Embrace AI as a Performance Tool to Benefit Higher Ed:

Changing Higher Ed podcast 211 with host Dr. Drumm McNaughton and guest Bruce Dahlgren

Table of Contents

Changing Higher Podcast 211 Embrace AI as a Performance Tool to Benefit Higher Ed | host Dr. Drumm McNaughton and guest Bruce Dahlgren
Changing Higher Ed Podcast | Drumm McNaughton | The Change Leader

11 June · Episode 211

Embrace AI as a Performance Tool to Benefit Higher Ed

36 Min · By Dr. Drumm McNaughton

Higher ed needs prioritize adopting AI as a performance tool. Gain Insights on leveraging AI and developing policy frameworks for your institution.


Artificial intelligence (AI) has emerged as a tool with immense potential to revolutionize various aspects of higher education. In a recent survey, 94% of professors reported significant time savings when using AI-powered tools like Anthology’s design assistant for course creation. As colleges and universities navigate this transformative technology, leadership must approach AI not with trepidation but as a performance tool that can yield immense benefits for all stakeholders.


In this episode of the Changing Higher Ed podcast, Dr. Drumm McNaughton and Bruce Dahlgren, chief executive officer at Anthology and a trustee at Stetson University, discuss the benefits, address ethical concerns, and highlight how AI enhances institutional performance. They emphasize the importance of starting with a clear AI policy framework and building trust through pilots and communication to harness the power of this game-changing technology successfully. The conversation provides valuable recommendations for higher education leaders looking to integrate AI effectively into their institutions.


Understanding AI’s Learning Process and Addressing Concerns

The evolution of artificial intelligence can be likened to human development. Just as humans accumulate knowledge and experiences that allow them to grow smarter over time, AI systems build on ever-expanding datasets to identify patterns and solve increasingly complex problems. AI is computer intelligence that’s predicated on algorithms, data, and computational power. The more data and experience the computer has, the smarter it gets.


Natural language models like GPT, which stands for Generative Pre-Trained Transformer, now draw upon neural networks containing over 1.7 trillion parameters – a significant increase from the 175 billion parameters in GPT-3. This enables an unprecedented level of sophistication in areas like writing, analysis, and problem-solving.


However, these advanced AI systems may occasionally produce “hallucinations” – outputs that don’t make sense or seem out of context. This isn’t the AI intentionally generating incorrect information but rather a result of the AI model drawing upon patterns of words and data that weren’t meant to go together. Just as with human intelligence, as AI is exposed to more data and experiences over time, the occurrence of these hallucinations will decrease.


While there are valid concerns around the misuse of AI, it’s important to recognize that many innovations have had their “dark sides” as well. When calculators first emerged, they were seen as a form of cheating if used during tests. The early days of the internet also had its pitfalls until better cybersecurity measures and usage policies were developed. Even Apple’s new facial recognition technology requires thoughtful implementation and applications to be used effectively.


Similarly, in higher education, AI has the potential for misuse in areas like plagiarism and cheating. But with proper data, training, and policy frameworks in place, these risks can be mitigated. AI is like many innovations – under control, it’s a useful tool, but out of control, it can lead down a problematic path. The key is implementing AI responsibly and with clear oversight.


Piloting AI as a Faculty Productivity Tool

One of the most promising applications of AI is in helping faculty save time on routine course preparation tasks. AI-powered “design assistants” at Anthology have already been used in over 226,000 instances to streamline the course design process.


By providing professors with AI-generated suggestions and content relevant to their subject matter, drawn from multiple sources, these tools can dramatically reduce manual work. In one pilot study, 94% of faculty surveyed said the AI assistant saved them a significant amount of time. It’s important to note that the faculty remains in control, with the ability to review, edit, and approve all AI outputs before use.


This type of AI application becomes a gateway to building trust and enthusiasm among faculty. Once they start using these tools and realize the substantial time savings, from hours to days, they become eager to embrace the technology more widely. Ultimately, the time saved allows faculty to dedicate more energy to working directly with students, which is a key priority.


Developing a Proactive AI Policy Framework

To build trust with faculty and other stakeholders, university presidents and boards should get out in front of the issue by crafting a clear AI policy framework.


