WEBVTT
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So I think we have to put in conscious moments of resistance.
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And um and for our for our students, I think we have to create those moments of resistance for them and facilitate that because I don't think they'll necessarily do it themselves.
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Welcome to Intentional Teaching, a podcast aimed at educators to help them develop foundational teaching skills and explore new ideas in teaching.
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I'm your host, Derek Bruff.
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I hope this podcast helps you be more intentional in how you teach and in how you develop as a teacher over time.
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You may be familiar with author and consultant Leon Furs from his series of articles on the teaching of AI ethics, or perhaps from the AI assessment scale, a popular tool for helping students understand what uses of AI are permissible on a given assignment, which Leon helped develop.
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I'm very excited to have Leon on the podcast today, although we didn't talk about his AI ethics articles or the AI assessment scale.
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We could have, but Leon is such a prolific thinker and writer that we focused on a few of his recent blog posts and the ideas they contain.
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It was his March 2026 post titled Resistance as a Framework for Combating Cognitive Offload that caught my eye.
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In it, he uses the idea of resistance and strength training to help us think about the ways we can help our students resist the problematic cognitive offloading that AI offers to learners.
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I invited Leon on the show to talk about his resistance framework, and we had a wide-ranging conversation about equipping students to learn well in this age of AI.
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I had multiple light bulb moments during the conversation, and I hope you do too.
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Welcome to Intentional Teaching, Leon.
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I'm very glad to have you on the show and to um, I think have a really engaging conversation about AI and learning.
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I'm excited to have you have you here.
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Thanks for being here.
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Yeah, thank you very much for inviting me.
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Um and I really want to make a joke about talking to tomorrow because you are many time zones away from me right now.
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Um but I'll I'll avoid that and start with my usual opening question, which is um Leon, can you tell us about a time when you realized you wanted to be an educator?
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Yeah, well, we'll go back through the archives.
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Um so I was uh I was a maths science kid in school, uh, but my my teachers didn't agree with me.
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And um, so I ended up at university studying English literature.
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And um after three, four years studying English and American literatures as a a dual honors course in the UK, um, I sort of fell out of the other side of that and thought, God, what do I do with an English and American literature degree?
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And um, a week after the applications had closed, I applied for a postgraduate certificate in education, uh, which is a UK teaching qualification.
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And um then just sort of rolled through the first few months of that before I discovered that I actually quite liked teaching.
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Um yeah, and then uh spent 15 years in the classroom.
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Nice, nice.
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And what what did you teach in the classroom?
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Uh for the most part, secondary uh English and English literature, um, typically with senior students.
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Uh but I I worked in a small regional Australian school for most of my career.
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So I've taught uh you know history, geography, religion, STEM, uh, media, drama.
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Okay.
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Uh pretty much everything except for maths, because um again, the maths teacher, even in adulthood, uh still didn't think that I was qualified to do the maths.
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Okay.
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All right.
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But small school, you teach what needs to be taught.
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That's great.
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Okay.
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Exactly.
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Um uh now, however, you are doing a lot of work, um, I think at multiple levels of education, helping people grapple with AI and what it means for teaching and learning.
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Um and um I wanna I'm gonna start with this question because uh this this is a little unfair.
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I'm gonna give you uh a post from Blue Sky without any context, but it but it it is it does seem typical of a kind of post I see a lot.
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And it's it read Study finds that students using Chat GPT learned and remembered less than students who did not use it.
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And then they link to a study.
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And so um given how often we just read a headline and move on with our lives, um, when you hear a headline like that, what what advice would or what advice would you give to educators who hear a headline like that and are concerned about AI's impact on student learning?
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Read the paper.
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Yes.
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Please, please read the paper.
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Um even that headline, uh I I'd be 99% sure it was the MIT paper.
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Um It was not, but Oh really?
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Well, the 1%, the 1% that fooled me.
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Um so many of those, many of those studies, as we know, you know, this this this version of this technology has been around since November 2022 in the public eye.
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Um any studies published on it have gone through peer review, hopefully.
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And you know, that takes maybe six months plus sometimes.
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If you're lucky, it takes six months.
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By the time the papers are published, the models that they're talking about are out of date.
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But I just I don't think it's possible just yet to make any big claims in one direction or the other about the the long-term cognitive effects of using AI.
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There's so much supposition and assumption there.
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Um, you know, I I joked about the MIT paper before.
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Really, I think that paper, which was really incredibly famous and you know, big splashy headlines, uh, what it essentially said was if a learner uses Chat GPT to generate a response and then submits it without reading it, they don't remember what the output was, which frankly is common sense, not necessarily a spectacular um research finding in the long-term effects of AI on cognition.
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The paper was was fine, it was great.
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And the authors um actually came out afterwards and said, look, there's a lot of people misinterpreting this paper and reading too much into it.
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Um, you know, and that's true of the other side as well.
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We see a lot of papers that are, you know, promoting the potential benefits.
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One of them in the last uh week or so has been retracted from nature.
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Um, really, really uh again, very popular paper that was very well received by the the positive AI hypsters.
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Um so it swings both ways.
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Yeah, yeah.
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I saw that retraction too.
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Um and that was one of my concerns with that with the the presentation of that study that I mentioned on Blue Sky is the study finds that students using ChatGPT learned and remembered less than students who did not use it.
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Which students?
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How did they use it?
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Were they prepared to use it?
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Were they directed to use it well, right?
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Like, like I mean, your your initial response, read the study, is super important.
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Um, because I I find that the the study design says a lot um uh uh about how we infer or what we what what we take away from the results.
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Yeah.
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And and do do read it, don't just use Chat GPT to turn it into an infographic and then post it on LinkedIn.
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Right, right.
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That is good advice.
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Um speaking of posting on LinkedIn, I think that was where I saw your recent blog post.
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Uh how's that for a segue?
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Um you wrote a blog post called Resistance as a framework for combating cognitive offload.
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And that's kind of the heart of what I want to talk with you about today, because I just found I found it really interesting.
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The the framework that you that you you started to scope out there.
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Um and I, you know, as I read it, you're not arguing that that we don't use AI, but we use AI thoughtfully in ways that are going to support student learning.
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And so you use this metaphor of resistance in this context.
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So so what do you mean by resistance in this context?
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How is how how what is that how does that metaphor work for you?
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Um yeah, so I mean that initially came from two directions.
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One was I was I was reading lots of uh research around AI and education and trying to just sort of look at the the major themes that were coming out, which were the the five areas that I ended up with in the framework.
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And then in trying to to think of what kind of tied all of those things together, and I've done some writing in the past about friction, but I I thought you know friction's not really the right angle for these areas.
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Um, so I had a you know a bit more of a think.
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And the resistance idea really came from the fact that um I'm reaching an age now where when I get out of bed in the morning it hurts me.
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Um and I've got three kids and I pick them up and swing them around and inevitably injure my back.
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Uh and so I go to the gym reasonably often and I do you know strength training or resistance training.
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And the reason I do that is not particularly because I enjoy it, it's because I have to.
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And if I didn't do it, um my body would fall to bits.
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Um, and I think it's the same with this technology, you know, it's so seductive, it's so compelling and easy to use, it's it's increasingly ubiquitous.
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They really want us to use it and to sort of justify its existence.
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It is going to be increasingly difficult to avoid it.
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So I think we have to put in conscious moments of resistance.
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And um, and for our for our students, and of course, I you know, I mostly deal with younger students in K-12.
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I think we have to create those moments of resistance for them and facilitate that because I don't think they'll necessarily do it themselves and adults, not just kids.
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Yeah, yeah.
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Um I um I've been working this year with a number of faculty who are experimenting with pedagogical uses of custom AI chatbots.
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And so part of that involves creating system messages for these bots to direct them to interact in certain ways, right?
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Maybe you're tutoring, maybe you're coaching, whatever you're you're simulating a patient or something, right?
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Um and uh and we're often working against the design of the AI platforms that want to be fluid and easy and overhelpful.
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Um, and we're we're like, that's not how learning works.
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We we we need we need we need them to do other things than that.
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Um I uh so I've been using a a uh uh a gym metaphor myself lately, and I I think it's compatible, but I wanted to run it by you.
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So I've heard folks say now many times that um using AI can be like taking a forklift to the gym.
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It will move the weights around, but that's not the point, right?
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Um and I've been arguing that that is true, right?
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Like it can be a forklift in the gym, and that's an unhelpful use of AI for learning purposes.
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Um uh, but it can also be like a treadmill.
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You know, when it's cold and rainy outside and I don't want to run in my neighborhood, I can go to the gym and I can run on a treadmill.
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It's creating an opportunity for resistance in a in a sense.
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Um does that work in your metaphor?
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I think it does.
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Yeah, I think so.
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I think um uh what one of the uh pieces of research that I um referenced in that original article was a paper from um University of Technology Sydney, Jason Lodge and Leslie Lobel uh produced a big report on cognitive offload and sort of the lay of the land really with with the emerging research.
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And um they talk in that paper about the potential for beneficial cognitive offload.
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Um, because sometimes we don't want to do certain things, and it we it does uh free up room for for you know bigger and brighter things.
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I think about my own use of AI is obviously quite broad and diverse.
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Um, but probably my number one use of the technology is recording articles verbally um and then uh using whisper transcription models to transcribe them, but then also running them through something like Claude to do the, you know, putting the subheadings and paragraph them and um pop them into nice tidy HTML that I can just copy paste into my blog.
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Um, because I could sit there and write directly into the blog, but I find um, you know, my attention wanders if I've got multiple tabs open and things.
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I can concentrate more on writing an article if I'm out on the farm where I live, walking around kind of verbally speaking through it.
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And that for me frees up uh you know a lot of uh the the tedium.
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Um I also have an AI built into my accounting platform that does my bank reconciliations, which is great.
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Yes.
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So there are there are there are opportunities for beneficial offload, I think.
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I don't need to be spending three hours a quarter doing my bank reconciliations anymore.
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Right, right, right, right.
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Yeah, yeah.
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And I think that's an example, like your your approach to writing is sounds sounds like an example of my treadmill metaphor, right?
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It's it's using a little bit of technology to help you engage in the work in a meaningful way to you that you know might be hard to do otherwise, right?
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Um, but it's not doing the writing for you.
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Like it's your thoughts, it's your ideas.
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Um you mentioned in the in the blog post uh these kind of five uh pieces of your framework.
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Can you kind of walk us through those those five pieces and what they mean?
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Yeah, sure.
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I mean, and these are like anything, you know, you can identify as many themes in research as you want, really.
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You you've got to narrow them down at some point.
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I was I was joking to my wife um yesterday that I think half of my time now is spent um just uh my attention wanders for a bit, and I I kind of get distracted with what I've been writing, and then I focus on something new and start inventing frameworks.
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And uh I think inventing frameworks is something that uh that researchers and educators really enjoy doing just as a hobby.
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So these the five areas that really um stood out to me when I was reading around were the um evaluative judgment or evaluation, uh, which is something that uh a number of Australian academics have have written a lot about.
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Um the that's really the capacity to evaluate the quality of work, including your own, and now the work of artificial intelligence.
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So fact-checking, hallucinations, all of that kind of work.
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Um, feedback was another area.
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And for me, that's more about students learning how to seek feedback, how to use AI well to get feedback for their own sake.
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Much less about educators using AI to provide feedback because the research is suggesting that students really, really don't like that and they don't respond well.
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Yes.
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Um the uh the the importance of expertise.
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So I've this is something I've been writing about for a long time now.
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Um, but I'm I'm really of the opinion that AI works best in the hands of someone with existing um disciplinary or domain expertise, which kind of makes it paradoxical to learning almost, because by definition, you you don't know what you don't know.
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Yeah, um yeah.
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I have a slide in most of my presentations that says expert AI use requires expertise.
