The Latest on AI and Work in Higher Ed

Sophie White: Hi everyone. Welcome to season three of EDUCAUSE Shop Talk. It's our EDUCAUSE podcast where we talk about big issues in higher ed and technology. I really love how we started this season, which was a discussion of just released EDUCAUSE research report on the impact of AI and work in higher ed. We've been talking a lot about AI and how it relates to teaching and learning, but it was really cool to be able to talk with Jenay Robert, the author of the report, as well as our guest, Jessica Palatka and Lancie Affonso, who represented the HR and faculty perspective on how it actually relates to how we do our work in higher education institutions. A few highlights for you. We talked about where to collaborate across different departments as it relates to creating AI policies or adapting current policies to incorporate AI, risks and opportunities related to AI and higher ed work and how to upskill staff effectively, how we can think about measuring ROI as it relates to AI and how difficult that is, might even include some mixed methods research.

And then how we really ended the conversation on how this relates to the larger institutional mission, supporting students and these larger conversations about the role of higher ed in society. Looking forward to you listening and make sure that you also check out the report.

***

Hello everyone and welcome to EDUCAUSE Shop Talk. Welcome back from the holidays for those of us who are just joining us for the first time in 2026. I know I'm still getting used to writing 2026 on things and keep writing 25, but we're getting back into the swing of it. And today I am really excited to talk about the impact of AI on work in higher education. We're releasing a report at EDUCAUSE related to this subject. We have one of our authors, Jenay Robert on the call as well, as well as two institutional experts to share their perspectives on AI and work in higher ed. But we're going to dive in today to the report and their perspectives. So I'll introduce them and then we'll dive into it. First of all, I'm Sophie White. I'm an EDUCAUSE content marketing and program manager here, and I am one of the hosts for today's show.

Jenay Robert: Hi, everyone. I'm Jenay Robert. I'm a senior researcher at EDUCAUSE. I co-host the show and today I have the joy of wearing two hats as co-host and also the person who wrote up this research. Yeah.

Sophie White: Great. And our two special guests today are Jessica Palatka and Lancie Affonso. I'll start by introducing Jessica. She is an SPHR, SHRM SPC, an expert in human capital management with distinguished service across academia, federal government, defense sector and industry, a wide variety of expertise. Her leadership is recognized for aligning complex organizational requirements with strategic mission outcomes, delivering measurable improvements in efficiency, workforce engagement and institutional resilience. As the vice president and chief human resources officer for Miami University, Jessica leads all HR functions throughout the university, including HR strategic programs and partnerships, operations and services, and compliance. Jessica is also an adjunct professor teaching undergraduate courses in human resource management and serves on the executive board of directors for the United Somelier Foundation. Wow, that is so fun. What a variety of things. We can talk about wine maybe on the podcast too. Thanks for being with us, Jessica.

Jessica Palatka: Thank you for having me.

Sophie White: And our second guest, Lancie Affonso, serves as an honors college faculty fellow and senior instructor of computer science and entrepreneurship at the College of Charleston. He teaches courses in intentional AI and cybersecurity with a focus on human-in-the-loop methodologies. He collaborates with faculty on intentional AI initiatives at the Center for Excellence in Teaching and Learning and bikes to unplug. Thanks for joining us, Lancie.

Lancie Affonso: Glad to be here.

Sophie White: Great. So let's dive into it. Again, we'll talk about their perspectives and also just wanted to mention that we're releasing a report on the impact of AI in higher education through EDUCAUSE. So we can start with talking a bit about the report. Jenay, I'm curious, are there any takeaways that you thought were really interesting that we should start with for the audience? Was there anything that surprised you as a researcher as you were working on this and evaluating the data?

Jenay Robert: Yeah, I can start with the thing that as I was analyzing the data, it's kind of funny. Doing this kind of research sometimes is a little bit solitary once you get the data, you're sort of playing with it and figuring out what the story is. And so as certain pieces emerge, I'm oftentimes screenshotting bits of things and sending them to people on my team, "Look at this thing." And I think what really jumped out at me as I was doing that work was how most of the survey respondents said that they were using AI tools for work. And at the same time, like about half of respondents weren't aware of any policies or guidelines at their institution that would guide that use. And so I would say not necessarily surprising in a general sense, but the magnitude of that was a bit surprising to me.

