No Country for Fast Answers: Safeguarding Critical Human Logic in a World of Instant AI Responses

No Country for Fast Answers: Safeguarding Critical Human Logic in a World of Instant AI Responses

by InstructureCast

Dale Leszczynski leads RMIT University's Generative AI Lab for Education (GAILE), where he's spent the past year building a skills continuum (instead of a single checklist) for roughly 5,000 educators , on the theory that nobody stays an expert in this field for long. Hosts Ryan Lufkin and Melissa Loble talk with Dale about what happened when other Australian university leaders mapped their own institutions against that continuum, and about the debate RMIT staged at its first education conference: are AI tools morally neutral, or does responsibility for them rest entirely on the people using them? And now that AI can do almost anything, what should humans still do themselves?

In this episode:

• RMIT's AI Skills Continuum treats AI skill-building as a moving target, not a fixed benchmark, since what counts as expert practice keeps changing faster than anyone can master it.
• When Victorian university leaders mapped their own institutions against that continuum, most landed at the earliest stage, a sign of a confidence gap more than a competence gap.
• Efficiency is the wrong goal for AI in education, since it's made his own work more expansive, not faster, which means the work itself has to change instead of just speeding up.
• Students at RMIT's new AI advisory board raised concerns about their own learning unprompted, and the thing they asked for most, across every discipline, was consistency, not more rules.

For further reading:
rmit.edu.au/gaile
https://aiskillscontinuum.com/
https://www.anthropic.com/research/global-workspace

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• No Country for Fast Answers: Safeguarding Critical Human Logic in a World of Instant AI Responses
  Welcome to Educast three thousand. It's the most transformative time in the history of education. So join us as we break down the fourth wall and reflect on what's happening. The good, the bad, and even the chaotic. Here's your hosts, Melissa Lobel and Ryan Lufkin.
  
  Hey there, and welcome to Educast three thousand. I'm your co host, Melissa Lovel.
  
  And I'm your co host, Ryan Lufkin. Melissa and I are very excited today to be joined by Dale Zinsky. Gail is head of AI education at RMIT University in Melbourne, Australia, where he leads RMIT's generative AI lab for education. Gale for short.
  
  Gale just launched a new AI skills framework for university educators. I think I've talked about that a couple of times in the last few weeks. He also recently had roundtable with AWS and every Victorian university and ran their inaugural GenerateEd conference earlier this year. Dale, welcome to the show.
  
  Thank you so much for being here.
  
  Dale, before we jump into today's topic, give us a little bit more about your background.
  
  My background is, I would say, wide ranging and more interesting, at least, to me. So I began the world. Interesting enough, wanting to be Steven Spielberg, I very quickly realized that was a pipe dream and got really in love with all the technology behind creating movies and telling stories. And then eventually, after walking around in different areas and building educational content for things like non for profits and the rest, eventually, I'll say, I fell into education.
  
  I remember telling my boss at the first university I worked at that I'd only been here for eighteen months, that was about ten years ago. Worked around various education moments, building curriculum content, working with educators, building the capability, but always a big focus on how technology, and more recently, AI, can really support people in amplifying the work that they do and, I guess, making them better, which is how I've kind of started to lead and get really excited about, and build capability around AI and journey of AI and what it means for education and the educator.
  
  Dale, I'm sure as part of that journey, both the falling into education as well as just always being part of the technology behind how we present really just interesting and innovative content to people, I'm sure you've had a favorite learning moment. And we'd love to ask our guests this. So it can be a favorite learning moment or a memorable learning moment from when you were a student, when you were a teacher, something you observed in your family, something you observed elsewhere. But share with us a favorite learning moment or a memorable learning moment to sort of ground us in who you are from a learner perspective.
  
  I feel like we will go into education because well, I personally go into education because of the moments. Yes. And I think when we're working with with students, when we're working with other educators, other adults who are kind of learning things, you kind hear those moments quite a lot. I'm going pull out a recent example that we had where there was almost an amount of moments per minute in a room, so we were hosting a make and take session.
  
  The biggest thing about Journey of AI and what it means for education, the people who are using it, is getting people to just play. So we set up an environment where there was effectively twenty or so stations and educators. RMIT has roughly, do or take, five thousand educators that we're working with at any one time. And educators just came into the room, started to play with technology, so whether that was making a game with AI, whether that was something very simple to just polishing up your slide pack.
  
  And every single station that I sat at, I heard someone go, ah, every single time that they went around. And it was just such a joy to hear, but there's probably two moments. There was the bit where they saw the other person doing it going, oh, wow. And then about five minutes later, when they did it themselves and had their own themselves and had their own personal moment.
  