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Yeah, absolutely.
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And you know, this is where they the sort of the areas start to overlap.
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You know, you need to be able to evaluate the quality of AI.
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And in order to do that, you need some expertise in what you're doing.
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And um the the last two areas are are metacognition, which is a bit of a buzzword in education, just like you know, cognition in general, it's it's almost its whole separate little field of education studies.
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For me, metacognition is less about um thinking about thinking or learning about learning, and more about understanding the feeling of learning, if that makes sense.
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So, you know, what what does it feel like to grapple with a difficult concept and then push through and come out the other side?
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Because I think AI flattens a lot of that experience.
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And you know, I think one thing that's going to be increasingly difficult to um convince learners is that that is a good thing.
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Um, because AI is so fluent and so you know confident sounding, so frictionless, I think it's gonna be really difficult to convince people that that struggling through hard thinking is a good a good idea.
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Um it was hard to convince students of that before we had AI, often.
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Oh, yeah, absolutely.
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Let's let's not be naive as well.
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You know, I've uh I've been around the block for a few years now myself, and I've taught uh kids and adults.
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Um, you know, I've taught uh postgraduate teaching qualifications and um and other aspects of higher education as well.
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And nobody really wants to learn um when they're feeling tired, or it's you know, we're heading into winter here in Australia and the the days are getting shorter.
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Yeah.
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There's other things that we'd rather be doing often than struggling with dense concepts.
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And I think you know, AI again is very seductive there.
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So teaching students to kind of just you know sit with those feelings for their own sake is I think really important.
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Yeah, I I think I've shared this story on the podcast before, but um I had a transformative moment my junior year of college.
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Um I was doing a study abroad program for math majors.
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I was in Budapest, Hungary.
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I was there with about 45 American math majors taking a whole bunch of upper level math courses all at once.
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Um super geeky, but I it was hard, it was challenging.
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And I remember there was this one course called Conjecture and Proof, and they would give us these math problems that we had to work through.
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And it would, I I was pretty good at math.
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I was good at maths.
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Everyone, everyone told me I was good at maths, and so um I get there and I'm like, it's never taken me more than like half an hour to work through a homework problem in the past.
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And these problems take four hours, six hours, eight hours, right?
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Um, sometimes there's three of us working it together, and it's like even putting our heads together, it takes hours to kind of get the insight that helps us move forward.
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And I never had that experience before, but having gone through that and realized that yeah, sometimes it takes six hours to solve a math problem.
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Knowing you've done that once helps you approach other hard problems very differently.
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And it is a feeling, like you say.
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It's less it's less about knowing it, it's it's really is more about having that confidence to go into something hard like that and to persist.
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Yeah.
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And I mean, that story has just triggered a memory in me, which is kind of um illustrative of all of the stuff that AI can't do.
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Um, the fact that you've just shared a personal anecdote and then um it's it's conjured up one of the memories from uh the depths of my brain as well.
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When I was doing my own postgraduate teaching course, um we went on a field trip to Florence, Italy.
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And um, you know, we were English media drama um teacher graduates, teacher students.
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And uh for one of the days we we had to follow a small theater troupe around the city of Florence as they very enthusiastic um enthusiastically acted out the Medici story.
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Um and we had to we had to get involved, like we had to uh you know join in with some of the improv and and incredibly daunting for a group of young, inexperienced, um, you know, pre-qualified drama teachers to stand on the streets of Florence and act with a theatre troupe um in the middle of Italy.
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But um, you know, that experience uh absolutely 100% made me a better and more confident drama teacher.
00:20:41.460 --> 00:20:59.539
And that I think you know, like as you've just said with your maths experience, just having to go through that process of sort of baptism of fire to improve your skills to kind of hone and sharpen your knowledge, that's not gonna come from interacting with a chatbot.
00:20:59.539 --> 00:21:03.939
Um, and there are lots of ways to gain knowledge from using AI.
00:21:03.939 --> 00:21:19.779
I'm not on the on the side of the fence that says that they, you know, they can't be used in education at all, but we have to look for the moments where the more human, relational, um, you know, like your experience and mine there, where we can bring those to the fore.
00:21:19.779 --> 00:21:21.460
Yeah, yeah.
00:21:21.460 --> 00:21:24.419
I think you had one more part of your framework.
00:21:24.419 --> 00:21:34.339
Yeah, so uh the fifth aspect sort of necessarily last is is this idea of sort of stretch to sit with that resistance metaphor.
00:21:34.339 --> 00:21:36.339
Yeah just to push further now.
00:21:36.339 --> 00:21:45.779
And you know, and the the analogy that I used on the blog post originally was you don't just walk into the gym and start lifting heavy things because you you're going to injure yourself in much the same way.
00:21:45.779 --> 00:21:53.139
You're not just going to walk into your your most powerful AI platform and expect it to start learning for you.
00:21:53.139 --> 00:21:55.220
Um, we do all of the other things first.
00:21:55.220 --> 00:22:14.819
We build evaluative judgment, we learn how to seek and use good feedback, we um we apply our own understanding of learning and our metacognition, we develop expertise, and then we apply the technology to move us into new uncharted territory, make weird connections, go down rabbit holes.
00:22:14.819 --> 00:22:22.419
Um, you know, and I think once you've got that basis, that foundation, it's definitely possible to use AI in that way.
00:22:22.419 --> 00:22:31.299
Um, you know, I certainly use AI in that way in in things like coding and web design and areas where I'm right at the edge of my comfort zone.
00:22:31.299 --> 00:22:32.099
Yeah.
00:22:32.099 --> 00:22:32.659
Yeah.
00:22:32.659 --> 00:22:44.659
So let me ask a couple of Kind of things that I think fit in the framework, but um, because I really love this approach of trying to say, okay, we don't want students to just rob themselves of the learning opportunities.
00:22:44.659 --> 00:22:47.299
But more specifically, what does that mean, right?
00:22:47.299 --> 00:22:48.500
What are the components of that?
00:22:48.500 --> 00:22:53.220
And I think you've done a great job of enumerating and identifying these.
00:22:53.220 --> 00:22:57.699
Like I the part that you have to have the ability to evaluate AI output.
00:22:57.699 --> 00:22:59.220
I hear that a lot, right?
00:22:59.220 --> 00:23:11.379
But the notion that, you know, uh learning is hard and it's gonna take some struggle, and I need to know what that feels like so that I know when I'm not doing it, right?
00:23:11.379 --> 00:23:14.500
I don't hear that articulated in quite that way very often.
00:23:14.500 --> 00:23:16.740
Um, there's a couple of other elements.
00:23:16.740 --> 00:23:31.539
So as I think about my own AI use, I feel like maybe I'm I'm off here, but I feel like I'm getting better at knowing uh for which tasks AI will be helpful to me and my process and for which tasks it will not.
00:23:31.539 --> 00:23:37.539
And that's a little bit about the AI and a little bit about the task and a little bit about me and how I want to work, right?
00:23:37.539 --> 00:23:40.179
And so where does that fit in your framework?
00:23:40.179 --> 00:23:47.299
Is that it doesn't sound like feedback, but it it has a kind of similar vibe of like when to go to AI and when not to go to AI.
00:23:47.299 --> 00:24:06.419
Yeah, so I mean, in both the feedback area and the evaluation area, um I've I've seen both research around this and then anecdotally, and in my own experience in using the technology, as you say, developing that awareness of when to go AI and when to go human, essentially.
00:24:06.419 --> 00:24:12.500
And um with students, uh I'll I'll look at feedback as as the example here.
00:24:12.500 --> 00:24:22.740
The students are telling us that they want human feedback for some things and that they do not like receiving AI feedback from human teachers.
00:24:22.740 --> 00:24:32.099
Uh, you know, they they recoil if they even get the slightest hint that a teacher has used AI to give them feedback, they they revolt.
00:24:32.099 --> 00:24:40.980
And on the the almost sort of contradiction is they are using AI en masse to give themselves feedback.
00:24:40.980 --> 00:24:42.339
Right, right.
00:24:42.339 --> 00:24:43.220
Okay, yeah.
00:24:43.220 --> 00:24:58.019
And then there's there's research coming through that says even if the students, you know, if if they don't know whether a given piece of feedback is AI generated or human, and then you tell them that it's AI generated, even if it's written by a human, they don't care.
00:24:58.019 --> 00:25:06.099
The the knowledge or the you know the the supposition that it's AI generated is enough to totally devalue the feedback.
00:25:06.099 --> 00:25:19.299
So for me, there's there's something there, you know, and it's not the quality of the feedback that matters because AI feedback can be quite high in quality, it's it's the quality of the relationship.
00:25:19.299 --> 00:25:35.299
And so one paper I'm thinking of um Tom Corbyn, uh Joanna Ty, um maybe another author escapes my mind at the moment, um, writing a piece last year, I think, um, about different types of feedback.
00:25:35.299 --> 00:25:43.859
And they talk about the importance of recognition and the sort of the mutual recognition and vulnerability, the shared vulnerability between an educator and a student.
00:25:43.859 --> 00:25:48.579
The student reaches out for feedback, which you know puts them in a position of vulnerability.
00:25:48.579 --> 00:25:54.419
The the teacher provides feedback and kind of says, Look, here I'm the expert, trust me.
00:25:54.419 --> 00:25:59.699
And that that kind of mutual recognition um is something that AI can't do.
00:25:59.699 --> 00:26:06.579
Um, they use the term extra recognitive, which I've told um Tom I don't like because I can't say it.
00:26:06.579 --> 00:26:08.339
So I've I've asked him to change the name.
00:26:08.339 --> 00:26:13.779
So if he ever hears this podcast, um then Tom, please do change that in future posts.
00:26:13.779 --> 00:26:29.939
But um, that extra recognitive feedback that AI can give can be just as good quality in terms of identifying strengths and weaknesses and all of that stuff, but it doesn't have that reciprocal human relationship component.
00:26:29.939 --> 00:26:37.859
So, you know, like you said, there you over time can identify what's a good way to use AI and what isn't.
00:26:37.859 --> 00:26:43.059
And I think more broadly in education, we need to come to those conclusions as well.
00:26:43.059 --> 00:26:48.500
Um, because we see students and educators using AI in ways which are probably not great.
00:26:48.500 --> 00:26:50.259
Yeah, yeah.
00:26:50.259 --> 00:26:56.819
And I think a lot about how you know some of that it it it may it could vary from student to student.
00:26:56.819 --> 00:27:02.659
For for this student, this use of AI is gonna slow them down, but another student that may actually be a helpful thing.
00:27:02.659 --> 00:27:09.299
And so, because I think sometimes as educators, we want to have a kind of single solution for everything, but our students are very different.
00:27:09.299 --> 00:27:12.339
Um and that relationship piece is key.
00:27:12.339 --> 00:27:19.220
I I I it's almost what you said, the the students will easily go to AI themselves to get feedback on their work.
00:27:19.220 --> 00:27:27.699
But if they get essentially the same feedback from an AI in the place of their instructor feedback, they recoil.
00:27:27.699 --> 00:27:27.939
Yeah.
00:27:27.939 --> 00:27:28.259
Right.
00:27:28.259 --> 00:27:33.539
And I it feels almost like the kind of the it's it's analogous to the uncanny valley, right?
00:27:33.539 --> 00:27:39.139
That's supposed to be a human, but it's not obviously not a human, and I'm freaked out by that, right?
00:27:39.139 --> 00:27:52.419
Um aspect there as well that you know speaks to the value of the relationship between the teacher and the student, because the the student doesn't want the educator to use AI to give them feedback.
00:27:52.419 --> 00:28:07.779
And then on the on the educator side, I I've seen this cause a bit of tension because the reasons that teachers are using AI to give feedback are not because they don't want to engage with the student or form relationships, it's because they because of their workload.
