Beyond the Code: AI, Ethics, and the Future of GIS Professionals

In this episode of UMBC's GIS-focused series, "Beyond the Code: AI, Ethics, and the Future of GIS Professionals," we delve deep into the transformative role of artificial intelligence in the field of Geographic Information Systems (GIS). Join host Dennise Cardona and guests experts Dr. Dillon Mahmoudi and Mr. Ron Wilson as they explore the cutting-edge integration of AI into GIS education at UMBC, discussing not only the technological advancements but also the ethical considerations that come with it.

Discover how AI is reshaping the way GIS professionals approach problem-solving, data analysis, and spatial thinking. From enhancing student learning and engagement to preparing graduates for the demands of the workforce, this episode sheds light on the significant impact of AI across the GIS domain. Our guests share insights on how AI fosters a culture of innovation and creativity, addresses fears of job replacement, and, ultimately, how it complements human capabilities to tackle complex environmental and societal challenges.

Whether you're a GIS enthusiast, a professional in the field, or simply curious about the intersection of technology, ethics, and education, this episode offers a comprehensive overview of the opportunities and challenges posed by AI in GIS. Tune in to gain a deeper understanding of how UMBC is leading the charge in preparing the next generation of GIS professionals for a future where technology and human expertise converge for the greater good.

🌍 Learn more about UMBC's Geographic Information Systems program and how it's equipping students with the in-demand skills needed in today's AI-driven landscape. Click the link in the description for more details and to explore our offerings.

https://professionalprograms.umbc.edu/geographic-information-systems/

#GIS #AI #EthicsInTech #FutureOfWork #UMBC #GeospatialTech

Transcript

Dennise Cardona  0:00  
Hey, thanks for tuning into UMBC's Mic'd Up podcast. My name is Dennise Cardona from the Office of Professional Programs here at UMBC. Today, I am joined by Dr. Dillon Mahmoodi, Graduate Program Director of the Geographic Information Systems program, and Mr. Ron Wilson, Assistant Program Director to talk about a very hot topic, AI. So, AI, it is all the rage these days. Right. And I would love to hear your initial opening thoughts on AI in general, before we get started in our deep dive conversation.

Dillon Mahmoudi  0:35  
Absolutely, it's, yeah, it's so hot right now. And one of the concerns that a lot of people have is in the way that students might use it to say, cheat or do the work for them. But I think what what Ron and I are seeing is that it's also a way for us to figure out how to challenge students in new ways, and requires us to change the things that we're asking, as instructors and mentors, as we prepare our students for the world.

Ron Wilson  1:06  
And, again, because what am I experiencing in the classroom, I mean, first of all, for me, what it's been doing, is allowing me to take advantage of my strengths, where I'm really good at creating ideas and connecting things together. And it's a way that I can interact with it and help me sort of fill in the the detailed work that takes up a lot of time that allows me to explore the ideas and make those kinds of connections. And so when I see the students in the classroom doing this sort of same thing, I think about my experience with it, and how to help them because I'm in I'm finding that they don't, they're worried about being thought of as cheating and they're reluctant to use it. I'm having a you know, here, it's all a hot thing. And I'm wondering, like, who are they referring to because it's not our students, it's other students that I'm seeing. And I've seen, you know, some people trying to cheat, but most of them are trying to use it responsibly. But they just don't know how to use they, they're hearing that it's the hot thing too. And they're hearing that it's going to maybe make them obsolete. But what we're finding that doing I've seen in the literature is, is that that's not turning out to be the case, because research has emerged in the last year about the workforce. And what they're saying is, they want candidates to come in already knowing how to use it. And so the business industry says yes, we want people to come in to know how to use it, how to make use of it, but also bring that human side to it, because they know that it's not going to replace them from everything that they do. And the research that sort of does this in a more rigorous fashion is basically they came to the conclusion, after examining all these workplace studies, surveys from the industry leaders and stuff, like know, who's going to get replaced is going to be the people who don't know how to get used AI by the people who do know how to use AI. So that's where Dillon and I kind of like, you know, maybe we should more formalized this into a training into our program, and really, you know, advertises like, we got your back. We were gonna make sure you go out there to be one of those candidates.

