How AI Is Creating Faster Pathways Into High-Demand Careers

by Presidents Forum | 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

by Presidents Forum | 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

by Presidents Forum | 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.

Charter Oak State College Awarded $300,000 Grant to Embed AI Competencies Across Undergraduate Programs

by Presidents Forum | Jun 8, 2026 | Innovation

New Britain, CT , April 6, 2026, Charter Oak State College has been awarded a $300,000 grant over three years from the Davis Educational Foundation to support a college-wide initiative titled Embedding AI Professional Core Competencies into Undergraduate Programs.

The grant, approved by the Trustees of the Davis Educational Foundation, will support the integration of artificial intelligence (AI)–related professional competencies across all undergraduate programs at Charter Oak State College. Trustees commended the proposal as well-organized, highlighted its strong leadership, and expressed enthusiasm for its comprehensive scope and institution-wide impact.

“This generous investment affirms Charter Oak’s commitment to preparing students with the AI-informed skills necessary for today’s workforce and for lifelong learning,” said President Ed Klonoski. “We are deeply grateful to the Davis Educational Foundation for recognizing both the importance of this work and the strength of our academic vision.”

Charter Oak State College received an initial payment of $100,000, with additional payments of $100,000 scheduled for April 2027 and April 2028, contingent upon continued progress consistent with the project’s stated goals and objectives.

The initiative will embed the seven core competencies of The Business–Higher Education Forum’s (BHEF) AI Enabled Professional Framework across all bachelor’s degree programs at Charter Oak State College. The framework identifies the essential capabilities every worker needs to thrive in an AI‑enabled economy. At Charter Oak, these competencies, referred to as AI Entablements (AIEs), include: AI literacy (understanding what AI is, how it works, and how to use it responsibly); data literacy (interpreting data to make AI insights actionable); critical thinking, problem solving, and creativity (evaluating AI‑generated outputs and identifying flawed reasoning); ethics, governance, and responsible AI use (addressing bias, transparency, and compliance); digital and computational skills (navigating digital environments and automation logic); collaboration and communication (working effectively with colleagues and AI systems in hybrid environments); and adaptability and continuous learning (cultivating the ability to learn, unlearn, and pivot as technology and business models evolve).

“The grant was received from the Davis Educational Foundation established by Stanton and Elisabeth Davis after Mr. Davis’s retirement as chairman of Shaw’s Supermarkets, Inc.”

How AI Can Strengthen Learning Instead of Simply Delivering Answers

by Presidents Forum | Jun 5, 2026 | Innovation, Outcomes and Accountability

How AI Can Strengthen Learning Instead of Simply Delivering Answers

The wrong question about AI in education

Many conversations about artificial intelligence focus on speed.

How quickly can AI generate content? How fast can it provide answers? How much time can it save?

According to Cengage Group Chief Digital Officer Darren Person, those questions miss the point when it comes to higher education.

The more important question is whether AI is helping students learn.

“If the AI is helping the student build understanding or is it just handing over an answer?” Person asks. “That’s the real difference between assistance and actual learning.”

For colleges and universities evaluating AI tools, that distinction matters.

Learning requires more than getting the answer

Person argues that educational impact should not be measured by how quickly students reach a solution.

Instead, institutions should ask whether students can:

Explain the concept
Apply it in a new context
Transfer that knowledge later

These are the outcomes that signal genuine learning.

The challenge is that many AI tools were designed to provide information as efficiently as possible. Educational environments require something different. Students need guidance, feedback, curiosity, and opportunities to work through problems rather than bypass them.

Why context matters

One of Person’s concerns is the growing use of general-purpose AI tools in educational settings.

He argues that education is not a plug-and-play environment.

“You can’t just drop in a general purpose AI tool into a course and assume that learning will magically improve.”

Instead, AI systems should be grounded in course content, learning objectives, discipline-specific context, and validated instructional materials.