00:28:07.779 --> 00:28:18.500
So, you know, we have students using AI to write work, and then the teachers feeling totally flat from being overworked, marking all of this AI generated stuff.
00:28:18.500 --> 00:28:24.579
So then they start using AI to give feedback and the students resent them for it, and then the teachers resent the students for resenting them.
00:28:24.579 --> 00:28:27.539
And you know, that that whole cycle kind of goes around.
00:28:27.539 --> 00:28:36.659
And so, you know, I was talking, I was um working face to face at a school in Melbourne on on Monday this week, and this came up with the uh the heads of faculty.
00:28:36.659 --> 00:28:38.419
We said, How do we deal with this?
00:28:38.419 --> 00:28:47.939
Because, you know, we want to use AI in ways which which reduce our workload, but we don't want to do it in a way which threatens the relationship with students.
00:28:47.939 --> 00:28:51.460
And I said, Look, I mean, I would just cut out the middleman.
00:28:51.460 --> 00:29:00.659
Instead of having teachers using AI to give students feedback, teach the students how to seek feedback from AI better.
00:29:00.659 --> 00:29:07.779
Um we, you know, we see students, students just throw their work into AI and say, is this good?
00:29:07.779 --> 00:29:08.899
Right, right.
00:29:08.899 --> 00:29:15.059
And then they get a very sycophantic, enthusiastic response from something like Chat GPT, which tells them they're a genius.
00:29:15.059 --> 00:29:21.939
Uh and you know, there are ways to use the technology better and also places where the technology is best avoided.
00:29:21.939 --> 00:29:32.500
So teach them those behaviors and attitudes, um, cut out the middleman, and then any time that you save, use it in just having conversations face to face with students.
00:29:32.500 --> 00:29:33.299
Yeah.
00:29:33.299 --> 00:29:41.220
So feedback is not just when to go to AI and when to go to a human, but how to seek feedback from different sources and when to seek it.
00:29:41.220 --> 00:29:59.779
Um we have methods of feedback, we have methods of formative assessment, programmatic assessments, you know, all of these things which we know are effective, but they don't scale very well, and they require a lot of time investment, um, and they're much more relational.
00:29:59.779 --> 00:30:04.259
And that's really, if we're being honest, that's the problem.
00:30:04.259 --> 00:30:10.740
It's not it's not the AI that's the problem, it's uh it's speed, efficiency, scale, and cost.
00:30:10.740 --> 00:30:12.259
Yeah, yeah.
00:30:12.259 --> 00:30:18.659
Well, um, I want to ask uh about I want to kind of move into some practical strategies for teachers.
00:30:18.659 --> 00:30:27.539
Um, and maybe I'll do this by saying, you know, in this framework, there are these these areas where we want to build resistance for students.
00:30:27.539 --> 00:30:37.539
Um if we're gonna teach resistance to students, what what do we have maybe to unteach them in terms of their existing uses of AI?
00:30:37.539 --> 00:30:42.579
Yeah, students and educators and everyone.
00:30:42.579 --> 00:30:53.699
Um I've got a another series of posts on on my blog at the moment, which is um broadly called If You Know, You Know, I Y K Y K.
00:30:53.699 --> 00:31:02.579
Um Based on this idea that like if you if you know what AI can do, it's quite easy to use it in ways which are fairly effective.
00:31:02.579 --> 00:31:07.220
Um, but if you don't know what it can do, the technology doesn't help at all.
00:31:07.220 --> 00:31:17.939
And one thing that I would love people to unlearn is a design fault, I would say, of open AI, or a conscious design decision, maybe not a fault.
00:31:17.939 --> 00:31:29.059
Um, when they released ChatGPT, the first ever very big, publicly available commercial chatbot style large language model, they made it look like Google.
00:31:29.059 --> 00:31:36.339
And that I feel was a very deliberate move to make it familiar seeming and comfortable and easy to use.
00:31:36.339 --> 00:31:41.699
But essentially, you go to chatGPT.com, it looks like Google, it feels like Google.
00:31:41.699 --> 00:31:47.699
It even to an extent, especially now that Google's just chock full of AI anyway, it feels like Google.
00:31:47.699 --> 00:31:58.419
And it is the worst way to use the technology, you know, from particularly from the early days before these things had reliable internet connections and the ability to search.
00:31:58.419 --> 00:32:01.539
In 2022, that was a terrible choice to think of it.
00:32:01.539 --> 00:32:02.019
Absolutely.
00:32:02.019 --> 00:32:12.819
And so, you know, what I've written about in that that series of articles is the first experience of AI for most people seems to set the sort of the expectation going forward.
00:32:12.819 --> 00:32:17.460
It sets their ceiling on what they see AI as, the kind of the mental model around it.
00:32:17.460 --> 00:32:25.779
So if your first experience of Chat GPT was the thing that's, you know, like a it kind of looks like Google, so I'll use it to do a search.
00:32:25.779 --> 00:32:29.299
The mental model becomes AI is a search engine.
00:32:29.299 --> 00:32:39.779
If your first experience is um, you know, Microsoft copilot and using it to summarize an email from the boss, it becomes the thing that summarizes text.
00:32:39.779 --> 00:32:46.740
And a lot of people's mental models, students and other, um, are stuck on that.
00:32:46.740 --> 00:32:59.779
And I think the only way to kind of deal with that is to ignore the way that the technology companies are actually presenting their technology and just kind of start experimenting and doing weird stuff with AI.
00:32:59.779 --> 00:33:23.220
Um, you know, the the example I've used recently, I'm working with a group of um curriculum leaders at the moment, is thinking that artificial intelligence, large language models are really, really proficient in writing code and therefore they can do a pretty good job of simulating anything that a computer can do because they can write and execute code.
00:33:23.220 --> 00:33:29.379
Even ChatGPT on the web can write code and run code and you know access tools and use stuff.
00:33:29.379 --> 00:33:38.500
So what does it look like if instead of thinking of AI as a search engine, we think of it like a sort of Swiss Army knife app for working with a computer?
00:33:38.500 --> 00:33:41.460
And and then people start doing really weird stuff.
00:33:41.460 --> 00:33:51.220
Like, you know, one of the educators had mentioned um they downloaded a whole bunch of PDFs and things with dates and stuff on and had ChatGPT converted into an ICAL file.
00:33:51.220 --> 00:33:59.699
And you know, that's that's such a such a kind of a niche specific use, which has nothing to do with how most people use AI.
00:33:59.699 --> 00:34:16.260
So my advice for students would be you know, get a little bit weird, stop thinking of Chat GPT as a search engine because it's a horrible search engine, and start thinking more of it as a just a kind of multi-purpose um computer application that speaks the language of computer.
00:34:16.260 --> 00:34:17.780
Yeah.
00:34:17.780 --> 00:34:18.740
Yeah.
00:34:18.740 --> 00:34:31.940
Um uh yeah, because the the what you just described, or like your own use of using uh AI to take your kind of raw blog post and put and format it with headers and such, right?
00:34:31.940 --> 00:34:44.739
Um just yesterday I was taking uh someone's syllabus, uh, which was a well-formatted word document, and we wanted to put it up on our website as an HTML document so it would be more accessible to screen readers and such.
00:34:44.739 --> 00:34:46.340
Um I did all that by hand.
00:34:46.340 --> 00:34:47.539
I don't know why I did that now.
00:34:47.539 --> 00:34:48.900
I should, I shouldn't have, right?
00:34:48.900 --> 00:34:52.179
Like, but it is, it's it's the it's your first exposure, right?
00:34:52.179 --> 00:34:59.380
And I've used I've used AI tools for that kind of thing in the past, but it's weird to have a Swiss Army knife that does such strange things.
00:34:59.380 --> 00:35:04.099
It's it's hard to get in the habit of realizing, oh, here's here's something it could do.
00:35:04.099 --> 00:35:12.340
I I mean I had an actual Swiss Army knife, I still have it somewhere in my house, and it has tools I've never used and that I don't know what they do, right?
00:35:12.340 --> 00:35:14.819
But I know what most of it does.
00:35:14.819 --> 00:35:17.699
Um yeah, that's a good metaphor, yeah.
00:35:17.699 --> 00:35:39.940
Oh, it's um it's just the the reaching beyond those initial mental models is really hard because um it it this this is in the blog post, but in in user experience terms, there's this problem called the discoverability problem, which means like how easy or how difficult is it to discover the latent features of a piece of software?
00:35:39.940 --> 00:35:45.380
So you look at Microsoft Word and you can discover most of the features just by clicking through the ribbon.
00:35:45.380 --> 00:35:48.019
You know, everything's laid out, there's a few tabs.
00:35:48.019 --> 00:35:51.460
If you want to add comments, you can find that if you search around enough.
00:35:51.460 --> 00:35:54.980
AI doesn't have any feature toolkit.
00:35:54.980 --> 00:35:57.860
And the companies have tried, I think, to do some of this.
00:35:57.860 --> 00:36:06.819
Like if you look now at ChatGPT uh or Google Gemini, it's got the drop-down box, you can turn on research mode, you can you can click a button to generate an image.
00:36:06.819 --> 00:36:07.299
Okay.
00:36:07.299 --> 00:36:18.420
But those are very narrow feature sets, and the abilities of or the capacity of AI is so broad that it would be impossible for it to have a toolbar like a traditional app.
00:36:18.420 --> 00:36:23.299
Um, we've almost, you know, you've got to kind of feel your way through and create your own mental toolbar.
00:36:23.299 --> 00:36:24.659
Oh yeah.
00:36:24.659 --> 00:36:25.539
Yeah.
00:36:25.539 --> 00:36:29.219
That is a high load for user interface.
00:36:29.219 --> 00:36:29.940
Oh yeah.
00:36:29.940 --> 00:36:32.179
Um terrible software.
00:36:32.179 --> 00:36:33.860
Right, right.
00:36:33.860 --> 00:36:38.819
And again, they're going for the, you know, the simple, the easy, the Google, the clean.
00:36:38.819 --> 00:36:41.860
Um the Silicon Valley boring.
00:36:41.860 --> 00:36:43.940
Right, right, right, right.
00:36:43.940 --> 00:36:44.420
Right.
00:36:44.420 --> 00:36:50.340
Um, I do want to ask about some classroom practices because you've got another blog series you're calling resistance training toolkit.
00:36:50.340 --> 00:36:55.219
And so these are activity structures that are kind of targeted at some of these areas in the framework.
00:36:55.219 --> 00:37:02.819
And so can you share a couple of examples of of what those classroom activities might be that that would help students develop this kind of resistance?
00:37:02.819 --> 00:37:03.460
Yeah.
00:37:03.460 --> 00:37:28.980
I mean, so so these for me, they come from um the the world of things like uh Ron Richards' making thinking visible, um, the the Project Zero, um, Harvard University visible thinking routines, um, those kinds of uh toolkits, um, the the Black and Williams uh formative assessment book, they have lots and lots of examples in them of really short classroom activities.
00:37:28.980 --> 00:37:33.860
So I wanted to expand that resistance idea into that territory.
00:37:33.860 --> 00:37:40.099
Um and what I've started to do is is build out a few examples um sort of section by section.
00:37:40.099 --> 00:37:54.739
So I started with expertise and had to think about like, okay, so you know, what's a good activity to use in a classroom, um, either before, during, or after introducing AI, and then also link it to that emerging research around the technology.
00:37:54.739 --> 00:38:10.259
So there's a paper that suggests that if you interact with AI as if it's a novice learner and then you teach it what you know, almost like a like a teach the teacher kind of approach, um, then that can be useful for identifying gaps in your own knowledge.
00:38:10.259 --> 00:38:26.980
So one of the activities is essentially an adaptation of um teach the teacher, and this the student interacts with the AI, kind of info dumps on them everything they know about a topic, asks questions, and then goes backwards and forwards and iterates for you that a few times.
