Our podcast with Dr. James Hutson has been in high demand, so we produced a transcript below. Next week’s podcast with Dr. Adam Pacton will also include a transcript. We are also distribution channels. As they say, “stay tuned.”
Highlights Dr. James Hutson —
(B)ecause the future of higher ed is going to be one that fundamentally serves a broader audience that fundamentally is more modular and has more on and off ramps. The four and 40 model which is the biggest problem that higher ed was built on after the GI bill which is you go to school for four years you have that career for 40 years and retire, that's gone
The visionary leadership will say, "No, this is going to within 5 years severely impact our institution if we don't have a plan."
The class of of 2030, the year we're all pointing to as critical for change is starting here in a few weeks. That we're going to see more change in the next 5 years as far as you know uh economics, restructuring jobs, the workforce than we did in the last 100 years.
How specifically is AI going to impact my field? That's the best thing that that that faculty can do right now to benefit their students.
*You really need visionary leadership that can see beyond the problems that we're having right now.
*The big one is going to be identity and tradition. So that being that's what we're talking about with computer science and English faculty. If AI can do what you do, then who are you?
Stefan Bauschard: (0:48) Now today we are delighted to welcome Dr. James Hudson, a leading expert at the nexus of art history, AI, and immersive technology. Uh, Dr. Hudson currently serves as the lead XR disruptor and department head of art, history, AI, and visual culture at Lindenwood University. His impressive background includes advanced degrees in both art history and artificial intelligence, culminating with his PhD in AI in December of 2023. Over his career, he's taught more than 120 online courses, developed 26 online degree programs, four of which are ranked in the nation's top five, and overseen the creation of 38 additional academic offerings. He's also the editor-in-chief of the International Journal of Emerging and Disruptive Innovation and Education and the author of a number of recent books and journal publications on AI advanced technologies and education. Uh, welcome Dr. Hudson. Thank you for joining us. Uh we're thrilled to have you as our inaugural guest.
Dr. James Hudson: (1:51) Glad to be here and sure would love to uh share what we've been doing with you.
Stefan Bauschard: (1:57) All right. And you know, given your your background and extensive publication history, there's so many topics we could cover here today. Um, but you know, as our inaugural guest on our AI and higher education podcast, we'd like to focus our time on the we'd like to focus our time on the impact of AI on universities, including challenges and opportunities. Um, but you know, first is a bit of background. What first attracted you to the intersection of art, technology, and AI and education?
Dr. James Hudson: (2:27) Exactly. The trajectory should sound familiar to many as we're moving into a period in which one does not focus on merely one thing. We're actually moving into a period of the generalists that can do many more things that cross disciplinary lines. So my story will probably sound more familiar once we get to 2030 because many people will be adding new tools to their tool belt and new skills, new certifications, etc. So my story started off like most of the um higher education professionals that might be listening to this. Um, I went through traditional schooling. My father was an artist. I got a degree in art. Went on for a master's in art history. And as you continually progress, um, you develop a sense of your specialization, your degree being of singular focus and importance, right? So that being that it can address many of the problems out there, just like if you talk to those in computer science or mathematics or philosophy, right? So that's what it means to be a professor. you profess your love for a given discipline. And so I started off really traditional. I came out here in 2010 to set up the art history department. Solely focused on that, you know, solely focused on art historical research, presenting at CAA, publishing the book as I was supposed to, publishing my articles, um, within my narrow discipline. Um, uh, and then I was given an administrative role where I was asked to make those online and graduate programs that you that you were mentioning, which I knew nothing about, but you kind of back up. So you start with like a 100 foot view. you then back up, you know, to your 10,000 foot view and you start to see the bigger picture of where your discipline actually sits within this larger picture. And so, uh, so with that, I went back and got additional degrees, set up an XR lab, which is where the XR thing comes from, um, uh, as we were trying to develop ways in which to connect, uh, student-to-student interaction and student to faculty interaction right before and during the pandemic. So, um, realized that that had a lot to do with game engines. So, um, to teach myself Unity and Unreal, I I knew I needed some accountability, so I got another grad degree in that. I knew nothing about the business behind higher ed, so I got another business degree. Um, so I could shore up those deficiencies. And with each one, you realize your initial role um, looks smaller and smaller and smaller in this large ecosystem that's actually even one university. Um uh so with that I mean obviously the pandemic catapulted billions of dollars of of R&D into XR research with the metaverse. Um uh and then 2022 actually 2021 I was given the mandate um uh when we reorganized to start looking at how AI was already impacting higher education. Um, luckily I had colleagues in the the sorry in the college of humanities, believe it or not, that could already code in Python, but we're teaching poetry, right? So, we're already looking at these intersections, these overlaps. So, we started, you know, developing our own algorithm, seeing how that might impact how creative writing students consider their their creative process, etc. And then of course fall of 22 we did a number of studies the the last um uh writing tool of which and we did studies and how it impacted art and design web design and development drawing um uh composition. So all of that then ended with the launch of chat GPT. So at that point that kind of launched over now over 200 publications. I had I had to count them up as well. Um with over a hundred international partners and each time a problem came up I realized that more and more you needed different perspectives and specializations to solve them. Um uh and so we started crossing further lines um uh of not only departmental lines, college lines, universities, but then you know national and international lines to find those specializations. And so that's when I knew I had to get a PhD in in AI because I saw which way the wind was blowing and how important that was going to be. Um uh and then that just kind of naturally developed. So that's kind of that's kind of the trajectory that it took is um is looking at what skills do we actually need to accomplish the job um at hand and then looking at where we're going. And that's kind of the main message we have with all of our research is we have to be, you know, forward thinking and consider not what was but preparing our students for what will be.