I expected to see a lot of people interested in using AI tools for work. I didn't expect 90 something percent, almost all of the samples saying that they were doing so. I expected that people might not be totally aware of policies or guidelines to support that, but certainly only half that was a little bit lower than I expected in terms of people who were aware. So yeah, I think, and putting those pieces together to say huge red flag for our community.

Sophie White: Jessica, Lancie, any thoughts on that based on what you've seen on your canvases? And Lancie, I know you also teach on cybersecurity, so this feels to me like a big cybersecurity risk. And I don't know if you want to speak to that a bit.

Lancie Affonso: Yes. And so I teach cybersecurity and what's happened, we're seeing that there are policies in place FERPA primarily to govern the dissemination of student data. And so I think the discrepancy that you're seeing over here, it may be individuals who have access to personally identifiable data who are aware of the policies and those generally faculty and stuff figuring out may not be aware of policies that are at institutions organization. But I think what we're hearing about is the general use case of AI. There's not been any necessary prohibitions on using AI tools. The question is, what kind of data that you use? I think we'll see a lot more clarity in terms of guidelines as to what information can be used with external tools.

Jessica Palatka: Yeah. From my perspective, what I found solidifying, I'll say in what you shared in the research was you mentioned a lack of coordination between IT and HR when it comes to AI policy. And then also that if both are creating policies that they're inconsistent. So they might be inconsistent between HR and IT. They might be inconsistent between academic and operational units. And what's so hard for us to do so often, and this is not academic only in any industry, is to coordinate and to co-own. So co-ownership of policies and the cross-coordination, this is where we do bring in CIOs or CDOs who can create governance boards where there are multiple stakeholders creating these policies.

What's interesting to me is that this has rolled out in real time years ago, right? AI's been around for how long, but we're starting to see the catch up of the policies that are occurring. Thirty minutes before we began to speak, I got a training opportunity from an organization about how to create AI policies in HR that are practical. And so we're doing this in real time, but AI's already out there. We don't have the policies yet, right? So it really validates what your respondents said and what we're seeing from an HR and IT and academic and operational perspective.

Jenay Robert: Yeah. Jessica, I have a question for you about that HR perspective. So when we asked our survey respondents what units, what you were just alluding to basically, what units at their institution have decision-making authority for work-related AI policies and guidelines, only... checking the statistics here, only 9 percent of respondents said that human resources was part of that decision-making process. And I'm wondering if this is ... Do you feel like this is accurately represented? Is it really only about 10 percent of institutions or including HR in those conversations? Do you think maybe people just aren't aware that HR is included in those conversations? And just mostly for the listeners, I want to remind people that we really are talking about using AI tools for work. So this overlaps with using AI tools in student-facing settings, but it's not exclusively about using AI tools with students. It's just really thinking about using AI in your day-to-day work.

Jessica Palatka: So I would say that that would be accurate and I would hope that that would be the case. I would hope that many people didn't suspect that HR was the one that was creating the policy on AI usage. I would hope that they would know that there were these governance boards, that there were conversations that occurred where I was a contributor to said policy, but should I be the owner of that policy? Not necessarily. It depends on what the outcome of that policy is to be. Now, if it's on the ethics and integrity of employee conduct, absolutely there should be some information on employee conduct, some information on employee ethics, on ethical use, possibly, starting to cross over a little bit. But I could see that embedded within an employee participatory policy, not so much an AI policy, how we expect you to use AI.

And at that point, I would, again, coordinate with an academic unit to make sure that what I'm saying from a faculty and staff perspective would match what we're expecting of our students or what even we're teaching our students, because there are so many different policies that can overlap and misalign. So it's very important for me to know as an HR professional, what are we teaching the students? What are the guidelines we're putting forward to expect the students to then not just teach them and how to advance in their careers? What rules are we putting in place for the students to follow? Are they similar to the rules that I then expect the faculty to follow? So no, I think that the data speaks absolutely accurately. I wouldn't expect that the policy would be spearheaded by HR. I would expect it to come from some sort of IT office or from a provost office possibly, depending on the content again, but I would hope they know that I got to see it.

I helped to create any ... If there is a policy, typically if there's some sort of disruptive activity that's occurring, what the outcome of that might be that I had a contributing to that, especially if there's unions involved. But no, that doesn't concern me that only 9 percent think that.

