Hello and welcome to Espeaks, the show where we explore how organizations build the skills they need to stay competitive. I'm your host, Corey Nolles, and today we're talking about one of the biggest challenges facing today's workforce. How to keep up as AI moves from experimentation to everyday business reality. From generative AI tools showing up across teams to the difficulty of scaling AI responsibly, the need for effective upskilling has never been more urgent.
To unpack both why this demand exists and how highquality AI education is actually built, we're joined by Dr. Luke Hobson, assistant director of instructional design at MIT XPRO. Luke works closely alongside MIT faculty and industry experts to design professional learning programs that focus on real world application. Luke, welcome to Espeaks. >> Awesome. Thanks so much, Corey. Looking forward to this. >> Same. Same. It's great to have you. Um, I guess to start from your perspective, what's really driving the surge in demand for AI education right now?
And where are where are organizations feeling the most pressure? I mean, simply put, it's all that people can talk about. It's it's impossible to go into any social media platform and talk to any colleague and of course they're always talking about like, well, what is the latest and greatest with AI? And as new things keep on coming about, you're trying to be able to keep up with the trends to say like, oh, now there's sort of 2 and oh, now notebook LM has made this new update and like what exactly, you know, is this entire direction of where we're going for everything and not just thinking about it as in the individual trying to learn all these various skills as you mentioned about you have now inside of different companies and organizations saying AI is where we need to be able to go.
It's like great, but what's the plan to be able to do that? And >> there's a great meme about it. >> Yeah. Oh, yeah. Of course. And there's this like, well, well, where in AI? There's a thousand different directions that we can go and where exactly do you want us to land 6 months from now, a year, 3, 5 years? What's the impact going to be? What are we actually going to measure for success? So, all these different types of metrics and capabilities that feel like it gets thrown out everywhere that people say like just do this and do this and say, hey, it's that's actually really hard to think of >> and to do it well.
So yeah, it's it's been really interesting seeing this different type of industry trends and where all this is going. >> Absolutely. You know, a lot of professionals feel the need to learn AI, but that's that's just so vague. What kinds of AI understanding or skills actually matter for experienced professionals today? >> Well, it's funny because when you say AI, what everyone now thinks of, we kind of like jumped a couple of different types of steps.
When you say AI in 2026, everyone immediately thinks about chat GPT where it's like, hey, AI has been off around for a while. This is it went from AI to everything is Gen AI to everything is chat GPT and you know and so on and so forth for all these different types of things which is definitely interesting to be able to see from that perspective. But when we're thinking about we're going to be learning on the fundamentals about how exactly does AI work because for a lot of folks if you hear from them they think that it's magic and that you could just put in any form of prompt and you get this magical response is life and wonderful you can go in to use this and you're like yeah that's not how exactly this all works out.
We need to be able to really understand the fundamentals and the capabilities of what exactly are we trying to be able to do? What is the goal? And then how do we work our way backwards, reverse engineer it basically to be able to get the different forms of desired results. And whether that does come from using something like with an LLM or if we're now thinking about it from video generation or for voice generation or thinking about trying to improve the workflows or processes, whatever it is, really trying to be able to nail that down for that type of niche and that specific skill set and then we can drive in that different form of direction and make sure that you're getting on the right track to where you want to be able to go.
Because Cory, I'm not sure if you've noticed, but uh it's kind of overwhelming to learn. >> It's so overwhelming. It's so overwhelming. I absolutely love a little bit to be like just just learn AI. And you're like, "Yeah, okay, sure. Let me get right on that with every single thing out there. If I attempt to search this or Google it or now just to you whatever the new phrase will be for Google it, it'll now be go perplexity that, go >> applaud that, go." I don't know.
You know, it's >> have the right ring yet, do they? No, we don't we don't have a name yet, but it's just so much to be able to try to unpack all within once. So, trying to be able to find that type of lane and then to go from there is is kind of like the first step for trying to be able to figure this all out. >> Absolutely. Well, you know, next I'd like to ask about a couple of the courses you've offered. So, one of MIT XPro's flagship offerings is driving innovation with generative AI.
Who is this course designed for and what problems is it meant to help solve? So when we're thinking about how we built that course and trying to think about who is going to be taking this, it's kind of funny because you always imagine that you have the audience in mind of working professionals and you think about different types of sectors because we do talk about drug discovery. So you're going to have the medical folks and the health care folks and then we're thinking about other different types of industries perhaps with thinking more about it from a business perspective or we're going to have uh accountants take it.
