Dr. Monique Ross: If you don't really understand what generative AI is, what's happening, how it's doing it like, I know we all sort of recognize there's a limitation to what we could understand, right, because some of it is black boxed. But there is a lot that we could understand about, like, what's going in and how do we examine what's coming out on the other end of it.
Narrator: This is the Transform Your Teaching podcast. The Transform Your Teaching Podcast is a service of the Center for Teaching and Learning at Cedarville University in Cedarville, Ohio.
Ryan: Hello, and welcome to this episode of Transform Your Teaching.
In today's episode, Dr. Rob McDole and Dr. Jared Pyles continue our series on AI literacy by chatting with Dr. Monique Ross, associate professor in the engineering education department at The Ohio State University.
Thanks for joining us.
Jared: All right, so, how long have you been at The Ohio State University?
Dr. Monique Ross: I just finished my fourth year.
Jared: Congratulations.
Rob: Congratulations.
Jared: Where were you before Ohio State? What did you do? How'd you get there? Stuff like that.
Dr. Monique Ross: Yeah. So I was at Florida International University in Miami, Florida. I was a faculty in the Knight Foundation School of Computing and Information Sciences. My research area is focused on broadening participation in computing. So how do we get more people involved in computing professions and pathways?
And it got the attention of the engineering education department here at Ohio State. They were looking to expand their reach beyond just engineering education to computing education. So that's how I ended up here at Ohio State. I grew up in Pennsylvania and lived in Indiana for sixteen years. Ohio was like a perfect middle ground for me.
Turns out, I'm really a Midwesterner at heart. I am not a big city slicker, certainly not a Miamian. And so, you know, we were really looking, for opportunities to get back to the Midwest, and Ohio State presented that opportunity. So that's how I ended up here.
Jared: That's great. That's great. Yeah. So we we wanted to have you on, Dr. Ross, to discuss, part of this series is, informing our listeners on the importance of AI literacy and how we need to be training our graduates, our future graduates on the importance of AI literacy and its need in the workforce. We've done several episodes now with industry professionals and they have all of echoed the same thing where, there is a need for it.
So, and either it's, they're going to be using it or we've even had some that say that there will be a necessity to train others at the workplaces on how to use it. But what's your perspective on it? What do you think that employers are expecting, from future graduates as far as skills, competencies, on using or, evaluating generative AI?
Dr. Monique Ross: Yeah. I mean, I think it's a good question. I mean, I think generally speaking, I'll speak a little bit on, like, my perceptions of Ohio State sort of stance on it and then a little bit about sort of, like, my personal belief system around it. As you may or may not know, right, summer of '25 , Ohio State had announced that they were gonna roll out a university wide AI fluency initiative that was gonna focus on undergraduate students and getting them to be AI fluent by their graduating class. So 2029 was the objective.
And last year when it was sort of rolled out, it was, you know, all in the news and all over the place and but nobody really knew what it meant. So they spent some time over the next couple of months trying to parse through that. And the idea was that, to your point in your conversations with industry leaders, like, this is not going away. This is a disruptor in a way that in the 1990's , the Internet was a disruptor. Right?
Like, everybody was in a panic. What does this do about learning? How do we embrace it or reject it? Do we need to do it? All the things, you know, the rise of the personal computer, all those things that sort of happened as an emergent technology that was a huge disruptor.
AI is similar generative AI, we'll say specifically, is similar in that sort of pathway and that we couldn't just ignore it and pretend it wasn't there. Students are being engaging with it. They're using it. We need to really get ahead of it in two ways. One of them is to to create sort of responsible users of generative AI, but also to expand students' understanding of the role artificial intelligence, the broader umbrella, has on any career path that you go into.
Right? Regardless of whether or not you're studying history or art or engineering or science, artificial intelligence is being leveraged in some way, shape, or form in the advancement of that field. And while we don't need a whole bunch of computer scientists running around creating models, we do need people to understand what are the limitations of it, what is the power in it, when is it reliable, when is it less reliable, how you critically evaluate those kinds of things. And so we need it. We spent some time as an institution evaluating every program at the institution to try and figure out where already artificial intelligence was already being touched on at the curriculum, where are there gaps, How do we fill it?
Are there new courses that need to be developed and so on and so forth? And so as an institution, there has been sort of a stance that we can't ignore it. We need to embrace it. We need to lean in on it. But there's also been a conscientious part of it that's like, we also acknowledge that there's a lot of AI resistance.