00:38:26.980 --> 00:38:33.139
Um, another one is a close reading activity, essentially.
00:38:33.139 --> 00:38:37.940
So what can a student do without AI before they start using AI?
00:38:37.940 --> 00:38:55.619
We do some close reading, we do some annotation, we take things really slowly, and then when we start to introduce the technology, we start looking for um opportunities to add complexity or layers to the things that the student has already noticed.
00:38:55.619 --> 00:39:05.779
So, you know, we don't just default to using the AI in the first instance, we actually showcase or develop or explore our own expertise first.
00:39:05.779 --> 00:39:07.860
Yeah.
00:39:07.860 --> 00:39:08.340
Yeah.
00:39:08.340 --> 00:39:14.019
So both of those are designed to help foster that expertise in the discipline or the domain.
00:39:14.019 --> 00:39:23.940
Um in part by I think what you were saying earlier is is we're having students use the chatbot not in their normal mental model, but in other modes, right?
00:39:23.940 --> 00:39:29.219
One thing a chatbot can do is it can pretend to be other people of particular types, right?
00:39:29.219 --> 00:39:30.259
Not always well, right?
00:39:30.259 --> 00:39:38.739
But but it can pretend to be someone who doesn't know about, you know, cell biology, and then that you have a chance to then teach it what you know about that.
00:39:38.739 --> 00:39:48.579
Um I I had on my podcast a while back, Heidi Nobles, who is a uh uh uh uh English teacher uh at the University of Virginia, and she has a lot of experience as a book editor.
00:39:48.579 --> 00:39:53.539
And so she had all these uses of AI that were kind of fundamental model of a book editor.
00:39:53.539 --> 00:39:54.579
What does a book editor do?
00:39:54.579 --> 00:39:59.539
What kind of feedback do you get structurally, conceptually, strategically?
00:39:59.539 --> 00:40:10.019
Um and again, that's not the kind of go-to model, but um but it's one that points towards the students developing expertise, perhaps, if they are adding that complexity and nuance in that way.
00:40:10.019 --> 00:40:10.579
Yeah.
00:40:10.579 --> 00:40:11.619
Yeah.
00:40:11.619 --> 00:40:15.699
Well, I hope you continue writing that blog series or maybe put all of that in a book.
00:40:15.699 --> 00:40:22.980
Um my intention is to to um I I purchase domains almost kind of compulsively.
00:40:22.980 --> 00:40:30.500
Um so you know, I've got my leonfurs.com, I've got practicalai strategies.com, I've got teachingaiethics.com.
00:40:30.500 --> 00:40:34.819
I think somewhere I've probably already purchased a domain that would suit this series.
00:40:34.819 --> 00:40:46.659
My my intention is normally to publish this kind of stuff, open access, and and just throw it out into the wild, um, which is I think that's the teacher in me more than the business person in me.
00:40:46.659 --> 00:40:51.940
Uh produced all of this kind of stuff, put it out into the wild, get a bit of feedback and see what people think about it.
00:40:51.940 --> 00:40:55.380
So yeah, I'm definitely working on all of those in the background.
00:40:55.380 --> 00:40:56.980
We are almost out of time.
00:40:56.980 --> 00:40:59.059
So I will ask uh, what's next for you?
00:40:59.059 --> 00:41:02.340
Do you have any any new projects coming that you're excited about?
00:41:02.340 --> 00:41:03.539
Always.
00:41:03.539 --> 00:41:07.380
Um so my PhD is officially finished.
00:41:07.380 --> 00:41:13.860
Umfirmation of that, and I'll I'll walk across the stage in June for the silly hat.
00:41:13.860 --> 00:41:17.139
Um I'm gonna continue with the writing.
00:41:17.139 --> 00:41:23.539
I've got a book sitting with the editor at the moment, the um imaginatively titled Practical AI Strategies 2.
00:41:23.539 --> 00:41:26.500
And I'm and I'm just working with lots of really interesting people.
00:41:26.500 --> 00:41:36.579
You know, recently I've had conversations with ACARA, which is the Australian Curriculum Assessment Um Reporting Authority, ATEL that does the professional standards for teachers over here.
00:41:36.579 --> 00:41:40.420
So there's really high-level conversations going on in Australia.
00:41:40.420 --> 00:41:50.819
Um, but I'm also running lots of uh individual school universities, one-on-one with teachers, you know, that that group of um curriculum leaders that I mentioned before.
00:41:50.819 --> 00:41:53.219
So I that I find that really enjoyable.
00:41:53.219 --> 00:41:58.500
You know, I've always enjoyed teaching teachers for for over a decade now, running professional learning.
00:41:58.500 --> 00:42:02.900
Um so I'm gonna continue writing and continue teaching.
00:42:02.900 --> 00:42:03.860
That's great.
00:42:03.860 --> 00:42:04.579
That's great.
00:42:04.579 --> 00:42:06.980
Well, um, all good work.
00:42:06.980 --> 00:42:08.739
Um, and we thank you for doing it.
00:42:08.739 --> 00:42:10.500
And thanks for coming on the podcast today.
00:42:10.500 --> 00:42:12.099
This has been a really fun conversation.
00:42:12.099 --> 00:42:13.779
Um, thanks so much.
00:42:13.779 --> 00:42:15.219
Thank you very much, Derek.
00:42:15.219 --> 00:42:20.099
That was author and consultant Leon Furs.
00:42:20.099 --> 00:42:24.579
I keep thinking about some of the ideas he shared in our conversation.
00:42:24.579 --> 00:42:33.619
For years in my presentations, I've said that AI tools like ChatGPT are more like wordsmiths than oracles, which is a great metaphor, I guess.
00:42:33.619 --> 00:42:40.579
But Leon helps me see that I was actually trying to redefine the mental models we have of working with generative AI.
00:42:40.579 --> 00:42:46.819
Our mental models of AI don't just inform our understanding of AI, they shape our use of AI.
00:42:46.819 --> 00:42:49.779
And there are a lot of ways to use AI poorly.
00:42:49.779 --> 00:42:59.059
I think we need to move away from the mental model of AI as Oracle, as Google search, as answer machine, and we need to help our students do the same.
00:42:59.059 --> 00:43:20.019
Leon mentioned a recent lit review by Jason Lodge and Leslie Lobel, and that review points to studies showing that A, default uses of AI by students are largely unhelpful to their learning, but also that when students are provided better guidance on using AI, then their use of AI can actually improve their learning.
00:43:20.019 --> 00:43:31.539
I think changing student mental models and, in some cases, educator mental models of AI is key here, and I wrote a whole blog post about this idea, a link to which you can find in the show notes.
00:43:31.539 --> 00:43:41.779
Also in the show notes are links to the blog posts by Leon Furz that we referenced in our conversation today, as well as links to some of the studies and resources that Leon mentioned.
00:43:41.779 --> 00:43:47.860
If you like a podcast episode that gives you lots to read after you listen, this is the episode for you.
00:43:47.860 --> 00:43:53.460
And if you liked what you heard from Leon here today, please do visit his website for lots of ways to keep up with his work.
00:43:53.460 --> 00:43:58.500
He's got newsletters and podcasts and LinkedIn connections to make.
00:43:58.500 --> 00:44:02.980
He really is a prolific thinker and writer about AI and learning.
00:44:02.980 --> 00:44:10.099
Intentional Teaching is sponsored by UPSIA, the Online and Professional Education Association.
00:44:10.099 --> 00:44:18.500
In the show notes, you'll find a link to the UPSIA website where you can find out about their research, networking opportunities, and professional development offerings.
00:44:18.500 --> 00:44:22.980
This episode of Intentional Teaching was produced and edited by me, Derek Bruff.
00:44:22.980 --> 00:44:31.059
See the show notes for links to my website, the Intentional Teaching newsletter, and my Patreon where you can help support the show for just a few bucks a month.
00:44:31.059 --> 00:44:36.739
If you found this or any episode of Intentional Teaching useful, would you consider sharing it with a colleague?
00:44:36.739 --> 00:44:37.699
That would mean a lot.
00:44:37.699 --> 00:44:39.779
As always, thanks for listening.
00:00:39.619 --> 00:00:43.939
So I think we have to put in conscious moments of resistance.
00:00:43.939 --> 00:00:54.819
And um and for our for our students, I think we have to create those moments of resistance for them and facilitate that because I don't think they'll necessarily do it themselves.
00:00:55.219 --> 00:01:03.140
Welcome to Intentional Teaching, a podcast aimed at educators to help them develop foundational teaching skills and explore new ideas in teaching.
00:01:03.140 --> 00:01:05.219
I'm your host, Derek Bruff.
00:01:05.219 --> 00:01:11.140
I hope this podcast helps you be more intentional in how you teach and in how you develop as a teacher over time.
00:01:11.140 --> 00:01:29.060
You may be familiar with author and consultant Leon Furs from his series of articles on the teaching of AI ethics, or perhaps from the AI assessment scale, a popular tool for helping students understand what uses of AI are permissible on a given assignment, which Leon helped develop.
00:01:29.060 --> 00:01:37.060
I'm very excited to have Leon on the podcast today, although we didn't talk about his AI ethics articles or the AI assessment scale.
00:01:37.060 --> 00:01:46.019
We could have, but Leon is such a prolific thinker and writer that we focused on a few of his recent blog posts and the ideas they contain.
00:01:46.019 --> 00:01:54.899
It was his March 2026 post titled Resistance as a Framework for Combating Cognitive Offload that caught my eye.
00:01:54.899 --> 00:02:07.299
In it, he uses the idea of resistance and strength training to help us think about the ways we can help our students resist the problematic cognitive offloading that AI offers to learners.
00:02:07.299 --> 00:02:17.219
I invited Leon on the show to talk about his resistance framework, and we had a wide-ranging conversation about equipping students to learn well in this age of AI.
00:02:17.219 --> 00:02:22.819
I had multiple light bulb moments during the conversation, and I hope you do too.
00:02:22.819 --> 00:02:26.819
Welcome to Intentional Teaching, Leon.
00:02:26.819 --> 00:02:33.699
I'm very glad to have you on the show and to um, I think have a really engaging conversation about AI and learning.
00:02:33.699 --> 00:02:35.459
I'm excited to have you have you here.
00:02:35.459 --> 00:02:36.659
Thanks for being here.
00:02:36.659 --> 00:02:39.139
Yeah, thank you very much for inviting me.
00:02:39.139 --> 00:02:47.300
Um and I really want to make a joke about talking to tomorrow because you are many time zones away from me right now.
00:02:47.300 --> 00:02:56.339
Um but I'll I'll avoid that and start with my usual opening question, which is um Leon, can you tell us about a time when you realized you wanted to be an educator?
00:02:56.339 --> 00:03:00.259
Yeah, well, we'll go back through the archives.
00:03:00.259 --> 00:03:07.300
Um so I was uh I was a maths science kid in school, uh, but my my teachers didn't agree with me.
00:03:07.300 --> 00:03:11.379
And um, so I ended up at university studying English literature.
00:03:11.379 --> 00:03:26.500
And um after three, four years studying English and American literatures as a a dual honors course in the UK, um, I sort of fell out of the other side of that and thought, God, what do I do with an English and American literature degree?
00:03:26.500 --> 00:03:36.579
And um, a week after the applications had closed, I applied for a postgraduate certificate in education, uh, which is a UK teaching qualification.
00:03:36.579 --> 00:03:44.660
And um then just sort of rolled through the first few months of that before I discovered that I actually quite liked teaching.
00:03:44.660 --> 00:03:49.219
Um yeah, and then uh spent 15 years in the classroom.
00:03:49.219 --> 00:03:50.660
Nice, nice.