This approach helps ensure students receive accurate guidance while reducing the risk of misinformation or hallucinations.

Where faculty fit into the future of AI

Person believes one of the biggest opportunities for AI is strengthening the connection between faculty and students.

Faculty members are being asked to serve more students, teach more sections, and manage increasing workloads. AI can help by identifying learning challenges earlier and providing instructors with actionable insights about individual student progress.

Rather than replacing instructors, AI can help faculty understand:

Which students are struggling
What concepts create difficulty
Where intervention may be needed
How learning patterns differ across a course

That information can make personalized teaching more scalable.

Why human connection still matters

Despite the rapid pace of technological development, Person repeatedly returns to a simple principle: education remains fundamentally human.

Students learn through interactions with instructors, peers, mentors, and support systems.

AI should strengthen those relationships rather than replace them.

Person notes that many students are reluctant to ask for help directly. Technology can help identify those learners and create opportunities for earlier intervention.

A faculty member reaching out to a struggling student may still be one of the most powerful educational experiences available.

What meaningful AI adoption looks like

For institutional leaders, Person recommends approaching AI adoption through partnership and co-design.

The most effective implementations start with questions such as:

What are the learning objectives?
Where do students struggle?
What does effective teaching look like?
Where should AI help?
Where should AI stay out of the way?

These questions place pedagogy ahead of technology.

The bottom line

Person believes higher education should evaluate AI using a simple standard: does it help students learn?

Technology that delivers answers faster may improve efficiency. Technology that helps students build understanding, supports faculty, and strengthens human connection has the potential to improve education itself.

As institutions continue investing in AI, that distinction may be the most important one to make.

Transcript

Wes Smith: Darren, thanks for joining us today.

Darren Person (02:46.011) Sounds good. Looking forward

Darren Person (02:58.171) Les, great to be here. Thank you so much for having me on.

Wes Smith (03:01.069) Hey, this is a topic that is very interesting to a lot of people, and that is, how do you balance innovation and education? How do you put students first in that? So a lot of people in ed tech are talking about this. Can you start us off with your argument about starting with students?

Darren Person (03:22.031) Yeah. So look, I think I’m a dad, right? So I have two kids, one that’s in the middle of their higher education and one that’s literally about to just start his higher education as well. So I get this really interesting perspective of also seeing education as part of it and seeing the perspective and the lens from the student side of the house firsthand as I watched them go through and learn in today’s world.

but also come from a background, both my in-laws were educators. So I kind of get this interesting view between two sides of the house. And of course I was a student, hopefully not too long ago at these days, but I was a student not that long ago. So I have an appreciation for the perspective of that. And especially now with AI being so prominent in students’ lives and in a lot of ways being pushed at them from many different angles, it’s really important that we take

a really responsible view, especially sitting in a company like an EdTech company like Cengage, and really making sure that we’re building the right solutions for both students and faculty to really help bridge that gap.

Wes Smith (04:30.085) There are so many AI tools out there. And I don’t know if your text chains look like mine, but I have a few text chains with different friend groups. And every now and then, I’ll get a text. This happened to me a couple nights ago. A friend said, hey, have you guys tried this tool? It’s crazy. Look what it does. It makes this and this and this. And then a conversation goes on about, oh, yeah, and I use this. And have you guys ever taken a look at this?

Anyway, it’s kind of interesting how AI is impacting our lives, but there’s a difference between impacting our lives with just new capabilities and complexity versus in higher education actually improving learning. So how do you address that issue?

Darren Person (05:21.647) Yeah, I know it’s really important question. think the clearest signal, I think is pretty simple. I think the foundational question is, is the AI helping the student build understanding or is it just handing over an answer? Right. And if you really think about it, like in education, you know, impact does not mean the student getting means they got there faster. Right. It actually means that the student can explain the concept. They can apply it in a new context.