This framework should define what types of AI tools and applications will be supported, what guardrails will be put in place to prevent abuse, and how the technology will ultimately be used to benefit students. Gathering input from faculty senates, administrators, and even trustees is advisable to develop policies that everyone can get behind.


The faculty, as an institution, need to come together on what they’re comfortable with, because that’s how they’re going to be represented. The goal is to help them build practical policies and frameworks they can adopt.


Empowering Staff and Administrators with AI

Beyond the classroom, AI can be a powerful tool for enrollment management, student support, facilities operations, and more, as discussed in a previous podcast. By analyzing patterns in student behavioral data, AI-powered systems can predict which admitted students are most likely to enroll, then target personalized communications to boost yield.


Demographic analysis can also help universities better understand which new programs or facilities might be most attractive to prospective students. AI can process data on student demographics, geographic preferences, and interest trends to predict demand for potential new offerings, like a health sciences program or an athletics facility. This data-driven approach allows universities to make more informed decisions about where to invest.


On campus, AI chatbots can provide 24/7 assistance to students with questions about everything from course registration to mental health resources. Behind the scenes, machine learning algorithms can optimize energy usage in campus buildings, detect network security threats, and streamline administrative workflows.


Three Key Recommendations for Higher Ed Leaders


  1. Immerse yourself in the new AI technologies, experiment with them firsthand, and consider their applications across all university functions. This firsthand experience is essential to leading confidently in the AI era.


  1. Use shared governance bodies like faculty senates to surface concerns, build consensus, and ultimately codify responsible usage policies for AI tools. Clear communication throughout this process is critical.


  1. Never lose sight of the ultimate goal – leveraging technology to drive student success. The goal is to make students better citizens and future leaders, and a great way to do that is by helping them understand how technology can support their growth and development.


By embracing AI as a performance tool—thoughtfully, transparently, and always with the student experience at the center—colleges and universities can greatly improve their institutions’ performance and student outcomes. The opportunity is ripe to explore the synergies between human and computer intelligence, which will pave the way for incredible advancements in the years ahead.


About Our Podcast Guest

Bruce Dahlgren is a seasoned technology executive with more than 30 years of leadership experience. As Chief Executive Officer at Anthology, Bruce leads our diverse team of higher education and technology experts to empower our clients with innovative solutions and services. He strongly believes in the importance of a company’s purpose and how organizations can serve the greater good.

Before joining Anthology, Bruce was most recently CEO of MetricStream, the global leader in enterprise cloud platform and applications for integrated governance, risk, and compliance. He also held senior executive positions with Kony and spent more than 10 years at HP and Hewlett Packard Enterprise–including serving as HPE’s SVP and managing director of the Asia Pacific and Japan region.

Bruce currently sits on the board of trustees for Stetson University. He received an MBA and bachelor’s degree in business administration from Stetson University and attended the Wharton Executive Education Program.

Bruce Dahlgren on Linkedin

About the Host

Dr. Drumm McNaughton is the CEO of The Change Leader consulting firm and consultant to higher education institutions specializing in accreditation, governance, strategic planning, and mergers. 


Transcript: Changing Higher Ed Podcast 211  with guest Bruce Dahlgren

Introduction and Guest Welcome

Drumm McNaughton: Thank you, David. Our guest today is Bruce Dahlgren, chief executive officer at Anthology, an ed-tech company that provides holistic solutions to help higher education institutions, and he’s also a trustee at Stetson University.

Bruce has a deep history in higher ed and technology, and with his 30-plus years in tech, you can well imagine the technological changes he’s seen over his career. Technology that’s impacting higher education in a major way. At the forefront of this is AI, and Bruce joins us today to talk about the benefits for colleges and universities that embrace AI as a tool.

Bruce, welcome to the show.

Bruce Dahlgren: Thanks, Drumm it’s great to be here.

Drumm McNaughton: I am looking forward to our conversation. You have such an incredible technology background, and right now, AI is at the forefront of all technology.


Bruce’s Background in Technology

Drumm McNaughton: That’s what we’re going to focus on today, but if you would give us a little bit of your background so that listers know who you are as a person and how this should make a lot of sense for them to keep listening.

