How Technology Could Modernize Accreditation in Higher Education

How Technology Could Modernize Accreditation in Higher Education

by | Jul 10, 2026 | Innovation, Policy

How Technology Could Modernize Accreditation in Higher Education

Higher education has embraced technology to improve teaching, learning, and student support.

According to Alison Griffin, it’s time to apply that same thinking to accreditation.

In a policy paper for the American Enterprise Institute, Griffin examined how industries such as healthcare and financial services use technology to strengthen quality assurance. Her conclusion: higher education has an opportunity to move beyond periodic compliance reviews toward more continuous, outcomes-focused quality improvement. Her full paper provides additional detail on the framework and recommendations.

Learning from other industries

Healthcare and financial services use real-time data to identify potential problems before they become crises.

For example, hospitals monitor key performance indicators continuously, allowing leaders to spot bottlenecks and intervene quickly instead of waiting months for a formal review.

Griffin argues that higher education could adopt a similar mindset by using technology to monitor institutional performance throughout the accreditation cycle rather than relying primarily on episodic reviews.

Focusing on outcomes instead of paperwork

One challenge Griffin highlights is the sheer volume of documentation involved in accreditation.

Some accrediting reviews involve hundreds of thousands of pages of material, making meaningful analysis difficult and limiting opportunities for timely feedback.

Technology creates an opportunity to shift attention away from managing documents and toward understanding outcomes.

Institutions already collect data on student retention, completion, financial health, enrollment trends, and workforce outcomes. Rather than waiting years between reviews, those indicators could help institutions identify emerging challenges and respond sooner.

Using data to strengthen peer review

Griffin is not arguing for replacing peer review.

Instead, she believes technology can make peer review more effective.

If institutions identify declining performance through continuous monitoring, accrediting organizations could connect them with peer institutions demonstrating strong results in those areas, creating opportunities for collaboration and improvement rather than simply evaluating compliance.

Technology should reduce compliance—not add to it

Griffin cautions that technology should not become another layer of institutional reporting.

Instead, its purpose should be helping institutions identify issues earlier, improve student outcomes, and strengthen quality assurance without increasing administrative burden.

As Griffin puts it, continuous monitoring should help institutions “address problems before they become a crisis, not attempt to create a whole new compliance industry.”

The bottom line

Technology has transformed quality assurance in industries where continuous improvement is essential.

Griffin believes higher education has an opportunity to do the same by using data to identify challenges earlier, focus accreditation on meaningful outcomes, and create a system that better supports both institutions and the students they serve.

Transcript

Wes Smith (01:20.952)
Hey Allison, good to see ya. Welcome to the podcast.

Alison Griffin (01:31.353)
Great to see you, Wes. Thanks for having me.

Wes Smith (01:34.488)
Hey, I I know you’ve been doing a lot of thinking around accreditation. And we we’ve had we have you on the show to talk through a little bit about accreditation and about large cycle, what’s happening in higher education, especially in terms of technology and how that’s impacting everything. And that’s a kind of a new conversation for us. What what is technology doing in terms of accreditation? You’ve done some thinking on that. Can you tell us a little bit about what you’ve done there?

Alison Griffin (02:04.144)
Absolutely. So about a year ago, I was asked by the American Enterprise Institute to write a policy paper on a topic of my choosing related to accreditation. And the thing that struck me most about accreditation was that we don’t often talk about technology when it comes to quality assurance. And so I asked my colleagues at AEI if I could actually explore this concept in a little bit more depth. And so as I got in

To that research, that desk research, I started to uncover that there are a number of industries that rely on technology for their quality assurance frameworks and their processes in a much more intimate way than what any of our accreditors across the higher education landscape do today. And so I took the pen, truly pen to paper, and started writing on this topic. And what I uncovered.

was was pretty interesting, particularly when it comes to documentation that our institutions are creating and producing for the quality review process.

Wes Smith (03:16.758)
It it doesn’t surprise me that education isn’t on the cutting edge of quality assurance monitoring using technology, but what are some industries that that you found were more on the cutting edge?

Alison Griffin (03:29.26)
absolutely. So, well, the two that I spent some time exploring in depth were healthcare, not a surprise, and financial services, also not a surprise. The similarities with healthcare is that they have a joint commission that actually evolved from episodic site visits to ongoing quality assurance indicators.

one of the examples that I was able to learn a lot more about was at Johns Hopkins. They run this patient flow dashboard with 10 KPIs, and administrators are able to spot quickly bottlenecks instead of seeing that months later. And so I just started thinking about what if we were to apply that same concept in the institution context. You know, all of our institutions have.

KPIs or strategy frameworks, they all show up differently. But what if you actually built a dashboard where you started to see some of those bottlenecks in the data that might be coming through? You know, whether they’re financial indicators or whether it’s staff transition or even student enrollment numbers, where institutions could be a little bit more just in time responsive as opposed to months or years later.

catching some of these issues.

Wes Smith (04:59.054)
I love in in higher ed, we take our accreditations seriously. And there are so many people that want to see accreditation to protect, you know, consumers. That being said, there is no more important industry for quality assurance than healthcare. It it is literally life and death in healthcare. And and those KPIs are saving people’s lives, right? They’re saying, hey, we have a problem here. We need fast intervention.

And so you it sounds like what you’re saying is if it’s good enough for for financial services, if it’s good enough for the healthcare sector, why aren’t we taking some notes from that and figuring out how we can have faster intervention in higher education? Does that sound about right?

Alison Griffin (05:44.901)
That sounds about right. I, you know, I think today our creditors are asking institutions essentially like, how can we help you make your case? Whereas I started asking the question, like, what do the data actually show? And so, what do the data show? How can we start looking at the outcomes of our institutions instead of trying to fit into

What our quality assurance framework wants us to be.

Wes Smith (06:18.774)
Right, right. Okay, so if you’re if you’re applying this to accreditation and you’re you’re saying, okay, we have so much information, we can we can review it, you know, as in real time, essentially, and we can have faster remedies for troubling situations. Can you give us an example or two about what higher education is in a position to monitor right now?

on a regular basis that we don’t monitor.

Alison Griffin (06:50.64)
Sure. I’d like to start by just giving your listeners an example that I laid out in the paper. And that was my review of some Department of Education records and the requirement that they have for agencies, so the accreditation agency, to produce documentation on what they’re doing. And the example was one of the

regional accreditors, I guess now operating, of course, across regions, produced over 800,000 pages for their review. So you think about even half of that, right? We’ll take 400,000 pages. A single reviewer who is reading 40 pages an hour, it’s gonna take them five years to do that work.

Wes Smith (07:43.362)
That is wild.

Alison Griffin (07:45.307)
Right. And so that’s that’s the and this is probably not a topic for today’s conversation, but you know, that’s the federal government’s oversight of the accreditor. And then you think about the accreditors’ oversight of all the institutions and or programs in its purview. And so if if if our agencies, our accrediting agencies aren’t staffed to be able to do

You know, this review, we are leaving institutions without a review that provides them with the feedback and opportunity for improvement that they may actually be seeking. And so your question about, you know, what what could technology aid in right now? There are a couple of things I feel like our institutions are ready broadly to do.

So completion and retention from a disaggregated with a disaggregated approach. We are already collecting a lot of that information. It’s already broadly comparable. Those are some of our leading indicators that our institutions are looking at. So your retention drop shows up years before your graduation rate does. Great. So we can check that box. Economic outcomes.

I think done really carefully, the measure to emphasize is actually the value-added earnings, the wage gain an institution generates relative to their cost of attendance, you know, not just raw graduate salaries. So, how do we start looking at some of those value-added metrics? And of course, there are institutions and systems that are starting to do that work, certainly given the federal rule changes around accountability.

I think we’re gonna start seeing that data emerge more readily. So that would be the second thing.

Wes Smith (09:43.51)
Right. I I love the focus on outcomes. accreditation has, you know, this this traditional approach, generally speaking, of taking a lot of time to review inputs. And getting to the outputs seems to be the most important thing we can do. You’ve named one that I think is just the highest level.

Output that you can measure, which is economic gain. You know, what what are the what are the impacts of you know this program from this institution on your bottom line as a consumer? So I think that we’ve we’ve hit on one of the most important outcomes. What other things could could you use technology to skip a lot of the inputs and get directly to the out outputs?

So we can focus on the most important things. Any other thoughts on that?