They can even transfer that learning later. And I think that’s the real difference between assistance and then actual learning. So when you think about AI in this context, we need to think about how we use it to break down problems, like create curiosity, encourage things like persistence and like keep the student in the work. Cause if the student just reaches the answer on their own, you know, is that really a good signal?

It’s more about how AI becomes basically helping the student really be confident in understanding how they got to the answer, not the answer itself. I think that’s the hugest opportunity.

Wes Smith (06:35.289) You know, that’s I think the difference between these kind of these conversations with with that I think everybody we’re all having these conversations that is hey Did you see this look what look what you can do? Look how quick you can do it and you know, you all of those conversations don’t take into Consideration are you actually learning more? Are you retaining more? It’s not a higher-ed use. It’s more like we get to the answer faster in some of these but

Your point is in higher education, the whole point is learning and students have to be able to learn, but we’re not really set to validate that kind of learning as well as we could be. What do institutions need to do in the future with AI in mind to create that environment of learning and measuring learning as opposed to measuring getting to an answer faster?

Darren Person (07:31.899) Yeah, look, candidly, right? If an AI tool adds friction for faculty or makes learning harder to validate, it’s not ready, right? A helpful feature that creates more workload or confusion is not really helpful, right? one of the things that, and look, coming from an ed tech company, so things that we’ve been trying to do is to be very intentional. And that’s including tools that we’ve been building like our student assistant.

It’s about being grounded in the course context, tuned to the discipline, built around the vetted materials. So we know that the quality of the content and that the answers and the guidance that students are going to get are actually factual versus hallucinations. It’s also designed to guide. Like our student assistant was specifically designed to never give the student the answer two years ago.

We started with that as the premise. So it’s about creating that conversation. What questions are the students answering? We’re already seeing things like four to five times higher engagement and roughly a 20 % uplift in end of course grades. But it’s because of that conversation and guiding and the pedagogy being built into the student assistant versus a generic chat bot that’s just quickly about getting you

the answer that you want.

Wes Smith (08:58.253) Right, right. That’s important and it has to be the case in higher education. It’ll be interesting to see a transition between how students use that to learn now and then the tools that are just built for getting to an answer faster. Those are two different things, but in a higher ed context, one is certainly preferable above the other.

Darren Person (09:14.949) That’s right.

Darren Person (09:21.401) Yeah, and it’s the foundations of the, you know, hopefully of the premise, right? Like I had a, I had, was giving a, I was on a panel not that long ago at a conference and I had a student stand up and ask the question like, Hey, you know, I could learn all of this stuff by not going to school and reading a book. And I brought it back to like, why I think college and education is important. And it’s

It’s not just about reading the materials and digesting materials, but it’s the overall experience. It is the connection with your faculty member. It is the connection with other students. It’s those projects that you do together where you learn real life experiences that you’re not just going to get out of just reading a book or taking something purely in a virtual environment. It’s those interactions that are really important and being in the university as part of your maturing process as well.

And you’ll get that in other areas too, especially in the workforce as part of that, but you want to go in as prepared as you possibly can.

Wes Smith (10:25.455) So I like the direction that this conversation is going. Our audience, have a lot of higher ed institution leaders that listen in. Can you help us understand what is a meaningful collaboration between technology creators, ed tech partners, and institutions? How can presidents help shape AI adoption rather than just reacting to the product that

that EdTech puts in front of them.

Darren Person (10:57.209) Yeah, I think the first thing that I would say is that education is not a plug and play environment. And I think we, lot of organizations and especially some of the new technology is starting to be treated like we could just slap this in and make it work. So you can’t just drop in a general purpose AI tool into a course and assume that learning will magically improve, right? It just hasn’t happened.

I would say more meaningful collaboration starts with the pedagogy. You’ve said this to me as well. And some really core questions like, what are the learning objectives? What does good teaching look like in this course? Where do students struggle? Where should AI help? We can go on and on. And by the way, where should AI stay out of the way? That’s your question to ask too. It’s not just about where we infuse it, but where doesn’t it belong?

