Managing Change in the Age of AI
AI isn't just a technology problem in higher education — it's a people problem. From enthusiastic early adopters to deeply skeptical faculty, institutional leaders are navigating a wide spectrum of AI engagement across their campuses, and figuring out how to bring everyone along is where the real work begins.
In this episode, we dig into practical, people-centered strategies for leading AI implementation in higher ed. How do you support faculty at different stages of readiness? How do you reduce resistance without dismissing legitimate concerns? And how do you build a roadmap that's both responsible and sustainable? Grounded in change management principles and shaped by the realities of higher education culture, this conversation gives leaders the tools to stop reacting and start leading with intention.
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Transcript
Luke (00:00)
Hey folks, question for you. What are you doing on July 8th? If the answer is nothing, you should come join us at Fusion. This is the learning conference hosted by D2L, and this year it's going to be taking place in Phoenix, Arizona. Robin Hammond Tree and I are going to be hosting a pre-conference workshop on how to redesign your courses and programs in the age of AI. This will be a full-day workshop, which means
By the end of this, you are gonna know everything as much as I do about AI. And you're gonna be walking away if actually making real changes to your courses that day. So come nerd out with us. You can sign up by using the link in the show notes and be sure to use the discount code RobinLuke150 to save $150 off your enrollment. So once again, this will be July 8th in Phoenix, Arizona for Fusion. And yes.
It's gonna be hot because it's the summer and in Arizona. But good news, we're gonna have drinks and we're gonna be inside an air-conditioned room. So if you are planning on joining, be sure to use that discount code. And please feel free to let me know and give me a shout. That way I can make sure to look out for you and can't wait to see you soon. All right, folks, let's start the show.
Luke (01:14)
Hello, learning nerds, and welcome on in to the nerdiest podcast you're gonna hear today. I'm your host. My name is Dr. Luke Hobson. I'm the head of instructional design over at MITX Pro, a lecturer at the University of Miami for the School of Education and Human Development. And I have a little bit of an obsession around instructional design. So I make a lot of content online, the blog, the podcast, the newsletter of a YouTube channel. I've written a few books about instructional design. It's safe to say
I really care about designing learning experiences and trying to be able to share everything I know with all of you. That's really the point of this podcast. And you can find all the information that I have for you over at drlukehobson.com. Today's podcast is a little bit of a different kind of episode. I did a webinar with the folks over at D2L the other day that was called Managing Change in the Age of AI. And I was actually a panelist on this conversation.
And I was joined by Erin Robson, who is a change management consultant at D2L. And the host of a show was my friend Robin Hammontree, who is the VP of Academic Partnerships at D2L. And at the end of the webinar, it was just phenomenal. It was a really good conversation. And I reached out to D2L afterwards just to say, can I?
Take this audio and flip it into a podcast version because I know that so many of you right now are really trying to be able to navigate where are you taking AI inside of your institution and your organization today, and you're trying to be able to figure out how do you adapt, what do you try to be able to modify, how do you try to be able to take the advice from senior level leadership and act upon it when maybe.
It's a little bit harder than they think it actually is in the real world. And many of our different types of challenges and issues. So a very long way of saying that I wanted to grab the audio of that, flip it into a podcast.
And that is what you're going to be listening to today. So, in this session, the three of us explore practical, people-centered strategies for leading AI implementation, especially within higher education institutions, but it does apply to any sector, I would certainly say. You're also going to learn too about supporting other stakeholders, especially with different stages as far as with readiness, trying to being able to reduce resistance.
Building upon a shared understanding around like what actually is AI and what can it do for us inside of the real world, and trying to be able to create a form of a real roadmap to help to guide you as far as for using AI responsibly, which of course we should be doing. And I really loved the different perspectives in this conversation because Erin comes in from this with a literal change management background, that is her expertise. So being able to hear
Her perspective on what she would advise clients to be doing was really interesting. And it was a little bit different compared to what I was talking about because I've been able to see firsthand at the different institutions that I have been a part of, as well as with being able to connect with so many of you and hearing about what are your institutions currently doing. And I shared more along the lines of the struggles from the higher ed perspective, what's actually worked.
What is kind of in the process, and hopefully it's going in that right direction. So, all in all, a great conversation around change in the day and age of AI. So, to save some time, I got rid of the introduction section of the webinar and a few other things. So, we are just gonna jump right into the conversation, and it's going to be starting off with Robin asking a question to myself, and then you'll hear Erin chime in as well.
And then it just goes from there. I hope you enjoy the episode, folks. I know it's very timely and I hope that's going to be helping you out for what you're currently dealing with right now in your institution. So let's get the episode started.
Robyn (05:09)
I want to start with a question for you, Luke. So the first question is: in your opinion, what are some of the defining qualities of a successful AI implementation at a higher ed institution? That's part one. The second part is: where do you most often see the gap between leadership's vision and what's happening in reality?
Luke (05:35)
Sure. So great question. And honestly, a lot of my answers is that they're not so crazy or complicated, but actually being able to pull all of them off is a little bit of a tedious task for things. But to answer your question, I think about really four main qualities for like the ingredients for success. And the first is that with this type of the vision, it has to actually start with people and not the actual platform, which feels like whenever we're going to be designing like a new type of course.
You don't just start first with a tool and then work everything around the tool. And same thing can be said if you're a different type of strategy within your institution. It feels like so much so is that it's just like we need to use Chat GPT or Gemini or whatever it is. But it's like, well, no, that's not really the point with everything. And if we're going to be focusing and trying to be able to think about innovation and change in all these different types of ways for how technology can help to take us to that next level.