Stefan Bauschard: (6:43) That is a fascinating background story uh and in particular the way it kind of melds traditional approaches to academia and study and areas of expertise to then developing this understanding of generalist with areas of expertise. And I think you're right at the beginning when you said this is what it's going to look like in 2030 for for many of us. Um one thing that you also noted that I want to pick on and and to be able to develop is the rate of of change that we're seeing. you know it's a rate of improvement in many of the models or the technologies the architectures technology adoption awareness of AI has grown rapidly but that awareness is sometimes fairly superficial in terms of what that might mean for you know higher education for our institutions tell us a little bit about how that shift has affected your work your outlook you know you've you've talked about it in terms of your approach to some areas of expertise how do you try to grow awareness then within your institution or within acade academia and how does that spill over into society and maybe those are different approaches but but thinking about how do you communicate some of that how do you navigate some of it and help others do so
Dr. James Hudson: (7:45) exactly yeah you're right that is two different approaches and so I do try both sides that being looking at um uh educating and upskilling we have a big workforce development initiative happening right now um uh in in my own college which is arts and humanities which is where AI is located in my institution which is fairly unusual um uh but so We have we have that from community centers to Rotary clubs to economic development councils, right? So, we have that side of it and really on that side as far as awareness is concerned, it's, you know, meeting people where they're at and demonstrating what the impact will be on on them, their area, their jobs, their lifestyle, etc. Um uh so it's relatively um um uh straightforward you know concerns about upskilling job displacement you know ethics privacy things like that you know what what they want to know about for for their children for you know deep fakes in the media etc. But then you have the institutional side which is a whole different beast um uh in and of itself. Um uh and what's interesting is um uh I'm much more effective at demonstrating the the coming wave right as um uh CEO of Microsoft kind of put it um uh external right to to my institution and ecosystem right- which is kind of interesting but um uh but the way in which we've done it we tried the traditional route which is doing human subjects research dozens and dozens of studies right so we did them from community colleges luckily we had partners across the North America So, community colleges in Arizona. I'm actually at a teaching institution, believe it or not. Um, uh, uh, vocational schools, historically black colleges, uh, state schools, and R1. So, we we we did studies on all of them. Bring that back and say this is what's actually happening, right, in the classroom. It's not theoretical. um uh this is the impact it's going to have. But with each advancement that you mentioned, it became more and more pressing as far as trying to say, you know, this is not something that we need to kick the can on and think about assessment in the future, think about curricular design, think about even what coursework looks like, which is really what's happening. That's the inertia, right, that we'll talk about later. But um um uh but the way in which we we attempt to do it um um really is to demonstrate the practical um uh and the current impact it's having in the classroom and in their discipline and how they're teaching and how the tools once again are um uh affecting the outcomes that they're looking for with students because the students can do different things with different disciplines and the different disciplines also have different identities, right, that they consider for themselves. So, for instance, you know, many many disciplines that are performative in nature um feel no um rush in order to to address this. That's why I'm working with our head of of the theater department. Um but if you think about something like dance, you know, it's like what are they worried about in dance or anything where they're performing when it comes to AI, you know, okay, well, that's something we'll worry about later. They they should worry about it now. We can talk about why, but you know, things about exercise science, right? So, so, so things that are really um uh out of reach for non-embodied AI, but remember embodied AI is coming. Um um well, it's already here, but it's going to become more widely accessible by the end of the year. Um uh so so that's kind of we did keynotes we did traditional um uh books uh traditional articles um uh international presentations we just were the state department in South Korea with 21 Southeast Asian nations um um uh as much as possible kind of speaking the language of academia of you know here's what this means for pedagogy for curriculum development for administration etc. um uh but really with mixed results quite honestly.
Dr. Anand Rao: (11:35) Let me pick up on that point about embodied AI because this is something that we've we've talked quite a bit about and really want to explore further with what it might mean for academia. Um how have your colleagues in dance or theater responded when you've you've talked to them about those upcoming threats? They really need to start addressing them now because it does look if you're just looking at chat GPT for instance, you're thinking about a chatbot. I could see the disconnect thinking there's no way that could ever encroach on the work I'm doing with students with body movement. But how do they how do they address that? How do you talk to them about that?
Dr. James Hudson: (12:07) Sure. So um uh luckily we were um working with uh various departments including the performing arts right um uh during the pandemic and right after uh because of the XR lab. So doing things like motion capture technology, looking at ways in which to improve, you know, kinesthetics and movement, reducing injuries, right, during practice for ballet or modern or tap. Um, uh, and so with that, you know, you you you normally get the sense in the same way that nurses would in a hospital, in an ER, they call it alarm fatigue. That being they hear so much that there are things they have to pay attention to, they shut down. And so um, uh, we have and and that's what we were doing in institutional studies. we keep getting um uh qualitative feedback going, "Oh, this is just another shiny thing that that the university is chasing." Oh, this is just, you know, another one of those tech things that's going to pass. And that's what we heard, you know, um like last year from many um of our studies was, "Oh, no, no, it hit a plateau. It's not it's not going to last, right? It's going to go away.
" Um uh so, so really they feel even you try to demonstrate that um uh and discuss what embodied AI means when you're going to have robots that also have these capabilities. um uh in the real world with us um uh they're thinking, "Hey, I I just got through a pandemic. I just learned how to teach remotely. I just learned how to once again um uh start thinking about VR in my classroom and XR and motion capture." And so they're just like, "Okay, so you know, slow down." And and that's that's how a lot of them feel is this is the change is too fast, too much, too soon.
Stefan Bauschard: (13:39) Now, how you know to you know, you talk really in a in a way about the humanities, right? like the first the first introduction a lot of you know professors had to AI was of course you know in late December uh finals finals times right chat GPT 3.5 is out and the kids are using it you know to write their papers and of course now now it's you know it's much better right it's more accurate it hallucinates less you know the top models are do doing all kinds of things to use tools uh to verify what they're doing and you know of course just yesterday we saw the release of Chat GPT-5 which incorporates kind of all the different functionalities uh into one platform and it's freely available at least for limited use right you get you get so many queries that you can you can use this any student can use it we see open a open excuse me we see Gemini offering their basically top model to students for free uh for for the year so you know how would you say first of all maybe even awareness of this you know as we're coming up into the fall semester are faculty even kind of aware of the devel developments, a significant improvement of the tools and that students have free access to them. And second, like, you know, how are they how are they responding to that? Are they are they changing their assessments or, you know, or do you see people just still like I mean, I see a little bit on LinkedIn, you know, writing detectors. I saw somebody publish like a five-point series against writing detectors and I was thinking, oh, I can't even read anything else about writing detectors. To me, you know, it's just it's just such a debate that that's, you know, kind of been passed by. it's it's over in my mind but I still see a lot of that with faculty but you know in your in your position you know maybe a university or all this collaboration you know you've done with other universities how how do you see the faculty reaction
Dr. James Hudson: (15:22) the reaction is really varied and uh and but department by department so for instance um I would say the majority of fac we we should also say in in our research we can get to later that roughly half the population will not adapt well to these technologies for for very personal, psychological and existential reasons.
So we need to kind of start there in which that some will never change their classes, never change how they think they're going to assess um uh because they are the domain experts. They're the subject matter experts and so they know better. So for instance, you know, in our research um uh computer science and English professors um uh have the most staunch resistance um uh um globally um and why is that? And we have more studies now because it hit them first, right? So it's not hitting dance professors and those in um um chemistry labs, right? That need some type of physical presence. Um uh it hit them first and and hard that being, you know, so um the domain expertise, they're they're the most um uh still the most resistant, right? because fundamentally the skills that they believe are the most important just like you know I would think that they would have to be able to identify slides right so like artist title date back in the day you know um being able to have this rote memorization skill um uh to move forward they believe they need syntax level writing for coding and they need sentence level writing for grammar syntax to understand how to actually communicate um um uh so to answer your question um uh many of them aren't doing anything and there's a reason they're not they don't have incentive to do anything yet.
but also those that do really want to do something are not given the the uh the guidance, the uh professional development or the alternatives of being okay. So now you're telling me that what
We need to also admit that the majority of college students take their classes online. Even the ones living on campus, the majority of classes they take are online. And so there's not a single right now online assessment that um uh an AI tool cannot hack in some way. Right? So but that's because we're leaning on um an assessment strategy that goes back, you know, to the founding of academia, even if you want to go back to the first academy in ancient Greece, which is, you know, um uh which is examinations, right? Whether it be oral or written, and then um uh presenting research, usually in the form of a research paper, right? So those are the two most common forms of assessment that we still have. Um both of which are horribly outmoded and don't work anymore. Um number one they don't work in terms of assessment um uh because once again as you mentioned right so the way in which content is created um uh undeniably moving forward I mean this is in Microsoft Word right so every word processor has this in it. Google has it integrated into every single product they have, right? Google Docs, Google Sheets, everything. And so, um, this is how we write. The question is now, you know, um, how do we know what students know when they turn in a paper, right, that they wrote with this? That that's the assessment question that people really have. Um, uh, but it but to, uh, to answer your question that was twofold. That being one awareness of the technologies. No, that being um all the conferences that we go to and both of you are painfully aware of this.