Alison Griffin (10:42.267)
So absolutely, I think one of I’ve been reading a lot of stories about this recently, but it are the financial health indicators and institutions that for years or in some cases a decade have been suffering through financial ups and downs. Of course, the economy impacts that, state funding, if you’re a public institution. But the surprising part to me is how many institutions now look back and say, wow, we

Could have caught that if we had only seen a full picture, if we could have only done some projections in a way that looked beyond three or five years. And so that financial health indicator, while not a learning outcome, it’s an outcome that students actually care about because it’s whether or not the institution that they’re attending is still going to exist.

One when they’re due to graduate, or two, when they want to come back 20 years as an alum. the other thing that I would suggest is that labor market alignment. So, you know, we have institutions that are collecting data. And in the case of public institutions, we have states and state systems, state agencies that are collecting information. How do we start filtering?

Some of that labor market information through an institutional mission. So I’m not even saying that we have to compare all the institutions in a single state. What if we started looking at them across Carnegie classification? Or we write? And so one, it’s a it’s an opportunity to also share information. I think that’s another place where accreditation could actually reform peer review.

Wes Smith (12:22.892)
Yeah. Interesting.

Alison Griffin (12:35.589)
I wouldn’t say we need to get rid of peer review. We need to leverage peer review in a wholly different way. So if you use technology to get after some of these indicators, get after your outcomes, you see a dip in performance. Wouldn’t you want to leverage the people in the network of higher education who are doing an excellent job at that indicator to come and be a collaborator with your

Wes Smith (13:03.17)
Yeah, absolutely.

Alison Griffin (13:04.177)
to improve on that outcome.

Wes Smith (13:06.604)
Right, right. That makes a lot of sense. some of our listeners out there, especially those who are very familiar with accreditation, I know what they’re saying right now. They’re saying, well, yeah, you can monitor some things, but you can’t monitor everything that accreditors do using technology. There are some parts of quality control that aren’t continuous. You know, there are new programs, there are, you know, seasonal enrollment, some things like that.

So what do you think the exception for continuous monitoring and input would be in the accreditation process, if there are any?

Alison Griffin (13:47.826)
So you’re asking of like the things that might be hard to standardize using another term. I actually I do believe it that one of the things that is hardest to standardize are the learning outcomes themselves, to be really honest. Like we don’t have a valid sort of comparable measure of what students actually learn across 4,000 wildly different institutions. And so pretending that we do.

Wes Smith (13:52.813)
Yes.

Alison Griffin (14:17.497)
Is almost like worse than admitting that we don’t. and so I I think that there is still room for improvement when it comes to those actual learning outcomes. And so I think recognizing that from the very beginning is really important. I would also say, you know, in in this environment of disagreeing better, you know, long-run sort of civic and just personal outcomes.

You know, the way in which people are finishing their program of study and contributing to their local community. I think that one, that’s not really something that accreditation is measuring now in a in a comprehensive way. And I do think that that’s something that is still hard to get after. So it’s almost like that return on investment that is fundamental to community building, I think is is really hard.

Wes Smith (15:16.226)
Yeah, that’s interesting. That’s that’s I I don’t see accreditation doing a lot of work in that area right now, but it you’re saying it it that’s a possibility.

Alison Griffin (15:16.266)
Alison Griffin (15:25.421)
Saying it’s I think it’s important, and I don’t know that it’s the role of accreditation. I think I’m saying that that is something that is still hard to standardize. I’m not sure that I would want accreditation to standardize that, but it would be interesting in this environment. again, where I think there is

opportunity for people when they disagree and they know how to disagree in a civil way than disagreeing uncivily and in an uncivil way. And I don’t know how we’re capturing that, but I think it would be important to to have a glimpse into that a little bit better than we do now.

Wes Smith (15:59.458)
Right. Yeah.

Wes Smith (16:08.3)
Yeah, it’s certainly a big issue in our society today. Okay, I’m gonna give you the last word on this. you you’ve done some thinking on it, we’ve talked through it. what would you say to our listeners is you know, your top takeaway and learning from from healthcare and financial services and other industries that we could bring and apply to higher education?

Alison Griffin (16:34.033)
So I would thank you for the last word. so I think the continua the idea of continuous monitoring should change behavior. So addressing problems before they become a crisis, not attempting to create a whole new compliance industry. And so my charge would be leverage technology where it can help make the process better.

For the learner and for the outcome, not adding another layer of compliance for the institution.

Wes Smith (17:09.358)
A fantastic on point for the president’s forum. You know, this idea of using technology to advance accreditation, make it more more relevant to the learner. That is right on message for the things that we’re working on in the forum. And we appreciate your insight on this and thanks for joining us today.

Alison Griffin (17:27.173)
Thanks for having me.

Wes Smith (17:30.454)
Okay.

Beyond the Hype: Measuring the Real Effectiveness of AI Learning Tools

Beyond the Hype: Measuring the Real Effectiveness of AI Learning Tools

by | Jul 6, 2026 | Innovation, Outcomes and Accountability

Beyond the Hype: Measuring the Real Effectiveness of AI Learning Tools

By Jessica Smagler, Head of Research and Outcomes, Kyron Learning

Proving that students are learning – especially in new and innovative programs – is harder than it sounds. And the rapid proliferation of AI tools has made this more urgent, not less. Most AI  tools promise transformative outcomes but often provide little evidence to back them up. For institutions trying to make responsible decisions about what to adopt and who to trust, the question isn’t just does this work – it’s how would we even know?

As an AI learning company working with institutions across higher education, we’ve had to think hard about what meaningful evidence looks like and how to build toward it when rigorous outcome data takes time to accumulate.

What we’ve found is that measuring the impact of a genuinely new kind of educational technology isn’t a single leap to a finish line. It’s a progression from early signals to deeper evidence, and each stage has real value if you know what it can and can’t tell you. We think of it as four stages: Engagement & Confidence, Formative Signals, Persistence & Achievement, and Sustained & Verified Outcomes.

This is a framework built from practice, developed alongside institutions doing this work in real conditions. We offer it as an approach that can help any institution navigate the evidence question more clearly, whatever tools they’re evaluating.

Engagement & Confidence: Early Signs That Something Is Working

Engagement and confidence are not learning outcomes, but they are valuable prerequisites. This is especially true when introducing new modalities. Before you can measure what students have learned, you need to know whether they are showing up, staying engaged, and experiencing the instruction as credible and useful. Research in educational psychology is consistent on this: time-on-task and perceived relevance are preconditions for learning.  Students who are disengaged aren’t learning, regardless of how good the content is. And students who feel confused or unsupported tend to disengage.

Early confidence data at Kyron was encouraging: more than 80% of learners reported feeling more confident after a Kyron lesson and wanted to see more of them in their courses. When a learning tool builds confidence, students are more likely to keep engaging with it. Meanwhile, at one fully online partner university, students were spending over 22 minutes on each Kyron module compared to roughly 3 minutes for traditional video content. Students who spend seven times longer with content are, at a minimum, giving learning a chance.

Formative Signals: Seeing Inside the Learning Experience

Formative signals start to tell you whether learning is actually happening. And this is where AI tools, if designed well, have a meaningful advantage over many other modalities.

A textbook can’t tell you where a student got confused. A video can’t surface a misconception. But an AI tutor, by its very nature, is witnessing student thinking in real time – the questions students ask, the reasoning they attempt, the points where they struggle, and the moments where something clicks. The question is whether a given tool is designed to make that visible and actionable.

Institutions should be asking this directly of any AI learning tool they evaluate: what formative insight does your platform generate, and how does it get into the hands of instructors?

At Kyron, formative insight is central to how the platform works. Learner misconceptions are surfaced to instructors at both the individual and section level. Instructors can access full transcripts of student interactions, seeing exactly how each learner reasoned through a problem, where they needed scaffolding, and how their understanding evolved. And we use those same interaction patterns internally to continuously improve the learner experience.

This kind of data is the bridge between early engagement signals and the outcome measures that ultimately matter. It won’t tell you whether students passed – but it will tell you a great deal about whether they’re on track.

Persistence & Achievement: Proof That Learning is Happening

Persistence and achievement are where the framework starts to deliver on its promise. Persistence – whether students stay enrolled, continue engaging, and complete what they started – is one of the most consequential measures in higher education, particularly for the populations most at risk of stopping out. Achievement measures whether they actually learned: grades, pass rates, competency demonstrations.