Well, we need to be able to definitely get that buy-in from everyone else within the entire institution. And it shouldn't be feeling like this type of a top-down communication. It should be feeling like we are working with you and alongside you for faculty, for students, for staff. And that's kind of the first thing that jumps out at mind. The second is that the policy should be around different types of practices that your people are currently doing.
Right now, because I promise that if you actually go around your institution and talk to people and just ask them about like what have you used so far for AI in the past? How have you solved a problem with something related to Claude or Gemini or something along those lines? And you'll find an answer because you do have folks who are early adopters of technology. They experiment, they tinker, they try to be able to do something, they launch a prototype, and then they keep on trying again and again and again. And if we can get those different types of solutions and bring that into the equation, that will definitely be a huge win.
Another thing too is being really concrete and specific around what is actually going to define this as a success. So not just painting in very broad brushstokes, but actually talking more about metrics and what exactly are we trying to be able to work towards for a goal. Cause I see a lot of the time too, especially within higher education, is that you'll see something within this five-year plan. And then by the end, we'll be able to have blah blah blah blah. And it's like, well, first and foremost, at the end of this year.
Are we thinking about using technology to enhance and to make sure that we are being transparent and aligning to learning outcomes and goals? Are we building up trust within faculty? Are we trying to be able to help with student success and retention or reduce workloads? Like what exactly is the goal and trying to be able to have those stats to make sure that we all clearly see this vision? And then also the very last thing from that type of perspective, too, is just the acknowledgement that this change is gonna take time.
Especially for some faculty where this needs to be something, but it's not just like a one-time event, but you are hosting weekly types of workshops or monthly town halls or Zoom calls and just making people really know that this is the direction we're going in.
But you're not alone, and we're gonna support you, and we're gonna have all these different types of ways of making sure that our teams are working with you, we're listening to you, we're setting up different types of workshops and ideas that are teaching and learning centers and things of this nature. So it's not just like a one-time thing and then kind of hoping and praying for the best. And you also asked too, Robin, about the gap within everything as well. And for the gap, I certainly do think about and from reading many articles and from talking with many people within higher education.
It still is this type of communication errors of this top-down communication. It's one directional, it's transactional, where folks are saying that this is the direction we're going in, but not everyone has been bought in yet. And then lo and behold, there seems to be like this surprise by people when all of a sudden they're facing resistance and they're like, Well, what do you mean? Like everyone told me this is a great idea. When you actually, if you talk to the folks, like the boots on the ground.
Who were saying, like, we're not so sure about this. And what about autonomy? What about my identity? And and everything else of the sort, to really being able to think about those next types of steps. So I would say that that's also a huge part of that type of gap.
Robyn (09:33)
Yeah, I'm hearing ⁓ begin with empathy a little bit there and begin with thinking about the people that the change impacts. And that leads me to a question I don't some of you might want to share in the chat. I like to ask this question sometimes around this topic, which is what is a change that has either happened in your life or maybe you feel it was thrust upon you, but you feel like it was done well. Is there a change that you can think of that you're like?
I didn't, I wasn't excited about that change, but it actually went rather smoothly and I ended up being happy about it. To start to think about that and what might have happened in that change to cause you to feel that way about the outcome. So starting to think a little bit about your own self and changes that you've experienced. The other thing I hear, Luke, is that ⁓ process that we do every day in instructional design, which is
Think begin with the end in mind, backwards design for the design folks in the room. I I ask two questions whenever I am in what I would call a design meeting for a course or a program. ⁓ the first one is, what do you do today? Like what are you doing in your in your actual class? And the second is, okay, let's dream. What do you wish you could do? And those two answers guide what I recommend in terms of tool usage. ⁓
It can happen the other way around. And I I find that it does feel worse to Luke's point when it happens the other way around. Erin, what's your experience with this?
Erin
Yeah, it's speaking the man the change management language, you know, understanding your readiness, your current state, and then what's your future state, and then putting a plan in place to get there. ⁓ yeah, it's right in in our realm. ⁓ there's kind of three buckets we usually talk about when we're talking about that current state and the future state we're working towards.
Mindset is the first. So, how are people going to think about this? Are they willing? ⁓ the second being knowledge or are they able to? So, training components like Luke mentioned, workshops and the support in place. But then there's the application. They have to be willing, know-how, before they can apply. So if you're jumping to the metrics of who's doing what, but we haven't thought yet about how do they feel about it, or are they equipped with the skills to be able to do it?
You're kind of missing those two first steps before you get to that landing of actually applying it. So it's a great place to be and to have this conversation because there's institutions at different places, and within institutions, faculty at very different places. ⁓ so starting to understand where your people are at and where you want to go is the beginning, the beginning of this conversation.
Robyn
Yeah. So hearing empathy again and hearing, you know, how important it is to think about each of the individuals that a change impacts. And that leads me into my second question, which is, you know, change management frameworks, what you've worked in, Erin, for a long time, were largely built for corporate environments. And so I'm curious as you think about your experience in change in higher ed, what do you feel like needs to be adapted when you're applying these frameworks to
an experience that faculty, staff, or students might have in academic ⁓ culture today?