Jenay Robert: Thanks. That's an interesting perspective. And that was one of the interesting things about doing this research for me and why we partnered with some of our partner associations. So at the front matter of the report, you can read about the various associations that we partnered with, AIR, CUPA-HR and CUBO. And really the purpose of those partnerships was because this topic of work at our institutions is so broad. Really think about all the different types of work that people do at our institutions, from teaching and learning roles to business operations, people working in finance offices, people working in the bursar's office. There's just so many different types of roles at our institution. And so for us, wrapping our heads around the many, many ways that people could be using AI tools for work in higher education was a real challenge and something that was very exciting for me to get to learn about.

Sophie White: I'm curious, I thought in reviewing the report that it was interesting that so many, I'll have to find the exact number, but a lot of 64 percent of employees reported that they're not required to use AI tools for work and don't expect to be required in the future, but we had about 90 percent saying they are using AI tools. So I'm curious, do you all think that expectation that staff won't be expected to use AI in the future is accurate? Or do you all see maybe that trend changing that we'll see AI required as part of our roles in the future? I know that's a speculative question, but I thought that was really fascinating, that difference.

Lancie Affonso: Sophie, I guess it depends on your definition of an AI tool.

And so even a simple, whether it's Google or Bing search right now has AI embedded in it, your spell checker, you're using Grammarly, etc. And so I think when in the question, the whole idea of AI tools, you're using it exclusively or as an aid or is it embedded? And for the end user, you may not even realize that the technology that you're using has AI embedded. A good example would be Zoom for Zoom calls with AI enablement. And so you'll use the tools, but it's not at the forefront that this is an AI tool specifically. And so I think that definition will change as people start to realize the embedded nature of AI, which makes it really difficult. So going back to the earlier question, the issue of data governance, we have those policies in academia. And so the technology really doesn't change the data governance.

I think it's just because of the number of tools that we are using and that speed in which information can be shared is that we are becoming a little bit more concerned about the policies being more explicit. So the data governance policies have always been in place in academic institutions at staff level, faculty level. It's just the nature of the tools and the information that can be shared across platforms and the speed at which that information can be shared is giving institutions reasons to be rightfully concerned in some place. So Jessica mentioned, it usually is a collaboration across multiple stakeholders within the institution, academic affairs, IT, HR, faculty, staff, and stakeholders across the board. So those collaborations take a little bit time to actually come up with an AI policy, but in reality, we do not really need an AI policy. The policies are already in place.

It's how does the use of AI tools, how do the policies really highlight the data governance and data security aspects that institutions need to be careful about?

Jenay Robert: Yeah. Lancie's touching on something that was a bit of an evolution in our research at EDUCAUSE related to AI policy. So early on when ChatGPT was making this big explosion in higher ed, a lot of people were asking about policy almost exclusively. And as we were engaging in the research, that's what we were looking into. And as time went on, we really started to understand exactly what Lancie's saying, which is that there are so many policies out there that already address the things we're concerned about. They may not directly be AI policies in and of themselves, but they're data governance policies or whatever the other relevant topic is. And that's where in our research we started to shift a little bit more to asking people about both policies and guidelines because it was very clear that the community, particularly those who are not as familiar with data governance in their day-to-day jobs, didn't quite grasp the fact that many policies were already guiding the use of AI tools.

And so that's where we talk a lot about, at this point, creating guidelines or creating clarifications, creating more communication around these things so that people working in higher education can connect those dots. I think we have to be really explicit if I have a message for the people who are creating and monitoring these policies is that we need to be really explicit about how many of these policies are helping folks understand the appropriate and acceptable uses of AI tools on their campuses because they may not seek out those policies that are not explicitly saying AI even though they do cover those tools.

Jessica Palatka: Well, and Jenay, that gets back perfectly to the question that Sophie passed to Lancie in that 64 percent are documenting that they're not required to use it and don't expect to use it in the future. To your point, if they don't realize those policies exist, there's a lack of education here. And it's also, I think it's almost intentional because there's a fear. There's a lot of fear that has been generated with the term AI. There's a lot of, it's going to come take my job. It's going to come do my work. It's going to ... So there's this unwillingness to accept that we are using it. And we have been for decades. If you think about what the true AI is, it's been around you guys. It's like when people talk about the cloud, I'm like, the cloud, it's not a thing, you guys.