00:03:50.660 --> 00:03:53.139
And what what did you teach in the classroom?
00:03:53.139 --> 00:04:01.139
Uh for the most part, secondary uh English and English literature, um, typically with senior students.
00:04:01.139 --> 00:04:05.219
Uh but I I worked in a small regional Australian school for most of my career.
00:04:05.219 --> 00:04:12.100
So I've taught uh you know history, geography, religion, STEM, uh, media, drama.
00:04:12.100 --> 00:04:12.339
Okay.
00:04:12.339 --> 00:04:21.219
Uh pretty much everything except for maths, because um again, the maths teacher, even in adulthood, uh still didn't think that I was qualified to do the maths.
00:04:21.219 --> 00:04:22.500
Okay.
00:04:22.500 --> 00:04:23.219
All right.
00:04:23.219 --> 00:04:25.540
But small school, you teach what needs to be taught.
00:04:25.540 --> 00:04:26.019
That's great.
00:04:26.019 --> 00:04:26.500
Okay.
00:04:26.500 --> 00:04:27.219
Exactly.
00:04:27.219 --> 00:04:37.300
Um uh now, however, you are doing a lot of work, um, I think at multiple levels of education, helping people grapple with AI and what it means for teaching and learning.
00:04:37.300 --> 00:04:44.500
Um and um I wanna I'm gonna start with this question because uh this this is a little unfair.
00:04:44.500 --> 00:04:52.740
I'm gonna give you uh a post from Blue Sky without any context, but it but it it is it does seem typical of a kind of post I see a lot.
00:04:52.740 --> 00:05:00.180
And it's it read Study finds that students using Chat GPT learned and remembered less than students who did not use it.
00:05:00.180 --> 00:05:01.779
And then they link to a study.
00:05:01.779 --> 00:05:20.500
And so um given how often we just read a headline and move on with our lives, um, when you hear a headline like that, what what advice would or what advice would you give to educators who hear a headline like that and are concerned about AI's impact on student learning?
00:05:20.500 --> 00:05:22.500
Read the paper.
00:05:22.500 --> 00:05:23.459
Yes.
00:05:23.459 --> 00:05:25.539
Please, please read the paper.
00:05:25.539 --> 00:05:32.500
Um even that headline, uh I I'd be 99% sure it was the MIT paper.
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Um It was not, but Oh really?
00:05:35.699 --> 00:05:38.500
Well, the 1%, the 1% that fooled me.
00:05:38.500 --> 00:05:48.740
Um so many of those, many of those studies, as we know, you know, this this this version of this technology has been around since November 2022 in the public eye.
00:05:48.740 --> 00:05:54.180
Um any studies published on it have gone through peer review, hopefully.
00:05:54.180 --> 00:05:57.859
And you know, that takes maybe six months plus sometimes.
00:05:57.859 --> 00:05:59.939
If you're lucky, it takes six months.
00:05:59.939 --> 00:06:04.019
By the time the papers are published, the models that they're talking about are out of date.
00:06:04.019 --> 00:06:13.939
But I just I don't think it's possible just yet to make any big claims in one direction or the other about the the long-term cognitive effects of using AI.
00:06:13.939 --> 00:06:17.139
There's so much supposition and assumption there.
00:06:17.139 --> 00:06:21.060
Um, you know, I I joked about the MIT paper before.
00:06:21.060 --> 00:06:47.939
Really, I think that paper, which was really incredibly famous and you know, big splashy headlines, uh, what it essentially said was if a learner uses Chat GPT to generate a response and then submits it without reading it, they don't remember what the output was, which frankly is common sense, not necessarily a spectacular um research finding in the long-term effects of AI on cognition.
00:06:47.939 --> 00:06:50.099
The paper was was fine, it was great.
00:06:50.099 --> 00:06:56.979
And the authors um actually came out afterwards and said, look, there's a lot of people misinterpreting this paper and reading too much into it.
00:06:56.979 --> 00:06:59.779
Um, you know, and that's true of the other side as well.
00:06:59.779 --> 00:07:04.180
We see a lot of papers that are, you know, promoting the potential benefits.
00:07:04.180 --> 00:07:08.500
One of them in the last uh week or so has been retracted from nature.
00:07:08.500 --> 00:07:16.819
Um, really, really uh again, very popular paper that was very well received by the the positive AI hypsters.
00:07:16.819 --> 00:07:19.459
Um so it swings both ways.
00:07:19.459 --> 00:07:20.659
Yeah, yeah.
00:07:20.659 --> 00:07:22.500
I saw that retraction too.
00:07:22.500 --> 00:07:33.539
Um and that was one of my concerns with that with the the presentation of that study that I mentioned on Blue Sky is the study finds that students using ChatGPT learned and remembered less than students who did not use it.
00:07:33.539 --> 00:07:34.659
Which students?
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How did they use it?
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Were they prepared to use it?
00:07:36.740 --> 00:07:38.579
Were they directed to use it well, right?
00:07:38.579 --> 00:07:42.419
Like, like I mean, your your initial response, read the study, is super important.
00:07:42.419 --> 00:07:53.699
Um, because I I find that the the study design says a lot um uh uh about how we infer or what we what what we take away from the results.
00:07:53.699 --> 00:07:53.939
Yeah.
00:07:53.939 --> 00:08:00.099
And and do do read it, don't just use Chat GPT to turn it into an infographic and then post it on LinkedIn.
00:08:00.099 --> 00:08:01.620
Right, right.
00:08:01.620 --> 00:08:03.699
That is good advice.
00:08:03.699 --> 00:08:08.899
Um speaking of posting on LinkedIn, I think that was where I saw your recent blog post.
00:08:08.899 --> 00:08:10.659
Uh how's that for a segue?
00:08:10.659 --> 00:08:16.819
Um you wrote a blog post called Resistance as a framework for combating cognitive offload.
00:08:16.819 --> 00:08:21.379
And that's kind of the heart of what I want to talk with you about today, because I just found I found it really interesting.
00:08:21.379 --> 00:08:25.300
The the framework that you that you you started to scope out there.
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Um and I, you know, as I read it, you're not arguing that that we don't use AI, but we use AI thoughtfully in ways that are going to support student learning.
00:08:33.460 --> 00:08:37.060
And so you use this metaphor of resistance in this context.
00:08:37.060 --> 00:08:39.460
So so what do you mean by resistance in this context?
00:08:39.460 --> 00:08:42.659
How is how how what is that how does that metaphor work for you?
00:08:42.659 --> 00:08:48.259
Um yeah, so I mean that initially came from two directions.
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One was I was I was reading lots of uh research around AI and education and trying to just sort of look at the the major themes that were coming out, which were the the five areas that I ended up with in the framework.
00:09:01.460 --> 00:09:15.139
And then in trying to to think of what kind of tied all of those things together, and I've done some writing in the past about friction, but I I thought you know friction's not really the right angle for these areas.
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Um, so I had a you know a bit more of a think.
00:09:17.700 --> 00:09:25.700
And the resistance idea really came from the fact that um I'm reaching an age now where when I get out of bed in the morning it hurts me.
00:09:25.700 --> 00:09:31.620
Um and I've got three kids and I pick them up and swing them around and inevitably injure my back.
00:09:31.620 --> 00:09:38.259
Uh and so I go to the gym reasonably often and I do you know strength training or resistance training.
00:09:38.259 --> 00:09:42.740
And the reason I do that is not particularly because I enjoy it, it's because I have to.
00:09:42.740 --> 00:09:46.820
And if I didn't do it, um my body would fall to bits.
00:09:46.820 --> 00:09:56.900
Um, and I think it's the same with this technology, you know, it's so seductive, it's so compelling and easy to use, it's it's increasingly ubiquitous.
00:09:56.900 --> 00:10:00.980
They really want us to use it and to sort of justify its existence.
00:10:00.980 --> 00:10:04.980
It is going to be increasingly difficult to avoid it.
00:10:04.980 --> 00:10:08.900
So I think we have to put in conscious moments of resistance.
00:10:08.900 --> 00:10:16.020
And um, and for our for our students, and of course, I you know, I mostly deal with younger students in K-12.
00:10:16.020 --> 00:10:26.020
I think we have to create those moments of resistance for them and facilitate that because I don't think they'll necessarily do it themselves and adults, not just kids.
00:10:26.020 --> 00:10:28.500
Yeah, yeah.
00:10:28.500 --> 00:10:37.220
Um I um I've been working this year with a number of faculty who are experimenting with pedagogical uses of custom AI chatbots.
00:10:37.220 --> 00:10:45.780
And so part of that involves creating system messages for these bots to direct them to interact in certain ways, right?
00:10:45.780 --> 00:10:50.500
Maybe you're tutoring, maybe you're coaching, whatever you're you're simulating a patient or something, right?
00:10:50.500 --> 00:11:00.820
Um and uh and we're often working against the design of the AI platforms that want to be fluid and easy and overhelpful.
00:11:00.820 --> 00:11:04.259
Um, and we're we're like, that's not how learning works.
00:11:04.259 --> 00:11:08.900
We we we need we need we need them to do other things than that.
00:11:08.900 --> 00:11:17.620
Um I uh so I've been using a a uh uh a gym metaphor myself lately, and I I think it's compatible, but I wanted to run it by you.
00:11:17.620 --> 00:11:24.420
So I've heard folks say now many times that um using AI can be like taking a forklift to the gym.
00:11:24.420 --> 00:11:28.020
It will move the weights around, but that's not the point, right?
00:11:28.020 --> 00:11:30.340
Um and I've been arguing that that is true, right?
00:11:30.340 --> 00:11:35.700
Like it can be a forklift in the gym, and that's an unhelpful use of AI for learning purposes.
00:11:35.700 --> 00:11:38.900
Um uh, but it can also be like a treadmill.
00:11:38.900 --> 00:11:44.580
You know, when it's cold and rainy outside and I don't want to run in my neighborhood, I can go to the gym and I can run on a treadmill.
00:11:44.580 --> 00:11:48.580
It's creating an opportunity for resistance in a in a sense.
00:11:48.580 --> 00:11:51.300
Um does that work in your metaphor?
00:11:51.300 --> 00:11:52.020
I think it does.
00:11:52.020 --> 00:11:53.060
Yeah, I think so.
00:11:53.060 --> 00:12:14.980
I think um uh what one of the uh pieces of research that I um referenced in that original article was a paper from um University of Technology Sydney, Jason Lodge and Leslie Lobel uh produced a big report on cognitive offload and sort of the lay of the land really with with the emerging research.
00:12:14.980 --> 00:12:21.460
And um they talk in that paper about the potential for beneficial cognitive offload.
00:12:21.460 --> 00:12:29.700
Um, because sometimes we don't want to do certain things, and it we it does uh free up room for for you know bigger and brighter things.
00:12:29.700 --> 00:12:33.700
I think about my own use of AI is obviously quite broad and diverse.
00:12:33.700 --> 00:13:00.020
Um, but probably my number one use of the technology is recording articles verbally um and then uh using whisper transcription models to transcribe them, but then also running them through something like Claude to do the, you know, putting the subheadings and paragraph them and um pop them into nice tidy HTML that I can just copy paste into my blog.
00:13:00.020 --> 00:13:07.940
Um, because I could sit there and write directly into the blog, but I find um, you know, my attention wanders if I've got multiple tabs open and things.
00:13:07.940 --> 00:13:16.660
I can concentrate more on writing an article if I'm out on the farm where I live, walking around kind of verbally speaking through it.
00:13:16.660 --> 00:13:21.780
And that for me frees up uh you know a lot of uh the the tedium.
00:13:21.780 --> 00:13:27.700
Um I also have an AI built into my accounting platform that does my bank reconciliations, which is great.
00:13:27.700 --> 00:13:28.820
Yes.