Erin
Yeah, it's a good question. I mean, there are different frameworks and you can zone in and just use one. ⁓ but from a holistic lens, I try to look at the the best of everything. ⁓ so I'd say regardless of what methodology or framework you're really leading from, ⁓ there's a few things you really want to focus on at this point. And this can be early in in your journey. so in my experience I've seen the most success
When institutions strive to improve their systems or their processes as a whole. So, in particular, don't try to treat everything like a technical project. Don't roll it out. Don't manage it just like a technical project. And this is what Luke was saying in his intro. You gotta think about the people. So while the technical aspect takes time to build and to train and it will run in parallel, there has to be another big focus on the people and managing it within the context of your institution's culture.
⁓ how do people work together? How do they communicate? How do they collaborate? ⁓ think of it this way: if there's 10 emails sitting in their inbox, who would they see that it's from that they have to read first? Or what would the subject line be that they have to open that? That's just the way people communicate, think, make decisions. so working on improving those systems is gonna serve you better than just focusing on an addition like an individual project plan or project rollout.
⁓ and that can look like leaders owning the why. So something Luke echoed before, you know, you can you can preach it at the top, but you also have to walk the walk there. ⁓ so having that vision align with a strategic plan or where we're going big picture starts to give the transition meaning and it starts to feel less maybe mandatory or forced or required. ⁓ policy only goes so far, right? You need people to buy in, you need them to be excited. That's the mindset piece.
Teach them, that's the knowledge, and then the the applying, actually do it and learn from others who are doing it well. ⁓ so this shift towards AI-enabled teaching and learning, it's making it meaningful, is the stage we're at, and less about mandatory or plain catch-up or keeping up with the Joneses, like really make it meaningful for what you're really trying to achieve in that that future state. ⁓ so it can be getting it on an agenda item every month for your leadership discussion or putting it on.
every town hall or whatever it is you do internally that's your culture and your ways of working and communicating, make sure it starts to get baked into everything you do, that it's not seen as a one-off technical project that's going to come and go or be kind of temperamental with with how people feel hot and cold, maybe about it. So it's not about just giving the access. It really is about bringing the people along and supporting them. ⁓ So I guess maybe I'll ask Luke for for more details on that. Like
Have you found in higher ed institutions that they're just providing maybe access to tools without the strategy piece or without the support? Are they slower to adopt? What have you seen?
Luke (15:56)
Yeah, absolutely. So giving access and encouraging faculty to be able to use tools is a one thing and it's a great thing. I'm glad that they're trying to be able to have them and encourage them to to be able to do this. But it's another, as you were just talking about too, to being able to show them to discuss the why, which is a huge factor because for a lot of things, I've been working in higher education for many years at this point in time, and it's always been here's a direction we're going in. And then you like raise your hand, you're like, but wait.
Well, why? Like why why are we making these sweeping changes? What's actually happening? How is this going to benefit us? And trying to be able to just understand more about that reasoning from the top down perspective is absolutely crucial. And what I found is that when you just give folks access to tools with no guidance, no communication, no understanding for why you're trying to be able to use them, well, then that's when accidents happen. And we don't need to look that far in the past. I'm not sure if you folks saw in the news the other day at Dartmouth.
There was an incident where a professor accidentally posted students' ⁓ personal information into what he thought was a private portal, but instead it was accessible to the entire Dartmouth community, and they were able to then see the students' papers and a few of our different things as well, which like wasn't so great. And as soon as he was notified about it, he immediately took it down. And it was a total mistake. But what I also see on this side, too, was that.
You actually had someone who was bought into the idea to say, okay, I'm going to use AI for my classroom. I want to be able to improve what we're going to be doing for my next couple of classes. And he was trying to be able to do something for thinking about ⁓ future research and assignments and trying to be able to use AI to guide to that next pathway. And instead, his experimentation essentially blew up and it went wrong. And now it was a public-facing issue. And of course, if I was that person,
I would then be terrified to experiment with something else because something else might get leaked into my school system and everyone can access it, or worse, into a public domain. And unfortunately, that's going to send people backwards, or then they're going to be resistant to trying something out yet because the strategy, the guidance, everything wasn't there. It was just we can try to be able to use this tool and then see what happens. And the thing is too, is that especially within higher education folks, there are a number of different types of LLMs, a number of different forms of tools that are coming out.
That can be incredible for us as educators.
To build out for students. So not just a conversation around from like a student learning perspective and from a forward-facing teaching perspective, but for being able to build out something within any of you who use simulations in your courses is just one thing that I keep on ⁓ thinking about right now. Is that for many of the courses that I have designed and used over the years, I had to purchase a simulation from a third party. It wasn't always exactly what I was trying to be able to look for, but it was enough to say I can make this work and fit it into this course.
And instead now we can make our own simulations based off of the data and what you are looking for, and then to then incorporate that into your class and have it be something that you own. It is literally yours from being able to build that, having the code and then using it as you see fit. And then I show that to faculty to say, you know, how you always spend thousands of dollars on simulations. Like, well, what if you built your own?
And of course, the immediate thing is that they're like, that's gonna be way too hard. It there's no way. And like, let me show you in three prompts how to build out your own simulation. And now you can do that. So trying to be able to then give those realistic examples and to be able to build up that interest and then being able to take it from there is something that we can definitely strive towards.