It's the same thing with AI. It's been around, you've been using it. Stop putting your smartphone down and thinking that it's not AI. But it's the exact same point to the policy point to the data governance point that Lancie was making is that there's just this misunderstanding of what AI is and that they are using it. I can guarantee more than 64 percent are being required to use it just from the tools that they're required to use in the day-to-day. But there's also that misunderstanding that the same policies that govern the use of materials that aren't yours, the same with AI.

Sophie White: Yeah, that's such a great point. And Jessica, I'm curious, you brought up the fear of AI taking our jobs. And I noticed in the report that was one of the comments that we got from respondents that that was one of the concerns. How as an HR leader are you having that discussion with employees at your institution about where they as humans, where their future lies at the institution and where that might overlap with AI? Do you have thoughts on that or strategies, advice?

Jessica Palatka: Right. So the initial thought is, and in a very transactional and even operational environment, I can see that concern. Well, AI is going to come in and take the transactional work. Now, if it were me, I'd be like, great, take it. It gives me time to do all the other things. And what was so interesting to me about the data that was shared were from an HR perspective, from a CHRO perspective, looking at how AI is reshaping job design and where the workforce anxieties exist and the overlap between those. So looking at what Jenay was able to discover, AI is used for brainstorming, and let's just look very basically at something like ChatGPT and what someone could use a ChatGPT for to brainstorm, to think of ideas, what am I missing? What is out there? That's not going to replace a person because someone still has to take that information, synthesize it, parse out what's good, what's bad, what's useful, what's not, and put it into a good purpose and to put it to use.

So while it can be useful in certain aspects, it's only going to then help take a position that we currently have and elevate it to a more strategic level. So I understand the initial concern from what I do and from where I sit, I welcome. I welcome the opportunity to pass on my transactional work so that I have the opportunity to, and as Jenay points out, to re-skill, right? Not hire new work, not hire new people, not hire new systems, to re-skill the individuals that I have to do the new and emergent work that we've always said we always wanted to do, but have never had time for because we're too busy in the transactional. So here it is looking at us in the face and going, okay, well, now you can do that. Now you can take your transactional, automate, whatever it is that you'd like to do, go to a more predictive analytical environment and re-skill, take that opportunity.

So for me, I see it as an opportunity. Now, I know that's a hard sell. I know not everyone buys into the strategic. Some people are very comfortable in the transactional, that's where they've been, that's where they're happy. I don't also think that AI is going to take every transactional operation that we have and be able to do that work. Bots are useful, automation is useful. It's not ever going to just be 100 percent of everything that we do. So I definitely think that I understand the anxiety. I think it gets back to the previous question of a misunderstanding of what it is and what the intention is for. And I think that also points to maybe a leadership lack of communication of what the intent and purpose is of implementing AI. So there really has to be trust built. There really has to be communication about what the purpose is of the AI that we're implementing.

Jenay Robert: Yeah. I mean, I can't speak to other industries, but certainly in higher education. None of my research so far has pointed or even hinted at big reduction in force or anything like that due to AI tools being introduced. Everything I'm seeing in higher ed says that we are really excited about figuring out how to make our jobs a little more efficient. So like Jessica said, we can shift that focus to the more human to human interactions that we want to spend our time with, but also keeping in mind all of those risks that exist. And this report definitely speaks to that, that the majority of respondents selected, I don't have the statistic in front of me, was like six or more, eight or more, something like that, risks when we said, how many of these do you consider urgent? People were selecting many risks as things that they consider urgent.

And so we're definitely seeing both sides of that coin, but there's nothing in my data so far that would suggest that even leaders at our institutions are thinking in this way to replace human jobs with AI tools. And one other point that I want to make while we're on this is that where I do get concerned in this conversation is for those people who are so fearful of AI taking their jobs, that they resist learning about what AI is, trying to shore up any sort of AI literacy, get those trainings that are offered on our campuses. I truly believe that those are the people who are the most at risk of being phased out of the workforce, but not because AI is going to replace them because they will not have the digital fluency necessary to do the work that is expected in the digital world, in the AI powered campus, for example.

So I think that that fear sometimes is kind of a self-fulfill or could be a self-fulfilling prophecy where because you're so resistant to understanding the new tools, you're not going to be able to evolve in your role.

