeah, yeah.