Robyn (19:36)
Something you discussed there, Luke, I think ties in really well to a question that came in through the QA. And I I think ⁓ it's predominantly around AI distrust. and I I if you've heard me on other webinars, I may be repeating myself here. But I really like speaking to this question because it's a personal one for me and I think it's a personal one at this point for most of us. So my initial experience with ⁓ LLMs when they first came on the market was
I looked at them, I tried them, and I said, no, thank you. I don't like it. And I kind of stepped away for a few months. And I think that sometimes people think, you work in technology. ⁓ you were surely on board from day one and you're gung-ho AI for everything. And I just like to start out by saying, absolutely not. And I don't want you to be either. Like I it is it is never the goal, in my opinion, to have someone say,
I trust ⁓ all AI, everything all the time. I think the goal is for you to feel empowered to use AI in the way that benefits you and your students and your institution. and that line is different for different people. I I've also told the story ⁓ before where someone said to me, you know, I have an agent I set up and every day it scrapes my
bank account and it scrapes my calendar and it scrapes my email and it gives me all this like information about tasks to do. And I was like, never would I do that. I just that that there's just data exposure in all sorts of places that I don't feel comfortable with. ⁓ so I think knowing ⁓ and being aware of those risks is really important. And I think skepticism is very healthy, but
You know, to the point of the person who asked this question in the QA, they said, I also know AI is here. ⁓ and I think whenever I get this question from faculty, I say, Your students are asking the same questions, right? They're wondering about the same things and they're asking for our help in navigating things like how do I know what's safe? How do I know what's helpful for for my process?
How do I know what's good for my brain versus not good for my brain? And if we don't engage with these things, then we can't help them learn how to safely and productively engage. So again, just coming back to empathy as a part of the process of creating change, you really need to be able to be ⁓ vulnerable yourself and talk about your own experience. And also, I think sit with the experiences of your stakeholders.
⁓ I have found in many rooms that I've gone into that there is an element of grief in all technological evolution, right? AI or something else, anytime we're moving to something ⁓ or transitioning, we're giving up something. And there's a part of us that grieves that. And I think when we try to rush past that and say, but think about this instead.
And we don't take time to acknowledge that there is grief there and what that might feel like and allow people space to talk about it, that's where you can get the resentment that can build in an unhealthy way. So one thing I always say when this topic comes up is are you listening to people really well and making sure that you're creating space, not just for trainings and how-tos, but like, how are you feeling in all of this? What are your concerns and how can we address them?
Erin, I know you had a few more things to speak to on this topic as well.
Erin
It's tricky because there's so many different reasons that people dig into you know why they might be against it. And you're right, if you don't give them the avenue to express it and how you want them to, so whether it's you know, use this email link, this is specific to our project plan, or if you can use this survey and voice it, or here's the the forum when you can come speak out. ⁓ if you don't in some way give it guardrails to control it or invite it in some way.
It might pop up in areas when it's not invited. ⁓ So I would say having a feedback loop is a critical part of your communication strategy in general. ⁓ And checking in with different stakeholders with different questions at different points along the way. So you want to ensure that those voices are heard and you're not trying to silence people or ignore people. You want to hear from them in the way that you're welcoming their feedback and their input, because ultimately,
We're trying to work on this together and move to a place collectively together. not just some, but we know in terms of change management, there'll always be laggers, they'll always be your last group of of adopters. ⁓ they'll be resistant maybe at first, but the voice that you give them, the the volume that they speak at, who who they who else they influence, you do have some ability to to shape that. So creating feedback loops is is critical along that communication strategy.
Robyn
Yeah, so you mentioned there, you know, it things coming out in not the way you want them, as opposed to, you know, places that are productive. And I that leads me into questions about planning. ⁓ and and so if I'm a leader and I'm watching this right now and I'm six months out from a a major AI rollout, what are some things that you would say this should be on your radar and maybe it isn't yet? And I'll I'll let Erin answer first.
Erin
Sure. Yeah. It's a tough place because where do you start from, right? Like there's so much to think about, so much to consider. But my short answer would be don't underestimate the impact on people. Even though it's viewed as a technology, you know, focus, it requires behavior change. It requires people to buy in, be willing, learn how, and actually do it. So the mindset, the knowledge, the application. so don't underestimate the impact on your people.
Say you have six months, like Robin said there, ⁓ you want to be very strategic now. Don't wait. Don't be reactive at the end. ⁓ be proactive now. Try to prevent that skepticism from growing, maybe quietly or silently at first, because that will turn out to be your resistors who are vocal and visible later. You want to try to get as much momentum going and as much ⁓ as many people on your on board as you can and have their voices heard.
⁓ so try to start from the place of having a mindset that this is going to be meaningful and finding that meaning and having that future state in mind rather than mandatory or just giving access to tools. Because we know that that doesn't work. so if you're looking for some some kind of tangible things, I would say the you know, the pro psi acronym for ADCAR, you know, start with awareness, desire, knowledge.
Ability, reinforcement. This is very common in change management. I'm sure many of you have heard of it. what's different from when this was developed in the mid-90s to where we're at now, 2026, is the speed that people receive and absorb information and make decisions and make their mind up is like immediate. So you're watching like a 10-second video on your phone, you know immediately if it's interesting to you, if you want to learn more, if you care. You know, you're jumping from that awareness to desire.
without even being conscious of it at this point, because we're completely surrounded in technology in a different place. ⁓ so while I would strongly encourage lear leadership to be visible and vocal in the change, like Luke had said, and aligning to that bigger strategy or strategic plan that you have, ⁓ you also need to keep things in one place that it feels like a coordinated effort. If you can say create a SharePoint or a team site or a website that
keeps everything in one place, you can start to direct people back to that location as it grows and evolves. That way you're not sending one-off emails and trying to find that in your inbox or trying to see one-off examples when a faculty does something great and we're championing it. Keep everything in that central location. And you can have your timeline, your plans there. You can have, you know, your future state kind of defined if you have some ideas there.