Dennise Cardona  3:19  
Absolutely. Can you explain how generative artificial intelligence can reshape the educational landscape, particularly in the UMBC GIS program?

Ron Wilson  3:31  
This sort of starts off, for me, it's all about sort of the economics of effort. And what it's going to free up and this isn't just the GIS program, we are still working on figuring it out in a number of ways. I mean, we're sort of on the frontier of it and why we're doing this podcast partially, is to sort of get that message out of what we're working on. So how it's going to reshape the landscape, and from what I've been seeing is that a couple things are going to happen. One is the students are going to start to learn how to use it. And they're going to start to really how to figure out how to interrogate it, how to cite it, how to work it into their own work, and it's going to free them up to do more of the analytical work that they're wanting to do. I really, really hate seeing students, for example, in programming, get stuck on syntax, and it's a minute thing or even something that doesn't work, right. Like, for example, one time I was what Dillon was, is that I was having this one technique not working out, right. And I pinged Dillon and said, Hey, I spent hours on it, and I think he'd spend hours on it too. And we figured out was just like, it was the way our, our geometry and our one of our GIS layers was built. And he says, Did you check for this? And I was like, Oh, my gosh, you know what I think I do. I actually saved my daughter in the same way. And I had checked it and sure enough, had I had GA III, I could have said, What is some of my, my problem going on here? Or, you know, I'll run a block of code. And it'll throw a syntax error. That's kind of funky. And I'm not sure exactly what it means. I can't, you know, you could do that and Google's like, take the error message, put it in there. But with the with the AI, it can really, you can really customize it to your context. Because on Stack Exchange, or out on the internet, you're finding somebody else's context. And you got to figure out how it fits into yours. Where with the with the GI, you can like, Oh, tell me more. Or why does this or explain how this happened until I don't do it again, and it because this is the environment that I'm working in. So we can free up students from doing that kind of silly work, that allows them to say, Hey, I've got time to explore doing something else with this code. Or if it's an analysis, it's the same kind of thing, they have to like, expand it out. And it takes the time from doing all the literature review and looking for all the sources and everything, they can just ask for those kinds of things, Dillon?

Dillon Mahmoudi  6:09  
Never will, I'm gonna riff off of a point that you've made to me a couple of times, and that it's, yeah, sure, their GI may provide some sort of time savings, but it's about how we can turn that time savings into a better quality product and better quality analysis. So we can take the kind of creativity that we might see out of Gi and turn it into a better explainable product for our customers, for our communities, etc. And I think that that aspect that, you know, again, I'm this is a point that Ron has made to me many times. Doing that I think, improves what our students are able to do when they get into these positions when they get into companies to demonstrate their experience. And what ends up happening is those that aren't working with GAI, get left behind.

Ron Wilson  7:04  
Yeah, they, that makes me think about, you know, the magnitude of the problems that we face, I mean, all the disciplines, you know, have have serious problems or wrestle with, but geography and particularly our program is wrestling with social and environmental issues. And if we can cut that time away that and that time is not trivial, that that time spent on writing code or digging up the literature, and getting it all assembled. And everything is an enormous I mean, in academia, there's this is just like, it's, you know, you spend 90% of the time getting ready in 10% of the time doing, where if we flip that economy, then think about how much our students are going to go out into the world and impact society in the environment, by now having much more time to focus on the problems. And in particular, to come up with new problems. One of the biggest tricks in science is to figure out, it's not for lack of ideas, we all got, like, oodles of ideas, we spread them out on the table, and we go, all right, we can only pick a few of these, what is it going to be? And you got to decide, and you got to hope that you made the right choice, or at least a wise choice. But now, not only can it help you, you know, reduce that time to spend more time on all of those options, it can help you figure out which one of those options is going to have the most impact and help you work it out. way in advance before it gets there. To that point where you're like, Okay, I've used AI to help you figure this out this this idea sounds good. Seems workable, but it's not. And this has a cascading effect. And that is is that. And I've seen this in the research, too, is is it's a game changer in terms of new candidates in the workforce, is this data, the couple studies that I've seen, and there's only a couple at this point is that it's reduced the gap between the experts and the newbies in being able to perform high quality, rigorous and complex work. And so when you have new people not needing to take as long to get to a stage in their careers, then think about the exponential impact that this is going to have on people being able to try to solve society's and environmental issues. I mean, that's just I'm thinking down the road like, you know, we're implementing this, but we have yet to see this. And I think you know, everything everybody's talking about is going to be a bigger impact. And I think people think

Dennise Cardona  9:41  
exciting things ahead, really, in what ways will the introduction of GAI in the GIS curriculum, enhance student learning and engagement?