These are the outcomes institutions care most about. They take time to accumulate, but when they do come, they are the most direct answer to the question this whole framework is designed to answer: is this working?

The evidence across our institutional partners is compelling. At one partner institution, students who engaged with Kyron showed statistically significantly higher grades on case study assignments and stronger persistence rates across multiple health information technology courses. At a community college partner, the pass rate for a gateway English course rose from 68% to 72% — breaking the 70% threshold for the first time in the institution’s history. And at a non-profit workforce development organization, Kyron’s integration into an HR track led to a 15% increase in course completion and a 20% increase in learner retention.

These are not anecdotes. They are the validation that the earlier signals were pointing in the right direction.

Sustained & Verified Outcomes: Building Evidence That Holds

Programs that can offer solid persistence and achievement data are in a great position to start thinking about even more sophisticated evidence. That might mean longitudinal tracking – following the same cohorts over time to see whether gains persist and compound. It might mean quasi-experimental designs that allow for more rigorous comparisons across sections or populations. Or it might mean pursuing independent, third-party validation that makes findings credible beyond a single institutional context.

At Kyron, this is exactly where we are headed. In the coming months, we will be incorporating an in-app assessment tool that will allow institutions to measure learning gains over time directly within the platform. We’re also deepening our understanding of dosage: how much Kyron does a learner need to see meaningful gains? And we’re pursuing third-party validation to ensure our findings are as rigorous as students deserve.

AI in higher education will only be as good as our willingness to hold it accountable. That means building measurement frameworks that are honest about what early signals can and can’t tell us, and patient enough to follow the evidence all the way to outcomes that actually change student trajectories. The institutions that do this well won’t just make better decisions about technology. They’ll be better positioned to serve the students who need them most.

Transcript

Wes Smith (00:04.091)
All right. Here we go.

Well, welcome to the President’s Forum podcast today, Nestor. Thanks for being with us.

napereira (00:13.199)
Thank you, I’m happy to be here.

Wes Smith (00:14.639)
Hey, before we get into some of the questions about AI and about how Miami Dade is using AI, tell us a little bit about Miami Dade and your role there.

napereira (00:25.806)
Sure, absolutely. So Miami Dade College is one of the largest and most diverse institutions of higher ed in the United States. And we serve over 120,000 students annually across eight campuses and MDC Online. And really our mission is really to provide accessible high quality education to a predominantly first generation, low income and minority student population.

really the communities that need higher education the most and have historically had the least access to it. And you know it’s my privilege as Vice Provost for Academic and Learning Technologies to lead the college’s digital learning infrastructure, overseeing MDC Online, which again serves those 120,000 students, along with our Canvas Learning Management System, our AI Student Success Platform, and really

trying to move our students forward in whatever chosen path they would like to take in their careers, in their professional lives. and and I just ensure that the tools and the platforms and the experiences that we build aren’t just innovative, but that they’re effective for the MDC students and really founded to serve them.

Wes Smith (01:29.231)
Right.

Wes Smith (01:40.612)
Yeah, we talk about that a lot at the presence forum. The idea there’s a difference between, you know, innovation and accountable innovation. So, you know, you y there are a lot of cool things you could be doing, but do they decrease cost for students or do they, you know, provide a better pathway to success for students? That’s the ultimate question, right?

napereira (01:48.555)
Right.

napereira (02:01.494)
Yeah, absolutely. And that’s something I’m happy to to talk with you about today.

Wes Smith (02:05.667)
Yeah, so I know Miami Dade has been out in front on on putting AI in cr classrooms and and you’re looking at at things that a lot of schools you’re doing things a lot of schools are, you know, still talking about looking at. They they would love to get to a position to execute on it. But tell us a little bit about what first got you interested in AI in in higher education and the problems you were trying to solve for your students.

napereira (02:32.223)
Yeah, so really the you know, the honest answer is that we weren’t really chasing a trend because there’s so many trends now in AI trying to to steer you in a particular way. We were trying to solve a persistence problem. So at Miami Dade College, we we serve this student population again, where the majority are first generation college students and many are working adults and balancing jobs and family. and really a significant number of them also facing language barriers or gaps in academic preparation.

So, really, the the traditional model of office hours and email didn’t really apply and wasn’t really designed for this type of student. They really needed you know, support at 11 o’clock at night, right before something was due, maybe in the language that they think in, and really calibrated to where they actually were in their understanding and not necessarily where we assume them to be in their understanding of the material in a particular course. That’s what really

drove us toward AI and and we started with the question, you know, where are the students most likely to stop persisting? Right? What what would have to be true for that moment to go differently? And you know, the answer kept pointing to access for us. And really to this idea of timely personalized support that scales across thousands of students without proportionally scaling that cost. And that was very important to us as well.

you know, and today we’re solving for that through Lucy. And and Lucy is our live user coach for you. She’s an AI agentic platform that that we co-created. and it really functions as an AI student success coach that’s embedded right in our learning management system. she brings together proactive outreach, real-time faculty alerts, and connected tutoring and advising services so that the right intervention reaches the right student at the right moment.

And really we’ve moved from this curiosity in AI to really making a part of our infrastructure.

Wes Smith (04:33.955)
Yeah, I love it. And and you had a a specific use case for that. You wanted to work on persistence. How can we leverage AI to to advance persistence or keep our students engaged and and progressing? Is that does that sound about right?

napereira (04:50.024)
That sounds about right. And and you know, we’re starting with our online modality where students can sometimes feel a bit disconnected just because of the asynchronous nature of how these these courses run. so that’s where we started and we’re expanding that across all the modalities at D C.

Wes Smith (05:05.081)
Yeah, I love it. So you teamed up with Kyron learning on this, is that right? So walk me through that a little bit. How did you actually get it into your courses? Like how how did you take it from an idea of we need more persistence to students have Lucy or students have other assets that that they can use to stay more connected?

napereira (05:09.254)
Yes, that’s correct.

napereira (05:30.319)
Right, so Chiron’s a great partner and it really functions like an interactive lecturer inside the course itself, right? And it engages students in real-time conversational dialogue, which was very important to us. Again, because of that asynchronous nature of of online courses, we thought that this kind of lecture would be really beneficial. But the real reason is that you know the technology for us identifies and corrects misconceptions.

As they’re forming. So as the students are having this conversation with the AI, the AI is kind of understanding what are the misconceptions that these students are having and how can I help them? How can I ask deeper questions and steer them in the right direction? And this was important to us in terms of persistence. This this data, this analysis of students and their misconceptions comes about you know before a quiz or an exam might reveal them, right? So it’s

prior to that happening. And that’s really a a different and a and a meaningful different model than traditional content delivery. And the way that we introduced it, we introduced it kind of deliberately and and not all at once. And we started with our ENC 1101, which is our introductory English composition course at MDC, and really made sure that our instructional design team was trained and ready to support our faculty really before they even touched the technology and the platform. So

For us, the infrastructure and the support really existed on day one and not really as an afterthought. you know, yeah. Absolutely, yeah. and really from there we expanded course by course using what we learned in each rollout to really refine you know what we saw next.

Wes Smith (07:03.097)
Yeah, that seems really smart.

Wes Smith (07:13.839)
How long does it take you to embed within a single course? Like is that like a a quarter long process or or are you being able to rapidly scale that at this point?

napereira (07:24.963)
We’re we’re able to rapidly scale that at this point. Initially we took our time because the platform was new to us. but once we were able to train you know, internally with our instructional designers and with our faculty, we found that that was actually a rather quick platform to be able to embed this in the courses, right? And and all the technical things in the background as to where how it gets inserted into the course and all that, that’s part of what my technical team does. As far as faculty, they just need to understand how the platform works.

Wes Smith (07:30.554)
Mm-hmm.

napereira (07:54.094)
and really serve as the subject matter experts in in their given course. and we found that that does not necessarily take that long. You know, it could be weeks where you can embed this technology in there and really train the AI is what what faculty are doing.

Wes Smith (08:08.613)
So it sounds like faculty pretty supportive, they’re moving it, they’re advancing it. Tell us a little bit about how students are responding.

napereira (08:15.905)
Yeah, so students have responded really well to this and we’ve seen we do take surveys you know after each session to kind of see where students are at, how they feel about the technology, and it was overwhelmingly positive. One of the one of the things that I thought was an important indicator was kind of what I talked about before. Like d do students have that a better connection now because they have these lectures with the AI and the AI is is kind of picking up on on

you know, what they need help on and that the AI is there twenty four seven for them. and all of our surveys showed a kind of positive reaction to that to that connection and the explanation of course material inside of of the courses through the use of Chiron technology.

