Subscribe
Share
Share
Embed
Career education is a vital pipeline to high demand jobs in the workforce. Students from all walks of life benefit from the opportunity to pursue their career education goals and find new employment opportunities. Join Dr. Jason Altmire, President and CEO of Career Education Colleges and Universities (CECU), as he discusses the issues and innovations affecting postsecondary career education. Twice monthly, he and his guests discuss politics, business, and current events impacting education and public policy.
Jason Altmire (00:04)
Welcome to another edition of Career Education Report. I'm Jason Altmire, and we're gonna do another session on AI because everywhere I go, people ask for more content on AI. It's obviously the hottest topic in the country and in certainly in business and in education. So today we have an expert to talk about the interconnection of those issues, and that's Darren Person. He's the executive vice president.
And chief digital officer at Cengage Group. Darren, thank you for being with us.
Darren Person (00:37)
Thank you, Jason. It's great to be here. I really appreciate your time. ⁓ this topic is so important. And as you said, it's kind of being talked about ⁓ all over the place all the time. So just really happy to be here. Really appreciate you having me on and and looking forward to the conversation.
Jason Altmire (00:53)
Let's start just for those who may not be familiar with Cengage. Talk about the Cengage group. I know you have three business units. What what is your role at Cengage?
Darren Person (01:03)
Cengage focuses on learning for a combination of our K-12 business. So ⁓ K through 12. We also have a higher ed business. And then we have a workforce skillings business. So all three of those businesses largely make up Cengage as a whole. We're traditionally an ed tech company. So we provide learning services across all of those functions. And my role really is to work with the company and our customers, right? Education institutions, workforce skilling. ⁓
offices and really try to put together a a ⁓ product roadmap that supports students learning, teachers teaching, and really kind of bridging that gap between the two to help make education better and improve outcomes for for learners and ⁓ institutions as well.
Jason Altmire (01:48)
I know one of the ways that people might see ⁓ Send Gauge referenced in the media is through research. You have a research arm that does very credible and and substantial research into topics related to higher education, artificial intelligence. How does that process work and what has your research shown as it relates to artificial intelligence?
Darren Person (02:11)
We've got an amazing research team. We spend a tremendous amount of time with educators, with learners. We do surveys, kind of what you would expect out of like a market research company, really spending time with our customers to understand, you know, where the market's going, where the industry's going, where enrollment is going at universities, what challenges students and teachers are having. And then really how are we positioned as the institution that kind of helps bridge technology and education?
How do we kind of come together to help solve some of those challenges for them? So we've been working across, you know, obviously a ton of time now on trying to understand the impact of AI in education and how to br deliver more responsible AI solutions into the market.
Jason Altmire (02:54)
Do you find now that we're three or so, three and a half years into this whole AI, I don't wanna call it an experiment, because it's clearly not an experiment, but where we're going in the future, do you find people have a better understanding and and a better acceptance and competence with AI related issues?
Darren Person (03:14)
It's a really good question. I think for the last couple of years it's actually quite fair to say the conversation around AI has been around experimentation. I think a lot of places have been really experiment, like what can it do? How fast is it improving? ⁓ how disruptive will it be? I think the conversation is now shifting to something a bit more practical. More about like how do we use AI ⁓ in ways that actually improve learning, support our instructors and basically prepare students for a workforce that's that's already changing.
Right. It's already kind of moving on. So for us, the way we've been thinking about AI is, you know, first off, education is not a plug-and-play environment. Right. So we simply can't just drop general purpose AI tools into a course and assume that learning will just improve. I think the real opportunity is to build AI around what's central to an ed tech company, our pedagogy, our course context, the trusted content and measurable outcomes. And I think that's
That should be the the goal should not be AI for the sake of AI. The goal should be about better learning. So I think when we kind of put all of those pieces together, AI is playing an interesting piece in it. And we're trying to figure out how to make sure that we're really being responsible about that deployment across it.