Ron Wilson  9:55  
I'm learning this one right now. I'm gonna pull straight Since out of their shells, it's been kind of tough. And, you know, because they, they, they're reluctant to respond. And they are truly just, I think, not sure what to do with it. I think they're a little bit afraid not from the cheating, but they just don't know how to engage it. And so what it's done is allowed me to create assignments, and things that are more engaging. And, you know, with the way that I've been flipping the classroom, I've been using the AI to get them more involved. So and get them out of the shells. Just last night, I decided to change my whole class up in terms of how we're going to operate rather than lecture. As like, alright, I've been telling you that we're going to learn to use gi I gave them. So I paired up with a TA. And he led one group and I led another group. And we both come up with different methods for solving the same problem, but we only gave them the sort of general you need to do this, you need to do this. And what we had them do was like, we guided them. But we were having them use chat, GPT and Gemini to say, well, how are you going to figure out what to do and what tools to do that? So they'd start using it and they started seeing that they'd get overwhelming information about it. And they'd be like, Oh, Professor, I'm not sure what, this was a lot. And I says, Okay, well, let's, let's think about what we've had in the classes. And so it all became one big activity that they were engaged in, they were talking, they were starting to get involved into it. And it was really good time it was it was better than the blank stares that I would get work from a two and a half hour lecture. Dillon, what's your experience in that?

Dillon Mahmoudi  11:41  
I love that. My experience is pretty similar. It's I've been focusing recently on the idea of reflection or reflective coding. Often students will search for a problem that they're having on the internet, they'll find an article that isn't quite right, but it has some code in it. And they'll say copy, or, or copy and modify that code into their own code. But again, it's not doing exactly what they want. So with the ability to now talk with that page, where that they got that from or to better or more clearly talk to a generative AI, they can go through these kinds of rounds of refinement, where they can say, well, that's not exactly what I was looking for, I need something that does this. And in that process, students are learning the differences in what the code might do. One of the things that we both kind of encouraged in the classroom is to check to run run code and check the expected versus actual outcomes of your work. And see if we can get students to continue to check that check the expected versus actual outcomes and use something like generative AI to refine that to find not only get the kind of, to make sure that the expected and actual outcomes are the same, but then actually go into and figure out ways that they can save time or better algorithms that they can use. And so that that process of being able to ask generative AI for refinement has been a really useful tool. And getting students to understand or think through what kinds of refinements they could be asking a generative AI for.

Ron Wilson  13:31  
Yeah, this is one of the things that we're we're that I'm wrestling with, too, is this sort of getting them to realize that I've been prompting them to sort of talk to it like they're talking to a human. Because I'm finding that I'm getting better results from that. It's a little bit longer than being succinct. But that's the thing. It's a large language model, it has the ability to take no matter what how much you give it, and figure out what you want. And so I found just like talking with Dillon, like about, you know, our ideas for the program, or this analysis that we're doing, you know, I talk to it in the same way and I get better responses because I've given it a large amount of information that processes it as fast as Dillon's processing it when I'm talking to him about and he can give me a rebuttal. 

Dennise Cardona  14:18  
Yeah, absolutely. It's really, I think it's a fun process to have those iterations over and over letting it marinate. And then I I'm the same way I talked to it. Like it's a human being, like, no, that's not what I'm asking for. You need to dumbing it down or you need to put clear language or don't be so you know, dramatic, less dramatic, be casual, and I just love playing around with tone and style, because it's almost seems like it's having fun with me too. It's like, oh, okay, I get it. All right, so just give me a different version.

Ron Wilson  14:50  
Yeah, I'm just waiting for one day, buddy. I'm giving you what I'm giving you you're gonna like it.

Dennise Cardona  14:59  
Absolutely. Oh gosh, it's a brainstorming partner that doesn't really give you slack back, which is, you know, it's, it's wonderful you can you can be as like, frustrated with it as you want to be, and then it just doesn't get flustered. It's like, okay, let's try this again. Now, given the rapid evolution of GE AI, how will the UMBC GIS program keep its curriculum relevant and up to date?