Wes Smith (09:01.581)
Nice. So I mean, we are living in an age where we’ve been talking about personalized education for a long, long time and the time and resources for one individual human to, you know, to provide individualized care for each student is it’s almost pro it’s prohibitive in almost every instance. But with AI, we can expand, we can we can see students get that personalized education at scale.

napereira (09:07.798)
Right.

Wes Smith (09:29.207)
And you’re you’re watching it right now, and what I’m hearing is students are reacting positively to that. Is that

napereira (09:35.829)
Yeah, absolutely. And we have fantastic faculty at at Miami Dade College. like you said, we’ve been talking about personalized experiences and learning for a long time, but sometimes it’s it’s kind of f it’s physically difficult to be able to do that you know, at scale with so many students where we see the AI as really as another tool for our faculty. You know, faculty are able to see those misconceptions in real time. They’re able to understand, better understand what specific students

are having issues with at a specific time. and the AI can kind of remember that across you know across the the course of of of that class that they’re taking and really keep probing the student to figure out ever if there are any other misconceptions.

Wes Smith (10:19.865)
Yeah, I love it is as a tool for faculty to be able to personalize their efforts more to each individual. So yeah, I mean it’s a win, it’s a win for faculty, it’s a win for students. And I know that a lot of education leaders that aren’t at Miami Dade and and they’re at different institutions out there watching and they’re thinking, okay, yeah, we love this, we’re we’re curious about it.

that they’re in that curiosity stage. They’re they’re interested though in making the leap and actually doing it. You’ve you’ve made the leap. Tell us lessons learned for for any of those education leaders out there watching. What would you recommend as far as taking the next step towards actually adopting and making it a reality for their students?

napereira (11:10.227)
Yeah, I think really the the biggest lesson learned is you have to start with the student problem and not necessarily a specific tool, right? So you kind of have to work your way backwards from from the results that you want to see and then go backwards. And it’s really easy to, I think, to get pulled into what can this technology do or that one or that AI platform or that one, instead of what is actually getting in our students’ way right now from persisting and from s for succeeding.

So really every piece of what we’ve built from Lucy to the Chiron integration really traces back to a specific point of friction that we could name before we ever evaluated a vendor. Right. and then secondly, you know, we really built the infrastructure before you built the rollout. and we trained our instructional designers ahead of faculty, like we discussed before, to just make sure that this new tool was

was easy to understand, it was accessible, and then had that faculty buy-in. I’d also say, you know, a third thing is try and treat it as a phased rollout and not necessarily a huge launch event. you know, we’ve had a a real roadmap, you know, what’s live now, what’s coming next quarter, what’s what’s further out. and that phasing really let us prove the value at each stage.

instead of you know betting everything on one big rollout and also gives us an opportunity to hear feedback in real time from our faculty and students and make adjustments in the platform as as we see you know as we saw necessary. And then finally I’d say bring leadership real data, you know, not just a demo of what it can do. And institutional buy-in I think from you know what was actually happening with students and and not just what the technology could theoretically do, but what

changes and what positive effects did we actually see.

Wes Smith (12:57.401)
Right. I love I love your focus on data and I like, you know, this idea of starting with a student problem that you want to solve as opposed to you know, flashy new technology that you think looks cool. No, let’s let’s let’s figure out the problem that we’re working to solve. So so speaking of that, tell us what you’re seeing about you know, on your persistence issue at Miami Dade. Are you seeing the data?

napereira (13:06.13)
Right.

Wes Smith (13:24.335)
That is encouraging to say, okay, what we’re doing here is advancing our goals and persistence.

napereira (13:31.482)
Yes, very very encouraging data we’re seeing, you know, particularly from our spring semester that just finished you know several weeks ago. We’re seeing students that have actively engaged with the AI where those misconceptions were detected. We’ve seen them have measurably higher pass rates and persistence than students who were kind of on the same level but did not necessarily interact with the AI. It was important for us to kind of see that. we’ve also had some faculty that taught the exact same course for us.

one using the AI model and one not, and then doing a comparison between them. And we did see higher persistence and higher pass rates in the course that had the AI. And then some qualitative data that we received from from students about how they felt. How did you feel about having that help, that assistant, that that guide there for you 24-7? And that was very positive. We also wanted to to have qualitative data as well. And that was very positive from both the students and the faculty. And

You know, one other thing that we that we developed in conjunction with the AI is kind of this AI dashboard that lives in the LMS for faculty to have the data at their fingertips. They don’t have to dig through and try to find out, you know, how are the students interacting with the AI, what has it identified as misconceptions. This is all in one location for faculty and they really appreciated that.

Wes Smith (14:47.065)
Yeah. I love it. I l I love that you’ve got the data to to show you now and that you can now start to refine your systems and improve. That’s that’s a great stage to be at. Okay, so finally, l I I want to pick your brain on one last thing and then we’ll let you go. That is, you know, a a lot of this series is intended to inform policymakers. People in Washington, DC or in the state houses,

Or or you know, those in in education that are building the future system for our students about innovation and technology and specifically about AI. Is there any advice that you would give to policymakers who are building that, you know, next next generation of policy that will facilitate higher education? What do you want them to understand about AI and what AI can do for students?

napereira (15:45.881)
Yeah, it’s a great question. the first thing I I guess I’d want poly policymakers to understand is that AI in education is not primarily about automation, right? It’s really about access. And for generations, you know, we’ve been discussing personalized academic support, and we’ve had that in the past. We’ve had tutors and advisors and we’ve attempted to have real time feedback and all that. that’s been available to students maybe who could afford it or who attended well resourced institutions.

you know, for example, the students at at Miami Dade College, most of whom are again working adults and many of whom are are the first in their families to pursue a degree, they’ve had those access opportunities at at MDC, but what AI lets us do is really scale that access further and extending that personalized support to every student at a scale that really wasn’t possible before.

I I’d also ask, you know, and advise policymakers to consider specifically what do those funding mechanisms look like that reward AI implementation? and tie those to measurable results. again, completion rates, credential attainment, workforce readiness, things like that, and not just adoption metrics, right? you know, yeah, absolutely. And and really

Wes Smith (17:03.311)
Yeah, I love that. Well look let’s let’s tie it to outcomes, not not implementation. Yeah. It’s not it’s not about the the fad of, you know, we adopt it. It’s about it’s really about persistence and completion.

napereira (17:08.927)
Yeah.

napereira (17:18.923)
Yeah, absolutely. And you know, I think that the the the recognition, particularly to colleges and community colleges and and other universities around the country that that enroll the largest student populations in higher ed, we really think that those students that are balancing their lives, their work, their family, school and all that, they really need to be central to this conversation about about AI. because if AI driven innovation doesn’t reach

these institutions and those students that really need it the most. I think we’ve missed the the you know, the students who need it and really the workforce pipeline that the economy depends on.

Wes Smith (17:56.112)
Yeah, I love I love what you’re doing down there at Miami, Dave, Nestor. Thank you so much for sharing your college’s approach and and just your lessons learned. I think it’s really helpful for everybody else that that’s watching and that’s thinking about, hey, we we should probably be moving in the same direction. So this is this is fantastic.

napereira (18:15.317)
Than thank you so much, Wes. I really appreciate you inviting me on.

How Southern New Hampshire University Is Using AI to Expand Student Support at Scale

How Southern New Hampshire University Is Using AI to Expand Student Support at Scale

by | Jun 17, 2026 | Innovation

When prospective students visit a college website, they often need answers immediately.

Questions about programs, admissions requirements, transfer credits, tuition, and enrollment timelines can determine whether a student moves forward or moves on.

Southern New Hampshire University is using artificial intelligence to ensure students receive support when they need it, while helping staff spend more time on the human-centered work that drives student success.

Helping students get answers faster

SNHU launched an AI-powered virtual assistant in early 2025 to help prospective students quickly find information about programs, admissions, enrollment, and university services.

The tool uses a compliance-first approach. General questions draw from approved website content, while sensitive topics rely on human-approved responses and additional safeguards.

This mean faster access to information for students and families while allowing university staff to focus on more complex conversations that require personal guidance.

Creating more time for student support

SNHU has also deployed Microsoft Copilot to more than 5,000 employees through a phased rollout focused on training and responsible adoption.