That’s also why I think the partnership model that you mentioned really, really matters so much, right? Institutions and technology partners, we need to co-design with faculty and test in real courses, look at the evidence, iterate based on what actually improves understanding. We’ve been spending a lot of time, we have panels of teachers who work with us to make sure that the way our student assistants are asking questions, that is what’s gonna give you the insights.

And I think we’ve seen this already, right? Like a cautionary tale is homework helper, right? Like there are these tools that have been launched into market by more consumer-based organizations. sure, maybe the technology may have helped the student move faster, but it then made it much harder for educators to validate real learning. And when you really think about that, that actually increases faculty workload and undermines trust.

That’s the opposite of what ed tech companies have been trying to do for the last 40, 50 years in this sector.

Wes Smith (12:51.715) Yeah, yeah, had Darren, we’ve had some conversations prior to this one. And in one of those conversations, you mentioned to me tools that will improve the ability for faculty to be able to construct courses, curriculum, and then deploy based on kind of the feedback, the regular feedback that they can receive from students using some of this technology. Tell us a little bit about the upside.

for faculty when they use technology that’s designed to assist them in instruction.

Darren Person (13:28.293) Yeah, no, this is probably the most important one. So when I think about education and learning, in a lot of ways, it’s like, how do we use technology? And in this case today, we’re talking about AI. Tomorrow it will be something else. But how do we use this technology to bridge that human connection between the faculty and the student? And I think that’s the more important part. And if you go into the workflow, on the student side, they’re really trying to learn the material and understand what

it means and how that’s going to apply to them in ultimately their future job, career, et cetera. For faculty members, they’re being asked to do more with less, right? As this technology rolls out, hey, more classes, more courses, more sections, more students. And that over time has driven this divide, right? The teacher has been pulled away from the students where the technology as we’re starting to look at deploying it is really about

gathering all of those insights and being able to support the teacher no longer in just helping them get the homework assignments graded, but actually identify problems that individual students have, driving more of that personalized learning. But it’s also about personalized teaching, right? It’s not just about making sure the student is getting the right question at the right time, but also that the teacher now is better informed across their entire course on how they can help each individual student.

and be able to bridge that connection where in lot of classes, just because of the scale and the volume, it’s nearly impossible for an educator to be able to make that human connection with every single student, right? They have to kind of select and pick. And a lot of times it’s the other way. It’s the student who basically reaches out to the faculty member and makes that connection first that way. Let’s be honest, a lot of young kids aren’t comfortable, you know, picking up the phone and being like, Hey, I got a bad grade on this test. I could use extra help. Can you help me? They’d be more comfortable if a teacher saw that.

recognized it and was able to reach out to them and say, hey, I see you’re having some issues with XYZ topic. Here’s some ideas and recommendations. That caring connection, I think, is what really helps drive education. We all have stories about a teacher who took an interest in us. And I think that really is foundations of education.

Wes Smith (15:43.437) Absolutely. Darren, love the way that you’ve grounded this conversation in how learning actually happens and not just around the technology, what the technology can do, but how it should support students and faculty. I think that that’s a great way to ground the conversation.

Darren Person (16:01.453) I it. I love this conversation. It’s such an important one. And I think the more we can stay focused together, like this isn’t about it’s not one company, it’s all of us partnering together. And I think if we keep putting the customer, both the people who have to deliver the education, as well as the people who are receiving the education, I think if we keep them at the center of everything that we do, I think that will help us drive the outcome versus moving away and moving to the outer edges of the technologies for the sake of technology.

Wes Smith (16:31.397) Well said, well said. Thanks for joining us today, Darren.

Darren Person (16:34.501) Thanks so much, Russ. Again, thanks for having me.

Wes Smith (16:36.645) You bet. OK.