Um uh the peer review process in academia and publication cycle is painfully slow much slower than the development of these tools which have bypassed or way far bypassed Moore's law right back in 2023. I mean the head of Nvidia said we've had a millionfold right um improvement since 2023. Right? So that is um that's an exponential rate that we can't keep up with verifying.
So as an example, I had an English professor who is staunchly opposed to any type even brainstorming, even outlining, even anything with these tools. And I said, "Okay, so then so why why don't you show them how to do it? Use it yourself." They refuse to use it themselves, by the way. Um uh and they're like, "Because there's not best practices research out there yet to show me why and how to do it." I'm like, "That's because you're not doing it right. So you have to do it in order to get the best.
But but but I I I'm in international conferences this spring that are citing what they think AI can do from studies from 2023. So if you think of, you know, GPT-3.5, you know, hallucination rates, they're talking about red teaming it, they're talking about biases, they're talking about like all kinds of things that today are not a concern. So anytime you demo or tell them that we have PhD level capabilities with the Pro series, that goes back to December of 24. um um uh were met with, you know, disbelief um uh uh vehement disagreement that no way can it do what they do. Um uh but um even when they do see it, it's a complete shock. It's a complete surprise. So no, they are not aware of it.
Dr. Anand Rao: (20:20) James, what you're outlining is something that unfortunately resonates with many of us. the the concern that um the the traditional model of higher education just isn't equipped to deal with the rapid change, the evolution. Um you know, the AI is a significant challenge to higher ed. Um but it's not the only challenge. And so I hear from many colleagues and administrators, you they're also facing the demographic cliff. They're looking at funding cuts, what's going on with the federal government and grants. Um there are a lot of different challenges they're facing, but you honing in on AI as a threat. Is AI really an existential threat to higher education? How might the reaction to AI by higher ed maybe even change the nature of that threat?
Dr. James Hudson: (21:00) Yeah, you're absolutely right that being um I was giving a presentation um uh to a K through 12 school system just yesterday and um and one brought up, you know, that these these AI tools that teenagers are using are going to exacerbate, you know, them not developing certain social skills. And I'm like, this is the exact same argument that we had back with social media and smartphones, right? So, um the same concern over isolation, etc. And in the same way, we we had a myriad of problems that were um uh uh compounded together, right? So, you had various constituents wanting different things from higher ed, right? So, that being, you know, the parents wanted, you know, career outcomes, the students thought it should be vocational, the faculty thought it should have to do, um uh with their particular focus or developing the whole human, right? So uh politicians you know wanted specific outcomes etc.
And so you know all of those various pressures are what most C-suites and boards are still focused on is okay this AI thing that's your problem right you deal with that in the classroom we're going to talk about the enrollment cliff we're going to deal with once again um uh retention we're going to deal with tuition right we're going to deal with the problem with these grants that that we're not getting the funding for anymore. Um uh so you're right the attention is still sucked away right being redirected um to the previous problems before we had all this but to answer your question as far as AI because it's not being addressed it's just growing bigger and bigger and bigger. So where we started um the the abilities of most college professors in even November 2022 when we had the first soft launch of GPT um uh weren't that far off. We could have done something about it at the time and and started thinking about as this moves, you know, how do we adjust with it? But because we ignored it for so long, the vast majority of those in academia, um uh the the gap the the gap, the gulf that we have here, um between where we're still at and how it keeps moving, you know, um um uh uh uh further and further away from the awareness and the abilities of academics, um the more it does become an existential threat. um uh it doesn't have to be um uh but it will fundamentally reshape um all levels of education at at different at different um um uh different levels and increments. Right? So, as I as I as I say, kindergarten is going to be hit last because I don't see them uh parents um uh uh wanting their their 5-year-olds to be eight hours a day with a robot alone, right? So, I I don't see, you know, the social emotional development aspect, but just they're not comfortable with it. They're creeped out by it. Um uh but the further up you go, I mean, you have most college students taking classes mostly online and then graduate school, right, to where you're supposed to work almost entirely independently and then report back periodically, maybe every six months, maybe even year, depending upon your your thesis or dissertation adviser. Um um uh yeah. So you're going to see this domino like change depending upon um um uh the the need for human intervention um uh the types of skills and tools needed and the assessments but uh expectations in the discipline. So so so everyone you know kept talking about hallucinations etc. But certain disciplines aren't bound by external um uh um accreditation or um um uh uh external types of of uh of rule settings and mandates that they have to meet, right? So like with paramedics or um uh uh some of these others that are more kind of metric driven um uh we kind of make up the rules as we go in a lot of disciplines, right? It kind of you know we set the standard, we set the curriculum, we decide you know what needs to be read and taught and and tested on, right? So um it's not an external right focus and that's kind of what academia is is like no this is this is us this is external we decide as the subject matter experts um what this is um so the short answer to your question is um uh it is an existential threat for many reasons the one that hit me in 2022 which we'd already done eight studies at that point was that the model of um uh secondary post-secondary and graduate education as how we traditionally move up was moot that being in uh the undergraduate um degree.
I don't care what it is, right? I don't care if it's business administration, chemistry, history, poet, whatever whatever the degree is, it's it's all the same. You have um uh the fundamentals of your general education generally agreed upon by a state. Um uh so your math, your state history, US history, your literature, your art, you know. So, so we have that. You have the the introductory level vocabulary and intro to a given field. So you're able to know the basic methodologies, the basic vocabulary so you can talk about it and then you work your way up to a capstone which is just here's where we are in the field, right? So really it's just getting them up to speed by the time they graduate with their bachelors. Well, you can do that immediately right now. That that's what AI does is it can summarize in incredibly complex ways exactly where we're at in a field. So now what? Right? So then really you know undergraduate education becomes graduate education because if they can already do that and output that and the point of graduate education is creating new knowledge for a field and innovating for a field um uh then really we just kind of collapsed that whole ladder right um uh so that's the existential threat I don't know a lot have considered so far
Stefan Bauschard: (26:19) yeah and you know we can you know I like been talking about you know threats and obviously difficulties right but you you know from what you're saying it there's at least the possibility of of moving the ship right it you know see back in December 2022 there's opportunity it was easier to move the ship I guess I should say is what you're saying and it kind of every day of delay right it be it becomes more difficult to move the ship or reinvent the ship uh to change the ship but ultimately like a ship has a captain right a ship has a leader like what what type of leadership do you think is needed you know maybe first like at a university level uh to start actualizing some of these change and you know maybe maybe it might come through some external uh type of leadership obviously you know professors and that like they have a like you say a lot of control over their content but maybe whether it's you know some type of accreditation body or or just government leadership where do you see some maybe potential leadership even stepping in above the university level but you know from people who have respect by un respect of university administrators uh and professors maybe being able to you know reinvent the ship or steer it.