You can have like a mission statement or goals. You can put progress and highlight things that go really well. ⁓ And this doesn't have to be like extremely time consuming. Once it's set up, it's already there. You just update it occasionally with where you're at in the process. So you start to shift that focus from something being mandatory and playing catch up with maybe other institutions to shifting the mindset of your whole organization that this is really meaningful. It feels like we're in control. It feels like we have purpose in doing this.
And it's less reactive. ⁓ so it's giving people the reasons to buy in and a place to go for information, even if they are maybe skeptic or you know, have some skepticism or they're a little bit resistant in different ways for different reasons. It's at least getting them to the right place collectively. So that would be one of my suggestions, Robin.
Robyn
There are a couple of logistical questions here that I think are kind of tied together. And look, I'd love for to for us to hear your thoughts on this. I have some as well. ⁓ So someone asked, you know, what about those folks who are overly eager to use tools and they're not necessarily thinking or know a lot about privacy and regulatory boundaries? Simultaneously, we have some situations where there is a very long process for how to validate tools and validate safety.
So how do institutions and people who want to use those tools but can't because their institutions aren't ready to sign off on them yet? Sort of two different ends of the spectrum, I would say. What's your experience? How do you find ⁓ balance in that? ⁓ and how do you respond to it in your career?
Luke (29:55)
Sure. So let's go with the first one where ⁓ you want to be able to think about giving people access to tools and essentially a safe way and making sure that they're not doing what that professor did from Dartmouth, unfortunately, the other day. And what MIT did, which I thought was interesting, was that they gave us this type of a sandbox, if you will, of all the LLMs at once inside of one login that became a access behind MIT's type of like SSO and and everything of a sort.
And they essentially combined all the LLMs into one, which is fascinating for us to be able to see the differences of Gemini versus ChatGPT versus Claude and so on and so forth. That was a pilot run that went out for several months. Once it was deemed that this was now ready for all to being able to use, then there was a rollout, there was an announcement. There were different types of open workshops that people can attend and such to be able to use that.
But first and foremost, it was the idea of like, let's give you a sandbox to play around in something. And please don't post anything that's going to be private or sensitive information or anything of the sort. You can try to break anything inside of here because we gave you this access to this thing. And that's something that we should then be thinking about and doing from that perspective. And then from those who are already light years ahead of everyone else, and you're trying to be able to like use different types of video generation tools inside of your courses because you're trying to make your scenarios.
As realistic as possible, and you're using the latest tools that make it seem like you're literally creating like a movie. Yes, that is so far ahead compared to what higher ed is ready for. That because of that, depending upon your institution, maybe that's going to be inside of one your type of little world for your classroom. And then being able to go and bring that back into the larger organization and then show about the results of the pros and cons from what you learned about.
There's been a lot that I have done where I have seen something and then I went and reached out to my fellow faculty to say, you need to know about this. Like my world is AI. It's it's all it is nowadays. So I'm on top of everything for the latest and greatest. And many of my counterparts are nuts. And it's not a part of what they do. So when I do talk about agentic browsers, where they're like, What is that? Like, let me show you, because it's kind of crazy.
So both for good reasons and bad reasons. So we need to talk too about this one. So if you are that pioneer, that champion of experimenting with things, keep on sharing those results and those findings. And if you do, ⁓ come to a conclusion that it can be valuable for your institution, making that pitch and that proposal to get it more adopted and most likely will go into a pilot phase and things of that nature. So that's
Robyn (32:31)
Yeah. Yeah. And there there's also some some comments in the chat about, you know, uncritical adoption of AI. And certainly it's something that I think about and present on all the time, which is what does AI mean for teaching and learning? How do we approach it in ways that again are good for our brains and are are good for education? How do we help students learn when to use it and also when not to use it?
⁓ because we know that they they may be asked to use it in their careers and we want them to be prepared ⁓ to make the right decisions, right? In the same way. And I think one of the ways that I have found in my career to feel most comfortable giving this advice to colleagues or students is doing it myself, right? So having experiences both of like, that was a use of AI that ultimately like I didn't like.
Like for instance, I don't use AI personally to do a lot of writing. ⁓ I find that writing helps me think through what I'm what my idea generation ⁓ in a way that I find very personally beneficial and that helps me in my process of learning. ⁓ but there are certain ways that ⁓ I use AI that I think I'm not getting a whole lot out of that if I do it manually. ⁓ so an example of a way that I used it recently is I was
doing a workshop and I had about 50 people in my workshop and they were at eight different tables, and I wanted them to mix and make mingle throughout the sessions, and I wanted to maximize the number of people that they were able to exchange ideas with. And could I have done that by hand? Yes.
⁓ I probably would have made some mistakes and that would have been okay. It would have taken me a long time. And I'm not sure I would have gotten a whole lot out of it. ⁓ instead, I was able to use AI to generate a Python script that then ran and gave me exactly what I needed. And I used that to print out cards to tell people where to go. And I tell this story again because telling stories like this helped people think, okay.