Jessica Palatka: The fear is really about change, right? The underlying fear is the change. Yeah.

Lancie Affonso: I agree. Change and the fear of the unknowns. I think one thing that we can all agree on is collectively, we are all together in this change across multiple institutions. And so this reminds me back when we think about when the internet first came around, there was this fear, including Y2K, the world would come to an end for one thing. But beyond that, the fear of what would happen and retooling, upskilling, I think Jessica talked about, it's a wonderful opportunity for individuals and educational institutions as a community to embrace this as an opportunity to upscale and to really focus on our missions, our goals in educating students. And if some of the back office transactional things that we do as educators or staff members to provide better service to our end users, which is our students, in meeting their lifelong learning objectives. And so here, the emphasis is on lifelong learning because that really is the upskilling that will provide value to organizations, whether it's for-profit or non-profit.

And so I think at the College of Charleston, one of the things we've done is really focused on intentional AI. And so can we be intentional? Can we align the use of AI with our educational mission, our strategic plan, and then go back to the drawing board in community to see how best to meet some of those outcomes out there and to further reinforce that it's part of our tenure quality enhancement plan. And so the intentional before the AI was so that we could collectively sit down, think, evaluate. And what I really liked about the report that you had, across institutions, the percentages where opportunities really were evaluating the opportunities and the risk. So the time to sit together, discuss what are the opportunities and how to mitigate the risks and or recover from any risks that are out there, but also encourage faculty and staff to use AI upskilling and then more importantly in a classroom environment, but also in a work environment for staff.

How do staff members use AI? And to Jessica's point, it really has to do with educating yourself, but more importantly, maybe having playgrounds where you can play with information while ensuring security of the information, but learning in community. And I think we will all agree that part of our educational missions is to learn in community. So as a faculty member, I do welcome AI. It will take my job to certain extent, but then it allows me to focus as a staff member, how can we add more value? Can we use AI tools to brainstorm to shorten the time that we have for this transactional task to look at the human in the loop? And so I think one of the things we try to do is, can we now focus on equipping our students with those uniquely human skills, communication skills that will allow them to be successful in this new age of AI, whatever it is.

We don't entirely know what this crystal ball will be, but what are those human skills that are unique to humans that cannot be replaced by a bot? And how can we then provide and encourage our students, faculty and staff, and our institution to slowly move in the direction, but intentionally and together? Therein lies the challenge. It's educational institution are slow to change, rightfully so. It's the collaboration and the opportunity to play around in a playground to understand how to use the tools and then tying it back to our missions.

Sophie White: I love the focus on the mission. I think that's so important as we think about all of the wildness and the quickly changing nature of AI. It's easy to lose sight of that. I'm curious, can you talk a little bit more about this ten-year quality enhancement plan? What is your goal for the intentional AI user and are there any takeaways for other institutions that you want to share with us, Lancie?

Lancie Affonso: Yeah. So this is relatively new. It's our first year. So as part of our accreditation, SEC accreditation process, we picked intentional AI at our institution for our five-year and ten-year quality enhancement plan. So we're still in the early stages, but the committee and the faculty groups that got together, we put the word intentional in front of AI because we didn't want necessarily the tools to drive the process and really go back to our strategic plan, which is about traditions and transformations. And so how do we transform education while still delivering value in our public mission to our students, our state and our stakeholders of work. So intentional AI really is to prepare our students to be professionals, to become ethical and critically literate global citizens with AI proficiencies that include examining the appropriate and ethical use of AI. So it really boils back to, regardless of your discipline, is to take some time within disciplines, within schools, and within programs.

So this is a bottom up approach rather than a top down edit. It's how will AI impact your major, your work as a staff member, and then take some time and then have a dialogue across campus to see how we can intentionally apply AI. And this also has to do with return on investment, but not explicitly. Can we think about the tools before purchasing licenses and spending a lot of money? And I think what we are is we're currently in an AI hype cycle. Everyone is selling you AI tools that may or may not deliver value. So you have to realign that to what your mission is and does it really change the outcome for us? Does it change the outcome for our students? And so that's part of the first ... We're in the first year of a ten-year plan at the College of Charleston.