How Learning-Focused AI Looks Different From General AI

by Presidents Forum | Jun 1, 2026 | Innovation

How Learning-Focused AI Looks Different From General AI

The challenge with using general AI for learning

When Rajen Sheth published 10 Lessons on How to Drive Learning with AI, one theme stood out: educational AI should not be evaluated like general-purpose AI.

Large language models excel at retrieving information: ask a question and they generate an answer. But learning requires something different.

Research shows that approximately 75% of students do not know enough about a subject to ask the right question in the first place. If learning depends entirely on student prompts, many learners will struggle before they even begin.

Why AI should ask the questions

Traditional AI systems wait for a user to initiate a conversation.

Learning-focused AI reverses that model.

Sheth argues that effective educational AI should identify where students are struggling, ask the right questions, and guide learners toward conceptual understanding. Rather than simply delivering information, the system should function more like an instructor helping students work through ideas step by step.

The goal is not faster answers. The goal is deeper understanding.

Why guardrails are essential

Another major difference between general AI and educational AI is the role of guardrails.

Most conversational AI systems are optimized to keep interactions going, but educational systems require a different objective. Educational AI must know when to continue a conversation and when to stop.

Students need support that remains aligned to specific learning goals rather than wandering into unrelated topics. They also need protection from inaccurate, distracting, or counterproductive interactions.

In many cases, ending a learning interaction at the right moment is just as important as starting one.

Supporting instructors, not replacing them

Sheth repeatedly emphasizes that AI should function as an extension of faculty rather than a replacement for instructors.

Educational AI must align with classroom content, course materials, and teaching approaches. Faculty should maintain control over learning objectives, instructional methods, and the student experience.

The technology can then provide valuable feedback by identifying concepts students struggle to understand and highlighting where additional instruction may be needed.

The future of AI in higher education

Sheth believes higher education’s greatest AI opportunities will come from systems intentionally designed around learning outcomes.

The distinction matters.

General AI helps users find information. Learning-focused AI helps students develop understanding.

As institutions continue evaluating AI strategies, that difference may determine whether technology becomes another digital tool or a meaningful driver of student success.

Transcript

Wes Smith (01:58.35) So you recently published a piece called 10 Lessons on How to Drive Learning with AI. And what I appreciate about it is that it really cuts through the noise with

practical and buildable principles. So rather than staying at 30,000 feet, I just want to walk through a few of those takeaways and make them concrete for campus leaders and faculty and frankly, the policymakers who we’re trying to get this information to right now.

Rajen Sheth (02:43.778) Perfect, that’s great.

Wes Smith (02:46.498) So OK, let’s start with one of the principles that you’ve articulated. You’ve said that effective learning is instructor-led. What does it look like for AI to ask the question instead of waiting for the student? And I know that you have a stat in there that 75 % of students, don’t yet understand the concept well enough to even ask the right question. So what does it mean for how AI has to behave?

Rajen Sheth (03:13.526) Yeah, I think it’s a great question. It’s interesting because, you know, at Google, I was part of the development of a lot of the underpinnings of what became Gemini. And what was interesting there is when a lot of that was built, a lot of it was built around the concept of information retrieval, which is ask a question, get an answer, ask for something, get content, that kind of a thing. But it wasn’t built for learning.l l

And that stat is actually true. What we’ve seen from studies is 75 % of students actually don’t have a question to answer. So if you ask them to just use a chatbot, they’re not going to know exactly what to ask for. We’ve now taught over 100,000 students with Chiron. And we’ve seen exactly that play out. And so what we chose to do from the very beginning is we asked the question. We figure out what is the right question to

ask to that student at that time, and then use that as a way to stimulate learning and stimulate their understanding and then guide them to the answer with the right teaching rules. And we found that as a result of that, students actually get to a deeper level of understanding. It’s very different than how students are using AI right now, but it leads to better results.