Dr. James Hudson: (27:29) Um uh that is you touched on the main problem that we're dealing with in the sense that there is no one party or individual that can turn the whole ship. Um uh it's actually four different major parties in order to shift it which is why we're not moving. Um uh so but but to answer your question about leadership right so you you really need visionary leadership that can see beyond the problems that we're having right now which is um uh enrollment retention um uh uh tuition um uh all this type of you know attrition from from institutions lack of government funding you have state and private um uh with different challenges etc. And so um but even if a president um a board and then the provost right chief academic officer um uh decides okay we're going all-in on AI um uh they're counterbalanced in traditional faculty governance by okay but you can't tell me what to do in my classroom right and so um uh so so they're they're at odds there and they cancel each other out quite often and the harder so for instance there was a wonderful book you both are very familiar with this point published in 2023 college president um uh entitled uh for for this is about faculty being I don't care what it is I'm against it right and so um so there is this innate sense of um and suspicion of anything administration wants to do especially if it involves meddling in their department their classroom what what they're teaching um and what they're expected to do highly suspicious and they will cry um academic freedom um uh um very quickly when it when it comes that even if it has nothing to do with academic freedom um uh that's still going to be silent
. But um but for leadership um uh really assessing and realizing this isn't going away. This is once again um another yet another which is very painful to think of. another factor they have to bring into their modeling um uh and their financial planning um uh in addition to the the demographic cliff, you know, federal funding um um uh uncertainties, right? So um uh so for instance, in my state here of Missouri, only 35% of graduating high school seniors this last May went on to college, right? Was starting college this fall. So that's a shrinking number. The rest a growing number. um uh vocational training, nothing at all, going to the military, right? So, those are growing. So, we're talking already about a shrinking pool of potential applicants they had to begin with. Um uh and AI is just another one. And so, it just seems like another nuisance that can be put off, you know, until later. But the visionary leadership will say, "No, this is going to within 5 years severely impact our institution if we don't have a plan." whether that's going to be complete integration.
There will be institutions by the way that will be completely AI free. I think of like sci-fi films like surrogates to where you have, you know, the these havens of human only, you know, areas where technology is completely banned, right? So, I'm sure you'll have a few of those, but that can't be the norm, right? So, um that'll be a vast minority of them. Um uh but yeah, so so that's the type of thinking that that you have to have. And then you have to socialize, right? So, you have to start um making people realize I I know this is an existential threat to you, your identity, your livelihood, your vocation. um uh etc. But it's not going away and it's going to exacerbate and uh speed up your fears if you don't address it, you know, now and have a plan to address it because it's not slowing down. What is happening interesting, you asked about GPT-5 and so we're finally starting to see what what's going to happen in that um uh we're looking at the development of general use tools, right? So, if you if you had the pro version of OpenAI's GPT um like I do um two days ago, you would have seen a plethora of models from 4.1 says good for coding, 4.5 advanced research, 03, you know, great 03 mini, you know, 01 Pro, right? So, uh but the general public didn't know what all that was or what the what the different models did. So, the whole point is they threw them all together um uh to have a general purpose model that's supposed to kind of guess. It's not doing a good job right now by the way, but kind of guess what you want, right? So if it needs to go coding, deep research, you know, just a a quick, you know, shopping list of things, but uh but that's that's where we're moving is kind of the expectation that AI will do anything at all times
. But that leads to a further disconnect with understanding what it actually is, right? And what's possible. So that's kind of where AI literacy and AI skills need to be because the general public have smartphones, they talk to Siri, they talk to Alexa, etc. uh fundament and they think it's similar, right? But it's fundamentally different in use cases and purpose. And when you pair that with a physical um body of some sort, right? So we're looking at a completely, you know, different society, let alone and supposedly the the goal of higher ed is to elevate society in general, right? So you have an educated populace, we have, you know, economic growth, you have, you know, less strain on um health care and um uh and law enforcement, etc. And so that lowers costs and once again is benefit for all. So you know you know a rising tide lifts all ships. But um uh but yeah, we're looking at more and more stronger but general use kind of tools to where um uh just as you saw the generalist thing kind of coming in.
Okay, now I need an analysis. Now I need a spreadsheet. Now I need a pitch deck. Now I need a slideshow. Now I need once again you to do um a deep research in a NASDAQ, right? Etc. So, you know, kind of this this level. And then you're going to have the hypers specialists that can take the tool that know what the different ones do and can go much further than they ever could before. Just like with research we're talking about, we have to relinquish control that we're not going to be able to keep up with everything that's being published, right? Because it can go very very quickly. The example um I gave to our administration in the winter of 2022 where I lost so much sleep realizing what this meant was okay. So, just imagine this. you set up, you know, a model that's going to um ingest everything on a given topic. Like I need a new um uh um uh uh prostate cancer drug, right? It's like, okay, go through immediately ingest almost instantaneously everything that's been published on prostate cancer up to this point. It can do that, right? Okay. So now that I know all that, okay, now um output um um a kind of protocol um a methodology to uh to um uh to develop that and then test it in an internet of things lab, right? So it's like, okay, here's the protocol. I'm going to send it over, right, some uh to an internet of things lab, which can then work with this with mice or whatever it needs to completely free of humans. um uh take the results, send it back to another model, um synthesize the results, publish them in a medical journal, right? Which then adds to that um uh previous body of knowledge and then rinse and repeat. But this process, right, can go in certain fields like physics, mathematics, you know, chemistry, right? So, um rapidly, thousands of times, right? You know, a second as we're moving forward. So, we're going to have to develop protocols of, you know, what are we um uh when it comes to researchers and the whole system of higher education, right? So, we're still subject matter experts, but we're more we're more editors, we're more quality control, we're more putting up the systems um uh to ensure that the um um the very powerful AI, and we haven't even talked about quantum computing yet, right? So, that that's that's without quantum computing um is going to be doing its thing. But um uh but yeah, that's the irony between that the models are exponentially growing in power and capability, but at the same time um uh knowledge of the different types of capabilities is is dropping in many communities.
Dr. Anand Rao: (35:24) I really appreciate the way you've outlined some of these forces in higher ed that many of us recognize. You know, it's the butdding of heads at times, but but there is something to be said for that model of shared governance. I mean, I've chaired our faculty council um and have worked with, you know, colleagues and department chairs and administrators to try and grapple with some challenges that we're facing. You know, I know that within your your new book, which has just been published, the adoption of artificial intelligence and inertia higher ed, exploring complex resistance to technological change, that you've looked at some of the some of the historical examples and and you're thinking about this. So, let me play devil's advocate a little bit and say maybe there's something to be said for how this traditional higher education model provides a more thoughtful response. Um, and so if we look at something like maybe even as simple as introducing calculators into mathematics courses. Um, and the way that that was grappled with and maybe it was a little messy at times, but now we've gotten to the point where we know how to best integrate it. Getting to your colleagues' response of we need best practices, we need to be able to explore this and be able to verify it. Um, it seems like, you know, one concern that that you've already raised and and you've been able to really elucidate well and I think most of our colleagues have heard is that this is happening so much faster than you would find with other technological change. Is there is there more to it than just that? Is it really about the nature of the change or is it really more about the rapidity of change that we need to adapt our model?