00:13:28.820 --> 00:13:33.220
So there are there are there are opportunities for beneficial offload, I think.
00:13:33.220 --> 00:13:37.220
I don't need to be spending three hours a quarter doing my bank reconciliations anymore.
00:13:37.220 --> 00:13:39.139
Right, right, right, right.
00:13:39.139 --> 00:13:40.020
Yeah, yeah.
00:13:40.020 --> 00:13:46.500
And I think that's an example, like your your approach to writing is sounds sounds like an example of my treadmill metaphor, right?
00:13:46.500 --> 00:13:55.060
It's it's using a little bit of technology to help you engage in the work in a meaningful way to you that you know might be hard to do otherwise, right?
00:13:55.060 --> 00:13:57.220
Um, but it's not doing the writing for you.
00:13:57.220 --> 00:13:59.379
Like it's your thoughts, it's your ideas.
00:13:59.379 --> 00:14:05.780
Um you mentioned in the in the blog post uh these kind of five uh pieces of your framework.
00:14:05.780 --> 00:14:09.379
Can you kind of walk us through those those five pieces and what they mean?
00:14:09.379 --> 00:14:10.340
Yeah, sure.
00:14:10.340 --> 00:14:16.259
I mean, and these are like anything, you know, you can identify as many themes in research as you want, really.
00:14:16.259 --> 00:14:17.940
You you've got to narrow them down at some point.
00:14:17.940 --> 00:14:35.139
I was I was joking to my wife um yesterday that I think half of my time now is spent um just uh my attention wanders for a bit, and I I kind of get distracted with what I've been writing, and then I focus on something new and start inventing frameworks.
00:14:35.139 --> 00:14:42.660
And uh I think inventing frameworks is something that uh that researchers and educators really enjoy doing just as a hobby.
00:14:42.660 --> 00:14:58.259
So these the five areas that really um stood out to me when I was reading around were the um evaluative judgment or evaluation, uh, which is something that uh a number of Australian academics have have written a lot about.
00:14:58.259 --> 00:15:08.180
Um the that's really the capacity to evaluate the quality of work, including your own, and now the work of artificial intelligence.
00:15:08.180 --> 00:15:11.540
So fact-checking, hallucinations, all of that kind of work.
00:15:11.540 --> 00:15:14.180
Um, feedback was another area.
00:15:14.180 --> 00:15:22.100
And for me, that's more about students learning how to seek feedback, how to use AI well to get feedback for their own sake.
00:15:22.100 --> 00:15:31.460
Much less about educators using AI to provide feedback because the research is suggesting that students really, really don't like that and they don't respond well.
00:15:31.460 --> 00:15:32.020
Yes.
00:15:32.020 --> 00:15:36.259
Um the uh the the importance of expertise.
00:15:36.259 --> 00:15:40.420
So I've this is something I've been writing about for a long time now.
00:15:40.420 --> 00:15:56.820
Um, but I'm I'm really of the opinion that AI works best in the hands of someone with existing um disciplinary or domain expertise, which kind of makes it paradoxical to learning almost, because by definition, you you don't know what you don't know.
00:15:56.820 --> 00:15:57.940
Yeah, um yeah.
00:15:57.940 --> 00:16:03.379
I have a slide in most of my presentations that says expert AI use requires expertise.
00:16:03.379 --> 00:16:04.820
Yeah, absolutely.
00:16:04.820 --> 00:16:08.020
And you know, this is where they the sort of the areas start to overlap.
00:16:08.020 --> 00:16:11.700
You know, you need to be able to evaluate the quality of AI.
00:16:11.700 --> 00:16:15.220
And in order to do that, you need some expertise in what you're doing.
00:16:15.220 --> 00:16:29.139
And um the the last two areas are are metacognition, which is a bit of a buzzword in education, just like you know, cognition in general, it's it's almost its whole separate little field of education studies.
00:16:29.139 --> 00:16:40.500
For me, metacognition is less about um thinking about thinking or learning about learning, and more about understanding the feeling of learning, if that makes sense.
00:16:40.500 --> 00:16:48.100
So, you know, what what does it feel like to grapple with a difficult concept and then push through and come out the other side?
00:16:48.100 --> 00:16:52.259
Because I think AI flattens a lot of that experience.
00:16:52.259 --> 00:17:02.660
And you know, I think one thing that's going to be increasingly difficult to um convince learners is that that is a good thing.
00:17:02.660 --> 00:17:17.379
Um, because AI is so fluent and so you know confident sounding, so frictionless, I think it's gonna be really difficult to convince people that that struggling through hard thinking is a good a good idea.
00:17:17.379 --> 00:17:20.900
Um it was hard to convince students of that before we had AI, often.
00:17:20.900 --> 00:17:21.940
Oh, yeah, absolutely.
00:17:21.940 --> 00:17:23.539
Let's let's not be naive as well.
00:17:23.539 --> 00:17:30.180
You know, I've uh I've been around the block for a few years now myself, and I've taught uh kids and adults.
00:17:30.180 --> 00:17:38.819
Um, you know, I've taught uh postgraduate teaching qualifications and um and other aspects of higher education as well.
00:17:38.819 --> 00:17:48.259
And nobody really wants to learn um when they're feeling tired, or it's you know, we're heading into winter here in Australia and the the days are getting shorter.
00:17:48.259 --> 00:17:48.579
Yeah.
00:17:48.579 --> 00:17:52.899
There's other things that we'd rather be doing often than struggling with dense concepts.
00:17:52.899 --> 00:17:56.500
And I think you know, AI again is very seductive there.
00:17:56.500 --> 00:18:06.179
So teaching students to kind of just you know sit with those feelings for their own sake is I think really important.
00:18:06.179 --> 00:18:13.939
Yeah, I I think I've shared this story on the podcast before, but um I had a transformative moment my junior year of college.
00:18:13.939 --> 00:18:17.779
Um I was doing a study abroad program for math majors.
00:18:17.779 --> 00:18:19.539
I was in Budapest, Hungary.
00:18:19.539 --> 00:18:26.659
I was there with about 45 American math majors taking a whole bunch of upper level math courses all at once.
00:18:26.659 --> 00:18:31.379
Um super geeky, but I it was hard, it was challenging.
00:18:31.379 --> 00:18:36.980
And I remember there was this one course called Conjecture and Proof, and they would give us these math problems that we had to work through.
00:18:36.980 --> 00:18:39.779
And it would, I I was pretty good at math.
00:18:39.779 --> 00:18:40.899
I was good at maths.
00:18:40.899 --> 00:18:49.539
Everyone, everyone told me I was good at maths, and so um I get there and I'm like, it's never taken me more than like half an hour to work through a homework problem in the past.
00:18:49.539 --> 00:18:54.259
And these problems take four hours, six hours, eight hours, right?
00:18:54.259 --> 00:19:02.259
Um, sometimes there's three of us working it together, and it's like even putting our heads together, it takes hours to kind of get the insight that helps us move forward.
00:19:02.259 --> 00:19:10.579
And I never had that experience before, but having gone through that and realized that yeah, sometimes it takes six hours to solve a math problem.
00:19:10.579 --> 00:19:15.699
Knowing you've done that once helps you approach other hard problems very differently.
00:19:15.699 --> 00:19:17.539
And it is a feeling, like you say.
00:19:17.539 --> 00:19:25.539
It's less it's less about knowing it, it's it's really is more about having that confidence to go into something hard like that and to persist.
00:19:25.539 --> 00:19:26.339
Yeah.
00:19:26.339 --> 00:19:36.179
And I mean, that story has just triggered a memory in me, which is kind of um illustrative of all of the stuff that AI can't do.
00:19:36.179 --> 00:19:44.579
Um, the fact that you've just shared a personal anecdote and then um it's it's conjured up one of the memories from uh the depths of my brain as well.
00:19:44.579 --> 00:19:52.659
When I was doing my own postgraduate teaching course, um we went on a field trip to Florence, Italy.
00:19:52.659 --> 00:19:59.059
And um, you know, we were English media drama um teacher graduates, teacher students.
00:19:59.059 --> 00:20:11.859
And uh for one of the days we we had to follow a small theater troupe around the city of Florence as they very enthusiastic um enthusiastically acted out the Medici story.
00:20:11.859 --> 00:20:34.099
Um and we had to we had to get involved, like we had to uh you know join in with some of the improv and and incredibly daunting for a group of young, inexperienced, um, you know, pre-qualified drama teachers to stand on the streets of Florence and act with a theatre troupe um in the middle of Italy.
00:20:34.099 --> 00:20:41.460
But um, you know, that experience uh absolutely 100% made me a better and more confident drama teacher.
00:20:41.460 --> 00:20:59.539
And that I think you know, like as you've just said with your maths experience, just having to go through that process of sort of baptism of fire to improve your skills to kind of hone and sharpen your knowledge, that's not gonna come from interacting with a chatbot.
00:20:59.539 --> 00:21:03.939
Um, and there are lots of ways to gain knowledge from using AI.
00:21:03.939 --> 00:21:19.779
I'm not on the on the side of the fence that says that they, you know, they can't be used in education at all, but we have to look for the moments where the more human, relational, um, you know, like your experience and mine there, where we can bring those to the fore.
00:21:19.779 --> 00:21:21.460
Yeah, yeah.
00:21:21.460 --> 00:21:24.419
I think you had one more part of your framework.
00:21:24.419 --> 00:21:34.339
Yeah, so uh the fifth aspect sort of necessarily last is is this idea of sort of stretch to sit with that resistance metaphor.
00:21:34.339 --> 00:21:36.339
Yeah just to push further now.
00:21:36.339 --> 00:21:45.779
And you know, and the the analogy that I used on the blog post originally was you don't just walk into the gym and start lifting heavy things because you you're going to injure yourself in much the same way.
00:21:45.779 --> 00:21:53.139
You're not just going to walk into your your most powerful AI platform and expect it to start learning for you.
00:21:53.139 --> 00:21:55.220
Um, we do all of the other things first.
00:21:55.220 --> 00:22:14.819
We build evaluative judgment, we learn how to seek and use good feedback, we um we apply our own understanding of learning and our metacognition, we develop expertise, and then we apply the technology to move us into new uncharted territory, make weird connections, go down rabbit holes.
00:22:14.819 --> 00:22:22.419
Um, you know, and I think once you've got that basis, that foundation, it's definitely possible to use AI in that way.
00:22:22.419 --> 00:22:31.299
Um, you know, I certainly use AI in that way in in things like coding and web design and areas where I'm right at the edge of my comfort zone.
00:22:31.299 --> 00:22:32.099
Yeah.
00:22:32.099 --> 00:22:32.659
Yeah.
00:22:32.659 --> 00:22:44.659
So let me ask a couple of Kind of things that I think fit in the framework, but um, because I really love this approach of trying to say, okay, we don't want students to just rob themselves of the learning opportunities.
00:22:44.659 --> 00:22:47.299
But more specifically, what does that mean, right?
00:22:47.299 --> 00:22:48.500
What are the components of that?
00:22:48.500 --> 00:22:53.220
And I think you've done a great job of enumerating and identifying these.
00:22:53.220 --> 00:22:57.699
Like I the part that you have to have the ability to evaluate AI output.
00:22:57.699 --> 00:22:59.220
I hear that a lot, right?
00:22:59.220 --> 00:23:11.379
But the notion that, you know, uh learning is hard and it's gonna take some struggle, and I need to know what that feels like so that I know when I'm not doing it, right?
00:23:11.379 --> 00:23:14.500
I don't hear that articulated in quite that way very often.
00:23:14.500 --> 00:23:16.740
Um, there's a couple of other elements.
00:23:16.740 --> 00:23:31.539
So as I think about my own AI use, I feel like maybe I'm I'm off here, but I feel like I'm getting better at knowing uh for which tasks AI will be helpful to me and my process and for which tasks it will not.