  And so I think I just sat there for a little bit just at the back of the room just going, this is great. This is if you could bottle all these moments into one little place and kind of sell this, this is the magic of education right here. People just finding what they can actually do with this technology.
  
  I love that. And that we've talked about it on the show. One of things I love to say is that we're in the the show and tell phase of AI. Right?
  
  Right now. Right? There are some people doing really great things. There are a lot of people looking for inspiration.
  
  And so, you know, your background is in that design and storytelling. So you're kind of in just an incredibly unique position at this moment to be leading RMIT's AI education. How does that align? How did that come to pass?
  
  Because I think, honestly, I think your role is so unique and so perfect for this, but it wouldn't be, you know, necessarily on paper the natural fit.
  
  That's completely fair.
  
  There is an awfully long list of people who know infinitely more than I ever will in artificial intelligence and even education. And I wanna we want to, particularly in Gale and the journey of AI for education. We want to learn from those people and how it applies to what we're doing at RMIT and how we actually apply it to the sector and share those lessons more broadly. And it's all about what does technology mean?
  
  How is it used to amplify education? And really, how is it used to that application layer? I really agree with you regarding there's a lot of people in that show and tell phase that we're moving slowly towards that, cool, I can use this now, and I can start to use this with confidence. And we're seeing that kind of proliferate and grow more and more.
  
  It's now just around kind of wrapping people around.
  
  I guess some warm hugs of guardrails and safety and making sure they're doing it in a way that kind of supports the educational experience versus just, I'm gonna say it, going rogue and going, oh, let's just give Chattypati to everyone and and see what does the educational experience.
  
  What happens.
  
  So making sure people are doing it in a safe, secure way as well.
  
  I love that phrase, a warm hug of guardrails and safety, by the way. That just resonates in my heart. And I think it's something that the sector needs to be thinking about, not just an individual institution. And this is something I really appreciate about Gail is that you really are focused on education and the full sector, not just what's happening at RMIT. Well, RMIT is clearly the experimental bed for this. So why was it so important for you to do that, to not just have it be an institutional thing, but to really think about how can you drive progress and innovation in the sector.
  
  I might talk more broadly about Australia. There's some bits where I feel like we're batting above for what is a tiny island of who used to be convicts. We've got some excellent, behavioral scientists and educational, psychologists right around Australia more broadly. We're of, I guess, we're standing on the the shoulders of some of the learnings they've done.
  
  RMIT is a place where it's always been about applying the technology and doing it very purposeful. We're doing it very much from a person led space. I think there's choices that are made by every institution in different sectors. They're not comparing what's right or wrong.
  
  I think as much as everyone's talking about the application of AI right now, the other really popular phrase was, I was wrong and I don't know. And everyone seems to take a deep breath where they go, oh, god. Someone doesn't know the answer. So we're very confident to be, well, what can we do here with our space?
  
  We've very much been about applying what it means to apply learning to what it means for employability as well. We've always been a teaching university, What that means for when students leave classroom and go into that job, are the employers asking them from there? We think we're at that nexus for us to understand what industry we want, what hours we should be teaching, and what it means to prepare everyone, and bringing that research line as well. So I think it's just a ripe bed for us to do that.
  
  By no means we're the best, but we're definitely the ones who are giving it a red hot go. I think we've got some really good foundations by bringing everything else across Australia and, I guess, the broader sector as well.
  
  No. I think the minute we talked, the more I was like, oh, we need to share this. We need they only need to be on the podcast because I think it's so interesting. And one of those things is, you know, we've had other guests talk about the AI skills frameworks, right, and AI literacy frameworks, things like that. And you recently launched an AI skills framework, but what's different is it's you built out a continuum educators move along from wherever they're starting, right? What made you choose that model? Because I think it's really and it could wherever you're starting, it gives you a place to find your footing and and move forward positively.
  
  I think for us as well, and I'll call out my excellent colleague, Tony Jones, who the continues all her work. I just worked on making it pretty. I will give you guys the link. It's AISkillsTutorium dot com.
  
  If you ever wanna go look at it, I need to do a plug there. But the main thing about the continuum is AI is so difficult in certain places because it's changing so much. And so, again, I said before that people who were right yesterday, they're perhaps wrong tomorrow and even today, they're very confident about the certain thing regarding, oh, I know what it means to be ethical using AI. I know what it means to be really intelligent and applying AI in my classroom.
  