Dr. James Hudson: (36:48) Yeah. And so, uh, you're absolutely right that being if we think back at calculators um, and the example I always give, you know, is as follows. We had time before right so the model of higher educ let's just talk about academia and scholarship in general right so the entire system of even the scientific method and publication is designed right in such a way to allow for those to review right um of what's already known in the field by other experts um uh offer feedback and guidance um uh uh refine things or reject them if they don't if they don't need to be in the body of of that field's knowledge right and then go through the process right so that that cyclical process though for most disciplines and Q1 journals still takes a year or two depending right so I I know social science and many of the science journals are speeding that up incredibly rapidly because they're completely useless a lot of the le e etc you know within a few months um um but um as far as um uh the the model for academia you're right it's it it goes back to you know um the year 1000 right you know kind of the University of Bologna and then later Oxford um uh in which you have this system of checks and balances much much like many you know governments um uh which ensure we don't move too fast and don't consider things and do our thoughtful research and reflection and verify um uh but that we move somewhat um once all parties agree right but um but that takes a while right so if we're thinking about the time like Google launched in 1998 um um uh uh English faculty were sure that students would no longer be able to write a sentence because they would be thinking in search terms, right? So they were absolutely Wikipedia comes out a few years later. It is the devil, right? So it once again, oh my god, anyone can put anything up there. Disinformation, misinformation, it it's just awful. Now try to get something on on Wikipedia, right? So it is in very difficult to get something moderated on Wikipedia, not taken down. Right? So as a matter of fact, Wikipedia a year ago stated that they absolutely ban the use of any generative tools. Right? So they're they're like the institution now which is very interesting. Um uh but that took right so nearly 20 years right um uh but then you have you know social media 2006 Facebook smartphones 2007 okay well now what do we do about smartphones in the classroom um uh and then you have cloud computing right so we the learning management systems right so once again we didn't we didn't have everyone on the same page with pulling things from the gradebook and learning management system until around 2015 right so if you think about that that took a while we had them but how we used them took time and um and as You mentioned from a leadership standpoint, the only way we got that done was making it um um a requirement. That being everyone has to put their grades in the learning management system because that's how we do grade pulls and get them out right on their transcripts. So only because that was forced and so um in in many cases that is what leadership is going to have to do is minimum use of uh of kind of AI, right? And define what that means, right? So, at least talk about it, at least demonstrate it, at least give students reflective exercises on it. Um, but it's going to have to be, you know, a minimum use kind of policy in that way. But, um, um, uh, but to your point, you're talking about the, um, um, the way in which this works. Yes. if we are um uh not basing it on what we're seeing and what we're actually getting out of our research on a truly earnest um uh sense of wanting to find out how AI is actually the pros and cons going to be integrated into academia as a whole and then each individual discipline right so that that that's what their jobs kind of are and then what does it mean for policy what does it mean for plagiarism what does it mean for academic honesty so all those other things um uh the problem with that number one, even if we did do that is if we made a policy for this fall, it would be u completely irrelevant by the end of the term, right? So, we'd have to have a completely different policy because completely different use cases and abilities are going to develop, not to mention cultural uses and how we're going to see this evolving in our daily lives. Um, so there's that problem, but that's not what's really happening.
What's really happening is um yes governance structures right you have this type of inertia built in because the president and the provost want to do something as an initiative faculty council and/or department chairs and/or deans push back right um uh uh for their own agenda right and then you're kind of at this deadlock and then of course we're not even talking about HR and IT right so kind of as far as leadership Stephan that's what we're talking about is um that's the that's the holy trinity you need to together and that's not happening is that you'll have a president saying we're going all in on AI and then you'll do all this um PD setup and this training and then it will say you can't use it. I did a training once again at an institution for nine hours straight at the end of which it says that's great you can't use it on campus right completely was like completely undermining everything that we're showing them they can do with it um uh and then you have HR that mean so so HR has to come in and say yes you can use this per your contract and it's not a violation right of your job so all three have to be in sync in their messaging and they're not right so I can't think of hardly any example to where they're all in sync because AI is the gatekeepers and this is this is an existential threat to them as well, right? Um sorry because it you know no longer can stop you from doing something which is a general use tool. Um uh but anyway so that that's kind of the leadership side if we're talking about what we would need to do to that. But so we have the the governance structure. Um we also of course have a lack of training and resources when this happened. Um we did studies first. um the instrument we shared our information with Ithaca Research um uh they took it and shared it with other R1s and what they got back is what we got back in that it's it's not it's not an administration problem it's a teaching problem right you deal with that you decide what you want to do with it on the ground and then nine out of 10 faculty still today have a statement in their syllabi stating you cannot use AI in this course because they don't know what else to do right that being it's like you know if if I let them use it um uh you have to specify when they can use it think of the high school student who sued because he was failed, lost a scholarship because he wasn't told he couldn't use it for research. They knew they couldn't use it for writing, but you didn't say I couldn't use it for research. So that mean they don't know what to specify because they don't know what it can do and the workflow. Um uh so we have that a lack of training and resources.
That mean if a faculty really wanted to integrate AI and um a lot of our business faculty actually are like we're seeing this in employers, Amazon stated that every employee has to show how they're using AI for efficiencies. Shopify CEO says, "Show me you can do something AI can't or you're not getting hired." So business faculty are saying they're going to need this and so but they don't know really what to do, right? So okay, but then what do we do to help them, you know, uh get prepared to be AI ready? So lack of resources and training and but really the big one is going to be identity and tradition, right? So that being um uh that's what we're talking about with computer science and English faculty. If it can do what you do, then who are you now? Right? in this way. Um uh and so it goes beyond job insecurity um in that way and displacement. Um it goes down to the core of your being. I mean most academics I know are that they are the poet, they are the historian, they are a a chemist, right? So that's who they are. And so you're you're threatening to take that away. But then there's so that that's one thing that we really need to get past in the discussions, right? So in the discussions in 2023 anytime we tried to do any training on on AI uh we had everything laid out where we have all these steps things we're going to show them etc. we hit a wall in the first five minutes with discussions over uh over ethics and plagiarism, right? So, they wouldn't even consider it because they would stop and go, "No, no, no. It's stealing from creatives. It's fundamentally wrong." We had some wild analogies, right, that were very disturbing for what they were coming up with, including slavery, for instance. Um, uh, it was it was like, whoa, where did this come from, right? Um, uh, but you'll note that those gave way. We don't even hear notions of ethics. to change to responsible use of AI instead of ethical use. It was all about ethics 2024. Now you're just hearing responsible AI. So they don't use that excuse anymore. So they move to a different one. But if you get past it, what's the thing behind the thing that's really they're they're fighting against. Um uh it's it's that it's their professional kind of identity or the tradition that they're in.
So the tradition of higher education is also under attack, right? in that way because the future of higher ed um uh once again um uh is going to be one that fundamentally serves a broader audience that fundamentally is more modular and has more on and off ramps. um the four and 40 model which is the biggest problem that higher ed was built on after the GI bill which is you go to school for four years you have that career for 40 years and retire that's gone right and so but we haven't changed the model the model still assumes you major in one narrow field and then you're going to be accountant the rest of your life which is why when we have students come in you know and they say yeah what what are you gonna major in etc and they say things like oh I'm going into accounting we wse we're just like that is the number one profession that's going to be automated first, right?