Bruce Dahlgren: Well, thanks, Drumm, it’s a kind way to start, and I appreciate your comments. This is going to make me feel old, but I’m going on four decades in I.T. And high tech. And wow, what a great four decades to see all of the work of computing and data processing and then networking and then, of course, software applications, and today, artificial intelligence and machine learning. I’ve been very fortunate to have spent my time in this industry. The first half of my experience has been in large corporations like AT&T, NCR, and Hewlett-Packard, I’ve lived around the world. And the second half of my career has been on the private side, private equity-backed companies, and SaaS companies, seeing the benefits of all of these great decades of building this industry.

Drumm McNaughton: And SaaS being software as a service.

Bruce Dahlgren: That’s correct. That’s correct.


The Role of SaaS in Higher Education

Bruce Dahlgren: Taking these software assets and instead of making the customer buy them and install them and use them, we provide them as a service in a prescription environment so that they can get benefits much, much faster.

Drumm McNaughton: That is really important for many higher ed institutions and any institution, you can’t really afford to go out and build a tool yourself or even bring people in and hire them to run the tool. Whereas if you do a subscription basis, you’re paying for all that.

Bruce Dahlgren: That’s exactly right. In fact, when you go into these universities higher education institutions, they do have great IT talent, but not to be able to span all of the complexity of all of these different software aspects. I mean, just think of how complex it is from the teaching and learning. Running the university, all the marketing aspects. And so what a SaaS company does, what this model does, is bring those tools to them in a simple, easy to implement, and then use those tools to help make that university and that student success.


Innovation’s Focus Must Always Be on Student Experience

Drumm McNaughton: And one of the things that we talked a little bit about before we came on the recording was the innovation aspects. And many folks believe, I’m one of those, that technology is probably the greatest innovator that can be used to change the course of a university or any kind of company.

Bruce Dahlgren: Yeah. So I think that’s true. I don’t think you want to ever become more important than the student’s experience, the student success, and that interaction with the faculty and the ability to be in an environment where you can learn. So you never want the innovation to be more important, but what we work on, and we have been now for many years, is how to make that experience better.

And that’s everything from determining what university to go to, how you can be successful, even how you can determine what area you want to focus in and learn and then become successful in that capability for sure. Innovation is all there.

Drumm McNaughton: And it’s got to be, especially given where we are in higher education right now, with the public perception of “higher ed is not worth the money it’s being paid,” et cetera, et cetera. I would argue vehemently against that, but these are beliefs that they have. And I think you just summed it up. The student has got to be at the center of every initiative every focus that an institution has.

Bruce Dahlgren: I would agree with you. I think the trends are actually positive in the end.


Impact of COVID on Higher Education

Bruce Dahlgren: Now, we did have a pre-COVID world, and now, we have a post-COVID world, which clearly changed things. The student experience, the student expectations are different post-COVID. You know, they want to be able to have a safe environment. They want to know that the capabilities are there if they have to learn virtually.

So I do think that it’s changed, but there are a lot of perceptions that the higher education experience has gotten too expensive. I serve on a major university board. We’re very focused on containing those costs, making the experience worthwhile, and making sure people understand the true value.

I would say one thing, Drumm, that I’m noticing as a trend. I think there was an experience level pre-COVID that students came to the university experience to; I don’t want to say grow up but to kind of figure out what they want to do. And now what I’m seeing is, that people are more focused on micro degrees and saying, “Hey, I want to make sure when I finish this, I’m not just smarter and I don’t just know how to learn, but I actually have a vocation. I have an experience. I have a specific area of expertise that I can bring into the marketplace”. Meaning that they’re more focused on the ROI than just the experience.

Drumm McNaughton: I think you’re absolutely right. Which is a nice segue into what we were going to talk about.


Introduction to AI and Machine Learning

Drumm McNaughton: AI is such a huge tool right now. Technology and most institutions have, put up the sign of the cross, keep away from us. Industry wants people. Who understand AI, how to use it, et cetera.

And we’re going to talk a lot about that today for those folks who aren’t real familiar with AI. Tell us a little bit about it. Cause you’re knee-deep in this kind of technology,

Bruce Dahlgren: Sure. And I’d like to think that four decades of experience in the High-tech world have made me more adjusted to what’s natural here, and we’ve worked hard to get to this point. But let me tie it back this way, and I’m going to get really high level here just to kind of maybe go beyond the hype of A.I.