  But the next question is that something's changed out there, and so where you're right today, you've gone back for some reason. And so a continuum was built that you can't we believe that it's really difficult to be right at and knowledgeable across a mastery stage or an introducing stage or adapting stage, whatever that might be, at any one time. And so being giving people, I'll say, a reflective tool to kind of go, at the moment, I think I'm here, which means that maybe I need to go explore over here and build these skills over there as well. We also wanted a place to kind of just showcase what people were doing, not only in Australia, but more broadly, we started to bring those examples in.
  
  So when we see someone who is really good at applying AI or choosing the right AI tool is one of the skills that we we talk about. What does that actually look like in an actual classroom environment? The other really cool thing about the skills continuum is that you can flip it to work in the classroom. So it's very much educator first.
  
  It's very much designed to be educator first. But what educators have started to do, we've seen it play out at the moment, is educators are taking some of the skills there and going, oh, what does this look like if we're gonna teach this particular AI skill in the classroom so that students can build capability around that? They can also use it as an assessment tool and go, oh, I actually don't think my students know what AI tool to choose. How do I scaffold that learning in at the start to make sure that when they are approaching their assessment, they have that skill baked in and they can start to play that in.
  
  So it serves a lot of purposes. I think the honesty about the skills continuum is that we've given people the map and there's a bit of a sigh of relief that someone has the map and they go, 'thank god this exists' and maybe they put it down because the map is only one bit, the next bit is we have to do all the work around it, you actually have to engage with the skills, you have to engage with the framework, you have to be like what does this mean to my actual practice?' versus it's so good that there's some prompts over there, I'm glad that they're at one point, but you do actually have to pick up the work and and do with it.
  
  But we are seeing that adoption kind of play through. So the numbers are the numbers are coming up, and people are really playing with that and going, okay. I hate to take this seriously. What does it look like in my personal practice?
  
  Yeah. I love the and, Melissa, this this goes back to a guest on a recent podcast, Martin Bean, where he talked about the importance of learning but then unlearning. Right? And you called that out where this this need to almost think I need to unlearn what I learned and learn this new thing from scratch because then you're not kind of beholden with that baggage that you may have had before. I I think it's whenever we find those patterns, I'm like, oh, yeah. That that fits in nicely.
  
  Throw it out again. Throw it again. But I did learn I'm learning from Martin Bean. He used to be our vice chancellor.
  
  So Yeah.
  
  It's a small world in education.
  
  If there was a with if there was a tie in there.
  
  Well, speaking about this idea of being out in the sector, right, and how are you giving people tools that they can use themselves, they can use in our classroom, you know, they can use at their institution or even outside of their institution. You recently brought a bunch of senior leaders together from Victorian universities as well as AWS in the room. And I'm really curious. Like, so often we don't get to be a fly on the wall, but we had been if we'd been on fly on the wall in those conversations, what would have surprised us or what surprised you most about the roundtable? And are institutions actually doing things differently? Are they doing things the same? Sort of how did that conversation go?
  
  I think it's fair to say that a lot of institutions are being on a very similar journey in terms of how they've approached AI adoption, in terms of they maybe spun up some form of incubator kind of unit that's gone out there and played and experimented about support of huge HashBT or clawed licenses and kind of see Brits played and maybe tried to corral and go back to warm hugs, kind of productionize that warm hugs of guardrails. But every university, particularly in the Australian context, is evolving beyond to what does this look like to normalize this, to actually go to reckon with what AI means in twenty twenty six as opposed to when it landed four years ago, we all had to deal with this brand new alien that's working with us.
  
  We did bit of an activity in the room, I think I'll talk about the activity. Was we used the skills continuum, and we put up sticky notes and said, where is your institution at regarding introducing, regarding that middle phrase, regarding that mastering stage? Where do you guys think you're at? I felt that we'd all be moving towards that adopting stage, but the amount of sticky notes that were still around that introducing stage was really telling.
  
  And I think people were still at that point of going, oh, we're still needing to grapple this stage. And some of that's confidence. I think there's a real honesty there is that some of the sticky notes that we're seeing appear around the introducing stage were actually probably more faced towards that reinforcing. And that mastery stage even, there's some really good practices coming through.
  
  But for the reality is that people, don't have the confidence yet because they're kind of looking at this pin going, oh, actually, I think this is what we're doing here. I think this is good. Do we know this? And that's really that's because the technology's so new.
  
  So that benchmark we have, I appreciate we a whole bunch lacking.
  
  Right? Like, don't have that.
  