Jason Altmire (04:29)
You're quoted i in a lot of places. You've done a ton of op eds. I've I've seen your name all over. You've been in Fast Company and and Forbes and and other places. And I I saw in one of the things that you wrote towards the end of last year, twenty twenty five, you did predictions for twenty twenty six. So now we're more than halfway through the year. I wanted to see how you feel like those predictions are going.
And maybe talk about what you forecast for the year and maybe if if things are changing even more rapidly than you expected.
Darren Person (05:04)
We spend a lot of time trying to anticipate where the market is going. I would say, you know, like any predictions, they're hit or miss. My focus has really been on trying to map where the technology was going or is going and the speed at which that change was happening. In some of the conferences, like I recently attended the ASUGSV conference and I've attended some other in conferences. And what's really exciting is the conversation has actually been changing.
I'd say, you know, almost a year ago the conversation around AI was it's gonna come in and completely destroy education and replace everybody. I think what we're really learning now is that even though the models are evolving and getting, you know, better at doing tasks that humans have done, it still is a technology that is here to support. And when done right, it's supporting humans and being better at what they're doing versus replacing them.
So I think a lot of the early stage predictions around where things were going are continuing. We're seeing even more institutions and teachers really lean into how we're going to deploy the technology to support them in the learning system. Similarly, I think we have the same challenge that you would expect with students, which is really making sure that ⁓ we're teaching them how to use this technology responsibly. So again, really thinking about how do we help them learn versus how do we just get them as fast as possible to an answer.
'Cause once you do that, you l and you skip the learning process, then really like what are we what are we accomplishing at the end of the day?
Jason Altmire (06:36)
Yeah, a l a lot of the things that you talked about related to the the learning gaps that exist and and outcome centric models and how educators can implement AI. Cause what you know, w when AI first came out, it was viewed as a way to help students cheat. That was the concern everybody had. And now, of course, everyone understands you can actually use AI both as a student but also as an educator.
to your benefit to help you do your job more effectively and teach better. And one of the things I've noticed, you know, I travel around and visit trade schools and and career colleges, but I also I I'm an adjunct professor at Texas Tech and I I teach a class where it's an online class and it's a business management program that entails a lot of writing. So there's discussion board, there's papers that you have to do and
I've been doing it for six years. And what I've noticed, you know, in the beginning, when I started doing it, some people are really good writers, and a lot of people are not very good writers. And you can see that. You can see the difference. And that's a way to distinguish between students when you're judging their competencies and your aptitude. But now when people submit their papers, they're all perfect. Every paper is incredibly well written, cited.
Meticulously, I have a hard time now distinguishing between the students. And unfortunately, for the occasional student who's not using AI and is really trying to do it the right way and working hard by comparison, their work, it shows it's just compared to these others. So how do you help schools and instructors deal with that issue? Everyone is going to be using AI.
And if everyone uses it, it's hard to tell the difference between student competencies. How do you help get around that?
Darren Person (08:39)
It's probably one of the hardest questions. And I think the the industry has been focusing on how do we improve our assessments. And in many ways, AI kind of brought us back to the stone age, right? We moved back to the blue books and started giving out paper assignments. I don't think that's the answer either. But what I do think is that assessments at the end of the day, other than for writing purposes, right? When you think about the history of
quantitative side of the house, right? We've usually have judged whether a student knows or masters a material based on a grade in a course. They got an 80 on the homework assignment, a 90 on the homework assignment. What we don't know is how the student got to that point, right? They could have just as easily went to an online site and Googled an answer, you know, and not use Chat GPT. They could have asked their parent and their parent gave them the answer. So I think the the historic way of doing assessments also had a certain number of flaws in it.
So, where we've been coming in with ideas and solutions is actually trying to flip the script so that assessments aren't an activity that happens and gets done, but it's actually part of the learning journey itself. So, for example, when we introduced our student assistants, our student assistants are very much like Chat GPT, but they're grounded in our pedagogy and our core content, like I shared earlier. And the goal of those student assistants are when those students are in.