But but their parents, you know, are thinking that's how you get ahead, you know, um uh they were told if they get get a degree in computer science and they program, they're set once again with the with tech jobs, number one unemployment among Gen Z computer science majors, right? So it it it just it it completely flipped on what that is. So um uh so yes um if it's done right and we are uh a good example of that though is for instance anand is um uh faculty will not vote to get rid of their positions right and so in a lot of the um uh the movement we need is resource reallocation um um but to do that you have to get you know the faculty on board with the elimination of certain positions. Maybe the faculty are moved over because we're looking at 39% of core skills, right? That being um are completely replaced in most professions by 2030. Uh we agree on that as far as the different reports. 39%. So that being maybe they have to get a completely different degree like I did, right? And hop over and do this because it's a new position or they're part of the 59% that have to upskill, you know, for basic um um a basic new skill that is stacked on top of what they already do, right? So they have to have basic AI knowledge. Fine. Um uh but again we don't see in any of these examples um um uh people voting themselves out right. And so it goes against self-preservation fundamentally.
Stefan Bauschard: (47:43) Yeah. What would you say you know we talked we talked about like you know a lot of the barriers and you know some opportunities for hope right like Lindenwood what are some of the things like you know you know you've worked on or led or you know even just maybe stuff in your own right whether working with students or hands-on that like you found to be like effective you know that have made some changes maybe you know even at a micro level and especially maybe any that you think you know are in spite of all these barriers practical to recommend that other people adopt.
Dr. James Hudson: (48:14) Yeah, absolutely. Yeah. So, we started from the beginning which led to at the very end our human- centered AI programming. So, that that's what all the research and best practices led to. But, uh at the beginning, the first thing I did in 2021 given the mandate of uh looking at um AI and higher ed overall um uh was I formed the AI ambassadors. And so, you know, step one, I built it on a previous VR champions initiative, which is how we got one person from each department who was interested in integrating immersive uh education into their areas. Um uh and that's how we determined what the use cases are. We built out libraries. We made new scenarios. Same thing, rinse and repeat. We Okay, so we need to see who's interested in or already using AI in their areas in all these fields already in in in our particular university. So, this is best practice. Everyone should do this if they haven't already. Um so uh because you need no one's going to listen to someone from the outside. They need someone um from inside of their department to say no no no this is how I use it, right? So um uh that absolutely has to happen because they're not going to listen otherwise. So um uh so from education to business to chemistry, math, um uh English, so we did art, right? So every single one we found a multitude of use cases for AI, but they were always under the hood, right? So they were always prepackaged, ready to go. Um, uh, uh, solutions where you were the passenger, not the driver, right? So they couldn't change or modify anything with the tools they were using. Um, but but that was number one was an awareness that being, oh, okay, it's already being used, right? So it's not a big deal. Um, uh, the second one as far as classroom, it took us a long time and and a lot of trial and error because we couldn't figure out why so many students were resistant to even using the tools. like many of them said, "Okay, we're going to use these tools in this class." They're like, "Uh-uh, I'm dropping." Right? I refuse to use it. I'm not going to. Or if they did stay in, we would get the survey results um at the end uh for mixed method studies and it would say things like, you know, it's it's creepy, it's soulless, it's stealing from people, this is wrong. But we couldn't figure out what that what that was. And so really um uh um coming down to understanding human psychology and a needs assessment for your students is really important. what student population are you teaching to? Right? So, um what we determined from all the various institutions that I mentioned earlier um uh we couldn't tell. It wasn't age, it wasn't gender, it wasn't race, it was none of that. Um uh it was what they were learning and how they thought they were going to use it. So, if they were learning something that was their identity, like writing poetry, they rejected it outright. So, you could even give them studies and go, "Here's here's five poems. We're all going to sit down and rank them and which ones are the best." They always chose the AI one and then when they told them that they're like, "Oh, it's awful." etc. Um, anyway. So, but we you have to be aware of if you're in a vocationally oriented class, um, being careful how you introduce it. That being if you introduce it in such a way and say this can do what you thought you were going to do, they're going to shut down, right? So, um, you always want to, this is, this is very interesting as well. Um, you want to talk about the negative last. Um uh so we did lots of studies and so discussing the ethics, here's the bad parts, here's the problems, etc. If you do that upfront, um uh which you think would be best practices, they stop listening and they shut down. It's just negative. You're setting the expectation up that it's negative. So you you you warn them and give them all the information, etc. at the end and then they can apply what they've learned through that filter and lens. Um uh but really if it is um a skill that they see as core to their future job or identity um uh you you you you put AI in the background. You don't you don't foreground the capabilities. You still state that the skills that they're learning such as sentence level writing or coding are significant but they need to be aware of right that this is the reality when they go out in the job market. It's good to know that I'm teaching I'm teaching a coding class right now in Python. um the first week you know this graduate student asked so why are we doing this because you just showed us with these low code no code tools how it can do the exact same thing etc like here's why right so then we have to explain here's the realities of what you're going out and why it's still useful to understand the habits of mind right in that way um uh that is how do you think like a programmer how do you think like a systems architect how do you think like a physician right so um what questions would you ask what is your framework what are your methodologies right rope memorization is not as important but they still need to understand those frames of mind. Okay, so that's a second best practice.
Um the third of course is going to be policies. You need a clear policy which states this is the one thing that a lot of people hid behind for the first year um uh at our institution was no the policy doesn't say we can use it. It didn't say we couldn't, right? But it didn't specifically address what tools could be used and for what. So you need a very clear policy of what can be used by faculty. um uh and staff, right? So different for HR, fiscal affairs, academic services, etc. Addressing the different types of concerns they have, which was really big in the beginning with data privacy is not as big of a deal now. So we stopped hearing as much about that. Um uh so policy, right? So clearly communicating no, you can do it. Okay. And then you need um uh to have your um professional development for faculty and staff there to show them how. Right? So just saying, "Yeah, go for it, Wild West." which is what a lot of people are doing. um uh then you get very inconsistent results because they don't really know what do I do with this right so um uh because we still have that type of resistance um uh so but moving on then to like you know curricular development looking at how specifically is AI going to impact my field that's the best thing that that that faculty can do right now to benefit their students right so um is is it automating note taking right for physicians when they're when they're going in and getting background information and medical histories. Is it going to be, you know, um checking for any type of negative um uh reactions that different medications would have for pharmacists going through and learning chemistry? Um uh so, you know, what is it actually used for now? And then knowing what they know about their field, which I don't. Um uh what likely is going to be the use case five years from now, right? And then start thinking about okay, how do we start building that in and at least discussing it, right? and then by the end giving them some skills so that there are going to be um uh um more marketable once they finish up. Um but but yeah, so that's kind of the broad picture of get those on board, have them talk to their colleagues, right, to kind of spread, you know, awareness, then enthusiasm, then use cases. Develop clear policies. Um uh the biggest problem we have right now is the stoplight policy for syllabi which is the um the um no use of AI some use of AI and then go nuts right you can use AI for anything the problem that we're having and that institutions you look at reports are having is they put in the okay you can use it for some things right those that are like AI forward but they and it says and we'll tell you where and when so like they're like okay but I I wanted them to be able to use it for brainstorming and for putting rough drafts together and this but I didn't want them to use it to take the exam right so but they didn't say that right so um the lack of clarity around that I'm sorry I probably went on too long
Stefan Bauschard: (55:40) please continue no that was really great James
Dr. Anand Rao: (55:44) yeah you know one thing that I I really appreciate throughout this discussion is this thread about identity um and you know as you pointed out we're all very used to a very traditional model where you go to school for four years and you know what you're going to be not just do but what you're going to be for for the next 30 40 years in your career. For faculty, you know, I think we all have a lot of sympathy for someone who has dedicated their life to their discipline and that's part of their identity and it's very hard to let go of that. But you've also pointed out that for students who haven't even invested that time yet, they've already worked with that assumption that what they're going to study is going to be their identity. Um at the very beginning of the conversation though you also noted the transformation that's going to occur over the next several years where we have to really come to grips with the idea that many of us need to be generalists uh and to have a very different concept of identity and and I think what it comes down to is purpose you know for for many of us talking about how AI is going to influence and change society it's going to change our understanding of purpose if you could say a little bit about that because I think much of what you've outlined is incredibly helpful especially when we're thinking about what to do in our institutions, in our classes, but it seems like some of it that's fairly short term if this transformation is going to happen.