Sophie White: Beautiful. We'll have to have you back in ten years to see how all of it went. But yeah, I'm really excited to hear about the project. And you made me think of the ROI piece of thinking about where the return on investment of these tools is was interesting. And I noticed that came up in the report too, Jenay, that a lot of institutions did not have a plan to measure the ROI of AI tools in place. I'm curious if you all have thoughts about how we can address that gap or how we can start to look at return on investment of these tools at institutions. I know it's a tough question.

Jessica Palatka: Well, what's interesting and what Lancie just said, there were many things that stood out. One is that whenever I get into a conversation with someone regarding AI, I always try to frame upfront, why am I here? Am I here from HR to talk to you about who you're going to hire to do AI? Am I here to talk about how HR is going to use AI? Am I here to talk about the implications of using AI throughout the ... Because there's each one of those conversations we could do a different podcast. So what was interesting to me about what Lancie just shared is that I could see myself as one of his disciplines. I can see myself as being one of the disciplines that he's trying to teach how to use AI in that discipline. What does AI mean in the HR discipline? And to your question, Sophie, about ROI, you're absolutely right.

And one of the reasons this all ties together so well that we see some of that distrust, that we see some of that concern from employees that's going to take my job is because we cannot articulate clearly productivity from AI, quality improvements from AI, employee experience from AI, customer experience from AI. And I'm talking from the HR discipline. And there's twofold. One, HR doesn't have a long history. We have a history, not a long history of great data analytics. So just measuring these things before we get into the AI, but then to have it implemented an AI function and then have the data afterwards to compare the two, there's a lack of proof in that pudding. There's a lack of proof of saying, "Here's what I got out of making that for you. " Now, what I have is I'll have staff that says, "Well, I had to do twice the work because I had to do it the original way and I had to do it the way while we were implementing this."

So then they're all just very unhappy. The next thing, Lancie really focuses on the word intentional, and I think that that's very important. I think that if this is to be done and done correctly, there has got to be from the HR discipline perspective, an intentional redesign. It's not just implementation, it's not just add a system. There are concerns in the workforce that are very valid, entry level jobs. I'm looking at individuals submitting their applications and they're false. I'm looking at individual...This has occurred with the job market right now where I've had peers who have gone to job interviews and they realize they're interviewing with AI. That's not a person on the other end of that computer screen. And there are implications to that. There is an experience score to that that decreases. And then against in screening the resume, and there's so many different things that unless this is intentionally redesigned and what is our goal?

What is the increased outcome going to be? There are a lot of factors in there that do need to be measured, but if we weren't measuring them to begin with, it's hard to then implement the AI and then measure it and claim an improvement when we weren't ever measuring it to begin with. So that's kind of a gap that HR has in our own little world that we're really trying to get past, but that's common everywhere.

Jenay Robert: I think a lot of people struggle with that. And even beyond that, even people who have been measuring certain KPIs and now want to see if implementing some AI tool modifies those KPIs, we are in real world settings that we can't control every aspect of. And so it's one of the biggest weaknesses of any kind of social science is that we cannot put people in test tubes and add different ingredients to each test tube and then see what happens. And so with any study of what technology is doing in any setting, we have to consider that there are constantly changing conditions and it makes it very challenging to measure ROI of technologies. This is a longstanding conversation we've had in the education space with educational technologies. We at the same time want to say that there's no silver bullet that's going to solve all of our teaching problems.

And yet at the same time, we want to very carefully measure the impact of each individual technology without understanding that it all works as a system, including the backdrop of the institution and the students and the faculty and the way people teach and the world and politics and society. And so this is such a difficult question to answer and I think that's why people are avoiding it. What I hope as a researcher is that people will take a more holistic view of what it means to measure the ROI of a technology that people will start looking at. Like Jessica was saying, those experiential pieces. Y'all were going to have to do some mixed methods research, okay? We're going to have to not just do our surveys or measure how much money we have to put into something. We're going to have to talk to people.

We're going to have to ask people questions and get some qualitative data on these things, but that's just my two cents and I'm biased as a researcher.

Sophie White: Mixed methods research is intimidating. So I don't know what else to add to that, but I think that was really helpful context. I know this feels like the issue that keeps coming up. We're banging our head against the wall and there's no good answer to it, and that's okay. I also know that we talked about upskilling a bit, and that came up in the report as one of the really key use cases and opportunities related to AI and higher ed. Do you all have thoughts on successful strategies for upskilling? I'm curious too, from your perspective, Lancie, I know we've had a few different folks on the Shop Talk podcast talking about faculty and upskilling related to AI. Faculty maybe have been teaching courses a certain way for a while, and now we're asking them to integrate these AI tools and maybe even teach some AI literacy themselves.