Staff use AI to summarize meetings, streamline communications, synthesize information, and reduce administrative workload.

While these efficiencies happen behind the scenes, the benefit for students is straightforward: advisor and support teams can spend more time helping learners and less time on repetitive tasks.

Expanding personalized learning

The university is also piloting learner-facing AI tools through partnerships with organizations such as Latimer and OpenAI.

The goal is providing more personalized support, expanding access to learning resources, and creating educational experiences that reflect how students will use technology in the workforce.

Faculty oversight remains central, with AI designed to support instruction rather than replace it.

Building for the future

SNHU’s AI strategy extends beyond tools. The university recently completed a six-week AI Bootcamp for its Executive Council, helping senior leaders develop the knowledge and practical experience needed to guide responsible adoption across the institution.

Successful AI implementation requires both technology and institutional capability.

The bottom line

Students benefit when they can get answers faster, access support more easily, and receive more personalized learning experiences.

Across its AI initiatives, SNHU is focused on using technology to strengthen human support systems, improve responsiveness, and help more students succeed.

How AI Is Creating Faster Pathways Into High-Demand Careers

How AI Is Creating Faster Pathways Into High-Demand Careers

by | Jun 15, 2026 | Innovation

How AI Is Creating Faster Pathways Into High-Demand Careers

For many students, entering a technology career has traditionally required a long educational journey.

Artificial intelligence is beginning to change that.

Across the Alamo Colleges District, AI is helping students gain workforce-relevant skills earlier, shorten the path to employment, and access opportunities that previously required much longer academic preparation.

According to Dr. Henry Griffith, Dean for Academic Success at Northwest Vista College, one of the most significant benefits of AI is its ability to lower barriers to entry into high-demand STEM careers.

Building AI skills across all pathways

The district has spent several years integrating AI into STEM programs while also developing a dedicated Associate of Applied Science in Data Science and Artificial Intelligence.

The goal extends beyond preparing future data scientists.

Griffith says Alamo Colleges is working toward a future where every graduate develops foundational AI competencies regardless of their field of study.

As AI becomes increasingly embedded across industries, those skills are becoming valuable for students in nearly every profession.

Why stackable credentials matter

One challenge facing working adults is the amount of time required to complete traditional degree programs.

To address that barrier, Alamo Colleges designed its AI pathway around stackable credentials and workforce certificates that provide value throughout the learning journey.

Students begin building skills immediately through an introductory Google AI microcredential delivered through Coursera. Rather than waiting months to begin coursework, learners can start making progress almost as soon as they apply.

As students continue through the program, they earn additional credentials that can improve their employment opportunities before completing the full degree.

For working adults, career changers, and learners returning to education, that flexibility can make a significant difference.

Expanding access to workforce opportunities

Griffith believes AI is helping community colleges rethink how workforce education is delivered.

Instead of requiring students to complete lengthy prerequisite sequences before accessing emerging career fields, institutions can create pathways that allow learners to develop marketable skills more quickly.

That approach is particularly important in rapidly evolving fields such as artificial intelligence, where employer demand often moves faster than traditional academic programs.

The bottom line

For students, the value of AI is not limited to learning about technology.

It is creating new opportunities to enter growing industries, gain workforce-relevant skills faster, and build credentials that have immediate value in the labor market.

At Alamo Colleges, AI is helping learners move from interest to opportunity more quickly than many traditional pathways have allowed in the past.

Transcript

Wesley Smith (03:02.072) Henry, thanks so much for joining the President’s Forum podcast today.

Henry Griffith (03:20.082) Thank you for having me, Wesley. I really appreciate it.

Wesley Smith (03:23.054) We will get right into the AI topics. So when we talk at the President’s Forum, we talk about everything in the measurement of student success. Innovation is only innovation if it leads to student success. So with that background, where are you seeing AI make a real difference for students?

Henry Griffith (03:45.936) I think a lot of my experience regarding that question comes from the current role that I sit in as Dean for Academic Success at Northwest Vista College. Although a lot of the conversation at the national scale at the moment seems to be centered around the disruptions in a negative fashion that AI is having on the job market, we’ve been very fortunate here at Northwest Vista College and more broadly across the Alamo College.

in order to really be able to see some concrete examples where AI has lowered barrier to entry for a lot of STEM-oriented occupations. So just to give a little bit of background, I know we’re going to talk about this a little bit later, but we’ve been integrating AI within our STEM courses and STEM sequences for about three to four years from now. And one of the best initial examples that I can give you from that is the introduction of an intro to AI module within our introduction to engineering course. And I think back when we originally integrated that in 2000,

2024, I should say, using material in partnership with Amazon Web Services, Machine Learning University. We had a student that semester that was transitioning. She already had a bachelor’s degree in art history, and she was intending to take the introductory course just to get a little bit of a flavor of what an engineering career may look like. And she reached out to me at the beginning and she said, I just want to see if this is for me. I realize I’ll probably have to take a lot of courses before I could actually get any tangible benefit from pursuing this pathway.

But fortunately, after completing that course, particularly referencing the skill set that she gained in data science and artificial intelligence at the time, she was actually able to transition into an internship at a local battery manufacturer. Clarios is the name of that company, with only a semester of courses. And not surprisingly, the thing that they were most interested in with respect to her background were those kind of AI awareness and competencies that she developed as part of that introductory course.

Wesley Smith (05:34.946) Yeah, I can imagine that that would be really important to most employers today. Are you finding that that introductory course is like pretty applicable to almost anybody in higher ed right now?

Henry Griffith (05:45.318) Definitely, and I think it’s kind of driven the broader AI strategy that we’ve had across the Alamo colleges. A lot of the success stories that we had integrating kind of introductory AI modules within our STEM sequences were instrumental in the development of our dedicated associates of applied science degree in data science and artificial intelligence. And then more recently within the past year, the broader work that we’ve been doing across the district to try to ensure that all graduates from any Alamo college, regardless of their pathway,

have the capability to demonstrate kind of fundamental AI competencies.

Wesley Smith (06:18.018) Right, right, right. It makes sense. mean, at a near point in the future, I think every graduate for every credential and every degree is going to have to have baseline skills within the AI domain. I mean, it’s just becoming so much part of our life that we’re going to have to get there. you mentioned some of your programs.

like data science and AI, and you’re doing some stackable micro-credentials as well that include some of this training. Tell us about these pathways, but specifically, I’m interested in how these pathways serve students, especially working learners.

Henry Griffith (07:01.554) Yeah, so with respect to the pathway that we have, the Associates of Applied Science and Data Science and Artificial Intelligence, we actually started the design work on that in about 2023, at about the same time that I mentioned the integration of that module within the Introduction to Engineering course. And when we did that, the thing that excited us the most is we realized that a lot of jobs were coming in line that required those AI competencies, only a fraction of what we see now, as you mentioned. But we realized that in order for those to be

Wesley Smith (07:18.808) Okay?

Henry Griffith (07:31.47) accessible to most students, the only educational pathway that they at least had within the South Texas or San Antonio market involved going into a degree sequence that involved having to complete a three-course sequence in calculus and maybe some additional mathematics even beyond that. And that was problematic for several reasons, the primary of which, as you mentioned, is if you’re an adult learner, do you really have that much time to kind of devote to upskilling or career transitioning?

Wesley Smith (07:46.883) Uh-huh.

Henry Griffith (07:57.682) or something like that. So when we designed the new pathway in artificial intelligence, the first thing that you mentioned, the kind of what we call an in-ramp micro-credential in partnership with a micro-credential that Google offers, is really what we feel a novel pathway and model that we want to move to for all of our workforce programs. And the idea behind that is, if you think back to when maybe a traditional student applies for college, they send in an application and it’s a multiple month waiting period and they get a letter in the mail and then

they enroll in classes and maybe six months later from that point of where they express interest to the point that they’re enrolling in their first course and they’re getting some of that content.

So we know that we compete in a market where a lot of the content that we offer is really a commodity with respect to different platforms that are available on the internet and things of that nature. So what we’re doing with the Grow with Google program is using Google’s micro-credential introduction to AI as an in-ramp indoor program where when a student actually completes their application for the degree, that next day they get an invitation to join the Coursera course. So they start making progress on day one. So really shortening that latency, I think that’s incredibly

important for an adult learner. They don’t have time to waste and this gives them the ability to start making progress on their degree immediately.