That's a way I want to use AI, or that's a way I don't want to use AI for XYZ reasons. Because ultimately, again, what we're trying to help people do is make good decisions for themselves, for their institutions, for the world, et cetera. And I think the ways that we can have people share and create communities where they feel safe to share those experiences are very valuable.
Erin, I know, you know, we talked a little bit about faculty resistance already. So when a department or a faculty member is is resistant for whatever reason to AI, what do you find is usually the reason underneath the surface? And how do you respond in a way that doesn't make it worse?
Erin
It's great question. It's loaded. And I'm glad I had this one in advance so I can put some thoughts together. ⁓ I'll start by saying for the most part, AI adoption in teaching and learning is overwhelmingly framed around productivity, efficiency, personalization. So just like your example, your story, your shared, you shared. You know, you can almost always check one of those three or all three things. And they're all good things. So that's that's the positive we want to focus on. ⁓ But there's about six
Perceived negative effects that your faculty could fall into one of those buckets, one or two, and it's their reason, it's their stance, why they are against AI. Like these are your detractors from the start.
Or academic leaders that are maybe resistant and speaking vocally or visibly against AI, they probably feel strongly about one of these. So don't ignore them. Don't ignore their stance. You don't want to try to silence them. You know, come out later when you don't want it. So I want you to be aware, but also purposefully include them in your discussions, in your decisions. Don't go too far with just a singular leadership lens without all voices heard.
And this includes students too. I know I've spoken quite a bit about academic leadership and faculty, but students can fall into these buckets as well. And it makes sense. So, in terms of best practices and in managing, you know, this resistance and people that maybe are really against whatever it is you're moving towards or the purpose that you have for bringing AI and introducing certain tools, by the time they're vocal and visible and kind of advocating to others, it's almost harder to
Control. It's almost like weeds are outgrown beyond where you can contain. ⁓ so try to work at preventing. Try to have two strategies that'll get you in the direction you want to go that those people are are less detracting and growing like weeds, but they can be vocal and visible in ways that you're asking. So one would be have them represented in all decisions. ⁓
Give them the avenue that you want their opinion shared, surveys or an email link or a certain forum or whatever it might be. And bring your students, faculty, senior academic leaders all into the mix. Don't just make decisions with one group. The second piece of advice that I would give would be early on in the change or when you're discussing this, and people are starting to become aware, you want to be able to give them enough information that they're not.
already against it. So if you don't give them enough information, skepticism will just grow in the background naturally. So if you say they have to do something or this will be mandatory or this is coming, they're going to say, what is it? Why are we doing it? When are we doing it? Who's going to help me? Who's going to, who's going to support me? Like where where do I go for more information? How do I complain? Like there's so many things that that have been left out.
So this would be my short and sweet list of like seven topics to include in your comms, regardless of how you're putting it out there, whether it's a presentation or emails or town halls, newsletter, whatever you're using, even in social media, make sure everything you're putting out there is targeted to the right group at the right time, and you can defend and answer all these topics, these seven on the screen there, so that you're in a good place.
To get people back to that central location and you're not leaving too many questions out there that skepticism will grow in the background. maybe I can ask Luke for an example. Like has it's been your experience that anything on this list has really been overlooked or ignored and and what have you seen that turn into? Well the
Luke (39:31)
Funny thing, Erin, is that you you mentioned ignored previously too. And you just said again, that has honestly been the main thing that I've experienced as a faculty member, is that my types of warnings or concerns, or I'm raising my hand to say like something's wrong here, that just gets ignored in entirely. Or maybe it gets a slight acknowledgement and a head nod to say, like, okay, Luke, we hear you. And then nothing else comes from it until later on down the road, we're all of a sudden now we're facing these types of like
serious issues and it's like, yeah, like we s we saw this, like we were trying to be able to tell you. So to give you one example, which I've always kind of found this kind of odd, is that when universities are deciding around what are there's going to be their type of an AI policy, there's a lot of institutions who just leave it up to the faculty members to say, you know your classroom best and we are going to allow you to have like this autonomy and this academic freedom, but you're going to have your own type of AI policy inside of your course.
And while I as a faculty member, I appreciate that. It shows that you trust me, that you want me to still have my flexibility. You think that I know what's best for my students in a learning environment. So I love that. But at the same time, I can absolutely acknowledge from a student experience perspective that this is terrible because what ends up happening is that if you have students who take one class and another, and another, and we all have different types of AI policies, they don't know what to do.
And they're not sure as far as for what is actually the right thing to do with AI versus what not to do. So to give you an example, I've had some students before copy and paste their entire conversation with Chat GPT and send it to me. I've had them make these references from different LLMs that make no sense whatsoever, but they're trying to be able to link back to what they did so that that way I could see their actual lines of thinking through the entire ⁓ assignments.
And I've had some people just send me a paper before the paper to say, hey, Dr. Hobson, here's what I did for my assignment using Gemini. It's like that, no, I no, like I don't want you to do any of that. Like, here is my AI policy. We talked about it for the first week. I mentioned about it when I met all of you. It's in the syllabus, but at the same time, I completely get the fact that it's creating this anxiety and this fear around if I do something wrong.
Am I going to get a zero? Is this going to lead to like an escalation of potentially getting expelled and you know anything of a sort like that? So I understand now they're being overly cautious and doing all of these things because simply they're not sure about it. So unfortunately, this leads then to the overall experience from the student really suffering. And then as a faculty member, now I'm trying to being able to direct us down the right path to say, like,
Please don't do that again. It's okay. I trust you. Let's talk about how to use generative AI for the right reasons for our courses topic and going into that type of a direction with everything. The other thing, too, and we've talked about this a little bit so far today, is that tools are moving so fast and so quickly with everything that it's becoming almost impossible for the average educator to try to be able to stay on top of everything.