Do you have thoughts on effective practices for upskilling that you've seen or experienced yourselves as professionals?

Lancie Affonso: Yes, actually. So one of the challenges, and I think this was highlighted in the report, Jenay, was the AI's pace of change. When we look at technology evolutions in the past, let's go back to Windows, okay? And you'd have Windows 95 and then you wait a year or two, Waterfall method, two or three years, you get a new update, and that's when you'd have to worry about changes, but apps on your phone get updated a lot quicker, but AI tools change significantly and it's within weeks, newer models come out. The issue is whether you should be concerned with the rate of change personally, I do not think so. It's really what value it has towards your processes, but we are overwhelmed with information. There's information overload and there are benchmarks about which AI tools suppressing each other out there. And so in many ways are those red herrings that get us distracted from our true mission and purpose.

But to your point, it is challenging in an environment where the tool sets are changing, the vocabulary, the capabilities and the ability of what AI can do to have some baseline AI literacy, because the goalpost keeps moving. And so what we are encouraging through faculty, our Center for Excellence in Teaching and Learning AI has faculty members who train other faculty members, but we also use micro-credentials. So we're using micro-credentials as a tool to get basic literacy, whether it's through Coursera, DataCamp, etc, is some baseline literacy where you don't have to be the expert, but you can go through the micro-credential if you're a faculty member and then you can share it with your students and you don't have to keep updating the course content for all of these new evolutions and changes. Usually the providers will do that for you. So institutionally, we have purchased license through Coursera Career Academy that will allow us to do that.

LinkedIn Learning also has that and our institution has access to multiple micro-credentials. So that's a stepping stone for basic AI literacy. But the more important step is once you've understood how these tools can be used and leveraged, how does it impact you in your job function? And so that's the discussion that we're having across campus where each school has teams of faculty and staff members together or part of our generative AI groups, AI exploring the tools, but more importantly, looking at how it impacts our experiences for our students. So that part is a threefold approach, micro-credentials, professional learning communities, workshops, speaker series, but most importantly is actually trying to embed it into our syllabi and into our workflow in a control measured way in and sharing best practices if they're successful.

Jessica Palatka: So we've also, we developed an in-house microlearning series on AI as well. So we've done something very similar. Ours has short modules, it has live webinars, it's open to faculty, staff, current students, and then our alumni and external participants can also from $30, they can participate too, but it's all on our website that's accessible to anyone within our community. But we did the same and we released a different module each month, spread some out, had our current faculty teaching and providing the examples, short quizzes that I passed all of with glowing colors. I'd like to say I did get the micro learning credential myself. It's like, I can't promote these things if I'm not taking them. So I agree, Lancie, I think that's definitely a way to help. And it's also a great intro step, right? It's not overwhelming, especially when you put micro in the title.

So it's a great way to introduce the topic without overwhelming.

Sophie White: Jessica, I'm curious, do you require these courses or do you have any incentives that you provide or other than obviously someone's intrinsic interest in learning these things, but how from HR do you, I guess, scale and operationalize this to make sure as many people as possible are taking them?

Jessica Palatka: I highly encourage Sophie. I highly encourage that HR take, and I let them know that I'm doing it too. So I'm in that class. I'm taking the same thing. I'm there the day that it opens really because I know I'm going to be asked to speak about it and I want to know what's on the forefront. What's interesting to me in reading the information in the report about upskilling, there is a push towards self-directed learning. There is a push towards HR led, which is why I ask, "Okay, so why are you bringing HR into this conversation? Is it about the training? Is it about the employees? Is it about the recruitment? Is it about what I do in HR?" Because each one of those is a very different conversation. But when we talk about it in this conversation, especially at a university level, so if I'm looking at academia level of this topic, I wouldn't say, and as I stated at the very beginning, HR's not going to lead from the policy side, right?