Dillon Mahmoudi  15:25  
We've got instructors who are in all aspects of corporations, federal government, and academia, and they're being exposed to the cutting edge. Uses of Gi in the field. And so what we're trying to do and and Ron has been doing such a great job spearheading, this is bringing all of those kinds of insights together among the instructors, so that we can share across those different kinds of fields. We are putting together a workshop for students with some of the kinds of best practices from GEI and GIS. I think we have a date in April. Is that right, Ron?

Ron Wilson  16:09  
Yeah, April 29.

Dillon Mahmoudi  16:10  
 Yeah. And would you talk a little bit more about that, and how you're running it with with Paul?

Ron Wilson  16:17  
Yeah. So sort of back to that point of keeping curriculum. And what I did with Dillon was at first sort of go through and say, Alright, we need to formalize this. And so we put together a meeting specifically for this just sort of put the faculty sort of, you know, on their radar, that the that it's coming. And so what we're doing now is, is where I'm particularly meeting with them one on one, to sort of say, hey, let's take a look at your syllabi, let's just start here, I letting them know is like, look, we don't expect you to adopt this, like, immediately, you're we're going to work it into you, because they're reluctant to. And so we're trying to do take a slower approach, more systematic approach is to like, here's where you can work it into, because some of them aren't sure how it's gonna fit into their courses. I wasn't sure what the course that I am now, but I figured it out. And I'm starting to experiment with it and play with it and figure out what's working and what's not working and go from there. And I'm using those experiences to sort of share and keep in regular touch with the, with the instructors and the students and trying to give them that feedback. Plus, I'm also looking to survey those students at the end of the semester to get an idea of like, what were your experiences with that? Why did you think this and what kind of impact that you have. And so this way, we're getting feedback from the students so that we can alter our curriculum in a way that is responsive to how they're feeling about it, not just what we're seeing from the experts, what we think are just casual interactions with the students. And be a little more systematic about it. So there's a guy at Montgomery College who has that background with that kind of thing, that he's and he also has some knowledge in the GIS area that is allowing us to put a big workshop on specifically for students in the GIS area, which was kind of a remarkable find. And this way, the students are starting to see, oh, they really are serious about this isn't, this isn't, Oh, this professor did this, this professor did that. Or they've been talking about it, and there's no connection between them, they're starting to feel it and see it so that they know that, that we are actually sticking to our plan of like implementing it as a as a training curriculum. And I think that matters, those subtle things that you send people add up in the end and say, Yeah, I was trained pretty well in the GIS program. Because I don't think we can say certified AI, but we want to be able to provide a rigorous, specialized training in it, because everybody else is doing this too. And we just think it's going to be more formal is a way forward in sort of standing out because we do have competitors and we want to beat them. For the student population out there.

Dennise Cardona  19:16  
Now employers are increasingly looking for graduates skilled in AI, right? How will the UMBC GIS program equip students with these in demand skills now you talked a little bit about having a workshop and things of that so are anything else that you're planning?