Right? There's a generation of students coming out that are like, no. We're really interested in protecting the environment. We're really interested in clawing back our privacy, all those kinds of things. How do we also create a space that those students can also move in this space and get the skills and competencies that they might need to be competitive, but also honor that there's a tension there that people are grappling with?
And that is on all levels. Right? We have faculty and staff and students, everybody that you could probably map it out, and you got some that are, like, all in, can't imagine life without it, and then others that are like, I will never touch it. I'm going back to paper and pencil. Right?
And then everybody else that's sort of in between. So as a university, I think we've decided or the decision has been made, right, to, like, embrace it and really try to figure out how we're gonna move and navigate through it. Personally, I mean, I see it even just working. Right? Like and I'm not even working in industry, just working here at the institution.
You can't get away from those little star icons that say there's an assistant just waiting there to help you. And and so I'm, in a lot of ways, we're all sort of being thrust into this pushed into it to to interrogate it and play with it and see when is it most appropriate, when it's not, when is it helpful, when is it a pain, like those kinds of things. Admittedly, I was not an early generative AI adopter. I was kinda old and curmudgeonly about it. But I have certainly I I took a I worked I was in a workshop with a bunch of creative writers, people that their whole career is to write, like, fiction and have imagination and creativity, and they gave a workshop on generative AI.
And I thought if they're not afraid of this tool, I shouldn't be either. And I need to be thinking about the ways in which it might make things more efficient where appropriate, where it might even be more powerful in ways than I am not. But I've also played around with it and found the thing the limitations. And then I'm like, oh, it's not that smart. You know?
Oh, it's not it's not as advanced as they say it is. And so I I think getting in there and playing with it. And I think that's what we want for our students. Right? We want them to get in there and sort of poke around and then play with it.
We want them also to be able to make those explicit connections to in what ways might artificial intelligence broadly or generative AI specifically enhance my career as a marketing person or enhance my career as somebody in logistics or enhance my career in medicine or so on and so forth. So sorry. That was a very long winded answer.
Jared: No. That's great. I I want to ask you. You said you guys or I should say, Ohio State had an idea of AI fluency, but you, everyone realized they don't know how to define that. Do you know how to define that now? And could you give a definition of it?
Dr. Monique Ross: There is an actual official definition on the Ohio State website. The definition they're giving is that students would be fluent in the application of AI in their field. Right? So it was, again, this idea of getting going beyond just here are a bunch of tools that exist, Claude and Gemini and Copilot and ChatGPT and all those. The list goes on and on.
But how does this directly link back and or support your success in your career? Right? Like, how do we better understand in which AI influences every piece? And and when when the university sort of gave this charter in the beginning to start doing that sort of reflective practice, we were charged with creating a road map. Like, we had to actually identify places in which AI was integrated or places where it should be based on a list of learning objectives that had been identified by the institution.
And so when they think about fluency, we could certainly debate word choice all day long.
Jared: Oh, sure.
Dr. Monique Ross: Really, what they're meaning is is they want people to not just be familiar with it, but be able to really integrate it into whatever it is that they've decided as their their pathway.
Jared: Gotcha.
Rob: So competency. They want them to be competent in the usage of it.
Dr. Monique Ross: In their discipline.
Rob: Their discipline. So Yes. A little follow-up there: Who's determining, I'm-- I guess, I'm going to assume the faculty, but, who determines what that looks like in terms of the curriculum? How's that being accomplished in figuring that out for each discipline?
Is that the deans and the faculty in in the discipline?
Dr. Monique Ross: Yeah. I mean, that's the beauty or the curse, hard to say, of higher education, right, is the faculty do own the curriculum. So they are the one like, they were given a list of I think it's six learning outcomes. Right? And then and if you think about, like, education in Bloom's taxonomy, how there's, like, the lowest level where you just have to memorize, and then there's the highest level where you're creating knowledge.
They have a similar hierarchy, Bloom's taxonomy, if you will, of of of AI, which is the similar thing. Like, I have used generative AI and know what it is and can play around with it all the way up to I can create artificial intelligence of some sort that is embedded in my discipline. That was sort of what was flowed down from the university, but each individual program, so academic program, was given the opportunity to then really evaluate the ways in which they already meet those learning outcomes or where they have gaps that need to be filled in which case courses could be developed, modules could be developed. The university also rolled out a bunch of training, right, that faculty and staff can participate in. They incentivized participation in those trainings as well.