And then we launched the course and then it seemed like really everybody who was like I need to grasp this because my manager or my skip level manager is telling me that we need to bring AI into what we do and I don't know how. And I remember the very first time that we launched the course. I was meeting with all the learners who were coming in and I spoke with one person and she was the head of marketing and I was just like marketing okay interesting.
I'm like why are you here? What are you hoping to be able to learn about? and she's like, "Well, I was told that we now need to be able to use AI. I really have a very small team and I don't know how to use it." Like, fair enough, though. Let's talk about image generation and video generation and let's talk about trying to do more from using like an analysis as far as you're thinking about with market research and whatnot. So, it really does serve Corey like anyone from different types of professional business spaces and different types of sectors.
And it still surprises me to hear from data scientists to your everyday person who's like, "Yeah, I'm here to learn." I'm like, "You got it. Let's let's do it." >> So, let's talk about your second flagship course, deploying AI for strategic impact. Focuses on deployment rather than tools. So, why do so many organizations struggle at this stage? So many organizations are giving the directive to essentially go and use AI within the organization without actually having a firm strategy in place and that's kind of wild especially for how much money gets thrown out there to be able to have folks try to go and adopt these new tools and then to go and to be able to use them and then oh hey it doesn't work the way that we thought and now this is failing now we got to go and try something again and maybe doing something different.
So it's overall not having a strategy to begin with is setting you up to fail and that keeps on happening time and time again. And you would think that at this point in time that folks would know not to be able to do that, but it still kind of keeps on happening. So having an entire type of a course that does focus more on a strategic impact for everything and trying to make sure that everything is aligned and cohesive across the board from the organization from the top down really is going to be your best form of an approach.
And that's where that course really does highlight many different types of details like that. >> That makes sense. That's really interesting. So why is why is systems thinking so critical when it comes to deploying AI? And what happens when organizations decide we're just going to skip that? >> Yeah. Well, when you have the right hand and it doesn't communicate with the left hand, like things are bad. They typically don't work out. They they they don't work out so well.
And that's what we're seeing. So as this mentioned about it from like a strategic impact well that's one but then what about for the team who's focused more on ethics on bias on privacy you can't just throw that away you have to make sure that you are securing the data properly and trying to make sure that you are doing everything in an ethical manner and making sure you're doing all the right different types of steps. So you have that and then at the same time you have the folks who are literally using the technology and the tools to be able to build out your new idea or to be able to improve the workflow or whatever it is.
So when we think about all these different types of lenses and they're currently not all aligned with everything from start to finish, well then it's really not going to go well. So trying to make sure that we're focusing more on how folks are using AI in a variety of different types of ways to make sure that we're still hitting the same goals, whatever they are set by the organization, the standards and such, but that you're doing so in a way that does make sense from start to finish.
Let's go behind the scenes a little here. How does MITxPro identify emerging skill gaps in the workforce and decide we need to go build a course on that? >> Yeah. So, it's super interesting trying to be able to think about it. So, I call myself a learning nerd. Everything that we do, it really does focus so much so on everything from behind the scenes of like how does learning actually work and how does it take place, but also at a part of being a learning nerd comes data.
So, a lot too of about reviewing data and market trends and just seeing from different types of tools like with Semrush and other SEO tools as far as what are folks actually looking up to be able to learn more about and of course how are they doing so and then once we try to be able to identify those different forms of trends to be able to say that like oh okay all of these top results are certainly in our wheelhouse we could try to be able to help out with this and to create some type of a learning experience that folks can then take.
Then from there it's going and actually talking to the variety of facult uh faculty and stakeholders and subject matter experts over here at MIT to be able to say what do you think all signs of data are pointing towards trying to be able to learn about X. Is this what you're currently experiencing? Is this what you're teaching your students? Is this the demand that people are asking you to be able to host workshops and lead seminars on this different type of information?
And once all of that, once again, alignment apparently is like the name of the game for this podcast. Once alignment does happen as far as we're saying that yes, we're on the same page here. Well, then let's think about what does this actually look like? How long is it going to be? Who is the audience? What are going to be the main key takeaways? What are our overarching learning objectives and goals for the folks to be able to take it? And then comes that nerdy part of putting everything all together and a lot of project management to make sure that everything from start to finish is going to work for everything.
And that's kind of the behind the scenes to at least get you to the kickoff call where we have everybody on board. And it can get pretty crazy cuz the driving innovation with Genai course, we have 13 faculty who are a part of that. So you got so you're learning from from 13 professors at MIT and then I'm trying to be able to work with all of them to put it together. Various folks in different types of industries as well trying to help me with building out everything and and my team is working behind the scenes to put it all together.