Dr. James Hudson: (56:59) Yeah, exactly. So, as I as I kind of reminded um uh we look at and rank schools based upon graduation rates of cohorts of five years. So, um we give them five years to finish up a bachelor's degree, not four years of what they're expecting. So, what that means, as I pointed out, is the class of of 2030, the year we're all pointing to as critical for change is starting here in a few weeks. All right? It's starting this fall. So, um uh it's not off in the horizon. The ones that we are educating as freshmen starting at the last week of August generally um uh are going to graduate the year all this hits. So, it's not far off and theoretical, right? it is we're going to see more at least economists um um predict that we're going to see more change in the next five years as far as you know economics structuring um jobs uh the workforce than we did in the last um 200 years right so that that is a lot of change in a small amount of time and very difficult to wrap your mind around um uh so so yeah so so in in other research we started um looking at we we hear and we know what's happening we see the downsizing in tech jobs tens of thousands of them. We see the return to work orders which um uh uh many economists state are are kind of smokescreen for down or right sizing their workforce because of what AI can do. We're seeing the freezing of entry-level jobs for Gen Z, right, that just came out and um and the rising unemployment for that for for that age group. So, we're seeing the impact already, right, of of what's happening. Um uh so the first step is of course just saying, okay, this is happening, right? Right? So that's kind of the first thing we need to get through um in order to propel or get any type of momentum uh moving forward. The second question is okay, what do we do about it? Right? Or what what's what's going to happen? The problem is no um uh prognostication or uh or model that we have because the Stanford 800 model was thrown out the window because we automated creative work first. Um and blue-collar work is coming with robots, right? So it completely flipped on its head. Um uh uh so no one really knows um um the time frame and disagree. We know it's speeding up. We know we're going to lose, right? You know, 90 million jobs and create 170 million more. But that means we still have to retool all those people for jobs. We don't know what they are yet. So that's why um that's why really looking at the future of education, it requires um a massive restructuring um uh because you're going to have to make room for the uh the millions of workers now that need new skills and the structure of the four-year degree doesn't really align with that. Um uh so we're going to have to start thinking and what skills are we teaching them. So, as of now, if we're talking about restructuring higher education, um, uh, at the undergraduate level, think about what's actually needed, what everyone agrees with. So, everyone agrees you need some basic AI skills, some basic back in the day was computer literacy. I had my first computer class. So, you need you need to work Microsoft Office. You need to be able to open Word, right, and do an Excel spreadsheet. That's the basic stuff, right, that we need to kind of build in. That's not there. There's no required AI stuff in general education. So, that's got to be there. Um uh then take all the gen ed as painful as this sounds you can still teach it through the lens of philosophy through the lens of literature through the lens of mathematics but the actual outcomes have to be those skills that we know are important right so that uh employers agreed on before the pandemic right so you know communication critical thinking empathy teamwork leadership right so those are your outcomes right so if you get to them by teaching Shakespeare that's great right but that's what the core needs needs to be of the education. Then moving on from there, you break down those that's the biggest problem we have right now is many business majors for instance have such specific requirements that there's no room for any electives and so but they're tied into like accounting right is a great example right so um they're doing all their coursework for an eventual career in one narrow area of business um uh which we know the the accounting firms realize is being automated because you notice now their big selling point is well we don't need you don't need us to do your taxes, but we're going to help you try not to get audited. I don't know if you've seen those commercials. Um, so like because they know that's not what they're they're needed for. So their days are numbered. So um so get rid of that very narrow focus. There are still skills that we need, but think about in terms of meta majors, right? So that being um a broad grouping of skills that are going to go together for this new generalist world. So you know, is it going to be the natural sciences? Is it going to be you know like um uh business right in this way but broadly speaking right is it going to be you know um um but the point is is that it is that with those you can group these kind of like skills together whether it's quantitative skills qualitative skills right so um uh various business skills um uh interpretive skills humanic skills whatever that might be but they need to be flexible right and they need they need to be able to think broadly and adapt quickly so um uh the Most important outcome though for education at this point is um and we did a very poor job of that when I was in school is metacognition, right? I was handed thousands of pages, you know, every week, you know, as a grad student and said go read that and then come back and talk about it. No framework, no reason why, no methodological anything. It's just go figure it out. Right? So that being so what we need to do now is say you are using this to learn this specific thing. So skillifying the syllabus um which is what which is what we do right this you're doing this for critical thinking but then also clarifying you're going to do this to learn how you learn best right so and fastest you the person that can learn best and fastest and upskill more quickly will win right so in a manner of speaking right as far as um being able to transition in this new economy by keeping your identity small and um being willing and open everyone we've talked to that that has transitioned well thus far has the same quality qualities and characteristics. They have a growth mindset. They don't they don't think that they don't need to learn anything else. They're done. They're they're always like, I oh, that's interesting. They always are learning new things. No one told them to. They're not being paid to. Their boss didn't tell them to, but they're always going out and learning new things. Um uh so you have that growth mindset. But the second one, which is interesting, is curiosity. They have an innate curiosity about the world. That being that's interesting, right? So those people when AI came out were like that's interesting. I wonder what else it can do. I wonder if it can do this. I wonder if I can use it here. Right? Innately curious, right? Which then led to them adapting easier because it didn't threaten who they were. But um the skill set for students has to be um uh what we know will prepare them for the future. That doesn't mean we have to do away with we can't. Right? So I'm not um uh um so for instance we're we're partnering with a company um on a tool called Integravise um uh which is where students upload their papers or exams and then a clone of the instructor comes up and then ask them about what they submitted right so that we can determine if they know what they submitted right so and you can grill them as long as you want you can add rubrics etc um uh that's a stop gap measure because you know for the next 5 years we're not getting rid of exams and papers right that's to get us through is tools like that um uh but completely rethinking in the interim. Okay, so we've got this going for the meantime. Now we need to completely rethink um what we're actually doing to assess those and that takes you asked earlier about best practices. You got to bring students into the the first conversations on how things are assessed, right? So you got to get buy in assess. You can say here are the outcomes. Here's what you have to learn for this class. Now let's agree how we get there. How do you want to demonstrate that? Right? But we all have to agree. And once again, it has to be it has to be accessible, right? It has to be actionable. We have to be able to do it. Um um but you still have to be able to do X, Y, and Z. And once you get that buy in, students will enforce it with each other. Um if they're the ones that made right um that particular strategy. Yeah.
