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

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

by | 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

How AI Can Strengthen Learning Instead of Simply Delivering Answers

by | 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

How Learning-Focused AI Looks Different From General AI

by | 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.

UTA launches AI tool to support student care

UTA launches AI tool to support student care

by | May 29, 2026 | Innovation

Lucy streamlines administrative workflow, giving CAPS providers more time to focus on students

The University of Texas at Arlington is piloting an AI tool that helps reduce administrative workload for counselors, giving them more time to focus on students.

Known as Lucy, the tool helps Counseling and Psychological Services (CAPS) staff work more efficiently while preserving the central role of human-centered care.

“Student success is shaped by far more than what happens in the classroom. At UT Arlington, we’re committed to creating an environment where students feel supported, connected and cared for throughout their college experience,” UTA President Jennifer Cowley said. “Innovations like Lucy help strengthen that work by giving our teams more capacity to focus on the people at the center of our mission.”

Lucy, named after the Peanuts character, was designed as a “precision retrieval” tool that provides internal-only information on UTA-specific forms, policies, workflows and documentation guidance, according to Yaroub Saleh, a UTA counseling specialist who created the tool.

“Every minute saved from searching for a form is a minute that can be used to help a student,” Saleh said. “Lucy is a good example of how we can use AI ethically to support our students. And it’s been working. Providers tell me it saves time and gives them accurate information.”

“For example,” he continued, “if a provider is treating a student who is a minor, they used to have to dig through lengthy policy documents that have undergone multiple updates. With Lucy, they can get the exact process to follow and the correct forms in seconds. It’s accurate, consistent and reliable. Because providers receive the same information, it also reduces mistakes.”

Early feedback has been positive, Saleh said, with providers citing simplified day-to-day operations and a reduced administrative burden. Saleh said other universities are already reaching out to explore similar AI tools.

Ultimately, Lucy helps CAPS staff fulfill their mission of helping students increase self- awareness, address mental health and emotional concerns, and make positive changes in their lives. CAPS services are available to all UTA students, with in-person offices in Ransom Hall and the Maverick Activities Center. Virtual care is also offered 24/7 through TimelyCare.

About The University of Texas at Arlington (UTA)

The University of Texas at Arlington is a growing public research university in the heart of Dallas-Fort Worth. With a student body of over 42,700, UTA is the second-largest institution in the University of Texas System, offering more than 180 undergraduate and graduate degree programs. Recognized as a Carnegie R-1 university, UTA stands among the nation’s top 5% of institutions for research activity. UTA and its 300,000 alumni generate an annual economic impact of $28.8 billion for the state. The University has received the Innovation and Economic Prosperity designation from the Association of Public and Land Grant Universities and has earned recognition for its focus on student access and success, considered key drivers to economic

How UMGC Is Building Accountable AI Around Student Outcomes

How UMGC Is Building Accountable AI Around Student Outcomes

by | May 22, 2026 | Innovation

How UMGC Is Building Accountable AI Around Student Outcomes

UMGC’s AI strategy starts with governance

University of Maryland Global Campus is approaching AI adoption with a clear institutional principle: innovation only matters if it improves outcomes for students.

President Gregory Fowler describes a strategy built around governance, measurement, and practical implementation rather than experimentation for its own sake. The university has already implemented institution-wide AI training and established an AI Governance Board to ensure adoption remains aligned with institutional mission and student support goals.

The approach reflects a broader shift happening across higher education. Institutions are moving beyond curiosity about AI and focusing on how it can responsibly improve student success and operational effectiveness.

Why UMGC built a closed AI testing environment

UMGC launched nebulaONE as a controlled environment where faculty and staff can safely test AI tools, concepts, and workflows before wider deployment.

More than 300 team members are already using the platform.

The goal is not unrestricted experimentation, it is structured evaluation that allows the institution to identify where AI creates value, where it falls short, and how it can be implemented responsibly.

This type of infrastructure is becoming increasingly important as institutions look for ways to balance innovation with governance and accountability.

How AI is being applied to support students

UMGC is focusing AI adoption on practical student-facing applications.

Conversational AI is helping identify and support struggling learners earlier in the student journey. In the Registrar’s Office, transcript review processes that were previously manual are now partly automated, allowing staff to focus more attention on complex cases that require judgment and intervention.

Career Services has also integrated AI into resume review and mock interview preparation. These tools provide students with more opportunities for practice and faster feedback than traditional one-on-one support models alone can provide at scale.

The focus throughout is operational support that strengthens human-centered services rather than replacing them.

Why measurement matters in AI adoption

AI should function as a strategic enabler, not a replacement for teaching, advising, or institutional judgment.

That requires continuous measurement.

UMGC is evaluating adoption rates, operational outcomes, and areas where systems underperform. The institution then adjusts implementation based on those findings.

This approach reflects a growing expectation across higher education that AI adoption should be tied to measurable student impact rather than broad claims about innovation.

What accountable innovation looks like

The question is no longer whether AI is interesting or technically capable. The question is whether institutions can deploy it ethically, transparently, and in ways that genuinely improve student outcomes.

For UMGC, accountable innovation means governance, human oversight, operational measurement, and a consistent focus on serving learners more effectively.

Transcript

0:03
When we talk about innovation at UMGC, I tell our team all the time we’re not here to chase bright, shiny objects.


0:10
Our approach to AI has been deliberate.


0:13
We’re providing AI training for every team member,


0:15
as a baseline, not as an aspiration.


0:18
We established an AI Governance Board to make sure adoption stays aligned with our mission and our obligation to our learners.


0:25
And we adopted nebulaONE as a closed environment where faculty and staff can test new tools, concepts, and strategies.


0:32
More than 300 team members are using it now.


0:35
That infrastructure matters.