But that's just simply a lot of people to be able to work with a lot of great perspectives but then making sure that we have everything correctly with the different types of topics and modules and putting it all together. eating into one another and >> yes because it it can't be that you take week one and then you go into week two and you're like none of this makes sense like yeah and I didn't do my job. That's the flow. So you got to start first with the basics and the fundamentals and then keep on doing that waterfall effect and then work your way towards what do we want you to be able to do for final projects and assessments and activities and such and watching the videos and the podcasts, the webinars and and all those things.
But yeah, that's that's a bit of the behind the scenes. >> So, what role do like MIT faculty and industry experts play in shaping course content and how do you balance academic rigor with real world application? >> Oh yeah. So from that perspective, we want to make sure that we really are getting that direction from folks to say that yes, this does make sense. These are currently the topics that I have been teaching about that we've been doing research on and everything from that sense because if let's say hypothetically speaking the data was saying go out and build this course but then I can't find anyone to teach it because it really wasn't the correct you know approach for everything.
Well then that's kind of a tricky thing. So certainly trying to make sure that you're weighing in the values and the opinions from faculty from all that perspective is huge to make sure that we're going to be on track. from the other part with your second question with trying to make sure that everything does apply into the real world. Uh, a fun fact about me is that I actually failed out of high school because I did not ever see how the real world and my education was actually connecting.
And whenever I talked to teachers as far as saying like, well, how am I going to use this in the real world? And they're always like, eh, you'll learn about it later on when you're older. And that never came. So I I really I truly just I stopped caring when I did not realize was going to happen is that for a career I was then going to design learning experiences that actually help people at MIT which is kind of weird like okay that's a interesting way of you know of how life works for things but they really did make sure for me that it lit a fire to say that whatever I design you need to be able to learn how this information works and then go out and actually apply it into the real world.
So for every single thing that I design after one week, you're going to know how to go out and actually do it and practice and fail and then learn and then come back and then you get better again and again and again. So everything has to make sure that there is an application focus. And I would say that for 75% of the time when you're inside of a learning experience that my team creates, you're doing something. I do not want you just getting lectured for five hours.
Like that's that's not fun. like we're we're not going to do that. So majority of the time you are going through different activities and assessments and you are watching the videos from the faculty who are there and attending webinars and such but I want you to learn by doing and that's >> takes all of that doesn't it? >> It does. It does. It's just you're not a computer. You can't just download something and be like I'm going to use that later.
It's like no that's not how this works. >> You need to really go through the these different types of motions to make the information truly stick or else it's >> it's just not usable. So yeah, >> well on that note that bleeds into this perfect how do you build and design AI education especially for super busy people and what kinds of assignments and project are learners actually doing in a class? >> Yeah, I I'm always thinking about the folks who have no time and they're like I want to learn as much as possible and as fast as possible and I have like 30 seconds.
I'm like okay let's figure this out. >> Yeah. Yeah. Are you really? So it's just like so for you microlearning is awesome where we take something and condense it very quickly you know but for trying to be able to create a full-blown learning experience from these different types of sorts it's thinking about how we have I was just saying that 25% of the time the videos the readings the podcasts the webinars are there but then 75% of the time we are thinking about those learning activities and the assessments and especially for AI courses I want you to experiment I want you to be able to go and to try out all these various ious different types of tools and whether that's going to be like an LLM from Chat GPT or Claude or Gemini or to be able to do something with video generation from Runway or from Sora or to even use something like with different types of uh voice generation tools like one of the tools that I'm currently using right now that I find so interesting and I'm not sure if you've ever used anything that's called um Hey Genen is the name of it but it will translate your videos into different languages.
So, I can take a video of mine and then put it into Spanish or Japanese or anything of the sort. >> And what I find so fascinating about using this type of a tool, too, is that it keeps your accent. So, it still sounds like you. It doesn't make you into some weird robot quirky kind of a thing. So, I want learners to be able to go in and say like, "Well, what happens if I use this tool? How do I make this impact? How can I change these different types of things?" So all of those different forms of elements inside of a learning experience is for there for those to be able to use it.
And the more that I can do once again from the real world, all that matters. So let's examine case studies where you do have a company that used facial recognition software in an unethical way and now they're facing tons of lawsuits and let's talk about how uh an LLM scraped all of this data from authors and that's how they built out their tool. And they're like everything's fine. And I'm like, "No, that's not how this works." So, where does that lawsuit go?
Cuz there's a lot that are currently pending. And that's what's kind of fun to be able to go through these different forms of real world examples and case studies and then tell the learners that, hey, what is your opinion on this? What are your thoughts? Because currently the court's trying to decide this and we don't have an answer. So, where do you think this is going to land? So all of that, once again, taking these real world elements and putting it all in to make sure it's actually helping out these folks and then having more conversations about all that is the name of the game. >> That's fantastic.