00:23:31.539 --> 00:23:37.539
And that's a little bit about the AI and a little bit about the task and a little bit about me and how I want to work, right?
00:23:37.539 --> 00:23:40.179
And so where does that fit in your framework?
00:23:40.179 --> 00:23:47.299
Is that it doesn't sound like feedback, but it it has a kind of similar vibe of like when to go to AI and when not to go to AI.
00:23:47.299 --> 00:24:06.419
Yeah, so I mean, in both the feedback area and the evaluation area, um I've I've seen both research around this and then anecdotally, and in my own experience in using the technology, as you say, developing that awareness of when to go AI and when to go human, essentially.
00:24:06.419 --> 00:24:12.500
And um with students, uh I'll I'll look at feedback as as the example here.
00:24:12.500 --> 00:24:22.740
The students are telling us that they want human feedback for some things and that they do not like receiving AI feedback from human teachers.
00:24:22.740 --> 00:24:32.099
Uh, you know, they they recoil if they even get the slightest hint that a teacher has used AI to give them feedback, they they revolt.
00:24:32.099 --> 00:24:40.980
And on the the almost sort of contradiction is they are using AI en masse to give themselves feedback.
00:24:40.980 --> 00:24:42.339
Right, right.
00:24:42.339 --> 00:24:43.220
Okay, yeah.
00:24:43.220 --> 00:24:58.019
And then there's there's research coming through that says even if the students, you know, if if they don't know whether a given piece of feedback is AI generated or human, and then you tell them that it's AI generated, even if it's written by a human, they don't care.
00:24:58.019 --> 00:25:06.099
The the knowledge or the you know the the supposition that it's AI generated is enough to totally devalue the feedback.
00:25:06.099 --> 00:25:19.299
So for me, there's there's something there, you know, and it's not the quality of the feedback that matters because AI feedback can be quite high in quality, it's it's the quality of the relationship.
00:25:19.299 --> 00:25:35.299
And so one paper I'm thinking of um Tom Corbyn, uh Joanna Ty, um maybe another author escapes my mind at the moment, um, writing a piece last year, I think, um, about different types of feedback.
00:25:35.299 --> 00:25:43.859
And they talk about the importance of recognition and the sort of the mutual recognition and vulnerability, the shared vulnerability between an educator and a student.
00:25:43.859 --> 00:25:48.579
The student reaches out for feedback, which you know puts them in a position of vulnerability.
00:25:48.579 --> 00:25:54.419
The the teacher provides feedback and kind of says, Look, here I'm the expert, trust me.
00:25:54.419 --> 00:25:59.699
And that that kind of mutual recognition um is something that AI can't do.
00:25:59.699 --> 00:26:06.579
Um, they use the term extra recognitive, which I've told um Tom I don't like because I can't say it.
00:26:06.579 --> 00:26:08.339
So I've I've asked him to change the name.
00:26:08.339 --> 00:26:13.779
So if he ever hears this podcast, um then Tom, please do change that in future posts.
00:26:13.779 --> 00:26:29.939
But um, that extra recognitive feedback that AI can give can be just as good quality in terms of identifying strengths and weaknesses and all of that stuff, but it doesn't have that reciprocal human relationship component.
00:26:29.939 --> 00:26:37.859
So, you know, like you said, there you over time can identify what's a good way to use AI and what isn't.
00:26:37.859 --> 00:26:43.059
And I think more broadly in education, we need to come to those conclusions as well.
00:26:43.059 --> 00:26:48.500
Um, because we see students and educators using AI in ways which are probably not great.
00:26:48.500 --> 00:26:50.259
Yeah, yeah.
00:26:50.259 --> 00:26:56.819
And I think a lot about how you know some of that it it it may it could vary from student to student.
00:26:56.819 --> 00:27:02.659
For for this student, this use of AI is gonna slow them down, but another student that may actually be a helpful thing.
00:27:02.659 --> 00:27:09.299
And so, because I think sometimes as educators, we want to have a kind of single solution for everything, but our students are very different.
00:27:09.299 --> 00:27:12.339
Um and that relationship piece is key.
00:27:12.339 --> 00:27:19.220
I I I it's almost what you said, the the students will easily go to AI themselves to get feedback on their work.
00:27:19.220 --> 00:27:27.699
But if they get essentially the same feedback from an AI in the place of their instructor feedback, they recoil.
00:27:27.699 --> 00:27:27.939
Yeah.
00:27:27.939 --> 00:27:28.259
Right.
00:27:28.259 --> 00:27:33.539
And I it feels almost like the kind of the it's it's analogous to the uncanny valley, right?
00:27:33.539 --> 00:27:39.139
That's supposed to be a human, but it's not obviously not a human, and I'm freaked out by that, right?
00:27:39.139 --> 00:27:52.419
Um aspect there as well that you know speaks to the value of the relationship between the teacher and the student, because the the student doesn't want the educator to use AI to give them feedback.
00:27:52.419 --> 00:28:07.779
And then on the on the educator side, I I've seen this cause a bit of tension because the reasons that teachers are using AI to give feedback are not because they don't want to engage with the student or form relationships, it's because they because of their workload.
00:28:07.779 --> 00:28:18.500
So, you know, we have students using AI to write work, and then the teachers feeling totally flat from being overworked, marking all of this AI generated stuff.
00:28:18.500 --> 00:28:24.579
So then they start using AI to give feedback and the students resent them for it, and then the teachers resent the students for resenting them.
00:28:24.579 --> 00:28:27.539
And you know, that that whole cycle kind of goes around.
00:28:27.539 --> 00:28:36.659
And so, you know, I was talking, I was um working face to face at a school in Melbourne on on Monday this week, and this came up with the uh the heads of faculty.
00:28:36.659 --> 00:28:38.419
We said, How do we deal with this?
00:28:38.419 --> 00:28:47.939
Because, you know, we want to use AI in ways which which reduce our workload, but we don't want to do it in a way which threatens the relationship with students.
00:28:47.939 --> 00:28:51.460
And I said, Look, I mean, I would just cut out the middleman.
00:28:51.460 --> 00:29:00.659
Instead of having teachers using AI to give students feedback, teach the students how to seek feedback from AI better.
00:29:00.659 --> 00:29:07.779
Um we, you know, we see students, students just throw their work into AI and say, is this good?
00:29:07.779 --> 00:29:08.899
Right, right.
00:29:08.899 --> 00:29:15.059
And then they get a very sycophantic, enthusiastic response from something like Chat GPT, which tells them they're a genius.
00:29:15.059 --> 00:29:21.939
Uh and you know, there are ways to use the technology better and also places where the technology is best avoided.
00:29:21.939 --> 00:29:32.500
So teach them those behaviors and attitudes, um, cut out the middleman, and then any time that you save, use it in just having conversations face to face with students.
00:29:32.500 --> 00:29:33.299
Yeah.
00:29:33.299 --> 00:29:41.220
So feedback is not just when to go to AI and when to go to a human, but how to seek feedback from different sources and when to seek it.
00:29:41.220 --> 00:29:59.779
Um we have methods of feedback, we have methods of formative assessment, programmatic assessments, you know, all of these things which we know are effective, but they don't scale very well, and they require a lot of time investment, um, and they're much more relational.
00:29:59.779 --> 00:30:04.259
And that's really, if we're being honest, that's the problem.
00:30:04.259 --> 00:30:10.740
It's not it's not the AI that's the problem, it's uh it's speed, efficiency, scale, and cost.
00:30:10.740 --> 00:30:12.259
Yeah, yeah.
00:30:12.259 --> 00:30:18.659
Well, um, I want to ask uh about I want to kind of move into some practical strategies for teachers.
00:30:18.659 --> 00:30:27.539
Um, and maybe I'll do this by saying, you know, in this framework, there are these these areas where we want to build resistance for students.
00:30:27.539 --> 00:30:37.539
Um if we're gonna teach resistance to students, what what do we have maybe to unteach them in terms of their existing uses of AI?
00:30:37.539 --> 00:30:42.579
Yeah, students and educators and everyone.
00:30:42.579 --> 00:30:53.699
Um I've got a another series of posts on on my blog at the moment, which is um broadly called If You Know, You Know, I Y K Y K.
00:30:53.699 --> 00:31:02.579
Um Based on this idea that like if you if you know what AI can do, it's quite easy to use it in ways which are fairly effective.
00:31:02.579 --> 00:31:07.220
Um, but if you don't know what it can do, the technology doesn't help at all.
00:31:07.220 --> 00:31:17.939
And one thing that I would love people to unlearn is a design fault, I would say, of open AI, or a conscious design decision, maybe not a fault.
00:31:17.939 --> 00:31:29.059
Um, when they released ChatGPT, the first ever very big, publicly available commercial chatbot style large language model, they made it look like Google.
00:31:29.059 --> 00:31:36.339
And that I feel was a very deliberate move to make it familiar seeming and comfortable and easy to use.
00:31:36.339 --> 00:31:41.699
But essentially, you go to chatGPT.com, it looks like Google, it feels like Google.
00:31:41.699 --> 00:31:47.699
It even to an extent, especially now that Google's just chock full of AI anyway, it feels like Google.
00:31:47.699 --> 00:31:58.419
And it is the worst way to use the technology, you know, from particularly from the early days before these things had reliable internet connections and the ability to search.
00:31:58.419 --> 00:32:01.539
In 2022, that was a terrible choice to think of it.
00:32:01.539 --> 00:32:02.019
Absolutely.
00:32:02.019 --> 00:32:12.819
And so, you know, what I've written about in that that series of articles is the first experience of AI for most people seems to set the sort of the expectation going forward.
00:32:12.819 --> 00:32:17.460
It sets their ceiling on what they see AI as, the kind of the mental model around it.
00:32:17.460 --> 00:32:25.779
So if your first experience of Chat GPT was the thing that's, you know, like a it kind of looks like Google, so I'll use it to do a search.
00:32:25.779 --> 00:32:29.299
The mental model becomes AI is a search engine.
00:32:29.299 --> 00:32:39.779
If your first experience is um, you know, Microsoft copilot and using it to summarize an email from the boss, it becomes the thing that summarizes text.
00:32:39.779 --> 00:32:46.740
And a lot of people's mental models, students and other, um, are stuck on that.
00:32:46.740 --> 00:32:59.779
And I think the only way to kind of deal with that is to ignore the way that the technology companies are actually presenting their technology and just kind of start experimenting and doing weird stuff with AI.
00:32:59.779 --> 00:33:23.220
Um, you know, the the example I've used recently, I'm working with a group of um curriculum leaders at the moment, is thinking that artificial intelligence, large language models are really, really proficient in writing code and therefore they can do a pretty good job of simulating anything that a computer can do because they can write and execute code.
00:33:23.220 --> 00:33:29.379
Even ChatGPT on the web can write code and run code and you know access tools and use stuff.
00:33:29.379 --> 00:33:38.500
So what does it look like if instead of thinking of AI as a search engine, we think of it like a sort of Swiss Army knife app for working with a computer?
00:33:38.500 --> 00:33:41.460
And and then people start doing really weird stuff.
00:33:41.460 --> 00:33:51.220
Like, you know, one of the educators had mentioned um they downloaded a whole bunch of PDFs and things with dates and stuff on and had ChatGPT converted into an ICAL file.
00:33:51.220 --> 00:33:59.699
And you know, that's that's such a such a kind of a niche specific use, which has nothing to do with how most people use AI.
00:33:59.699 --> 00:34:16.260
So my advice for students would be you know, get a little bit weird, stop thinking of Chat GPT as a search engine because it's a horrible search engine, and start thinking more of it as a just a kind of multi-purpose um computer application that speaks the language of computer.
00:34:16.260 --> 00:34:17.780
Yeah.