Stefan Bauschard: (1:05:10) Anyway, yeah, just you know, in wrapping up a little bit, you know, we've mostly talked about uh you know, what professors can do, what administrators can do, leaders can do. You know, you mentioned students coming in, right? They're going to be this class, you know, someone I work with referred to as this class of AGI, which I thought was creative, right? But heading into 2030, whether we call it AGI or just some radical change, uh, you know, that's the world that they're going to graduate into. One one of those is my own son. Uh, he he'll be starting college this fall, you know, and I was thinking, oh, I want to give him some advice about AI, which is a little hard because if I bring up AI, he he kind of doesn't want to talk about it, but other times he's brought it up. uh you know once he he said well what kind of work are people going to do? This was just like he was just sitting there you know we were kind of watching TV and he kind of brought this up and then the other day he brought up he's like have you seen these things like where you can't tell if the person's actually the person um you know that really I think he just saw it on TikTok or something but that really disturbed him. So I was thinking, oh, you know, what kind of advice, you know, would I give them? I mean, the one thing is, of course, to develop, you know, whether we call them the soft or durable skills, work on opportunities for that in college, you know, to try try some of the tools. You know, here I'm definitely willing, you know, to pay for pay for a tool and you can access some of these for free. You know, the thing I thought is like if you're ever falsely accused, you know, come see me. I got you on this one. Uh the fourth, you know, I was like, but also like, you know, don't you know, professors are in different places, right? So you know try to take courses with professors are going to help you integrate this into the workflow and understand but you know you don't you know you just have to accept you know that people are in different places. I wonder you know what what you might think of some of those pieces of advice or what what advice might you encourage me to give my son or you know just advice you have to kids entering college or in college now?
Dr. James Hudson: (1:06:56) you brought up a great point um uh which our research confirms that being Gen Z is the the hardest to upskill an AI, right? Um uh and rightfully so. They were the last generation you you mentioned, you know, kind of AGI as far as higher education is concerned, but they were the last generation to go completely through secondary education with one set of expectations and then the world changed and those are no longer uh valid. So um so they feel cheated. They feel um very ambivalent about technology even before AI. They uh they they they really idolize the 1990s, right? They like look not a cell phone in sight, right? Everything was simpler, right? So they have this love-hate relationship with technology to begin with.
And you're right, I was talking to, you know, the mayor here and uh and his grandson, he was saying his grandson loves to write and so won't even talk about it, right? Just like I love writing, etc. Can't even imagine that that's part of the world in this way. So um uh so the first um um and so it's it's a very complex again um uh shifting of uh uh expectations as you were mentioning of identity right so what they thought they were going to be is being fundamentally challenged um because when they graduate that job might not exist I mean many of the jobs they're going to might not exist and so uh that's kind of step one right is teaching them that their job that they think they're going into is not their identity right so That's the that's the hardest thing for us to shake in America. You go up, you meet someone, you go, "Stephan, how's it going?" "All right, what do you do?" That's the first thing we ask, right? Because we tie identity with job. So that's kind of the first thing is showing them that you are as a parent modeling it more than your job, right? So you go out, you're father, husband, you might like fishing, you might like, you know, whatever it is that you like doing, but showing a diverse range of interest, skills, and hobbies. So that's one of the things in which we we can recommend for them. um uh as well don't focus solely in one specific area. That is fundamentally one of the best recommendations that I have for students um uh because we don't know. What we do know are the skills that matter. Um uh but um uh so don't think uh don't think that they need to major in something um uh in order for them to have a successful career or to be safe financially because that is not true anymore. what they need to do is um uh major in if they're limited right to majors now you can do interdisciplinary studies no one knows what that is but you know um uh uh major in something that they're passionate about so that if they're in a class as you mentioned with a teacher that um doesn't fully embrace if they do like to use AI if they don't that they're still passionate about the subject they're still passionate about you know what's happening instead of just checking a box um uh and they're more likely to learn those skills we talked about um uh if they are um passionate and invested in what's happening in this relationship um uh I I I would always say you know um you know 20 years ago right when I started teaching um that even if you to my to my mentees um uh even if you absolutely hate the person that's teaching you hate the subject matter you see no point in it um uh trust me um or you think you know everything right so you go in there um uh you you focus on um what they can offer you because you can get something and an experience and a skill out of every scenario if you're open to it. You need a growth mindset and going into it. But really, yeah, it's it's diversifying a set of interests. It's getting basic AI literacy. If he doesn't want to, you know, learn all the ins and outs of the different types of models, that's fine. But what the number one thing that we are cheating students on is not showing them what it can actually do. So that's what I tell them. that being you can't you can't get hired for Shopify or Amazon now unless you know what the AI can do and you differentiate yourself bare minimum you need to know that so like keeping up with oh okay it can code it can do this all right but it can't do this right so um it can't debug itself with these specific you know these low code things we still need backend you know kind of um developers to do that etc so uh so that's kind of it so just keeping up with you know roughly what the capabilities are um uh u diversifying interests, right, and skill sets and um focusing intentionally on developing those um uh durable transferable, we call them power skills. Um uh for instance, um uh and so there are different ways to do that. If he's an introvert or an extrovert or some of those, you know, very broad categorizations, you know, my son would just stay in his room and play video games all day if he could. Um he wants to be a game designer, but um uh so but or does he like sports, right? So developing teamwork by a sport. if he doesn't like sports, how do we develop that type of that skill of teamwork or leadership, giving him the idea of being over a project? Um, or once again, you know, developing different communicative strategies. That's kind of the big confusion that a lot in the humanities have is a jobs report just came out and the top skills, the number one is communication. So, what they think that means is traditional writing, right? Like composition. But what they what employers actually mean is knowing how to communicate um uh to different audiences, different ideas, right, across different media. That's really what they mean is, you know, how do I communicate short and sweet in this pitch deck, right, to this person? You got 10 seconds to give me your elevator pitch, you're done. Or do I need to once again give backstory and let them know a little bit about, you know, who I am and buy in and develop a relationship. but being able to know the difference and be able to switch between them, right? So, that's really what they mean. Um uh but but but anyway, I have the the book we can talk about, you know, um uh next time which addresses specifically that as far as from a parenting standpoint.
Stefan Bauschard: (1:12:49) All right. Well, thank you uh thank you very much for your time, a lot of time uh today. Um and uh we look forward to following up. Yeah, I saw you as I was going through all all your publications. I saw many and the book uh for the giving the advice to the parents. Yeah, you want to follow? Yeah, good.
Dr. Anand Rao: (1:13:08) This has been fantastic, James. Thank you for your your time and and so much to think about. Um for any of us who want to follow your work, where can we find you?
Dr. James Hudson: (1:13:17) Yeah, easiest way, LinkedIn. So, you just um put in my name, James Hudson. H U T S O N. I'm the first one that comes up. um uh uh the more we can have um in this network of varying perspectives and um different ideas the better. So um uh contact me. You can of course find me on the university web page as well at lindenwood.edu. Um happy to discuss further.
Dr. Anand Rao: (1:13:39) Excellent. Thanks again for being here and we look forward to following the discussion hopefully in a future episode.
Dr. James Hudson: (1:13:45) Thank you so much. Thanks for having me everyone. Have a great day.