  Exist yet. Yeah. And so the first person who stands up and says, I am an expert. The next person says, well, you're not because I am eccentric, etcetera.
  
  We've got a whole bunch of LinkedIn and and x based thought leaders. Yes. Sometimes I'm in there, sometimes I'm not. As I imagine you guys are as well.
  
  But reality is there's everything that's right today is wrong tomorrow. So I think that was really interesting to see where people are at. The biggest thing that I saw and I appreciate the room was full of education leaders. It wasn't full of there was chief information officers in the room.
  
  People have a very best interest in technology, but a really strong theme that came throughout. And I took a big sigh of relief, particularly with our partners, AWS, in the room, is that pedagogy comes before technology. Yeah. Was nuts.
  
  And I took a big old sigh of relief, I said, the tools, the tails aren't wagging the dog. The dog is leading here. We're in a space where we're really focusing on what the education means, particularly in an educational institution. And how good is that?
  
  So I do feel that pendulum is swinging back to what is our core business here. It's about bettering education, supporting students, and definitely supporting educators to support those students. So when pedagogy is with the foundations, isn't that just a great place to be in? So I did take a bit of a sigh of relief that that's where we're at, but always more work to go on that one because the pendulum will swing the other way as technology gets more and more exciting.
  
  Yes. Zane always says that back and forth. Right? Wave and trough. Well, Generate Ed was Gale's first conference, and we'll actually put in a couple of links in the show notes because I think there's so much to learn about Gale, your findings from the conference, and things like that.
  
  But from what I understand, you had a debate on whether tools these tools are morally neutral or whether ethics live entirely in how they're used. You have an AI adjudicator involved, I believe. So tell me a little more about that debate and also kind of which side do you find yourself on there?
  
  I was gonna put it back to you guys. Do you think their tools are evil?
  
  Where do you sit? Do are the tools evil? Or because the argument is for anyone who's listened to this early in the morning like I am, and the coffee hasn't set in yet.
  
  Is the hammer able, or is the person halting the hammer who puts a hole in the wall, is that the bad thing? Where do you guys sit currently?
  
  I mean, I will tell you, I've been fascinated from the very beginning of the human tendency to anthropomorphize these tools and give them human characteristics and assign human motivations, right? They're being indignant, they're being And so, I'm always hyper and I find myself even doing it, right? Even though I'm aware of it, hyper aware of it. And so to me, yeah, the tools are evil. How they're used can be, you know, unproductive and even, you know, unhealthy. That's where I fall as, you know, it is how we use the tools, we and we've gotta fight that urge to anthropomorphize.
  
  Well, and I agree. I mean, humans created the tools. Right? So they're only as good as humans are.
  
  And so it's gonna they're gonna reflect us. It's a mirror back. I think somebody's taught I've I've seen somebody speak about that where they're just a mirror of where are we at. I don't think humans all humans, some humans certainly are intending to use them for nefarious reasons.
  
  But I think the intention as humans is to use the tools for good reasons. We just don't understand. I mean, our literacy is so poor, so they end up getting used for evil, I think, in places even not even deliberately for that use.
  
  And even those early cases of bias that we've ran into. Right? They weren't intentional bias. They were just pointing out the bias in the existing data reflected. Correct. Yeah. Yes.
  
  So And, like, I'll talk about the aim of the debate because I was pretty firm on my decision in that it's very gray.
  
  Is the the answer is yes, maybe yes, maybe no, etcetera, etcetera. There's recent research that perhaps we need to talk about that may influence. But for the most part, the aim of the debate was not about anyone being right or wrong. Debates in Australian context were very fun.
  
  I had to give my DBC an AI partner because otherwise he talks too much. So we made the AI do a lot of the comedy styling and removed some of the, the bias there. So I think it was four adjudicators that kind of brought together. So but we tried to be as balanced as we could, as as balanced as the tools can be.
  
  And I believe one of the debate panelists also tried to do live prompt injection on the stage to try influence the AI debater. Didn't work, but that's maybe that's maybe because my model wasn't that good. But there's recent research from one of the Frontier Labs, I believe it was Anthropic, that there is a conscious space inside some of the AI tools called a J space. And so everyone, for a long time, always used to talk about the fact that AI, for the most part, is really good autocomplete.
  
  It's really great autocomplete. But now there's there's research because Dario Modi has been really excited about building, like, an MRI for what AI looks like, and we're getting a bit nerdy here. He's been doing some scanning, and found a little box inside AI where it is thinking about things. And if it's thinking about things, perhaps it's thinking maybe some nefarious things.
  