Ron Wilson  19:33  
This is one that actually I think UMBC has an advantage on for, over the other programs. Since we are a department or division of professional services program. We are required to make sure that we have ethic considerations in our programs. And we are also required to emphasize and enhance communication skills. And so this is another game changer for the economy effort that I was talking about earlier with G AI. Because G AI is if we're going to be effective with anything that we do, we have to communicate it. And the G AI is going to help students improve on those communication skills. I mean, matter of fact, the employers are, and the research is also saying it's just like, it's those softer skills that are now going to have to be pushed forward. Because if the GI is saving the time, that was once had people sort of heads down in their offices, or wherever nook it is, they hide up and do their work, they're going to be freed from that more, and they're going to be more engaged with people out there. And so they're gonna need to learn to communicate in a number of ways. One, they're gonna need to learn how to communicate with blending the GI work with their own ideas in a way that's ethical, that doesn't just copy something out, paste it in, or it's somebody else's, clearly that the GI spit out and blend it into their own. So that's we're hitting that ethics, part two, we're teaching them how to do it responsibly. But also the communication effort is like, the the research is showing is like, okay, so you're going to be communicating with people more, because you're going to have more time to do that, you're going to have to wrestle with bigger ideas. So people are going to have to be able to develop the ability to communicate in their writing, which the GI can help them, but they're going to have to develop it and being able to communicate what they have and work with other people. So they're going to have to learn things of being more diplomatic when they're dealing with people more empathetic, because we're social scientists and environmental scientists is like, we care about people. So you know, we're going to have to refine those skills. So, you know, those are things that, since they are a part of DPS is core requirements. And it's aligning with what the research is showing about what skills are going to need to be available, then I think we've we found the sweet spot, because I've not read, I've been looking at the other programs out there. And they don't really talk about those kinds of things. And quite frankly, on our GIS page, we don't really say it, the DPS page says it. But what I'm going to work to bring that down into our into ArcGIS, page two, so that we highlight. So what they're reading out there about at GA right now is they'll turn to our page and say, Alright, I'm seeing a connection may not be like Oh, A, B, but in their mind and the subliminal. They're picking up on it. I'm a I'm a big, big student of behavioral economics. And so nudging in those subtleties and things like that are things that that primed me up in terms of those nuances. Anyway Dillon?

Dillon Mahmoudi  23:02  
I mean, I think I think it's nothing new in terms of what we prioritize in our program. One of those things that we prioritize is the idea of reproducible research. And what comes with that as being able to communicate your work and communicate how you problem solved, in order to get there, what how did you break down the problem into these different steps? And so with the introduction of GAI, we're encouraging students to keep track of how they interacted with GAI, how can they communicate their use of GAI to employers or to the broader public? And then how can they communicate its benefit? How did it improve the product that they were able to produce? And that's, that's something that employers are looking for, not necessarily the GAI, GAI part, or the reproducible research part. But for example, one of the things that I've gotten in when I was working at Microsoft, one of the most challenging interview questions that I got, was not about me solving the problem, but then testing me and finding out how did I break down the problem and communicate my thought process to them? So in that same aspect, we're encouraging students to be open about how do they break down the problem? Where is the best place to use GAI? or other forms of tools? And then how do we make that so that it is reproducible so that others could come to the same conclusion that our students are coming to?

Ron Wilson  24:43  
Yeah, that, that makes me think about sort of the again, this is another area where the human has to come into play, and we teach them how to be the human in this and we want our students to not only go in and say, Hey, I know how to use it. AI, I want them to also say, Hey, I know how to be human too. And so sort of taken off from those two examples, is when we have when I have students that sounds like Dillon doing the same thing, or similar anyway, that we have the students, like, I haven't do all these methods, right now in processing this data as part of their class. And so that's where we're getting used to. So I'm using it to sort of get them to like, assess their method, and then build out on it. And because they're gonna say, Well, why the AI sort of given me everything I need I need here. And I said, Yeah, but see, that's the thing is like, you think about your reader, or who's going to be reviewing this, they aren't going to have as much information in your head. And so you have to take what it's given you in those bullet points, or those short sentences about it. And a make sure that it is clear, and then add your part to it, to enhance it so that it is readable and communicable for an effective, so it's a way of the it's another opportunity to say, here's how you get to be the human in this equation. And so as you know, the reproducible raw results is a great thing. I mean, that's, we've seen that in left and right in the sciences, particularly the social sciences, where nobody's been able to even reproduce the biggest and most impactful studies. So I think back to what Dillon's doing with his class is like, here's the expected, here's what you got, you know, you have to you not only have to reconcile that, but you have to communicate how you reconcile that, because that's important to somebody else who's trying to assess if your work was quality, but also is, is this another piece of evidence that we can add to this growing body of knowledge that says, this idea works. So that communication between how you did it and why it did what it did, is important to be able to distinguish the two, because those nuances are what convinced people, Oh, I see how that was reproducible now and what the original did. So

Dennise Cardona  27:15  
That is going to be a powerful takeaway for students.