Let’s talk about human intelligence and human intelligence, much like higher ed starts at birth, and you grow, and you become an adolescent, and you have experiences, and that experience is more data. It’s more knowledge. And then you go to school, and higher ed, and your knowledge becomes bigger. And so human intelligence grows.

It’s an evolutionary path. Well, so is artificial intelligence. In fact, artificial intelligence is kind of a fancy way of saying computer intelligence. And artificial intelligence kind of comes from the same thing. It’s used on algorithms and data and computational power. And so the more of that, the more data, the more the computer has, the smarter it gets.

And in a sense, what machine learning is, if you think about it from this basic thing, is that we’re trying to perform tasks that simulate or require human intelligence. In other words, we’re trying to get the computer to be able to learn and to reason and to problem solve. Having a perception, just like humans, as we evolve and as we become smarter.


Understanding AI: Supervised and Unsupervised Learning

Bruce Dahlgren: Now, there’s two types of machine learning, and I want to be careful not to get too deep here but think of it as training and then applying what you trained on. And two different types of training. There’s supervised training, and this is where we would start to label data. We’d structure it in a data model, and then it would build patterns.

Thank you very much. So just like human intelligence, artificial intelligence or computer intelligence builds patterns from that experience. Now, when you get into unsupervised training, this is when it gets more complex. And all of a sudden, there are unknown patterns. And what we consider these unknown patterns are in the industry, neural networks, and I’m not going to get too deep.

I’m going to try to keep this high because it’s easy for us tech guys to get deep. Okay, so work with me. But do people really know what GPT stands for? It’s Generative Pre-trained Transformer (GPT). And in a sense, think of neural networks as clustered data. And I’m going to give you a really simple way to think of it.

Think of the room that you’re in right now and think of it full of words, just random words. And you as a human being would be able to start interpreting those And clustering and bringing them together, just like a computer could potentially do that. And then you could start building patterns. And Drumm, let’s test something.

If I said to you, I want you to finish the sentence. If I said to you, it’s the best thing since sliced,

Drumm McNaughton: pizza,

Bruce Dahlgren: ah, no, you did that on purpose. Sliced bread. And now you say, why do you know Sliced bread. It’s the next best thing since sliced bread. Maybe that’s a bad example, but the point is from your experience as human intelligence, you knew that was the next word.

A computer can do the same thing. A computer can start to understand that these patterns, but the problem is it doesn’t have the human experience that we have. So it all comes from the size of data. And I’ll tell you one last cool thing that you might find interesting in 3. 0. There was 45 terabytes of text of these words hanging around.

That’s, think of it as an inflection point. That’s approximately 175 billion neurons in this neural network. And it was an inflection point to where it could start knowing what the next best word is, how to put this pattern together in GPT 4. 0, believe it or not, it went to 1. 7 trillion neurons. And this is why it’s become so sophisticated and why it can all start to determine these patterns and why artificial intelligence is now starting to simulate humans.

And it shouldn’t be something scary. It’s not intended to take over human nature. It’s intended to start thinking like humans so that we can start building those patterns. I tell you one last thing, I think you’ll find interesting hallucinations. So I was doing a little bit of work and I talked to some of our technical folks and I said, Hey, Tell me about hallucinations, and you know what this really is?

This is really the computer’s collection of words that aren’t necessarily supposed to be together. It’s not like the computer is intentionally coming up with something wrong. It’s because the pattern that it brought together wasn’t really meant to be together. And as we get more data, just as we get older and have more experiences, that intelligence will be stronger.

I hope that was okay.

Drumm McNaughton: Oh yeah.

Bruce Dahlgren: up with a better sentence.

Drumm McNaughton: Yeah. Yeah. And the best way since sliced pizza. I like it, you know, just happened that we had pizza for dinner last night, but, but anyway, You bring up some really interesting points with this.


Applications and Challenges of AI

Drumm McNaughton: There’s tons of applications on how AI is being used today. Surveys at conferences, turning technology into something that isn’t really scary, innovation, et cetera.