Another thing that we’re really excited about is the way that the degree is structured with integrated occupational skill awards and workforce certificates is as students progress throughout the program, their value in the market is continuously enhancing. They don’t have to wait to complete the entire degree. We’ve seen a lot of evidence of that so far, largely in the door at this point, but after completing the first course in the sequence, we get emails from many of our students, about a quarter of whom already have a bachelor’s degree or a master’s degree,

Henry Griffith (09:44.72) in another discipline, just really reflecting on how those foundational AI competencies that they built as part of that course have provided them value in their job already. And we’re serving amazing adult learners, some are working for the city of San Antonio, and they’re not only from the San Antonio market, but across the state and even a few across the country.

Wesley Smith (10:03.288) That makes so much sense to me that the credentials within the credential, the mile markers within the larger credential add value to an individual in the workforce before you actually finish everything. can imagine that especially in AI where there aren’t a tremendous amount of

of established pathways, especially past an associate’s degree. mean, I think it’s very rare at an institution that there are bachelor’s degrees and then beyond in AI specific training. But I would imagine that this is one of the first opportunities that employers have to see an actual credential for AI at any level is coming out of your program. Is that your experience?

Henry Griffith (10:58.642) I think that’s incredibly accurate. The majority of this work kind of developing dedicated pathways in workforce artificial intelligence really started around the 2022 timeframe. And a lot of that, what was wonderful, it was really driven by industry partners. Intel had a dedicated program to advance that, AWS as well as we spoke of earlier. And a bunch of programs were kind of spun out under that additional model, Miami Dade College, Houston Community College, all of those were just incredible motivations for us to stand up our pathway.

I think we had an excellent advantage at Alamore colleges in the sense that we entered the market about a year and a half later. So we kind of aligned directly with the rollout of generative AI. So when we look at our pathway, we do think that we call it a DS AI 2.0. We think that just based upon our timing that we were able to capture and integrate a lot of the advancements in generative AI, agentic AI, and so on, it really give us a unique value proposition to the employer market.

Wesley Smith (11:58.178) Yeah, that makes sense to me. Well, a big reason that we’re talking to our institutions about AI and how it’s making a difference in the lives of students is this March meeting we had in Washington, DC, where we took the presidents to visit with Hill, members of the Senate and members of the House. So Congress talking about

hey, what does the policy for higher ed look like? And what are the ways that we can collaborate with you? And what we received from those meetings was significant interest in what are our institutions doing in AI and how is it impacting students? So one of the questions that I want to ask you, and I want to conclude with this, if you had five minutes to talk to a group of

of bipartisan Hill staffers that are engaged in education issues that are driving a lot of the work behind the scenes. What would you want them to understand about responsible AI and higher ed? What would you want them to know about safeguards and other policies that would help your institution and other institutions scale these kind of benefits without creating new risk?

Henry Griffith (13:18.0) Yeah, I think that’s an excellent question.

If I had five minutes, I’d probably take three to four minutes and just highlight all the incredible work that students, faculty, and stakeholders more broadly are doing in higher education at the moment to really drive value out of artificial intelligence. So we spoke a lot about our dedicated kind of pathways on the STEM side of things, but I’m just continuously amazed. I really want to thank Dr. Mike Flores, who’s the chancellor of Alamo College’s district, for the work that he’s doing to really scale artificial intelligence competencies across all five colleges and across

across all five pathways. Really a wonderful byproduct of getting to serve on that committee is just seeing all the incredible things that faculty and administrators are doing. Just to highlight one specific example here at Northwest Vista College, for quite a while we’ve been a Microsoft data center and academy and trying to build ideal workforce pipelines to the emerging data center workforce here in South Texas. And we have recently partnered with a tool that is known as Goldie. It is a conversational AI agent that’s built on top of

And what that has given us the capability to do is to really scale kind an onboarding experience for Microsoft that directly aligns with the competencies that they’re looking for.

So I think immediately you’re starting to see just all these different ways that AI is kind of impacting the entire learning experience. If I did have some time with staffers on the Hill, I’d like to just thank them for all the work that they’ve done recently in order to really modify the kind of funding streams in higher education to focus more on community colleges, particularly all the work in workforce Pell has just been amazing in unlocking opportunities when we stand up rapidly, these kind of upscaling pathways and advanced technology like AI, that’s really enabling learning.

Henry Griffith (15:00.24) to have affordable access to those programs. I think that if I could suggest anything in addition, as we’re starting to build out our own unique policies at the college level, we’re looking at benchmarks and kind of exemplars at universities across the state level. It’s always easier to build on policies that are established at the federal level. So just any kind of guidance, I think our students and faculty, they’ve been incredibly innovative with respect to their applications of AI, but like anyone, they’re a little bit concerned about the

potential disruptions on the job market, about what the implications may be on privacy as well. So just any kind of establishment of recognizing the tremendous potential of this technology and kind of developing firm guidelines for education at the national level that we can build on as we work to kind of establish our unique institutional policies.

Wesley Smith (15:51.394) Yeah, there’s no question that this is a high priority for members of Congress right now, figuring out how AI impacts society generally. And a lot of good thinkers are thinking about how can we use AI to personalize education, to be able to enable students to move at their own pace, be able to just essentially fuel innovation that can have a tremendous impact.

amount of disruption, but good disruption in our current system to lead individuals to opportunity. So I can see that they’re working on that. We really appreciate your thinking on this. And we hope that this series and this conversation can help members of Congress and their staff wrap their arms around this in a way that they can provide value for the institutions that are looking.

to responsibly develop AI.

Thanks for joining us today, Henry. We appreciate your time and we’ll be in touch with more on this. Would love to check back in with you at a later date, but as for right now, we appreciate your input.

Henry Griffith (17:05.744) Wonderful. Well, thank you so much for the opportunity, and I look forward to seeing all the wonderful innovations that come out of this work.

How AI Is Helping More Students Persist and Complete Their Degrees

How AI Is Helping More Students Persist and Complete Their Degrees

by | Jun 12, 2026 | Innovation, Outcomes and Accountability

How AI Is Helping More Students Persist and Complete Their Degrees

One of the biggest challenges in higher education is not getting students enrolled. It is helping them stay enrolled long enough to complete a credential.

According to John Baker, founder and CEO of D2L, artificial intelligence is creating new opportunities to improve student persistence by making learning more engaging, personalized, and supportive.

The impact is already measurable.

Institutions using AI-powered learning strategies within D2L’s platform are seeing improvements in retention, course completion, grades, and student engagement. In many cases, students are performing better while spending less time trying to figure out what they are supposed to learn.

Building better learning experiences

Baker believes one of the most promising uses of AI is helping faculty create stronger learning experiences.

AI can help instructors transform static materials such as PDFs and slide decks into more interactive content that includes formative assessments, flashcards, embedded feedback, and engagement opportunities.

The result is not simply more content. It is content designed to help students understand whether they are learning effectively before high-stakes assessments occur.

Early evidence suggests these approaches are improving outcomes in some of higher education’s most challenging courses.

Personalization is about people

Personalized learning is often described as creating individualized pathways for students.

Baker argues that definition is incomplete.

True personalization, he says, is about strengthening human connections.

AI can help instructors identify students who may be struggling and automatically provide encouragement, resources, and guidance before problems become barriers to success. It can also help faculty deliver more meaningful and personalized feedback at scale.

Those interactions matter.

When students feel seen, supported, and connected to instructors, they are more likely to persist through challenges and continue toward completion.

Using AI to support persistence

One of the most significant benefits Baker sees is the ability to proactively support students before they disengage.

AI-powered systems can identify patterns that suggest a student may be falling behind and trigger timely interventions.

A simple message, a reminder, additional resources, or personalized feedback can often make the difference between persistence and withdrawal.

Baker says institutions deploying these strategies frequently see retention gains of five to eight percent in the first year.

For students, those improvements represent far more than institutional metrics. They represent completed degrees, stronger career opportunities, and a reduced risk of leaving college with debt but no credential.

Why AI is different from previous technology shifts

Over the past three decades, higher education has adapted to the internet, mobile technology, and cloud computing.

Baker believes AI is a bigger transformation than any of them.

Unlike previous technology shifts, AI affects the core of teaching and learning itself. It changes how students learn, how faculty teach, how assessment works, and how institutions provide support.

That reality creates new responsibilities for colleges and universities.

Institutions will need to invest in research, faculty development, curriculum redesign, workforce upskilling, and thoughtful implementation strategies to fully realize the benefits of AI for students.