  But who knows? Regardless, the debate was intended to give people more arguments than AI is bad and AI is good. It's that kind of there is endless kind of permutations of this argument for people to kind of wrestle with, and really it's about your own personal use and everything else. So the answer is still not black and white.
  
  Okay. We need to lead to that too because this you know, we've had the idea from the beginning that large language models are a black box. We don't we fundamentally don't and can't know what happens on the inside of those boxes. And so the idea I love your term, an MRI to see into the Correct.
  
  Yes.
  
  We're scanning them.
  
  We're we're seeing through them somehow.
  
  Seeing what it's doing then.
  
  Well Yeah. And if okay. So I recently had a Saturday morning with CHAT JBT. Long story short, I I do a lot of public speaking and and in person presentations, and I always wear black.
  
  And somebody gave me a hard time for that. I'm like, okay. I don't know what colors to wear. So ChatGPT and I had a whole exchange around what are my best colors.
  
  And, I mean, it started to dig in. It took photos. Like, it has it has clearly done this for more than one person. Yeah.
  
  Yes.
  
  But if you think about I thought about doing it myself.
  
  Oh, it was that the whole thing was fascinating, and it nailed it. But wow, was it positive. And so as you were saying that and, like, just the way it reinforced the exchange and have you thought about extending it to think about, you know, what your home looks like and, like, that whole thing. But when I as you were talking about this and its ability if its ability to do that, it also has a thinking ability to do the opposite of that, right, to be nefarious. And so I think it's like you've started to shift my thinking a little bit on this that if it can show up Wait.
  
  I didn't make it bring it in now.
  
  No. It's good. I love it. I love it. Because if if it can be if you can have these exchanges that are what we would probably gloss over as so positive exchanges, there are places where it can have very negative exchanges too because it's gotta be able to do both both sides of that spectrum. Anyway, when you were saying that, I was like, oh, wow. I gotta rethink my answer.
  
  That it's maybe a little bit bad thing.
  
  Episode two with Dale on test just to dive into the putting it through our hats and everything else.
  
  Exactly. Exactly.
  
  Exactly.
  
  Okay. So as part of conference, you also did a cook off where there was recipes, right, that were shared. Give an example of one, and how did that cook off go?
  
  Well, I think the thing that MIT I'm not we're seeing it quite broadly at a lot of universities is that one of the things that AI is great at is things like simulations where you can make an AI either be a board presentation that you need to interview to, you might be interviewing a patient, etcetera, etcetera. And so a lot of the conference initiatives we had was very similar versions of that. Now, as much as those were really quite exciting presentations, I couldn't put each of those up. So I was like, what would happen if we compare them all together? And put them all up on the stage and kind of and go, what does each one do? What's the special sauce in each one? I got far too involved in the whole cook off metaphor and wore a chef's hat and and everything else.
  
  Oh, I did.
  
  It was like the other You're not wearing a toque.
  
  What are you you know? Correct. Yes.
  
  It was it was it was very exciting. The thing that I really liked about this, we had things like user experience as your voice, and so people very much talking to models. You had other people doing self care, and three am tutors where they would speak to someone because the student support hotlines were not open at three am in the morning, what does that AI support look like, etc etc. So a whole bunch of different ones.
  
  I think the thing I liked from the audience is the amount of it was silent to the room, except for my hilarious jokes, of course. A silent to the room other than pen scratching for everyone going, oh, this is how we can do this. This how we can do this. I think for me, it was less about the fact that here's what it looks like.
  
  We really tried to make it approachable. We're gonna let you ready to go. You can go at any point. You can enter any point depending on where you're at and pick up one of these things.
  
  Actually, that difficult. You can use some of our we have an in house tool at RMIT built on OpenAI's platform and a few other models, you can use that. Or you can pick up any old plain AI and put that in with these guardrails and build your own kind of simulation experience that has some really strong guardrails based on what you want your learning experience to be in your particular class. And I think I just I love just seeing people go, oh, I can take this.
  
  This isn't that hard. It was really just about dingstifying what seemed very, very complex.
  
  Even terms like I mean, there's one that I'm practicing for a presentation next week and defining vibe coding. Right? Even vibe coding sounds like, You have to learn code. You have to know how to even if it's quicker. And you're like, No, it's the same it's having a conversation with the AI model about what you want to create and it translating that to code, right? So, even some of the verbiage we use.
  
  But one of the voices I think we don't include enough is the student voices. I think we are often very guilty of making assumptions, often incorrect assumptions about students and their needs. And I know at Generated, you hosted a student panel. Apparently, it was very popular.
  