But there’s a dark side to AI as well. And I think you just alluded that is. What you called hallucinations is drawing conclusions that really aren’t valid.

Bruce Dahlgren: That’s correct. There’s a dark side to every innovation. Let’s go back. You could go back all those decades. You could go even back to, innovation long ago, human nature, unfortunately, any of these innovations and any of new concepts have dark sides. Now, there is a dark side with the computer actually coming up with its own patterns or its own next step in these hallucinations.

No question. There’s been some things The next big thing is called Uncanny Valley, and it’s another kind of dark side of this. But, but I would also tell you that there’s dark side in some of the higher education areas as well, such as plagiarism or cheating. And I’d like to go back to when I was in college, and again, this is going to date me, but I can remember when calculators came out.

Now, Drumm, I think you’d probably be with me on

Drumm McNaughton: Oh, definitely.

Bruce Dahlgren: And it was perceived, it was cheating if you brought the calculator to a test.

I think that, you know, just like a lot of innovations, these dark sides, I mean, think of the internet was very similar when you first started doing all of this, unfortunately, you needed more data in order to use the internet correctly.

But there was a dark side and eventually we figured out ways around that with cyber security protection and things like that. You know, even Apple’s new mask, it’s all predicated now on getting applications so it could be used. So I think as the data increases and these neural networks increase, we’ll find there to be less mistakes.

And then we also need to have, Frameworks and policies so that we protect from plagiarism and cheating and other aspects of the dark side.

Drumm McNaughton: And , what I’ve heard from a lot of folks that I’ve spoken to about AI is one of the things that Schools are doing is they’re teaching students ethical uses for A. I. And the critical thinking necessary to be able to use it effectively. Now, what are you hearing from university presidents about A.I.

Bruce Dahlgren: I think there’s a wide spectrum here. There are universities that are really trying to control this to make it more of An extra piece. And then there are universities that are just saying, open it up and let’s just let it happen. Let the technology. I do think that there is a middle ground that the great majority of higher education institutions are going to, which is let’s start understanding how this is going to impact their experience.

And I want to talk to you about some of the things around faculty, but let’s make the experience better. Let’s try to show how artificial intelligence can help the educational experience, but full stop. I think the next big wave is students are starting to require They need to understand artificial intelligence, because when I go out into the business world or the medical profession or the law profession or wherever this education takes me, I know artificial intelligence is a reality, and I need to understand how to harness it and use it.

And I believe artificial intelligence is like many things, many innovations. Under control. It’s a useful tool. Out of control. It’s a raging tyrant and it can start to take, in some areas into a bad place. And we owe it to the students. We owe it to these universities and faculty to be able to make it a useful tool.

Drumm McNaughton: and to do that.


AI in Higher Education: Policies and Frameworks

Drumm McNaughton: I think when we talked a couple days ago, we talked a bit about where do you start with this, a university policy, AI on university policy on ai or something like that. How, what does something like that look like and why is it important?

Bruce Dahlgren: Yeah. I’d like to. I like to call it the middle ground of appropriateness. That’s a fancy higher ed term or what,

Drumm McNaughton: to, got it. Okay. Let’s send it, let’s send it to committee. It’ll be out in two, two years. Yeah, no problem.

Bruce Dahlgren: where you have to have, everyone vote. Yes. I think, I think there is. A benefit of having a trustworthy AI program. And I think that the university needs to get together and decide how it wants to use this and then be able to, to drive policy throughout the university and throughout all of its different programs.


AI’s Role in Faculty and Staff Productivity

Bruce Dahlgren: Now what we chose to do and I’ll get a little specific on Anthology is we chose to start on the faculty side and we developed, we were the first ones to develop an AI Design Assistant and basically it was set up to be a faculty productivity tool using chat GPT and our design assistant. We save that professor countless hours in building their course and putting it together.

But remember, it’s still the professor’s final sign off. It doesn’t do it for them. It doesn’t make the decision. It provides information. And now that professor is able to pull from multiple sources to help build that course faster. And I wrote down, this is literally last week. I was over in Europe and we were doing some studies on this.

We already have two over 226,000 . use cases where professors have used the design assistant to help build this and 94 percent have surveyed that it saved them time. And ultimately what does time give them more time with students? And that’s what every professor wants. They want to spend time with students.