Wes Smith (04:31.938) That makes so much sense to me because usually when you’re starting into a new subject, a teacher can assess where students are. That back and forth with students gives them a little bit of an ability to assess to say, okay, we’re missing a few key concepts. So that’s essentially where you’re starting.

Rajen Sheth (04:53.974) That’s exactly right. And the highest, hardest bar here is conceptual understanding. And if you don’t understand the concept, you can’t keep practicing. can’t get deeper and deeper in the subject. What we find with lot of students is that they’ll have holes in different concepts that they haven’t been able to get over. And then that hurts them down the line. And so we wanted to figure out

how do you use AI to get them to that conceptual understanding and aid the teacher and aid the instructor and faculty in helping their students get there.

Wes Smith (05:28.63) Right, right. Well, OK, so you also write that most AI tools, they’re designed to keep conversations going, not to keep them on track. And I think we all see that in our daily use of AI, right? It always ends, your prompt always is answered, and then another question is posed. Do you want me to do this? Do you need help on this? But you’ve talked about guardrails need to be up here.

Rajen Sheth (05:41.889) Yep.

Rajen Sheth (05:55.138) Mm-hmm.

Wes Smith (05:57.39) What do those look like when it comes to learning? What kind of guardrails do you have there?

Rajen Sheth (06:01.644) Yeah, safety is paramount here because a general AI system can take you in all different directions and can be distracting and in some cases even destructive. And so what we need to do is keep it on topic and keep the learning objective in mind as we talk to the student. And so that’s really what we’ve done is that we’ve enforced really strong guardrails to keep it on topic and guide the student in the right learning direction towards the learning objective.

The other thing is, of course, know, gargling against harmful conversations and making sure that those are captured as well. Another thing you’ve brought up that we’ve had to work really hard to do is not only learn how to do that, but learn when to stop the conversation. And that has been actually one of the trickiest parts about AI, because as you said, the tendency is to keep going and going and going. In some of our early trials, know, the AI would ask like,

20 questions and keep going back and forth with the student and the student would eventually give up. But we now have gotten smart about when to end the conversation to know how to get the student to where we need them to go to and then move

Wes Smith (07:12.546) Yeah, that makes sense. I mean, that’s different than just general AI in my experiences. You’ve had to program it for the purpose of learning. That also kind of leads me into this next question. We have decades, maybe centuries of learning science. We know how people learn. And so if an institution now is evaluating an AI learning tool, what

Rajen Sheth (07:33.548) Yeah.

Wes Smith (07:40.736) are the learning science principles that they should look to or look for that these tools can use. So it’s teaching and not just answering questions.

Rajen Sheth (07:53.292) Yeah, absolutely. And I think the interesting conundrum here is that everyone wants to look for proven outcomes. And AI is so new that we’re just starting to show those proven outcomes. But what is proven, to point you made, is learning science. We know what techniques work and we know what techniques don’t work. And so what we’ve decided to do is build our system around learning science and around those proven techniques to

show that those can actually lead to impact. And a few of the key things that are there. One is this concept of backwards by design. And so when a student has a learning objective, we go backwards by design. So what we do is we take that learning objective, we think about what are the questions we want that student to be able to answer at the end of it. And we work backwards from there to try to get to the right learning modules to get them to that answer.

The second thing is kind of this concept of the zone of practical development. What is the right question to ask to get them into that productive struggle? And then that is shown to be a way that you can actually really guide students towards getting and stretching themselves. A third is analyzing what are the right teaching moves to put in place. And so a generally our system will always go towards kind of giving you the comprehensive answer.

What we’re doing is we actually classify it to the right teaching move. And then we build the next response based on that teaching. And so that makes it such that we’re acting in the way that a strong, pedagogically strong instructor would do. And then the final thing is analyzing and making sure that we understand not only that we help the student, but where does a student have more holes that we can help them with? And that can help the progress on going.