Having issues or having a challenge and answering a question that they can engage with a tutor-like capability that's first been designed to never give the student the answer, but also to apply the Socratic methods. So to ask them questions. And what's really interesting, Jason, is that the data that we're collecting of students asking questions is much more material than whether or not they got the answer right or wrong. Even when they get the answer right.
I now understand what they didn't understand in order to get the answer right. And that's the kind of stuff, the information and data that we're collecting that we are now using to feed back into what we also call our instructor assistance. So now as a teacher, you know, if you think about the scale, and you know this quite well being an adjunct, right? Is you're being asked to do more with less, like more classes, more sections, more courses. So we're building AI tools that aren't for you to sit there and just like chat away and say,
Hey, tell me which student, you know, you got to know the right questions to ask. We're actually trying to take the AI out of the problem and actually enable you to jump in, look at a dashboard, but also use AI to automatically recognize patterns, which is what the tool is actually really good at doing, and give you the insights to make you the better teacher so that you can touch a student more often and specifically to the needs that they have. And I'll tell you a funny story.
One of the things that we do research on is like when students are actively pursuing new courses or or taking a course, one of the things that students say is really important to them is knowing whether or not the teacher cares. Now that's an interesting component. Like, how do you decipher whether a teacher cares? So when we took a sample of students and asked them what that meant.
What it really meant was a teacher identifying proactively when a student didn't do that well on an exam, on a quiz, and then basically reaching out to the student proactively. And that's really what this instructor assistant is designed to do. It's designed to help teachers identify students at risk and also help automate a lot of the work to make sure that you can build that one-on-one connection. So a lot of it, we're using AI to bridge the human gap that's been created by the
you know, the amount of heavy lifting and work that the teachers are having to do.
Jason Altmire (12:26)
The ultimate goal of education, especially in workforce programs, but education generally, is to prepare the student for life after they leave school for the real world, right? The workforce. So I think about we don't know yet, right? These students are are utilizing AI. We're all kind of learning together as instructors and businesses and and you know, everyone's role in in that workforce development pipeline.
And I think about when calculators first came along. And I am old enough to remember when as an elementary school student, we were discouraged from using calculators because it was better to do the long division. And when you get out in the workforce, you're not going to have that calculator. And, you know, all the reasons why. Well, of course, over time that proved to not be true. And calculators, when you need them.
are ubiquitous. You can find them anywhere and you can use them. So I I think about that with AI. Like right now we think about don't use AI to cheat. It's supplemental to your educational, but it shouldn't replace the current way. But I'm I wonder if years from now AI is going to be so ubiquitous that everyone's going to have access to it at their fingertips. I think it's pretty clear that will be the case. So
What is the thinking with regard to preparing students for that future? Not the way the workforce looks today or that we all remember it looking more than three years ago, but what it's going to look like in the future forever.
Darren Person (14:08)
Jason, it's a great question. And I think this one is one where I think there's a lot that may fall into the predictions category, just to be fair. But I think a lot of this is, yeah, you're right. Like technology has become more ubiquitous, right? Not just AI, but all sorts of technology, whether it's, you know, if you think about programmers, for the longest time there have been tools that have evolved. We started with, you know, tools that would let a developer debug code that they never had before. Then we came up with tools that would autocomplete.
So like guess what the developer was gonna write to help speed up the development of tools. And now obviously AI where it can actually write a whole bunch of code and then now have the human in the loop to verify that the code that was written actually does what it says it's gonna do and doesn't cause ⁓ challenges. I think the same thing applies in education and specifically students is that look, two parts of this. One is we need to teach students how to responsibly use AI. You know, when I use it, ⁓ like I rarely ask it.
To generate, I almost never ask it to generate something for me that I don't know. Like I use it a lot to help me maybe reshape words, but they're mostly my ideas, things that come out of my brain. I put in, I give it the paragraph. I mean, may ask it to help write or clean it up or something like that. I think students the same way. They should be using the technology in a way that has a conversational component to it in, hey, help me understand something.