0:36
Because the real question is not whether AI is interesting, it is whether it actually helps us serve students better.


0:42
So we’re being very specific about where to apply it.


0:45
Conversational AI now guides earlier outreach to learners who may be struggling.


0:50
In our Registrar’s office,


0:51
transcript review, which used to be largely manual, is now partly automated – freeing staff members to focus on the cases that need real judgment or intervention.


1:01
Similarly, Career Services have integrated AI into resume editing and mock interviews, giving students more practice and faster feedback than we could ever provide one-on-one.


1:10
Let me be clear.


1:12
AI is not replacing teaching, advising, or judgment.


1:15
It is a strategic enabler.


1:17
The way we know it is working is through measurement of outcomes, of adoption, of the areas where it comes up short.


1:24
Then we adjust based on what we learn.


1:27
That is what accountable innovation looks like here – 


1:29
practical, ethical and always tested against the benchmark of whether it genuinely serves the people who partner with us on their learning journeys.

Accountable Innovation with AI: Building Trust in Higher Education

Accountable Innovation with AI: Building Trust in Higher Education

by | May 13, 2026 | Innovation

Jessica Smagler, Head of Research and Outcomes, Kyron Learning

Across higher education, the most common question about AI is no longer “what can it do?” It’s “how do we know it will behave?” That question reflects something important about where the sector stands right now: enthusiasm is no longer the barrier to adoption. Trust is.

Trust in AI isn’t built through promises – it’s built through systems. Without clear internal accountability structures, AI tools operate on good faith alone – and good faith isn’t a governance model.

Institutions evaluating AI-powered tools should look for four interlocking commitments, treated not as product features but as obligations: guardrails, benchmarks, educator control, and a foundation in learning science.

The first line of that accountability is governance – specifically, the guardrails that define how AI is permitted to behave.

Guardrails Built for Learning

AI chatbots routinely welcome off-topic conversations, taking focus away from intended course content and derailing learning goals. Without strict guardrails to ensure that AI behavior stays aligned with education, safety, and institutional expectations, the integrity of the learning experience is at risk.

In the edtech space, guardrails should operate at two levels. One is educational, making sure the AI stays within lesson boundaries, supports reasoning without shortcuts, and redirects students who go off course. The other is around student security and privacy, ensuring student data is protected, sensitive information is automatically redacted, and access to systems remains tightly controlled. And these guardrails should be structural rather than add-ons, built into how the system works from the ground up, not applied as a filter after the fact.

Neither layer is visible to students but both matter to the administrators and instructors who are responsible for what happens in their courses. Together, these are guardrails that institutions can trust and hold companies accountable to, because in education, responsible behavior must be verifiable, not assumed.

Benchmarking for Measurable Results

Guardrails define how an AI system should behave. Benchmarking is how companies prove that it does – and how institutions can hold them to it. Without continuous measurement, guardrails are a promise rather than a practice.

In practice, benchmarking should also operate at two levels. Continuous benchmarking should be run against real learner interactions to detect behavioral drift and measure ongoing alignment with learning objectives. Periodic broader evaluations – run across curated datasets in multiple academic domains – should test for safety, instructional integrity, and consistency.

Critically, institutions should expect AI providers to share benchmarking results openly. A track record that institutions can point to is what separates accountable innovation from well-intentioned experimentation.

End to End Educator Control

AI should amplify great instructors – not replace them. Human oversight is an essential component of AI-powered instruction and should extend across the entire learning cycle, from content creation to the student experience.

At Kyron, for example, no lesson reaches a student without educator review and approval. Instructors set the learning objectives that shape what our AI generates, and retain full editorial control before anything is deployed. This process ensures that what students experience is always aligned to institutional goals.

Educator control should not end once a lesson is deployed to learners. Products should offer visibility into what students are struggling with in aggregate and at the individual learner level, allowing faculty to intervene, adjust, and improve. Insight into misconceptions creates a feedback loop that keeps instructors and institutions informed on student progress.

Grounded in Learning Science

Responsible AI providers don’t stop at governance and oversight. They are also accountable for whether students actually learn. Rooting instruction in established learning science frameworks – like Chi and Wylie’s ICAP model and Vygotsky’s Zone of Proximal Development – isn’t just good teaching. It is a standard that AI providers should hold themselves to and a standard institutions should expect when adopting AI-powered instruction.

Decades of research have made clear that real learning doesn’t happen by giving students answers. It happens when students are encouraged to think critically, reason logically, and develop conceptual understanding. It happens when lessons are intentionally structured to achieve clear learning goals.

A landmark study by Graesser and Person found that 92% of student questions focused on surface facts rather than deeper reasoning, meaning most misunderstandings go undetected and unaddressed. Because students can so easily appear engaged without building true conceptual understanding, instruction must be intentionally designed to surface reasoning and guide deeper thinking.

When AI providers root their tools in learning science, they are making a verifiable commitment to student outcomes – not just a promise of engagement.

This is Accountable Innovation

AI in education demands more than innovation, it demands accountability. Institutions have a right to ask AI providers how they know their tools will behave, and AI providers have an obligation to answer concretely. Organizations using AI must do so in ways that are safe for students and transparent to institutions. By setting guardrails and benchmarks, keeping educators in control throughout, and grounding tools in learning science, we can be confident that we are innovating responsibly.

At Kyron, our commitment to safe, accountable AI has helped us build enduring partnerships with institutions like Miami Dade College and Western Governors University, while creating opportunities to collaborate with forward-looking colleges like Rio Salado College and established curriculum companies like McGraw Hill. When we answer questions with evidence rather than promises, we build the kind of trust that makes responsible AI adoption possible, both for our partners and for the students they serve.

Interested in learning more about Kyron Learning? Visit www.kyronlearning.com or connect with our team to get started.