Wes Smith (09:51.951) Yeah, I mean, this is just music to higher education ears, understanding that AI alone can be helpful. There are ways that it could be helpful. But when it’s built on the right pedagogy, when it’s built on learning science that has been refined and proven out over centuries of learning,

It makes it just so much more reliable for instructors to be able to use. And I know that’s a huge issue. You have to have instructors that feel confident in how these tools are used.

Rajen Sheth (10:31.264) Yeah, absolutely. And I think that is the key thing is it cannot be the AI alone. It has to be the AI in concert with the instructor. And how do you kind of go back and forth in the right way? How do we dovetail to be kind of an authentic extension of that instructor? And then how do we feed the right data back to that instructor so the instructor can know what to do next for their class?

Wes Smith (10:55.33) Yeah, yeah. So how do you make sure that AI reinforces what’s happening in the classroom instead of teaching something that’s totally different or it’s a different version of the course at least?

Rajen Sheth (11:02.423) Yeah.

Rajen Sheth (11:08.566) Yeah, I think that is something we’ve had to a lot of time and effort into because AI by itself might teach something in a very different way. So for example, if I’m learning a concept in math, there are probably about 10 different ways to teach every one concept that’s there, but how do we reinforce the way that it’s being taught in the classroom? So part of that is that we dovetail with the material that the teacher has. They can upload in their

material and then we build the lesson with that in mind, with those concepts in mind and the way that they’re teaching in mind. A second thing is to give them full control. And so rather than them just kind of handing this tool to the student, they can control what does it say? How does it say it? What is it leading the student to? They can tweak it so that it is truly an extension of themselves. And then the third is that loop back that

we analyze the conversations and we come back to the instructor with, hey, you know what, 12 of your students are really struggling with this thing. Five of them are struggling with this thing. And that helps them figure out what to do next. And that kind of makes it such that it is a true extension of the classroom.

Wes Smith (12:17.57) Yeah, yeah, OK, so I want to finish with this question. The goal is impact, right? And when we’re talking to members of Congress and their staff, the question wasn’t just, how is AI being deployed in higher ed? It was, how is it impacting students? So it’s not just necessarily about deploying it. It’s about, how are we making a difference? And how do we measure?

what the impact is in higher education. Do you have some insight on that?

Rajen Sheth (12:47.362) Yeah, absolutely. think what there are two parts of this. One is how it’s built and the second is is what are the proven results. And we talked a lot about how it’s built with a lot of learning science and pedagogy in mind. And we’re now seeing that in the results. We’re seeing institutions where their pass rates are going up significantly six to nine percent in classes. They’re getting to the highest pass rates that they’ve ever seen as a result of putting chiron in.

their engagement with students is going way up. In one case, we saw engagement go up by about 7x in comparison to other material that’s there. And students have said that they really love the experience and they’re learning more out of it. And so it really kind of drives towards that goal, which is how do we help the students that need the help the most? And how can we get them through the higher education experience so that they can get to their goal?

And that’s really what we’re seeing in the results.

Wes Smith (13:48.056) Right. OK, so I want our listeners to remember you have a lot of experience in AI. I know your Google experience, pretty significant, and working with AI in a lot of different ways. But now at Chiron Learning, you’ve focused in on the use case for education. How has that focus changed the way

that you see AI.

Rajen Sheth (14:17.954) Yeah, the way that that has changed the way I see AI is that when you’re looking at a particular goal, you can do everything to reach that particular goal. Not only the technology, but how we work with customers and how we work with institutions. All of that goes towards this. You could take a raw LLM technology and get that to students, but it’s not purpose built. And so all of the things we talked about building in

learning science, understanding the student, understanding what they need, driving that engagement. All of that is what it’s taken to them lead to these great results.

Wes Smith (14:54.412) Yeah, okay. Well, Rajan, thank you so much for coming back on the show and for translating what you’re seeing into practice. And these are good practical lessons that our leaders can really act on. We appreciate your time.

Rajen Sheth (15:08.842) Great, thank you, we really appreciate it.

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