  I'd love to hear more about how that went.
  
  Room only. I think, we brought up our chance to a Yeah. And he came in. He was like, oh, there's there's people sitting on the floor.
  
  This is people are very excited to hear what's happening here. Honestly about the students is we all know that they're using it. We've got any pull, any survey that we can get for around student AI use, and the usage numbers continue to grow and grow and grow and grow. I think I'm finding what's really interesting is the students who are actively choosing not to use it, the ones who are really conscious around, hey, listen, I'm here having an educational experience and I want to learn.
  
  There's still that concern, more broadly, regarding, oh, that kid over there is driving a Ferrari, I am not. So I'm choosing not to drive a Ferrari, I'm choosing to walk to school in the Ferrari as they are taught in this particular example. And I think that's still that equity concern is still quite large.
  
  But, Dale, to your point about the students' usage, I see it with my kids. I bring my kids up all the time because they're like guinea pigs, honestly. Like our little They're our little, like, test lab. Right?
  
  And my daughter, who's a junior in college, you know, she was ending high school when AI came out, they were told to me, like, don't use it. It's cheating. She's been really reluctant. And she's also super type A, so she wants to learn the skill.
  
  She wants all of it. My fifteen year old, he's just like, these are tech tools that are at my fingertips. They're for me to use. Right?
  
  And so you I see that very different user experience based on even different age groups and different types of students.
  
  I think the hardest bit for all of this is that we can set up these guardrails and there's people who are gonna consciously use it. But when people have that time pressure, when people have that concern or that deadline that's coming up, they're probably gonna reach for something that's easier. I always think about when I get home at the nighttime and I'm gonna admit something horribly, I write a birthday card for a friend and I was exhausted and I'd known this person for thirty one years of my life and I said, can you just write me a quick birthday message quickly?
  
  And you know, I didn't feel great.
  
  Mention time we were on the I didn't feel great, but there was that ton of time pressure there that was was there, and I and I did reach for it, and I'll call it a weak moment.
  
  We launched our AI student advisory board yesterday, and I did notice a I'll I'll call it a change from even the short period of time. Our student panel was about four months ago, three months ago. Time flies in having fun. But we had our AI student advisory board yesterday, and we have a whole bunch of students from a wide range of panel, backgrounds, not just STEM subjects, so business subjects, ethics subjects, design subjects, undergrad right through to post grad.
  
  And I sat in that room as they started to talk to me about their concerns for metacognitive offloading and what it means for AI interrupted their learning and their concern for self regulated learning. I've never taken so many notes from our student cohort, ever. So they are hyperconscious around what it means to do it properly. The only big ask there is they want consistency.
  
  And I think it's such a challenge because we keep saying this keeps changing so much. And unfortunately, you come out here and say, we are consistently communicating x, but that message may change as it kinda tracks through and being really conscious of that as well. So I think there's a there's a desire for clarity around what's what is right, what is wrong, and we're all trying to develop that in our own kinda practice and as a as institutions, and our students are craving that. And that is always gonna be a gap that's gonna be difficult to fill.
  
  People want the clarity. Everyone wants the understanding. Students want it. Our educators want it. Because it's changing so fast, it is gonna be a challenge.
  
  But our students are very switched on. I think that's the big the big call out there.
  
  Yeah. That's so interesting too about the consistency piece. And one of the challenges is many institutions and actually many businesses are going after the just add AI to the mix. Right?
  
  Or just add AI to it. Right? And those projects are failing. So it's like, how do you have consistency in learning experiences or give students consistency in the how they're gonna be using it in their learning and and how does that match to the workforce?
  
  How do we do that in a world where we're just throwing AI in and those projects are failing? I think you even cited recently an MIT study that talks about a lot of them had absolutely zero return. Right? And so what are the mistakes maybe that you're seeing be made, or how do we navigate that world where it's like tossing extra salt in and it just makes it saltier?
  
  It doesn't actually solve a taste problem. How are we to use your cooking metaphor? You know, what are you seeing the mistakes there or any advice around that?
  
  It's a good call out, and I think we need to caveat the MIT study with all the things that came under regarding the it was too early to tell whether those pilots actually were gonna succeed, etcetera, etcetera. But it's really interesting headline because I think everyone maybe has been experienced too. Here's a new tool. It should make your life easier. Good luck. And then you disappear.
  
  Yep. Yep.
  