And that makes things very confusing for giving feedback and concerns and things of this nature. And I talked to one friend of mine recently who was a professor at a different college. And with complete confidence, he was telling me about how Chat GPT still produces hallucinations for different types of references. And I looked at him and I was just like, that was primarily solved with GPT four, which came out years ago because I never had access to the internet. And that's why it was creating hallucinations.
And he looked at like I have three heads, and he's like, No one's told me that. So I've been telling my students this for the last three or four years. I'm like, that's that's that's a fair point because you already learned that. And you learn that probably in a workshop from your institution that this is the thing, this is what you need to be able to be on the lookout for. But with the latest updates and advancements, he had no idea. And because of that, unfortunately, these are things he was telling his students. It's still in his syllabus, it's still in his course.
And then now this kind of keeps on happening with every different form of advancement that there then becomes like this new type of baseline for us to work towards as educators. And it's becoming honestly very hard to be able to keep on doing. So we need to have first that type of a baseline as far as for what are we really doing with these different types of AI tools? How does this all link back into our strategy and where we're going, trying to be able to track all of these different types of things and making sure that we are actually setting everyone up to success.
And ⁓ and trying to make sure that we know how to be able to respond to things accordingly as things do change.
Robyn (44:08)
Yeah, I think again, this is where I I really emphasize the importance of being able to use these tools yourself so that you can guide students. So I'm, you know, I'm seeing answers in the chat about how, yeah, it does still hallucinate, which is absolutely true. All of them do to some extent. ⁓ I've got a really funny story about that that I won't tell right now about trying to find a really good airline pillow. And I was curious what it would respond to, and I just totally made one up. ⁓
They and this is prime predominantly because LLMs lean towards sycophanti. So they they want to give you an answer. They want to tell you yes. And so ⁓ again, this is something that's really, really critical for your students to understand ⁓ so that they know when they're interacting with an LLM what to watch out for. ⁓ the other thing that I bring up a lot of times when I'm doing some of these workshops with faculty.
Is they will say kind of blanket statement, students need to have critical thinking skills. And I always push back a little bit on that to try to get faculty in their various subject areas to think more about what that means. So when you put a prompt into an LLM and it gives you a wrong answer, what is the information that you have or the experience that you have that tells you that it's wrong?
Okay, that's what we need to teach students, right? So making sure you're always aware of what the experience that students are having is so that you can speak to that, I think is going to remain critical. There, I want to get to a few questions in the chat that I saw come through. ⁓ one was a really good question ⁓ about like, but I don't want to be a sales rep for an AI company, right? Like I don't want to sound like I'm like, use this, use that. ⁓
I'm curious about your perspective, Luke, especially because you know, Harvard has the same mechanism of having the like mass LLM ⁓ and being able to experiment with all of them. And something that I didn't necessarily anticipate, but some of my classmates were like, I don't actually use that because I'm trying to build like a memory database. And I know that when I leave Harvard, I'm not going to have access to that account anymore.
So they won't use it. They want to use their own accounts so that they're building up all of the memory in those accounts. And so I'm curious, you know, from your perspective, Luke, how do you encourage the use of AI tools and experimentation without sounding like you're like working for Sam Altman?
Luke (46:50)
Yeah,
I'm not working for Sam Altman. I I think Anthropic has my number though, because all all the things I keep on now building are in Claude, because that's the best ⁓ types of things for simulations and whatnot. But but yeah, you don't want to definitely be tied into one different type of an LLM, especially to where I know that for some ⁓ institutions they do actually partner with just specifically one, and then that limits them for what they can do. So we do have in our case with Dartmouth prepping with ⁓ Anthropic, and then we have
ASU, who made the partnership with Chat GPT and OpenAI. So it kind of locks you into one. So therefore your experimentation kind of just like lends itself into that. But if you do have your own type of private account and being able to access and the change and to do like different types of experimentation with all of them, they all have their own pros and cons. So if you can to figure out as far as where like, well, Gemini is able to use notebook LM and I can create different types of podcasts based around open sources and materials.
Claude can do different things with interactive timelines and with simulations and building out apps and prototypes. Where for ChatGPT, it's great at fixing my grammar and helping me with my emails and such. Figuring out which one is which makes sense. And truthfully, the one with MIT and giving us access to everything all at once. It it like even changed the tone and the voice for how all the LLMs talk to you too. So it's weird. It's not even like the same thing. So I don't even use it. I was like, this is kind of confusing for things.
Robyn (48:14)
Yeah, I think this leads me back to something you said, Erin, about, you know, keeping everything in a central location. I think for me, a a lot of this is about transparency in the process. And so if your school is choosing a certain tool or giving you access to certain tools, ⁓ something that can be really valuable is transparency on
why they chose that tool and it's in a central location you can access and transparency around maybe what they recommend or don't recommend that tool for so that folks who are at the institution can see that and know, okay, here's what I want to look out for. Here's what I want to use this for or not use this for. Again, in the same way that we hope to equip students to do in their careers. Do you have any other thoughts around that, Erin?