They had a lot of workshops. They brought you know, all of the partnerships that they've created with these different companies have come out and also done a lot of work with faculty and staff to also but that it's what's interesting is that sort of attends to the generative AI piece of it, right, like how to use these specific tools. But I think what I, in a lot of ways, sort of admire about the initiatives is let's go beyond that. Like, let's not just reduce this just to a tool.
Let's think about what artificial intelligence looks more broadly instead of just the tools.
Rob: So is that going beyond generative AI, or is it sticking just with generative AI? Because I think for our listeners, what you've done we've done it here on the show many times is just to make sure that they understand that AI is a much broader category than what everybody is using in terms of terminology in the popular world today. So when we talk about ChatGPT, that's generative AI. And specifically for, you know, ChatGPT, would be image generation and then text generation. Used to be video, but they got rid of that.
Jared: Mhmm.
Rob: So I appreciate you saying that. Are you all moving beyond just generative AI, or are you
Dr. Monique Ross: Yeah. That that's the objective. It's, like, not to go beyond that narrow I mean, we we're certainly not ignoring it.
Rob: Right?
Dr. Monique Ross: Like
Rob: Right.
Dr. Monique Ross: There are certainly a lot of intentionality in even, like, the there's a class called launch, which is the incoming freshmen come in. Everybody has to take it. Like, they're being very intentional at introducing students to generative AI and sort of ethical and responsible use of it, especially when you think about, like, your academics, like putting some guardrails for students on when it's appropriate and when it's not appropriate to use generative AI. But as an institution, I think we are trying to continue to educate both faculty, staff, and students that when you say AI, not generative AI, it is a broader idea. It is this this idea that decisions are being made automatically.
Right? And even though we may not have called it artificial intelligence before, it's been happening. Right? When you think about statistical models that predict things
Jared: Right.
Dr. Monique Ross: That is AI. Right? We just maybe didn't call it AI before. We called it statistical models, but that predictive element is exactly what AI is. And so even making those explicit connections to students of, like, when you learn how to automate something, even in an Excel spreadsheet, if it makes a decision based on something you put in there, that is AI.
And so helping people make those connections because I also think those when those connections are made, I think people can better make the connection to also the limitations of the tool. Yeah. Like, if you're just putzing around in generative AI, man, it seems like it could solve all the world's problems. Right? Like, I just need to put it in AI, and Chat GPT, and it's gonna solve all my problems.
But when you start to realize that it's guessing Mhmm. For a better word Yep. On what's gonna happen. It's like an educated guess by a machine rather than a person. That makes you think a little bit more critically about its outputs.
You look at it a little with a little bit more scrutiny. And so I think doing that abstracting out to talk about AI broadly, right, is it left or right, It seems like it's doing great navigation, but it's really only going left or right, and you can start to see it for the simplicity that is there in some cases. I think it helps to contextualize how you view the whole system. Mhmm. And so I think I think that's kind of the gift of like, let's talk about artificial intelligence broadly, then let's go in and play with these tools.
You might look at them differently.
Jared: Why do you think now it's so important for students to encounter these these skills? And why what do you think the danger is of them not developing these literacy skills at this point?
Dr. Monique Ross: Yeah. I mean, I think the obvious reason is because I think employers are expecting it. I think they are for sure expecting more efficiency because there's a tool available that can help achieve that. Right? There might be an upramp on learning how to use the tool, but once you can, right, you can do some things faster.
Right? You can offload some of the cognitive tasks that maybe don't hurt people, right, to the machine.
Jared: What's the danger of them not developing it now?
Dr. Monique Ross: I think you fall behind. And I also think and I think we made this mistake early on with, like, the personal computer. Right? We rolled it out. A lot of people used it mostly just for word processing, but in a way, you know, no longer typewriting and having to delete.
Like, there were things that made it easier to be able to communicate information. But I think what we did was we didn't teach people how it worked or what was going on or what was happening. And what you do is you created, like, an elite class of people who had the information and everybody else didn't. And there was a scholar some time ago, I think his last name was Snow, who made sort of this prediction that we needed to we needed to educate people on how those things work so that way we can make informed decisions and not just be sort of the the victim of it
Jared: Mhmm.
Dr. Monique Ross: Or the victim's not the right word, but I hope you get where I'm getting at.
Jared: Yeah.
Dr. Monique Ross: You're not just receiving it. Right? You are having input into it. Right?