  So I think we're really conscious around when we roll out a new tool, build that new tool in, whatever that looks like, is that it has to come with a pretty decent training program. And also a big reconsideration around this may need to change your work. We've also been really, really conscious about using the big e word, the little e word, depending on where you see it. Efficiency is the one I'm talking about.
  
  I don't know about you, but I think AI perhaps makes this efficient in certain spaces. There's a whole bunch of other stuff where I'm like, I feel like I'm doing a lot more work now. And because I'm doing a lot more work, maybe I'm not actually as efficient as I once was because I'm exploring all these other things that's enabled me to do, which is great, but I definitely can't say it's efficient. I think it's it's given me more.
  
  It's amplified Dale in some ways, for good and for bad, depending on which peers of mine you talk to. But for the most part, it just needs to be really about efficiency isn't the answer here. It's going, okay. It's gonna amplify my work, but my work probably needs to change here as well.
  
  And we start to talk about things like agentic workforce, which is a lovely buzz wickets throwing around and what agents mean. It gets even more complex Cause you've got things kind of running on a loop all the time. I always like the thing regarding agents is that they can reply to you within within seconds. It's gonna upset customer service, gonna upset things like how you respond to people, etcetera, etcetera.
  
  A lot of my mental models around how I interact with the world and how even our students interact with universities expect that there's gonna be a bit of a time delay. It's gonna be like a twenty four hour period before someone gets back to you, etcetera, etcetera. So all of a sudden, your whole workload changes because you've got a response almost immediately or reply almost immediately to maybe it's assessment feedback or something That's a completely different workload model that perhaps we're not really set up to consider as humans, so perhaps our workflows as well. So all these things need to be considered rather than here's a tool, go forth and use it.
  
  So I think that starts with kind of that base level. Okay. Well, we've got this tool. Here's the things, what it means to do it really to use it safely, use it securely, use it in a way that may support you.
  
  But the next one is a really professional and personal journey. I would say AI is a very personal thing because where it benefits me and my particular work because I really like perhaps doing my my PowerPoint slides. That's something I get great joy in. I love doing my PowerPoint slides.
  
  I want AI nowhere near Well, I could tell you, I love to design a slide deck.
  
  It's one of my favorite things.
  
  It's it's a thing I love doing, but but I really want AI to maybe do the Excel spreadsheet stuff over here. Maybe I've got two thoughts. I need to jam them together. Can you help me bring them together?
  
  Because I can't quite think of it yet. But that was work that need to reconsider now and go through. Because maybe the way I did that previously was working through a PowerPoint slide and designing that and bringing those thoughts together, and now that completely changes. So it is really quite a a challenge of going, okay, well, the only way you get efficiency or you get that amplifying fact is that you need to go and do the work and sit with it and go, what does AI actually mean to support me in my own work?
  
  And you can also do that from your own point of judgment. You're gonna be an expert in the bit you're really good at, and you can judge the output of AI from that lens.
  
  Yeah. Well, and to that point, Gail's mission talks about AI amplifying educators rather than replacing their judgment. Right? Which is something we've often said there, you know, this is the educators are Tony Stark, and AI is the this, you know, super suit, the Iron Man suit, everything.
  
  It amplifies that.
  
  Good.
  
  Yes.
  
  But where do you draw the line?
  
  Especially taking input from educators, from students, where do you draw the line? What should always stay as a human decision regardless how do the tools get and what can you offload?
  
  Oh, it's a really interesting question regarding where the split is, where the human should do things and where the AI should do things. I don't have an answer yet and I think whatever answer I say now will change. So I still think academic judgment and the judgment and professional care needs to be a distinctly human component, and I think that will stay. I think everything else, educators' life and academics' life and their professional context is incredibly varied regarding that they love synthesizing information, like bringing things over here together, they love creating the PowerPoint slides, some of them love doing marking, etcetera, etcetera.
  
  So I think there needs to be, again, a collection of models considered, enabled by the tools and technology for what is gonna be distinctly human and what is gonna be distinctly AI. So I don't think it's decided by any one individual. I think it's decided as a community, and that's kind of how we're approaching it. We're building out a human AI curriculum pipeline at the moment.
  
  And effectively, it will be a collection of widgets across a how you build curriculum, how you teach, and there's gonna be a conscious decision for educators to go, oh, wanna add this to my shopping cart. I wanna do this. I wanna build this little bit that's can help me build my course learning outcomes. This bit here, I really like how that maybe supports my authentic assessment.
  