But it's a back and forth thing versus telling me the answer. I think we've come too quickly to give me the answer. Even when we were using Google back in the day, Google didn't actually give you the answer. It led you to a place where you can learn enough to know the answer. And and the fact that the results were not always a hundred percent, it kind of forced you to have that research and critical thinking. I think we've got to teach students the same way. Just because Chat GPT or Gemini or any of the tools out there says this is the answer.
Reality is is it could be or could not be the answer too. And that reliance on these tools to be the answer without us still building the knowledge, I think will create a lot of pain. And that's where you'll see people not be as productive with the technology as those who can be productive also know how to challenge and question and provide that critical layer of thinking.
Jason Altmire (16:22)
Without talking about St. Gage in particular, or at least St. Gage's offerings in particular, schools all the time ask us and others, I'm sure, for advice on, you know, we we need help, right? We we don't really know how to handle this the onset and and this the speed with which AI is changing in this environment. ⁓ what should a school look for when they seek outside help?
Because what I have noticed is all the time I have companies tell me we're experts in AI and maybe I want to come on your podcast and talk about it or I wanna get in front of some of your schools and we have to verify, right? Are they really an expert? Do they have a product that we would be comfortable sharing with others? If you're a school and you're inundated, sort of if you put out information and RFP, whatever it might be, we're looking for help, you are going to be inundated with people.
Who tell you that they're the best, what should a school look for or a business when they're looking for help with AI?
Darren Person (17:30)
So I think the first part I would I would even say not even the AI anymore. I I think for me, what I look for is ⁓ does the company understand the business? Where have they been in that business model before? Do they understand the problems? Like we should always start with the customer and the challenges that they're having before we start with the technology. I think that's actually the thing that's led a lot of companies a bit awry. We've heard a lot of companies like start and stop with AI.
It's because we started with AI as the solution, but we never really defined the problem that we wanted to solve. So when you're looking for a partner, a partner that tells you, hey, this first the starting point of the conversation is we're going to use AI and we're going to put it here, we're going to put it there. Probably a risky conversation. We should start with what are the problems that you're having and where is AI the tool that we should use? And by the way, there's still thousands of other tools that are cheaper.
More efficient for things like automation. So basic machine learning models. Just because AI came out or generative AI has come out doesn't mean that like all the machine learning models don't work anymore. They actually work highly efficiently for specified tasks and they're awfully a lot cheaper as well. So I think part of the conversation is making sure that someone doesn't take the square peg and shove it in the round hole with you and always just want to use generative AI capabilities. Because I think one of the things you'll also see as we go through this phase of AI.
As companies are rolling this technology out, you're also starting to see a significant cost pressure go the other way. Instead of it being a tool that's taking out costs, it's actually dramatically increasing costs. So really making sure that when you pick your partner, whoever it is, that they're thinking about it one, responsibly, two, that they're also thinking about is it the right technology? They should challenge themselves in that. And then the third part is are they being cost effective when doing it? Are they thinking about the you know, and when I say cost effective, I don't mean just in
you know, dollars, but also the environment, ⁓ and other areas of the ecosystem that AI is profoundly impacting. So that those would be the big things that I'd be looking for is how a company thinks about that as part of the solutions that they're gonna deliver.
Jason Altmire (19:37)
What would you say are the biggest mistakes that a business or a school would make when th when thinking about AI and and how they can utilize it and and what are some of the biggest misconceptions that people have?
Darren Person (19:52)
I think the biggest mistake is thinking that it can do everything and that it it's not wrong. I think the the misconception that you'll see is that the tools don't hallucinate that much, that the model improvements are incremental. I would tell you from experience that sometimes model improv improvements actually take you backwards sometimes and not forwards. So that notion of like five dot two, five dot three, five dot four, five dot four doesn't mean better than five dot two.
Right. It it may be better in some things. It may actually get worse in other things. So just like any technology, there should never be a blind acceptance. This is technology. These are tools that are, you know, they're being trained on imperfect human content. So the assumption or the underlying assumption that they're perfect tools also I think is a fallacy. And I think that's where a lot of companies or or people that use the tools get themselves in trouble.