We wouldn't expect that, but I would hope to be an architect, if not the primary architect, for the capability development for that upskilling, for that re-skilling. And that's where we do need to be at the forefront of that conversation, help drive that conversation of how we can help with that. So I do think that I do need to be a voice for how to train, what to require. I think some people should be required, and they are, to do much more advanced courses than what we've put out in the microlearning series. We do things like badges that employees can put in their email signatures that they've taken the courses to get recognized for that. So there are different ways of offering some of that recognition, which is helpful, but it's really, again, you're going back to the same group of individuals who aren't afraid of that change, who do hear what's coming, who do want to see the art of the possible.

And also knowing that the next wave is already out there, right? The next AI is already on the ... We're going to have to offer something on quantum here next. I just know it. If I can't get them on board for AI, I'm never going to get them on board for quantum, Lancie.

Lancie Affonso: Oh, quantum. So this is how do you look into the crystal ball and see what's coming? I think Sophie and Jenay, one of the things that really excites me most about AI as a faculty member and even as a staff member is this whole element of personalization. And we see this already in consumer apps where things can be personalized to your goals. And in many ways too, what Jessica talked about is upskilling, but also learning and learning, you can start with micro-credentials, but once you dig in deeper, as you start to see the value it adds and how if we can, especially what I call low-hanging fruit, the transactional things that AI can do with integrated systems, if done well to improve the consumer experience, to really allow more ... In my case, in work, as a faculty member, AI has allowed me to spend more time with my students in conversations, getting to know my students and their own individualized, big, hair, audacious goals that they have so that we can then reverse engineer and spend more time trying to get them that first internship.

So my personal goal is to work with students so that they have two internships before they graduate. That is the easiest way based on our online experiences where they can get real hands-on experience. But to do that, they sometimes have to start in the freshman year with micro-credentials to demonstrate the skillsets, but then they have to think of the humans in the loop. Who do they communicate with? Do they get mentors out in the workplace, get some experience? And so internships have this interesting scenario, the chicken and the egg syndrome. How do you get an internship if you don't have experience? Because most internships now require that you have experience upfront. And so finding creative ways to embed that in the learning process where we have industry experts in the classroom. So by using AI, I'm able personally as an instructor to flip the classroom, use Coursera micro-credentials, and then invite alumni speakers on a weekly basis to reinforce what the students have just learned and why it's important in the workplace.

And those same alumni guests because then hire our students. And so I think AI has freed up some time to have what I call a Ponzi scheme of getting in real humans in the loop to make these real connections with our students in here. And so it does mean that faculty have to be willing to experiment. It is high risk, especially if you're on a tenure track, you want some consistency and students aren't entirely happy with new surprises or being asked to do something that's challenging in many ways, but when you frame it in the context of it will lead you to be successful. And here are some other students who've done exactly what we are asking you to do, it's storytelling. And the intentional part of AI starts to make a little bit more sense to not only students, but to faculty and to staff.

Sophie White: That's such an important point. As we're in this larger period where we're looking at what is the value of higher education in terms of public perception, I think it's so important to think about where can AI make sure that we're preparing students for those workforce demands in the future while encouraging them to be ethical users of AI, great global citizens, all of those really important liberal arts type of values as well. I think this is a really inspiring place to end the conversation on how can we think about AI really supporting these institutional missions. So thank you for bringing that up. Thank you, Jessica and Lancie for the fascinating discussion today for sharing with us all that you're doing related to AI at your institutions. I know it's a work in progress and it can be scary to share that while we're all figuring it out together, but we really appreciate your time and thanks Jenay for the wonderful report.

Jenay Robert: Yeah. Thank you, Sophie, for your amazing hosting today.

Sophie White: Thank you. My brain's still getting started after the holidays, but I think it's coming back.

Jenay Robert: We didn't even get to talk about wine, so that means we have to schedule, or bike riding. I had a whole thing in my head to talk to Lancie about my husband and I are e-bike riders, and I got all excited to have a chat about that, but I guess it'll have to be for next time.

Sophie White: Love it. There was an e-bike voucher here in Denver that I thought about doing, but I already have three bikes and I was like, four bikes feels like just excessive. I'm already cramming them all in my little apartment.

Jessica Palatka: Bikes for N plus one. You can never have enough. Never enough.

Lancie Affonso: So the good news is AI is freeing up time so I can spend more time biking.

Sophie White: Yes. And drinking wine. There you go. Drinking wine. This is the future I want for all of us.

Jenay Robert: Not at the same time. Be safe, everyone. Ride responsibly.