Ron Wilson  27:18  
They're going to be able to assess their work in ways that they just weren't able to do before, because now they're they're going to have the time because that's way we're designing our classes is to allow them to have that time and building into the exercises to say, you need to assess this map. And so why we did what we did last night with the class with the two different methods is to say, All right, this is a classic thing in the sciences, where he has, particularly the social sciences where you have when I was at the University of Michigan, we always had to be paired up with somebody to say, All right, we need to come up with these statistics, two of us would work on it. And so we go about it in our own method. And then we'd compare results in the end. And if they matched, we're good. We take the documentation from both of them, put them together, but if they didn't, we'd have to go back and communicate well, this is what I did. Why did you do that? So it's that communication kind of thing. And so that's what we started in the classroom last night with that kind of exercise. Because when we were done, I showed them Harrison's work. And I showed my work or my methods, and we compare it like here's what he did, we came to the same results. But along the way, here's why he did this. Here's why I did this.

Dennise Cardona  28:36  
Now there is a common fear. We all know that AI may replace human jobs. How will the GIS program address this concern and prepare students for a future where AI complements human capabilities?

Dillon Mahmoudi  28:52  
There's a fear. And there's some truth to it that any technology is going to de-skill, the workforce or automate jobs. We see our job as re skilling workers re skilling the workforce, so that they can meet the new demands that are being placed on them, and figuring out how GAI can kind of fill those gaps. How do you How would you add on to that?

Ron Wilson  29:19  
Um, well, I think it just go back to the curriculum. And some of the examples we, we've been trying to make it is a fear and some jobs are just going to get replaced. And so the rescaling becomes the kind of an important thing because it needs to be so the so we were going to be sending students out there that will go into something and GA may evolve, it's kind of puts them at a disadvantage. So they need to be adaptable. And because that's going to change fast and they're gonna have to learn those skills and saying, oh my gosh, this just now does this. I gotta figure out what what else I can do or how to emerge from this, showing that I have something to offer still. So the design of our assignments, and the constant use of it in a way that shows them how to be that human and adapt in those ways is, is is going to be critical. And that's how they're going to survive that. 

Dennise Cardona  33:10  
Now, my last question, what role does GAI play in fostering a culture of innovation and creativity within the GIS program?

Ron Wilson  33:21  
Well, that one is the big thing for, for me in terms of the impact of GAI, I'm, I, it makes me think of a time when I was at Housing and Urban Development, I was paired up with a guy who was really mathematically capable. And he wasn't, but he admitted to me, he's like, you know, I really can't put the ideas together and the concepts and all the things you know, about criminology together to ask the kinds of questions that we should be asking in relation to crime and housing. And, and so I was like, not him in the sense of being able to put these really rigorous mathematical models together to prove that what the idea was, was actually sound in terms of its statistical properties and things. And I think about the guy who was the father of behavioral economics, he had this idea about behavioral economics. But there was so much you had to prove to bring a whole discipline to life to prove that it was something that had merit, he had to go to all these people who were high functioning mathematicians and say, Alright, here's what I want to do. He says, I can't do that. I'm like that in that situation. I can't do that. I need to leave that to them. So if you take that kind of situation and apply it to GAI, then you could let it do a lot of the harder things for you and interrogate it and query it. And that frees you up for the time to think more about your bigger plan, your creativity to come up with ideas, it goes back to what I was saying earlier about, like, I have all these ideas, which ones are the ones that are going to allow me to, to to go forth with. But also, one of the things that I was really good at back at DOJ was, I had, I was I ran a program. And we had to put out grant proposals. And I constantly had to like read the literature and fee read all of the incoming research that was coming from the current grants, I was looking at the proposals of what people were thinking, I was going out to the criminology journals in the behavioral science journals. And it took a lot of time to digest that. But when I did, I was able to, like, look at it all and say back and like, I see these connections here. And here, I see this connection here and here. And I'd start putting ideas together. And then I'd say, alright, what's the merit to these kinds of things. So because I had to do all that reading, all that listening, all that conference attending, I didn't have as much time to do that part. And I had to be very good at being able to select out those connections that I was seeing, because your job at that moment, is to put out a proposal that is new, that is going to create new research. So you as the grant manager, you have to say, alright, this is what I think, to the director, what we should fund. And so that funding leads the science, because well, that's where the money is, then the universities and the think tanks go for it. And they have to go with what you propose to them. So you have to propose good stuff that has merit because you get, you know, you get down the line, you fund, let's say, a half a million dollars worth of research. And at the end of it, everybody's finding like, yeah, there was nothing to that you lost yours, you lost half a million dollars, you've, you've not really derailed somebody's career, who'd spent that time researching that, but they could have been doing other things. So with AI doing all that, hey, give me give me a summary of these things. And these things, it's produces in something more digestible, and you can reach out and cast further net seems like it can pick up something that you would not have had time to look at in another journal or another discipline. You know, one of the things that you always have trouble with in your own discipline is like branching out to other disciplines, because it's like, Oh, my God, keeping up with the journals that I get from my own disciplines hard enough, you know, this kind of changes, the game, all goes back to you being, you know, giving you more time to be creative about the work that you are the material that you have in front of you.