I think we create better products, outcomes, more thoughtful things when people have a say in it. But if only, you know, a very small percentage, you know, less than 1% are the only ones that have access and understanding, the rest of us are just sort of left with result of whatever they decided. Mhmm. And so I feel like the danger is, like, this long term danger of, like, if you don't really understand what generative AI is, what's happening, how it's doing it. Like, I know we all sort of recognize there's a limitation to what we can understand.
Right? Because some of it is black boxed. But there is a lot that we could understand about, like, what's going in and how do we examine what's coming out on the other end of it. Like, even just understanding that when you give something to generative AI, the whole world has it now. Right?
It is no longer it's not it's not this conversation between you and this chatbot. It's the whole world now has access to that. And I think a lot of people don't even quite understand that, and so they feed it personal information. Or even here at the institution where people do research, something that was novel, once you've put it into generative AI is no longer. Right?
And so just even understanding that process or interrogating what comes out because, again, it's kinda guessing. So I think the the scary thing is that we could end up I don't know. Like, on the always end up just like with the tech boom, end up on the end where we are just receiving what comes out of it rather than helping. Yeah.
Rob: I think I think you're you're right. Plus lack of one of the things I think that goes into what you were talking about there would be what we've already recorded, here in terms of critical thinking and the role that that plays, where you're not just being acted upon, you're an actor, and and a purposeful one. And here at Cedarville, we would say one who acts in wisdom or one who walks in wisdom. So talk talk to us about how you feel or what you think about that role of critical thinking in terms of evaluating or utilizing generative AI and AI in general.
Dr. Monique Ross: I mean, I think it I think it's critical. I think I think one of the things that was, like, enlightening to me. Right? I was, again, playing around with generative AI, and I wanted it to, I don't know, synthesize literature related to student success in undergraduate programs with literature from the last five years. Right?
And you put in a prompt just as simple as that. I'll say something like, as a graduate student, I'd like to write a paragraph of a literature review on research on student success over the last five years. And it pumped out beautiful prose with citations and a bibliography. And I was like, oh, snap. This thing is way more powerful than I thought it was.
But then I started looking at the citations, and they weren't real. Like, they were real people, and they might have been even real titles, but they weren't the real people with the real titles. Like, it had really, like, just made it all up, dumped it on the page. And I think if you'd if I hadn't thought, well, let me just dig a little further. Right?
Like, you could think you had created something. And and for students in particular, like a fast track to an assignment that needed to be done. Right? But I think because I was more curious and wanted to really know, like, how I wanted, well, I wanted to dig and see, did it really synthesize it well enough? Let me go pull these papers and see.
But the papers weren't real, so I never even made it to that part. Right? But, like, I think having some of that discernment or that critical thinking, that that that question, that nagging question of is this accurate or is this you need that, I think, in order to not get caught in the trap that can be generative AI if you're not really careful about it. This is, I think, where my biggest, like, personal tension comes in because it's like, in a lot of ways to me, generative AI is a lot like a calculator. Right?
And at some point around education, somebody had decided before we let students play around with the calculator, we have to help them understand how to do the arithmetic first. And once they have demonstrated some command of arithmetic, we'll no longer force them to add in their head or on paper. We'll allow them to use the calculator. In a lot of ways, I feel that way about generative AI. I feel like we have to help people understand the arithmetic first.
Right? Like, do the and not only that, but there's so much lost in learning if you're not doing things like writing. Right? Like, you think while you write. Right?
It has been proven with time. Right? But if you cut all of that out, you cut out some of that deep learning, you cut out some of that critical thinking development. And so I feel like in a lot of ways, one of the tensions I think I struggle with is, do we understand what's happening first before we go and play with this tool instead of, you know, flip because if you don't if you didn't understand the arithmetic, you never knew if the calculator was right or wrong or if the Excel spreadsheet was right or wrong, right, or if you got something. You had to understand the arithmetic first to be able to critically analyze the output.
If you skip over that, you don't even have the tools to critically examine what's being spit out on the other end.
Jared: Right. So you have stressed it, so many others have stressed it, we've stressed it ourselves, the importance of preparing our students, with these skills and these competencies, the fluency, as you said, and the need for critical thinking in this. And I'm sure you've come across faculty members as well who hear all this, but they're completely overwhelmed with the idea of doing that. What do you tell them? What's the first step to help them better prepare their students for this future?
Rob: And themselves, really.