The bottom line

For Baker, the most important measure of AI is not efficiency.

It is whether more students succeed.

When AI helps faculty build better learning experiences, provides more personalized support, and strengthens human connections, students are more likely to persist, complete credentials, and achieve their goals.

That is where the real value of AI in higher education begins.

Transcript

Wes (00:32.984) Hey John, it’s good to see you today. Thanks for joining us.

John Baker (00:40.689) Excellent.

John Baker (00:46.2) great to join you, Wes. Looking forward to the conversation here today.

Wes (00:49.41) Hey, I I mentioned, you know, in the intro that you’re a new member of the forum. We’re glad to have you as a collaboration partner. you’ve been at this for a long time since I wanna say D2L was founded in nineteen ninety-nine. Is that right?

John Baker (01:03.599) Yeah, that’s right. I was a third year university student at the time. You know, for me it’s always been about what’s the most important problem we could solve that would have the biggest impact on the world. I can’t think of anything more important than transforming the way the world learns because learning is at the heart of solving all the world’s challenges. and so we set out in our case to build a learning platform that could engage, that could inspire, that could break down barriers, and not just help people achieve their potential, but to help them achieve more than they’re ever even dreamed possible.

through these transform learning experiences. So, you know, been at it for almost twenty seven years. and yeah, excited for the future too.

Wes (01:38.784) Yeah. Yeah, it’s kind of amazing.

Well, I the thing that’s that’s very interesting to me is you created a pl this platform while you were a student. So I mean it’s like learner created, right?

John Baker (01:50.661) Yeah, exactly.

Yeah, no. a lot of the features that we built in the early days were very much with the students in mind, including giving them a lot of transparency in terms of what was happening in the platform.

Wes (02:03.906) Yeah. Yeah, I love it. Well, let let’s start out with this. Can you think back in those twenty seven years? And is there an experience with a student or an experience as you’re setting this up that really sticks with you throughout the years and and informs what you do today?

John Baker (02:10.598) Mm-hmm.

John Baker (02:24.249) Yeah, well, there’s many. you know, I can think of one example where there was a student that spoke at her conference a few years back now, and she told her own personal journey. You know, when she was eight years old, she had a dream of becoming an Olympic athlete for the US. and her gene came to a crushing blow when she learned that she was going blind. And so in her case, she had a choice to she stay in the community and try this new experimental.

Wes (02:46.102) Oof.

John Baker (02:51.941) learning using one of our clients Gwynette online campus, or does she go to a school for the blind and and she made the choice of, you know, going to this experimental trying this online learning platform that was supposed to support her and it worked out. She became a Paralympic athlete for the US, she won medals and then she’s now studying at at college. So it’s you know those types of moments where your technology can break down a barrier

Wes (03:10.382) Well, that’s amazing.

John Baker (03:19.611) that would normally hold someone back from their dreams, is, you know, those are those are pretty magical moments.

Wes (03:24.77) Yeah, that’s a that’s a great story. That’s that’s one that’ll stick with you for a for a long time, seeing that kind of success. What kind of an athlete was she? Or is she swimmer?

John Baker (03:33.184) she was a swimmer. So in her case, McLean Hermes is if you want to look her up.

Wes (03:38.664) that’s cool. That’s great. Well, let’s talk. we’re here to talk a little bit of the future of higher ed and how AI impacts that. And we talk a lot at the forum about, you know, students first. It’s a student student first mentality. And I’m interested if you’ve seen some tangible ways that AI can reduce friction for learners today in just day-to-day learning experiences.

John Baker (03:52.272) Mm-hmm.

John Baker (04:08.497) Well, I I think the key with AI is making sure that we’re scaffolding the AI into these learning platforms in a way that’s gonna support a better learning experience. So we’re gonna graduate doctors and nurses and engineers that are better at the profession. And we want to avoid some of the risks around cognitive offloading. And so, you know, in our case, we think we can do this very successfully. you know, we’ve seen good evidence of that now with a lot of our clients where

We’re leveraging AI largely in the in the in the use case for for faculty to help them build better learning experiences for the learners. So how do we help faculty build better formative assessments, build more engagement, take you know, maybe a PowerPoint or a PDF and turn it into something much more inspiring, maybe with some flashcard exercises and some quick embedded inline assessment that helps the student understand if they’re on the right track and can hit that next button with confidence.

So making the job of faculty building really high quality learning experiences is already through a number of efficacy studies that we’ve already done with third parties, really having a big impact on increasing retention, driving better completion rates for some of these tough bottleneck courses, lifting grades. The time on tasks for students is actually coming down. So they’re scoring better on their exams, but they’re not having to spend as much time trying to figure out what they’re supposed to be learning.

Wes (05:26.709) Wow, that’s interesting.

John Baker (05:26.949) Great great metrics across the board. Yeah, no, it’s it’s really having a positive impact. We’re also seeing impact in terms of giving feedback to students or tutoring or all kinds of other areas within the system.

Wes (05:37.976) So you’ve built this in, you’ve used AI as b I mean, building it into the LMS, so you can you can use it seamlessly.

John Baker (05:44.847) Yeah.

Exactly. And there’s there’s actually a a recent article that just came out in one of the journals that really speaks to this. cog you know, the cognitive offload is there if you’re just using an AI on the side. Think just you know, students using it to support their work outside of the learning platform. But in the learning platform it actually has an increase in cognitive ability for the students because and it makes sense because we’re we’re leveraging these technologies to scaffold better learning experiences which engage, inspire and help students really

Wes (06:02.818) Right.

John Baker (06:17.071) get through the material in a in a much more efficient, more engaging way, which helps them achieve better results. And so you there are good ways of doing the you know, AI and there’s there’s bad ways of doing it. And we we definitely have been spending the last fifteen years trying to figure out how to harness this technology in a way that’s gonna really have a positive impact on students.

Wes (06:36.28) So John, when we talk about personalized education in in the future, how does AI accelerate that?

John Baker (06:39.845) Yeah. Mm-hmm.

John Baker (06:44.623) Well, I I’d I’d I’d argue there’s two key things when when we talk about personalization. So there’s the traditional individualizing the adaptive learning pathways for students. So if a student is struggling with something, here’s some remediation pathways that automatically open up that are predicted to have a better outcome for that individual student to help them get back on the right track, or maybe some enrichment pathways that open up. So we spent a lot of time doing that work and it does have a big positive impact on student experience. There’s no question about that. But there’s a second piece to this, which is

Wes (06:53.485) Right.

Wes (07:02.168) Right.

John Baker (07:13.753) I I don’t think personalization is meant to be individualization, not not by itself. I think personalization at the heart is about building better human connections. So better connections between students and other students, or students and professor, or students in the profession they’re pursuing, or the big questions in their field. You know, if we can really harness these AIs in a way that’s gonna help those students feel better connected, help them get inspired, help them with their problem solving, their creativity, their you know, their profession they’re pursuing, that’s when we get this right.

And it’s not just about that, you know, individualized pathway which is traditionally thought of as for personalization.

Wes (07:49.036) Yeah, that’s not that’s not very intuitive to think about personalization as better human connections through AI. Tell us a little bit how that can happen.

John Baker (07:52.451) No.

John Baker (07:56.817) Yeah.

John Baker (08:00.657) Well, it can just be little things, like when something you should pay attention to is in the platform, we just alert you like, hey, John, noticed you might be interested in this particular article that was just posted. So you like just being able to at mention someone’s name and all of a sudden they’re now their attention is now drawn to it, or better collaboration suites within the system or communication. but one of the best ways of doing personalization is around feedback. So we have all kinds of intelligent agents in the system that

watch what students are doing, can understand if they’re off on the wrong track and can send them a little nudge. Hey, I noticed you did poorly on the last two assignments. Don’t worry. Most students struggle. It’s part of learning. here’s some support for the next assignment. Like pay attention to the following three things. And if you ever need help, here’s my here’s my information. Here’s a picture of my cat. You know, stuff like that that enables that personalization at scale, but then it frees up time for the instructor to be able to give feedback to the student.

And feedback for me is is something separate and apart from assessment. And quite often people intertwine these two things. And with feedback, you can actually be very personal. You can say, well, congrats on the football game. That was a fantastic outcome. you know, on now on the last assignment I said to you I wanted to see improvements in these three areas. I saw it on this assignment. On the next assignment, I’m gonna be looking for the following. And you know, so the students don’t just submit something and forget. They’re they’re getting that personalized attention, that feedback.