  How can I take that? However, this bit here regarding I'm gonna say marking as a bad example or building up my Canvas shell, for instance, they're mine. They're the ones that I'm gonna hold onto strictly for me. But also, what's this over here?
  
  Something else to explore. I can build out a learning activity in ten seconds by vibrating out a game. So there's there is gonna be stretch goals in that, and people are gonna have to explore that pipeline as they go through. And as they go through that pipeline, I think what they love doing and what that means, what a human should be doing, what AI can do, will change.
  
  I kinda stand by the fact that AI can do a lot of things on delivering curriculum. I think really it's around what humans should do. We often talk about the, oh, we need to question what AI should do. No.
  
  No. I think we need to question what humans should be doing because AI can do a lot. And as every day goes past, it can do a lot more. Apparently, it's running a broccoli farm in Japan at the moment.
  
  So you can do a lot of things.
  
  Wait. Human aren't meant to do spreadsheets with all of their time?
  
  What? Some people love them, and I'm not here to judge those people.
  
  True. True. I I have those friends. But I love what you said though because we've talked a lot about, like, the unlocked potential of personalized learning, but that idea of like personalized teaching, right? Focusing on the areas that as an educator you enjoy, you're good at, you want to do while automating those other things. I like that kind of the yin and yang of both of those. That's good.
  
  I swear you're singing to my heart because first of all, I'm as passionate about simulations. I've taught them for a long time, and I teach learning design. But also, I believe teachers inherently, the good ones, are artists, and we have to find our medium. And AI enables us to find a medium and embrace that medium.
  
  So now I might be a potter. I don't have to figure out how to, you know, oil paint. I can really focus on my potting, you know, while my students and while AI is helping me do the things that I am not spectacular at. And then that's where we're gonna see the uniqueness in learners.
  
  At least that's my my personal dream or the uniqueness in the teaching experiences. But Love it. Okay. Last question.
  
  Crystal ball time. We love to do the crystal ball time, so I'm gonna warn you. Year from now, what would you like to see? What kind of shift would you like to see across Australian or even global higher education with use of AI?
  
  And what might Gale be doing to support that shift?
  
  I hope it gets, and I may regret saying this, infinitely more gray. I think there's so much tribalism in this kind of space at the moment. There's so many people who are, I am very anti AI, either this or reaction to data centers. No one data centers.
  
  They find their sort of purpose in certain spaces, and there's there's some issues around it. But for everyone who is so passionate about how bad AI is, there's other people who are just wanting chachapatea tapped into the vein, give me that brain chip neural interface, just there's going nuts. I think we just need to have a lot more human conversations and bring that together. So I'm gonna take a very optimistic view.
  
  It's that things get incredibly more gray as we start to learn more about this and we stop being so tribalistic about this. We hear from some of those students that I was speaking about before that I have those that awareness and that that knowledge, and they're saying, oh, I'm actually questioning my metacognitive offloading here.
  
  And we start to bring that into the the mainstay, and so we start to be a bit more conscious around it, what how we use AI. I always talk about purposeful AI use, and I feel like it's a lovely umbrella term for ethically using it, responsibly using it, and using it with guardrails. Because it's not just going nuts and getting it to perhaps write your birthday card on the weekend, but it's about letting it being really conscious about how you're using it, why you're using it, and what actual benefit it's going, which for me, it covers a lot of bases around using it responsibly as well. So I think that's kind of my big focus. And Gail's really keen to kind of host those environments and have those conversations with people regarding it is more gray. There's no right answer at the moment. I appreciate it may be a gruddig at one point, but we're a bit beyond ones and zeros now because it's becoming very, very human.
  
  Well, I love this. Well, Dale, this has been fantastic, and we will stay plugged into Gail and to you. We will we'll probably have you back on the you know, a year from now, if not sooner. We will have you back on to talk about your prognostication and and see where we stand.
  
  Whether it's really gray and I regret it, I want everything to be much more black and white, please.
  
  Yeah. Like, it's just as gray. It's just gray. It's very gray.
  
  Yes. Thank you so much for your time.
  
  No problem, guys. Really appreciate the chat. It's been excellent.
  
  Thank you, Dale. And thank you everyone for tuning in to this episode of Educast three thousand.
  
  Thanks for listening to this episode of Educast three thousand. Don't forget to like, subscribe, and drop us a review on your favorite podcast players so you don't miss an episode. If you have a topic you'd like us to explore more, please email us at InstructureCast at Instructure dot com, or you can drop us a line on any of the socials. You can find more contact info in the show notes. Thanks for listening, and we'll catch you on the next episode of Educast three thousand.