Jared: Yeah. Really, that too.
Dr. Monique Ross: So I think and there's many different approaches. Right? I think for me in particular, like, going to that workshop where I was with a whole bunch of people that were not scared of it in ways that I was scared of it helped a lot. It helped to lower the bar barrier to entry. It helped to lower sort of the anxiety maybe associated with it.
It also forced me to play around with it. And in a lot of ways, they had given me some, like, rules of engagement. So I think and I think now there it's all over the place. Right? Like, LinkedIn Learning has things, and Coursera has things.
Like, everybody has some low entry point. Even like, IBM is giving away, like, their skills build training. Like, there's a lot of free and available tools out there, but there's also just playing around with the tool and familiarizing yourself, I think, with the limitations and the benefits of using it, I think, goes a long way. I think everybody feels like they gotta go get some type of an endorsement or some type of certificate, which maybe will help. But I think just getting in there and playing around with some of the tools goes a long way.
I have been reading a book that I think has also, I think, helped in a lot of ways, which is The Opposite of Cheating: Teaching for Integrity in the Age of AI, which helps, I think, also with faculty, teachers, and instructors to reevaluate how we define cheating in this new age and era. Like, what and what does that mean for the way we design assessments and how we measure learning now? I do think generative AI and probably long before that, places like Chegg and all those things that sold everybody's everything everywhere all the time anyway, was sort of forcing us to start reevaluating the way we assess. But I think with generative AI, we we absolutely have to rethink what that looks like. But I I think there's a lot more books, a lot more podcasts like that of your own, a lot more free tools out there.
I think though I think picking the medium that gives you comfort. Some people are readers. Some people are doers. Some people are listeners. Go into that entry point that is more peaceful for you and play around in those spaces.
I think that's how it gets started. And I think I think once you do, already your content knowledge in an area, you'll be able to put the pieces of the puzzle together of, like, where you think it would be best to introduce the tool to your students in a way that's productive to their learning. But I think first is getting over that initial barrier of, like, how interrogate how you feel about it, why you feel about it. And I people my age or older, I always go like, well, like, you survived the Internet. And I feel like if you could survive the disruption of the Internet and the personal computer, generative AI really should be a breeze.
Right? Like, I feel like you should you just have to reflect on, like, what was that like when that happened? What did that change? What did that in change what did that change for educators? Because you know it did.
Or even just the disruption so close and traumatic of COVID and how much that changed the way we saw education, the way we saw social engagement, the way we interacted with online everything. Right? Like, disruptive moments are uncomfortable and weird and scary and nobody knows what they're doing, but somehow we always end up on the other end of it. And we need to see this one sort of as the same thing.
Rob: We usually ask this question as kind of a a closing question. If you could give students and faculty one message that would help them be prepared for a future with AI, what would it be?
Dr. Monique Ross: I think it would be proceed cautiously. I think I think, like I mentioned before, I think you have people that are either, like, running to it fast and then others who are resistant to move at all. And I think both are bad. I think to move cautiously is the way to go with it, but to keep moving. Right?
Don't be don't be stale or stagnant, resistant, reluctant, but also don't race to it. Move with it with caution. And because I think, generally speaking, we tend to move super fast. And when we move super we break things. Right?
And and I just think moving through it cautiously, but continuing to move is the way to go with it. You know? I think absorb the training. You can still have tension with it. I mean, you can learn the thing and still have tension with it.
And I think that's what I would give folks, both faculty and students. Like, even if you have are in conflict with it, become more educated. Even figure out what is your real conflict with it. Because I think that's the other thing is, like, people take a stance, and they don't really know why they're taking the stance. Right?
Like, really interrogate why it is that you're so reluctant. Learn more about it and see if there's ways to move in and through it, but keep moving with caution.
Jared: We're very appreciative for you coming on with us and helping us out, Dr. Monique Ross, with this good stuff. I just really appreciate it.
Rob: It is. And it's not gonna stop. Like she said earlier, it's not gonna stop.
Jared: Yeah. We would love to have you back on to talk with us again about this.
Dr. Monique Ross: Absolutely.
Ryan: Thanks for listening to this episode of Transform Your Teaching. If you have any questions or comments about our conversation with Doctor. Ross, feel free to send us an email at ctlpodcast@cedarville.edu. You can also connect with us or message us on LinkedIn. And finally, don't forget to check out our blog at cedarville.edu/focusblog.
Thanks for listening.