Now, the idea is Drumm if I’m seeing AI now as a productivity tool, It becomes less scary. It becomes less of a dark side and more of a, wow, I had no idea. Instead of me going out into the internet and doing all this search and all this stuff, it all came to me. And then I built the course that I wanted and it’s been really successful for us.

Drumm McNaughton: Mhm. Well, that’s that is a great way of working with a stakeholder and faculty is, certainly one of the key folks there. So you start with a policy. You’ve got to change the culture of the institution to where it starts to embrace that with faculty. That is a great way to do that. How about staff and administrators?

How do you benefit them?

Bruce Dahlgren: There’s no question. Cause you know where this goes, right? So it goes from the faculty and the course design into the marketing programs. I mean, it could be anything. It could be housing. It could be the aspects that you have around your sports programs. It’s really endless in the way that universities can use this data.

Data and to use these tools, what we’re recommending. And we actually have a program that we’re bringing out to universities. It’s what we do as Anthology. And it’s called our “AI Policy Framework”. And it’s basically a structure that comes in as a framework and allows the university, the president, the administration, the faculty, even the board of trustees, if they want to engage in understanding that framework and starting putting parameters.

on where AI gets used, where it starts to apply. But understand that the Gen Z student is going to start expecting AI in the classes. They’re going to actually, I’ve even heard in some cases where there’ll start to be AI degrees, like data scientists, data, actuaries, data, analytics, all of the things that are happening in the computer world.

I think AI will start to show up even as courses.

Drumm McNaughton: Oh, I think you’re absolutely right. I’ve had two or three people in the last three months approach me wanting to start a university graduate program around AI.

Bruce Dahlgren: Yes. In fact, I’m headed to Europe next week and we’re working on some programs with some new concepts around that. This is all happening. And. And it can be just such a useful tool. We have to demystify it. We have to go beyond the hype. There’s a lot of marketing now. I have to say something funny.

I’ve seen a couple ads where there’s an AI tire that can start predicting when there might be a flat. And I was thinking, Oh, that’s probably a stretch. Or how about the AI refrigerator? That can tell you when something’s going to get spoiled based on when it was put in there. I think people are tending to use this a little bit too liberally.

The reality is it’s fundamentally neural networks built around words and being able to help the computer start to identify how these patterns play out. And then all of that becomes. Impact and helpful for us, and it plays out in the university world, just like we as humans learn computers learn, and there’s so many great synergies.

Drumm McNaughton: Give me some examples of how staff. Enrollment staff could make good use of an A. I. Tool

Bruce Dahlgren: Sure. So, step back for a minute and think of the different demographics. And by the way, this is a big challenge. I can say, serving on a university board statistics, like retention, getting a first year student to come back the 2nd year. There’s a lot of challenges now where, it used to be when somebody applied an application.

We in the industry call it melt and they said, yes, I’m coming. A great percentage of those students would show up and now those statistics of, open applications and accepting and then not showing up. And so I think there is a lot of statistics. of knowledge from this data to start helping the university understand demographics of students, geographic preferences, how to attract different types.

Let’s say you want to open up a different program, a health science program, or a different sports program, starting to understand the demographics of people. Who would attend those programs? What are the costs? What things you need to have? All of that data is out there and exists and we can use artificial intelligence.

We can use our systems to help them start predicting and understanding where this is headed.

Drumm McNaughton: and you bring up a great example in the open application. You have so many people apply accepted to your college. As you say, the melt is, it’s getting worse. And so with that, you can make a good predictor using AI, provided you’re using the right prompts, you’ve got the right data access to say, this particular demographic, these people from this particular town, et cetera, they have an 80 or 85 or even 90 percent chance of accepting and coming in.

So you can focus your target marketing on them to make sure that you do get to that 90%.

Bruce Dahlgren: Sure. There’s a lot of exciting new advancements around adaptive learning, starting to tailor some of the learning direct to the students experiences and their desires and what they’re good at. Think of the ability to start actually positioning students to give them ideas of what they ultimately could be successful.

What careers should you select based on how you do certain tests and how you interact? Think of the ability for professors to start actually tailoring some of that learning based on how they do and giving feedback to students long before they’re tested. There’s just really endless opportunities.