It's a It's definitely a complex problem. So based on what you've seen though, what separates professionals who successfully apply what they've learned from those who struggle and what AI related skills matter the most over the next few years. >> Sure. So that's a great one. So where I would say for the folks who will come in go through a learning experience with everything from uh different types of elements with Genai I would say that they are no longer confused or afraid by the different types of results that they're getting.
So I've spoken with many folks who come in basically saying I don't understand this black box. I don't get it. It's just it is a mystery and then all of a sudden I get a mystery result and like what I I just don't get it. I'm like great. Well, to tell you the truth, AI can be a bit of a black box. So, like you're not wrong, but there are different types of results depending upon what you do, what you're looking for, and the tweaks and the tunings that you do make.
So, seeing the results and the transformation from people who come in at the very beginning to say, I don't know what I'm doing here, but I know I need to know this because it's all that people are talking about. And it's like, got it. And then by the end of, you know, the week six or or whatever it is for for the end of a course and then you get to see people and hear from them where they're saying like, "Yeah, that initiative I was working on at work and I couldn't figure anything out.
I'd actually use AI to be able to help me." And now all of a sudden like we're going in this direction and I'm able to speed things up. The quality is better. And you're like, "Yeah, like that's that's the point of this is to take these different types of ideas and then go into experiment with them. Try it out and other times you hear from folks to say I tried something it didn't work but at least now I know what not to do in the future because before I didn't know you're like right exactly so trying to be able to get folks um confidence about AI really is the name of the game for that and then for thinking about the second part of your question as far as for skills and where really all of this is is going for things if folks are listening to this and they are feeling overwhelmed and confused because there's so much that's going on like well congratul ations.
Welcome to literally like 99% of the rest of us. >> Yeah, it it is insane. Like you and I are both super in the weeds of AI and it's still hard. There's there's a new thing that I just saw that Google just created that is called learn your way that is creating essentially some new form of of uh of flexible personalized learning using AI and learning about someone's backgrounds and then trying to be able to plan out what does curriculum look like with your interests your skill level where you're currently at and then it kind of maps out everything in a whole new direction and that just came out as far as for like I I don't even have access to it.
I can only see it. So, it's like, all right, let me figure this out. Where does this fit into my schemes? But most importantly, too, it's knowing about what to tune out and what to pay attention to. Because we're in this AI space, we pay attention to a lot of things. But for the average person, you don't. And if you can just focus on one tool and get really good at it, you will be so much better compared to so many other people who just do not understand how AI works.
And then before you know it, you keep on using it and essentially become a pro with these different types of technologies and then you can jump from one to the other and then transitioning isn't so hard to be able to do. So for a skill set perspective, of course, we'll keep on talking about prompt engineering. I think video generation really will be fascinating to see where that all goes because the latest updates are getting >> way better to the point of being really scary.
Yeah. Yeah. There there's there's a little things that I I saw an update that came out recently from uh someone on Instagram who created this perfect replica of him moving with a video to the cast of Stranger Things and you can't tell the difference. It it looks exactly like them. It's like woo. Which also, you know, we have to uh talk about that too in the courses as far as for here's how you don't use AI. >> Yeah. You know, it's a that's a big part too is making sure that we're talking about it from that perspective of not like damaging society more from that, you know, but it's even trying to think too that if I had to think about it from a few years that one skill that'd be so fascinating to think about and that that type of um line too is being aware about what is AI and what's not AI because that's now getting to the point that you can't tell.
That's a fuzzy line these days and it's getting uh increasingly fuzzy. >> It is. It's getting really blurry. So, knowing about that and that is going to be really key around all these different types of things of where we're going. So, not just using it, but identifying what is and isn't AI might be a huge skill for the future, which is >> it may very well be. >> Yep. >> Well, Luke, thank you so much. This has been a a really fascinating look at both the urgency behind AI upscaling and the thought that goes into doing it.
Well, before we slide off of here, where should people go if they want to learn more about MIT XPro or explore these AI programs for themselves? >> Sure. So, they can absolutely go and to Google. Currently, we're still googling things. So, if you do go to xpro.mmit.edu, edu. That'll pop right up and I'll have all the different types of courses and you'll probably put them in the show notes below. As a fellow podcaster, I'm guessing you'll probably do that, too. >> Right down here. >> Yeah, there you go. >> Well, for everyone watching or listening, we will have those links below.
And if you found today's conversation helpful, don't forget to like, follow, and share this episode with a colleague who's navigating AI adoption or professional development already. Thank you so much for joining us, and we'll see you next time on Espeaks.
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