Erin
Yeah, I mean once you get it started, you can evolve it into whatever it needs to be as as time passes. I mean, early days, yeah, it's great to have that mission statement, that vision, the where are we headed. ⁓ also a great place to give voice to things that have been successful, ⁓ create that champion network and and be able to go somewhere for examples. ⁓ it can be as simple as putting up like job aids or how-to guides, like start at the foundations.
Because there will be people in your institution that need that. But then there's also the most ex you know, advanced experts that are using this frequently across different ⁓ tools. So you think of the whole spectrum. Don't just focus it at one group in particular, but at least put it all in one place. So it's it's great advice, Robin.
Robyn
There's another good question in the the QA. ⁓ and I've I've seen different numbers around this, but I'll just read what what the question is. So
90% of higher ed students use AI, but only 22% of faculty are regular users, and 20% of universities have a formal AI policy. I've seen similar numbers as well. And ⁓ just to be safe, I think in general, ⁓ this isn't broad stroke, but students are generally above in front of or above in terms of numbers of AI usage versus faculty. So
The question here is how do you lead change when the gap isn't necessarily resistance? It's just that students are moving faster than institutions do. Erin, do you have thoughts on that?
Erin
Yeah, I mean, it's it's tricky to like the cart before the horse, or what should we do first? ⁓ but start with the conversation and include the students in your conversation. ⁓ goes back to where I was he leading before in in making decisions in early days of, you know.
Don't start with the policy in mind. Policy means mandatory. Policy means rules. Nothing creates resistance more than having a rule that people don't want to follow. So I would say if you if you're not in a place to have the policy that will be widely adopted and well received, start from a place of hearing all those voices. So bring in those students that are the high users that have, you know, experimented across different platforms and are doing different things and or maybe are advocates. ⁓ but also bring in the voices of faculty who are resistant.
You want to collect and be aware of what's going on so that they can collectively have the vision for what the future is. And it's one step at a time. If students are making up the majority of users at your institution, doesn't mean they need to lead it, but their voice needs to be heard. ⁓ I'm working with an institution right now where they have ⁓ you know, a volunteer opportunity that students can step forward and be part of the transformation. What better way for a student?
Say a traditional student right out of high school into higher ed has minimal maybe job experience to be able to go into an interview or put it on their resume and say, This is what I did in the past few years, and this is what I was a part of. This is what we helped build during a time of uncertainty and a time of transformation, right? What a great learning opportunity that can be to involve the students rather than try to contain it or control it. Have all those voices heard when you're making decisions.
Robyn
Yeah, absolutely. So the importance of making sure that everyone's voice who touches it or is a part of the process is a part of that decision-making process as well. One more question. I think it's more on the logistical side, Luke. So I'm going to direct this one to you. ⁓ good question about academic integrity. ⁓ so this person says that ⁓ at their institution, they have a new academic integrity policy in place and it puts most of the onus on the faculty. ⁓ this person is in a support role, and so they're wondering.
You know, as a support person, how do I assist faculty in dealing with this? ⁓ and I'm specifically thinking of, you know, like as an instructional designer, how would I assist faculty? How do you think about it?
Luke (53:10)
Yeah, I would definitely want to be able to have a conversation with them and to understand about what is most important to them from that type of integrity perspective. This also would greatly change too, depending upon the context and the topics and things of this nature. So I can definitely see for some cases AI not being very helpful if for a different type of a subject matter versus something else that would be going in a different direction. So I think about like a difference of a creative writing class.
Versus public speaking course and using AI in a variety of different types of ways and use cases and whatnot. So I would just love to have an honest conversation around what they currently think about different types of AI tools, what makes sense, and for where they want to be able to take that in a type of different direction. So it would just be trying to be able to do more and to to listen to that. I hope you enjoyed that conversation, folks. Like I was saying, it was a great convo and I wanted to be able to share this in a different type of way, just in case if you couldn't attend the webinar that happened the other day.
If you did like this information and you want to learn more about AI and instructional design and online learning and everything of a sort, and you want to be able to learn for myself and Robin, we are tag teaming a type of a pre conference workshop for D2L fusion that's happening on July 8th. That is going to be in Phoenix. Yes, Phoenix in July. I know. So if you want to come sweat with me and learn about AI,
I would highly recommend being able to sign up for that. And of course, if you were going to be there and you can't attend the pre-conference type of a workshop, please feel free to say hi if you're gonna be there regardless. I'd love to meet as many of you as I possibly can. Everything for today, folks, is gonna be down below inside of the show notes. So definitely check that out. The everything that I have for you for a recommendation.
Is I've been reading about this topic more and more, and I picked up a really good book that I would highly recommend, which is called HBR's Guide to Generative AI for Managers. So for those of you who are leading teams and you're trying to be able to talk to your team about AI and usage and best cases and everything of a sort too, responsibilities, ethics, everything like that, I would really recommend to pick up that book. It is by Elisa Fari.
And Gabriella Rosani, I apologize if I said those names wrong. It's my Harvard Business Review. Excellent book. Would really recommend to pick up that one. A lot of what I was reading about intersected with many of the things that I talked about on this webinar today. And I know it will definitely help you out. So once again, go in the show notes. I'll put everything there for you.
As always, I would encourage you to do what other wonderful, smart, and brilliant people do, which is to leave this show a five-star rating. Wherever you are listening, Spotify, Apple, or anywhere else, those reviews always help out with the show. And now looking at the time of things, this was a long podcast. That is really all that I have for you today. Stay nerdy out there. I'll talk to you next time.