For sure.

Drumm McNaughton: How does somebody learn how to use ai effectively you just say like a faculty member at this point It’s pretty much, hit or miss trial error, . Oh, that was a lousy prompt. I got to try something different How does somebody learn about these things other than trial and error?

Bruce Dahlgren: Well, I hate to say, I don’t know if it’s something that you just jump into and maybe that’s what makes this a little scary. I think it’s incumbent on companies like Anthology to start providing these tools that demystify this, that make that a tool for them to say, okay, I understand if I put in these parameters, if I ask these questions, if I’m looking for this information, Wow, look at how smart the outcome of these capabilities and these systems are to now start providing that and then start to do some what ifs to where you can actually start to say, Hey, what if this was a scenario?

What would be the outcome? And I think this is where the whole computer science is headed. If you go back to early days with decision trees and how we built programming and here we are today. Now where the Yeah, The computer in a no code environment is actually starting to program itself to say, here’s a question you should ask.

I think it’s very exciting, to me.

Drumm McNaughton: Yeah, it’s funny that you say, the early days of computer science. I had reason the other day to take a look at my transcript from my freshman year at the Naval Academy. And the only A in the first semester I got was introduction to the computing.

Bruce Dahlgren: Oh, for sure. It’s, yeah, you know, this will date us. We talked about calculators and a lot of stuff, but I can remember early days. And when I decided I really was excited about this part of the future and the whole world of information technology, I can remember using punch cards. And being excited to see the green bar reporting coming out.

And now you look at today with the mobility and the way this works. I can remember there were only a few companies in the world commercially that had over a terabyte of data. And now you can get it on your phone, but this is the re but it is the reality and it’s back to artificial intelligence, just like humans grow and experience and learn.

And our answers become more sophisticated. So does a computer with more data. More of these neurons and this is what it’s become and it’s fascinating and the students are expecting it and some in the faculty are concerned about it But it’s a reality and it’s going to make in my opinion things very exciting

Drumm McNaughton: Oh, absolutely. The big thing that I see, especially for higher ed, is you have to do a, in some ways, a culture change. At a college and a university and get them to align with this. How do you build trust in AI to where you can have this culture change and people start to embrace it.


Building Trust and Embracing AI in Universities

Bruce Dahlgren: I’m glad you asked that and again, I’m gonna be a little specific for Anthology, but I think there’s many options here but One of the things that we’ve done is we’ve created an AI advisory council and we’ve started to get different sources, and geographies, by the way, so that because there are different aspects to higher education around the world and start to be able to bring this together in councils that say, here’s some of our learning.

I find one thing great about higher ed. Is they love to share, there’s this openness about, new concepts and things. I think that’s wonderful. Another thing that we’ve done is we’ve put a trust center together Anthology trust center. And basically, this is a way to get questions. And a source for people to go to as to how do you put this together?

But I strongly believe when you work with a president of a university, you work with the faculty Senate, you work with the board of trustees and the shared governance, they, as an institution need to come together with what they’re comfortable because that’s how they’re going to be represented. And we want to help them, build those policies and build that framework to be able to do this.

Drumm McNaughton: So how does an institution get to this point where it embraces, outside of Anthology, because what you’re doing is really good with this, but what does a quote, normal university have to do?

Bruce Dahlgren: Yes, I would strongly take, I would recommend take a strong look at this design assistant, find an area within the faculty that is, looking for artificial intelligence. And there are many different, aspects, within the university. So depending on where that is, you’ll need a thought leader to start embracing this, to experience it, and then using the faculty senate,

I think the president’s important to embrace and to be able to start sharing these concepts so that it spreads and what we’re finding is that once they start using this and then one faculty talks about the other and they realize, wow, I just saved three hours. I just saved six hours. I put this together in one night.

It would have taken me three days. Then all of a sudden it’s like, well, I want to give that a try to go. And I don’t think that’s much different. Drumm, you know, if you think about other innovation that’s come around, I mean, think of when BlackBerry came out with the first automated email, and yeah, for sure.

And then it advanced and all that, and I think that’s where we are with this technology.