Dillon Mahmoudi  37:52  
There's there's also this, I love this idea of building connections and using GI for those connections that also connects to this kind of old saying, old in internet terms, this old saying on the internet that if you want to figure something out on the internet, don't post a question, post something that's wrong. And everybody will flood the responses with you're wrong, here's what you should be doing. Not dissimilar, right? If, you know, we asked G AI to do something for us, or we ask for a solution, or we ask it to help us figure out where we might be able to take this work in the future, when it does give us something wrong, because it's not perfect. When it does give us something wrong. That's a real moment of learning where we can build from and say, Oh, well, actually, this is wrong, because and develop something from there. So it's actually a way to not just build connections, but a way for it to push us in identifying when it's wrong, and why it's wrong and how we can how we can be better produce better products.

Dennise Cardona  39:16  
Yeah, I mean, it's great with critical thinking it it really if you look at it, and you apply those skill sets to to AI, it can help with critical thinking aspect of educational journeys, professional journeys,

Ron Wilson  39:30  
yeah, next time I encounter a being wrong. I'm gonna go and say, Amen, you're wrong. What do you think about that? Let's see what it gives me back.

Dennise Cardona  39:40  
That's awesome. So, on that note, are there any final notes that you want to share any final thoughts I should say that you want to share? Before we close out this episode? We

Ron Wilson  39:52  
look forward to our next one of these with you. sort of give you an update on where we're going What we've learned because I think that's one of the things that, you know, because Dillon and I are testing things right now. You know, that's how this all started was is that, you know, I had a feeling Dillon was doing the same thing I don't know about my other colleagues in the department, but I knew that he was he's been engaged with it, too. And so I was just decided, I was just like, hey I've been working on it this way, what about you? And again, typically, in typical fashion with Dillon and I, we're often thinking in the same parallel lines, and we're just applying things differently in the context of our classes. And we're just different ideas that we have, they're all for the same thing. And so we started comparing notes. But then we started hearing from our other faculty, that, hey, I think they're cheating. I'm not sure what to do. They use GAI, I gave him permission, but they didn't use it in a way that I thought was right. And then I'd hear from another one that they used it, and they used it in a way that wasn't quite as well as they wanted to. And so we started talking about that. And we started doing things in our classes. And then we just decided after we hear those things from our instructors, like, I think we need to get this in shape into something formal, because we're not going to escape this and it's going to be chaos across the program. And I think this is gonna be a unifier for our program, because our programs new. And this will be a way to bring it together and sort of not add to the chaos of bringing a new program to life, as always is the case. So Dillon, anything you want to add?

I mean, I think you you nailed it. GI is here, you can't put the genie back in the bottle, let's figure out how to best leverage it to make the world a better place.

As the only way you're going to protect the society out there as send your students out there with the kind of do the best you can to train them. So that they go out there and be responsible with it rather than just winging it, because they're going to figure that out in the workforce. And it's much more dangerous out there to do that. Not only just in the sense of what you do to other societies, to people that you're trying to apply your solutions to, but also just using it in a way that is a responsible, you know, for your own for your own career.

Dennise Cardona  42:19  
Well, this has been an amazing conversation. I thoroughly enjoyed it. I love digging into AI and listening to both of you. Just so insightful on the on the topic. It's incredible. So thank you so much for being here with us. And thank you to all the listeners if you're viewing this on YouTube or listening to this on your favorite podcast channel. Thanks for tuning in. We hope you enjoyed it. And if you'd like to learn more about our offerings, do a quick Google search for UMBC geographic information systems or simply click the link in the description. Thank you.