We've seen cases already where, you know, consulting companies have blindly trusted the tools. You know, there's a couple of good legal examples where when AI couldn't find a case, it would actually make up a case. So I think those things, you know, while they happen and they're unfortunate, they're very good reminders of how far we've come, but also how far we have to go and why that immune in the loop, at least as of today, is so important. So those are some of the misconceptions I think that are out there as part of it.
Jason Altmire (21:19)
I think one of the things I get asked most often is where can I find information on AI and how do you stay current on it? And you're you're someone, I mean, you're a chief digital officer at a prominent tech company, right? I mean, this is your job for a company that it's their job. So I can't think of anyone else, you know, who needs to stay current more than you. So what what are your sources of information? How how do you try to keep up with it all?
Darren Person (21:47)
I rely on lots of areas. So I I would tell you there's it's it's almost impossible for one person to stay on top of everything. I'm part of different groups where we talk about this. So the nice part is, or my recommendation would be become part of a group. I'm part of a group called the New York City CTO Club. ⁓ it's a bunch of CTOs that get together on a monthly basis. We have a forum.
People share, like what's working, what's not working, is a wonderful way hearing other people's experiences, both good and bad, to stay in touch. We also run a lot of experiments internally with the technologies. That's another way to really kind of stay connected. We're part of a private equity portfolio of companies. So SendGauge is just one of a portfolio. And one of the things that we do is we get together with that portfolio of companies twice a year. And there we get to even share. And that's by the way,
Like companies that are not even in our industry, so that we can really learn about what other people are doing and how they're using it. And again, it's it's both the successes and the failures that are really interesting to learn. Sometimes the failures are always more fun to learn about, right? Because they're they just tend to be. But I think the sharing of stories of what's working, what's not working, that is the type of information we need. And of course, like we have our research team that does the research. We have a lot of testing that we do with all these new models that are coming out.
What's really interesting is we test not just the frontier models, right? The paid models, but we also look at the open source models that are coming out and how they're evolving. ⁓ what's really interesting is how the open source models are largely like a half version now behind the frontier models and their capabilities. So I think both of those two ecosystems will will start to create other kinds of business opportunities for companies that are adopting AI to think about not just the frontier models, but also how they start get leaning into the open source space.
as well. So there's a ton of learning to be done, but I usually stay connected through conversations with my peers, coming on here and talking to people like you and kind of also learning what you know you're seeing and the questions you're asking. And then that kind of informs the research that we do as well.
Jason Altmire (23:54)
If somebody wanted to get in touch with you or learn more about Send Gauge, how would they do it?
Darren Person (24:00)
First off, right away, LinkedIn is like my lifeline. So you can find me on LinkedIn. I think there's only one of me these days. Find me on LinkedIn. Happy to connect with anyone, happy to follow. I like you said, I publish a lot of content. That tends to be my platform also for publishing. My email address is darren.person, just like it says and sounds, at sendgage.com. ⁓ so you can reach out via email. But also we are go to sendgage.com, our website and
You know, you can always reach out through there as well.
Jason Altmire (24:32)
You did not ask me to do this, but I would just offer if you are interested in ed tech, somebody out there listening, and and you are especially interested in in AI and keeping current on the changes, I would say give Darren a follow on LinkedIn because he he's right on top of everything. And he's been our guest today. Darren Persson is the executive vice president and chief digital officer at Cengage Group. Darren, thank you for being with us.
Darren Person (24:59)
Jason, thank you so much for having me. Really enjoyed the conversation and ⁓ hope everyone listening also enjoyed it as well.
Jason Altmire (25:07)
Thanks for joining me for this episode of the Career Education Report. Subscribe and rate us on Apple Podcasts, Google Play, Spotify, or wherever you listen to podcasts. For more information, visit our website at career.org and follow us on Twitter @CECUED. That's at C E C U E D. Thank you for listening.