Drumm McNaughton: I think you, you make some real interesting points with that. One of the things that I think about is pre COVID post COVID pre COVID. The only people who really did a lot of, Oh, what’s online education was the online education schools,

Bruce Dahlgren: Yes, I agree.

Drumm McNaughton: COVID forced schools to get to that point. And now.

And it was always the faculty saying, it’s not as good, students don’t learn as much. Well, they’ve had studies come out and say they can if the courses are structured properly. So I think it’s, I think it’s something like that.

Bruce Dahlgren: I agree with you. And there are some dynamics happening in the United States right now. In fact, I think it’s spreading around the world where there are some challenges on university campuses right now. And isn’t it, I think, a positive that you can, go to a virtual learning to make sure you can finish the semester and students can graduate.

So, I do think as hard as the COVID period was for us, and as challenging as it was to keep people safe, I do think that the technology advanced dramatically in our ability to do things in a more virtual environment. I do think person to person is better learning, and I think faculty like that. But now there is a hybrid scenario, and we’re using technology in ways we never expected.

 You know, I was thinking about this. I’ve been traveling around the world representing Anthology at different user conferences or different things, individual meetings and all. And one of the things that started to dawn on me is that a university environment is a complete, almost holistic community.

So obviously we have the learning experience, right? And we have the teacher student, but remember it also operates like a bank. You have a lot of security with things like credit cards and social security numbers and addresses. So all of privacy and security is huge. By the way, Anthology were number one or number two in global, student repository of student data, huge important to be secure and private.

Then you’ve got hospitality. You’ve got to manage dorms and where people live. Oh, and you’ve got retailers. You’ve got to sell stuff. Yeah.

No, for sure. And then you’ve got healthcare. You got to keep people safe. Oh, and by the way, there’s sports programs. I mean, when you think about it. It’s, it is truly all of these vertical industries that are so complicated and are driven so much around data and how you pull us together. A president of a university is very much like a mayor of a small community and, or a big community in some cases.

And, and technology is so critical to make all of that work.

Drumm McNaughton: Bruce, this has been a fascinating session for me. I really appreciate your being on. We’re about out of time, so let’s go ahead and get to those three takeaways for university presidents and boards.


Three Recommendations for Higher Ed Presidents and Boards

Bruce Dahlgren: I think the first thing I would do is to make sure that you immerse yourself into the new technologies, specifically artificial intelligence, so that it becomes a natural piece as you lead the university. I think it’s a great opportunity to be on the forefront of technology.

The second thing, obviously, it’s a big challenge. You’ve got shared governance trying to pull it all together. Don’t hesitate to use data and new technologies and capabilities as you manage this community that we’ve talked about.

And then third, and I think they would all agree with me. Never forget the ultimate purpose and the passion, and that’s for student success, and we want to make them better people, future leaders, and a great way to do that is have them understand how technology can help them.


Conclusion and Future Innovations

Drumm McNaughton: Great takeaways Bruce, what’s next for you and Anthology?

Bruce Dahlgren: Well, You know, we’ve got some exciting things happening at Anthology. Our big tagline is power of together. So that’s together, obviously between us and our clients, these universities, and institutions, but the power of together between the students and the university, but then the power of together of all of the technology.

And so we’re in the process of unveiling some really exciting new innovation. It’ll be coming out early summer in a big event we have called AT24. It’s in Orlando, middle of July 14th through 16th of July. And we’re going to be involved in, releasing a lot of new innovation AT 24 and all around this power together.

Drumm McNaughton: Well, thanks again for being on the show, Bruce. It’s been a thorough pleasure on my part, and I look forward to the next time we get a chance to talk.

Bruce Dahlgren: Me too. It’s been a real pleasure Drumm I’m thoroughly impressed.

Likewise. Thank you.

Drumm McNaughton: Thanks for listening today and a special thank you to our guest, Bruce Dahlgren, for his insights on how AI can improve productivity and efficiency at higher education institutions. Next week, join us to welcome back Dr. Zach Mabel and Catherine Campbell from the Georgetown University Center on Education and the Workforce.

Zach and Catherine will be joining me to talk about their latest study, The Great Mismanagement, addressing the mismanagement between the supply of certificates and associate degrees and the future demand for workers in the U. S. labor markets. Thanks again for listening. See you next week.


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