And it will give them a reason to persist. Even if they’re struggling, all of a sudden I’ve got a a professor that cares. that is engaging in with me. And and and I think, you know, those are just a few examples of of where it could have a big impact for students.

Wes (09:36.579) Yeah.

Wes (09:44.706) You’ve seen this in your own data, right? That persistence is increased when these tools are leveraged.

John Baker (09:47.786) yeah.

John Baker (09:52.793) Yeah, exactly. It like you know, we our argument is I I don’t care if our competitors give away their software for free, we’re gonna save institutions way more when it comes to retention of students. Quite often we’ll see a client the first year see about a five or six or eight percent increase in student retention because of these strategies now being deployed across their campuses. And so it has a huge measurable impact. And think what that means for the student. You know, if if they can progress, you know, and finish their four year program on time.

and successfully. That has a huge ripple effect for their life downstream. So yeah, we care deeply about this.

Wes (10:28.888) So I the way that I see this is, you know, student first, and it has a huge impact for those students who are they’re they’re more persistent, they they finish their degrees, they actually get through. So that’s the the first area that we can celebrate. The second is it’s great for the institutions themselves. Like keeping students moving, seeing them go through the system and and succeed is great. The the third one.

that doesn’t get talked about a lot is really good for the system generally to be able, I mean there’s nothing worse than a student for students and for the system, than students who attend for a while, incur debt, and then don’t complete and don’t have a credential that helps them in the workforce. So this this way to invest and to help students initially actually is really

John Baker (11:19.791) Yeah, exactly.

Wes (11:27.362) Beneficial to the system itself.

John Baker (11:30.061) absol absolutely. I I think you know, anytime you can have this kind of a measured impact on the quality of the experience, it has a human impact. It has that ability for that student to now build a great life, a big a great career. you know, and ideally it encourages them to recognize that, hey, my university was a fantastic learning experience. Maybe I’ll come back and do some upskilling, you know, to help me advance in my career. because we’ve built a better system, because we’ve built a better learning model.

Wes (11:59.468) Right. Well, John, I really appreciate your time today. I’m gonna I’m going to leave you with this last question and we’ll conclude. Tell me how you feel about the future of higher education with regard to the AI impact on education that that is we’re feeling right now and that is coming.

John Baker (12:19.727) Well, I I’ve been in the space long enough. I’m dating myself a little bit here, but where I’ve ushered in internet into many classrooms, helped them with mobile transition, because in the early days no one thought they would ever learn on a mobile device. So I need to think back to that now. cloud was a b another big transition, but AI is bigger. AI is gonna be more transformative because it is getting at the heart of the real transformation. You know, we’re gonna change how we learn, we’re gonna change how we assess.

We’re gonna change how we actually tutor. And so this is a big, big transformative moment for higher education. And so there needs to be significant investment. So there’s investment into the research. So how does the scholarship of teaching and learning change now with the advent of AI? Because it’s significant. you know, these new tools are in the hands of students already. So it’s not like you can put the genie back in the bottle and pretend they’re not there.

And so the the natural tendency for a lot of institutions will be kind of go back to the way things used to be, you know, twenty years ago. That’s not right. That’s not the way w way forward. So we need to now retool, rebuild. And so there’s strategies like formative assessment, which might be a good, you know, stop along the way that we’re really leaning into, but there’s there’s more to work to be done on that research. Curriculum change, upskilling of the workforce, you know, the adoption of AI technologies into the institutions. There’s a lot of capacity building.

Wes (13:21.891) Yeah.

John Baker (13:42.327) And research that’s got to be done to support all this. And so, you know, for me, you know, I I keep coming back to the the main point here, which is like the work that our university and college clients are doing right now today has never mattered more. Because learning is how we get through this transition, through the disruption that gets created, and also seize the opportunities that gets created. And it’s also at the same time, like if people are displaced, like they got to go back to upskill.

Wes (14:02.295) Absolutely.

John Baker (14:09.177) And so we need to invest in our institutions right now to sort of, you know, leverage these technologies in new ways to help support society at large. And so the work that’s being done right now has never mattered more and you know we’re trying to do our best to partner very closely with our educational clients to help them through this next phase of adoption.

Wes (14:29.442) Great, great concluding remarks there, John. We’re so happy to have you on as a collaboration partner. And that experience that you just outlined, going through the internet, going through mobile devices and cloud and now to AI, it’s really remarkable. You’ve got you bring that experience to all of this that will really help our institutions and the system. So we appreciate you having having you as a partner and we appreciate your input on today’s podcast.

John Baker (14:58.555) Thank you very much, Wes. None of us can do this alone. The journey matters. Thank you for the collaboration. Thank you for the partnership. All the best.

Wes (15:04.684) You got it. Thanks. Talk to you soon.

From Prediction to Intervention: How AI Is Reshaping Student Success at Excelsior University

From Prediction to Intervention: How AI Is Reshaping Student Success at Excelsior University

by | Jun 10, 2026 | Innovation

Priyo Chatterjee, Chief Analytics Officer, Excelsior University

The Big Picture

During recent Hill meetings, one question came through consistently from policymakers on both sides of the aisle: How is AI actually improving student outcomes today? At Excelsior University, we have a direct answer — grounded in operational experience, measurable results, and a conviction that AI’s greatest value in higher education lies not in generating smarter reports, but in driving better decisions.

“Insight does not create impact. Decisions do.”

Why It Matters

Too often, AI conversations in higher education center on tools rather than impact. What policymakers and institutional leaders need is evidence that AI can improve persistence, enrollment, and operational effectiveness in tangible, measurable ways.

For years, analytics in higher education evolved from descriptive to predictive — answering what happened and what is likely to happen next. But a critical step has been missing: what should we do about it? The challenge is no longer access to data. It is translating insight into consistent, scalable action.

The Approach

Excelsior’s response was StIR — the Student Intervention Recommender — a suite of machine learning models designed to optimize the student journey across the enrollment and academic lifecycle. Rather than building isolated analytics tools, we embedded AI directly into the workflows where decisions are made.

StIR was built around three core questions:

  •  Which students are most likely to need support? (WHO)
  •  Why are they struggling or at risk? (WHY)
  •  What intervention is most likely to help? (WHAT)

Figure 1. StIR platform illustrating the data-to-decision loop across the student lifecycle.

What Makes It Different: Human in the Loop

Today, the platform spans multiple modules — enrollment conversion, student melt, course success, and persistence. The most mature and impactful module targets “student melt”: students who register for courses but withdraw before beginning.

What distinguishes Excelsior’s approach is a deliberate “human in the loop” design. Rather than treating AI as an autonomous system, human judgment is built into every stage of the workflow. Our data science team works in close, ongoing collaboration with advisors to ensure model outputs are clear, interpretable, and directly actionable within advising workflows — not handed off and forgotten.

Equally important is the feedback loop. Advisors are not passive consumers of model recommendations. Their observations and frontline judgment are actively incorporated back into the system. This continuous dialogue between the people who build the models and the people who use them has made both the technology and the practice sharper over time.

By The Numbers

  • 6 consecutive academic terms: lowest melt rates in institutional history across
  • Approximately 309 full-melt students preserved over the six-term period
  • $2.55M in annualized retained revenue impact
  • Advisors shifted from reactive to proactive, prioritized outreach, improving how support capacity is deployed across the student population

What’s Next

Excelsior is also thinking about AI through a broader ecosystem lens. As higher education evolves toward more interconnected models — partnerships, stackable credentials, and multi-institution networks — AI becomes an enabling layer across complex learner pathways. We refer to this vision as a “constellation” model: institutions and learning experiences connected through shared intelligence and data-informed decision-making.

The most transformative opportunities in higher education AI lie not in generative tools for content creation, but in operational intelligence, intervention systems, and decision augmentation. Institutions that can identify friction points earlier and intervene faster will be better positioned to support students and manage enrollment pressure.

The Bottom Line

For policymakers asking how AI is improving student outcomes today — the answer is already here. Meaningful deployment is not a future aspiration. It is an operational reality, producing measurable results right now. Institutions must approach this work responsibly, with thoughtful governance, transparency, and human oversight. But the future belongs to institutions that make better decisions, consistently and at scale.

The real promise of AI in higher education: not intelligence for its own sake, but intelligence that drives action, impact, and outcomes.