00:34:17.780 --> 00:34:18.740
Yeah.
00:34:18.740 --> 00:34:31.940
Um uh yeah, because the the what you just described, or like your own use of using uh AI to take your kind of raw blog post and put and format it with headers and such, right?
00:34:31.940 --> 00:34:44.739
Um just yesterday I was taking uh someone's syllabus, uh, which was a well-formatted word document, and we wanted to put it up on our website as an HTML document so it would be more accessible to screen readers and such.
00:34:44.739 --> 00:34:46.340
Um I did all that by hand.
00:34:46.340 --> 00:34:47.539
I don't know why I did that now.
00:34:47.539 --> 00:34:48.900
I should, I shouldn't have, right?
00:34:48.900 --> 00:34:52.179
Like, but it is, it's it's the it's your first exposure, right?
00:34:52.179 --> 00:34:59.380
And I've used I've used AI tools for that kind of thing in the past, but it's weird to have a Swiss Army knife that does such strange things.
00:34:59.380 --> 00:35:04.099
It's it's hard to get in the habit of realizing, oh, here's here's something it could do.
00:35:04.099 --> 00:35:12.340
I I mean I had an actual Swiss Army knife, I still have it somewhere in my house, and it has tools I've never used and that I don't know what they do, right?
00:35:12.340 --> 00:35:14.819
But I know what most of it does.
00:35:14.819 --> 00:35:17.699
Um yeah, that's a good metaphor, yeah.
00:35:17.699 --> 00:35:39.940
Oh, it's um it's just the the reaching beyond those initial mental models is really hard because um it it this this is in the blog post, but in in user experience terms, there's this problem called the discoverability problem, which means like how easy or how difficult is it to discover the latent features of a piece of software?
00:35:39.940 --> 00:35:45.380
So you look at Microsoft Word and you can discover most of the features just by clicking through the ribbon.
00:35:45.380 --> 00:35:48.019
You know, everything's laid out, there's a few tabs.
00:35:48.019 --> 00:35:51.460
If you want to add comments, you can find that if you search around enough.
00:35:51.460 --> 00:35:54.980
AI doesn't have any feature toolkit.
00:35:54.980 --> 00:35:57.860
And the companies have tried, I think, to do some of this.
00:35:57.860 --> 00:36:06.819
Like if you look now at ChatGPT uh or Google Gemini, it's got the drop-down box, you can turn on research mode, you can you can click a button to generate an image.
00:36:06.819 --> 00:36:07.299
Okay.
00:36:07.299 --> 00:36:18.420
But those are very narrow feature sets, and the abilities of or the capacity of AI is so broad that it would be impossible for it to have a toolbar like a traditional app.
00:36:18.420 --> 00:36:23.299
Um, we've almost, you know, you've got to kind of feel your way through and create your own mental toolbar.
00:36:23.299 --> 00:36:24.659
Oh yeah.
00:36:24.659 --> 00:36:25.539
Yeah.
00:36:25.539 --> 00:36:29.219
That is a high load for user interface.
00:36:29.219 --> 00:36:29.940
Oh yeah.
00:36:29.940 --> 00:36:32.179
Um terrible software.
00:36:32.179 --> 00:36:33.860
Right, right.
00:36:33.860 --> 00:36:38.819
And again, they're going for the, you know, the simple, the easy, the Google, the clean.
00:36:38.819 --> 00:36:41.860
Um the Silicon Valley boring.
00:36:41.860 --> 00:36:43.940
Right, right, right, right.
00:36:43.940 --> 00:36:44.420
Right.
00:36:44.420 --> 00:36:50.340
Um, I do want to ask about some classroom practices because you've got another blog series you're calling resistance training toolkit.
00:36:50.340 --> 00:36:55.219
And so these are activity structures that are kind of targeted at some of these areas in the framework.
00:36:55.219 --> 00:37:02.819
And so can you share a couple of examples of of what those classroom activities might be that that would help students develop this kind of resistance?
00:37:02.819 --> 00:37:03.460
Yeah.
00:37:03.460 --> 00:37:28.980
I mean, so so these for me, they come from um the the world of things like uh Ron Richards' making thinking visible, um, the the Project Zero, um, Harvard University visible thinking routines, um, those kinds of uh toolkits, um, the the Black and Williams uh formative assessment book, they have lots and lots of examples in them of really short classroom activities.
00:37:28.980 --> 00:37:33.860
So I wanted to expand that resistance idea into that territory.
00:37:33.860 --> 00:37:40.099
Um and what I've started to do is is build out a few examples um sort of section by section.
00:37:40.099 --> 00:37:54.739
So I started with expertise and had to think about like, okay, so you know, what's a good activity to use in a classroom, um, either before, during, or after introducing AI, and then also link it to that emerging research around the technology.
00:37:54.739 --> 00:38:10.259
So there's a paper that suggests that if you interact with AI as if it's a novice learner and then you teach it what you know, almost like a like a teach the teacher kind of approach, um, then that can be useful for identifying gaps in your own knowledge.
00:38:10.259 --> 00:38:26.980
So one of the activities is essentially an adaptation of um teach the teacher, and this the student interacts with the AI, kind of info dumps on them everything they know about a topic, asks questions, and then goes backwards and forwards and iterates for you that a few times.
00:38:26.980 --> 00:38:33.139
Um, another one is a close reading activity, essentially.
00:38:33.139 --> 00:38:37.940
So what can a student do without AI before they start using AI?
00:38:37.940 --> 00:38:55.619
We do some close reading, we do some annotation, we take things really slowly, and then when we start to introduce the technology, we start looking for um opportunities to add complexity or layers to the things that the student has already noticed.
00:38:55.619 --> 00:39:05.779
So, you know, we don't just default to using the AI in the first instance, we actually showcase or develop or explore our own expertise first.
00:39:05.779 --> 00:39:07.860
Yeah.
00:39:07.860 --> 00:39:08.340
Yeah.
00:39:08.340 --> 00:39:14.019
So both of those are designed to help foster that expertise in the discipline or the domain.
00:39:14.019 --> 00:39:23.940
Um in part by I think what you were saying earlier is is we're having students use the chatbot not in their normal mental model, but in other modes, right?
00:39:23.940 --> 00:39:29.219
One thing a chatbot can do is it can pretend to be other people of particular types, right?
00:39:29.219 --> 00:39:30.259
Not always well, right?
00:39:30.259 --> 00:39:38.739
But but it can pretend to be someone who doesn't know about, you know, cell biology, and then that you have a chance to then teach it what you know about that.
00:39:38.739 --> 00:39:48.579
Um I I had on my podcast a while back, Heidi Nobles, who is a uh uh uh uh English teacher uh at the University of Virginia, and she has a lot of experience as a book editor.
00:39:48.579 --> 00:39:53.539
And so she had all these uses of AI that were kind of fundamental model of a book editor.
00:39:53.539 --> 00:39:54.579
What does a book editor do?
00:39:54.579 --> 00:39:59.539
What kind of feedback do you get structurally, conceptually, strategically?
00:39:59.539 --> 00:40:10.019
Um and again, that's not the kind of go-to model, but um but it's one that points towards the students developing expertise, perhaps, if they are adding that complexity and nuance in that way.
00:40:10.019 --> 00:40:10.579
Yeah.
00:40:10.579 --> 00:40:11.619
Yeah.
00:40:11.619 --> 00:40:15.699
Well, I hope you continue writing that blog series or maybe put all of that in a book.
00:40:15.699 --> 00:40:22.980
Um my intention is to to um I I purchase domains almost kind of compulsively.
00:40:22.980 --> 00:40:30.500
Um so you know, I've got my leonfurs.com, I've got practicalai strategies.com, I've got teachingaiethics.com.
00:40:30.500 --> 00:40:34.819
I think somewhere I've probably already purchased a domain that would suit this series.
00:40:34.819 --> 00:40:46.659
My my intention is normally to publish this kind of stuff, open access, and and just throw it out into the wild, um, which is I think that's the teacher in me more than the business person in me.
00:40:46.659 --> 00:40:51.940
Uh produced all of this kind of stuff, put it out into the wild, get a bit of feedback and see what people think about it.
00:40:51.940 --> 00:40:55.380
So yeah, I'm definitely working on all of those in the background.
00:40:55.380 --> 00:40:56.980
We are almost out of time.
00:40:56.980 --> 00:40:59.059
So I will ask uh, what's next for you?
00:40:59.059 --> 00:41:02.340
Do you have any any new projects coming that you're excited about?
00:41:02.340 --> 00:41:03.539
Always.
00:41:03.539 --> 00:41:07.380
Um so my PhD is officially finished.
00:41:07.380 --> 00:41:13.860
Umfirmation of that, and I'll I'll walk across the stage in June for the silly hat.
00:41:13.860 --> 00:41:17.139
Um I'm gonna continue with the writing.
00:41:17.139 --> 00:41:23.539
I've got a book sitting with the editor at the moment, the um imaginatively titled Practical AI Strategies 2.
00:41:23.539 --> 00:41:26.500
And I'm and I'm just working with lots of really interesting people.
00:41:26.500 --> 00:41:36.579
You know, recently I've had conversations with ACARA, which is the Australian Curriculum Assessment Um Reporting Authority, ATEL that does the professional standards for teachers over here.
00:41:36.579 --> 00:41:40.420
So there's really high-level conversations going on in Australia.
00:41:40.420 --> 00:41:50.819
Um, but I'm also running lots of uh individual school universities, one-on-one with teachers, you know, that that group of um curriculum leaders that I mentioned before.
00:41:50.819 --> 00:41:53.219
So I that I find that really enjoyable.
00:41:53.219 --> 00:41:58.500
You know, I've always enjoyed teaching teachers for for over a decade now, running professional learning.
00:41:58.500 --> 00:42:02.900
Um so I'm gonna continue writing and continue teaching.
00:42:02.900 --> 00:42:03.860
That's great.
00:42:03.860 --> 00:42:04.579
That's great.
00:42:04.579 --> 00:42:06.980
Well, um, all good work.
00:42:06.980 --> 00:42:08.739
Um, and we thank you for doing it.
00:42:08.739 --> 00:42:10.500
And thanks for coming on the podcast today.
00:42:10.500 --> 00:42:12.099
This has been a really fun conversation.
00:42:12.099 --> 00:42:13.779
Um, thanks so much.
00:42:13.779 --> 00:42:15.219
Thank you very much, Derek.
00:42:15.219 --> 00:42:20.099
That was author and consultant Leon Furs.
00:42:20.099 --> 00:42:24.579
I keep thinking about some of the ideas he shared in our conversation.
00:42:24.579 --> 00:42:33.619
For years in my presentations, I've said that AI tools like ChatGPT are more like wordsmiths than oracles, which is a great metaphor, I guess.
00:42:33.619 --> 00:42:40.579
But Leon helps me see that I was actually trying to redefine the mental models we have of working with generative AI.
00:42:40.579 --> 00:42:46.819
Our mental models of AI don't just inform our understanding of AI, they shape our use of AI.
00:42:46.819 --> 00:42:49.779
And there are a lot of ways to use AI poorly.
00:42:49.779 --> 00:42:59.059
I think we need to move away from the mental model of AI as Oracle, as Google search, as answer machine, and we need to help our students do the same.
00:42:59.059 --> 00:43:20.019
Leon mentioned a recent lit review by Jason Lodge and Leslie Lobel, and that review points to studies showing that A, default uses of AI by students are largely unhelpful to their learning, but also that when students are provided better guidance on using AI, then their use of AI can actually improve their learning.
00:43:20.019 --> 00:43:31.539
I think changing student mental models and, in some cases, educator mental models of AI is key here, and I wrote a whole blog post about this idea, a link to which you can find in the show notes.
00:43:31.539 --> 00:43:41.779
Also in the show notes are links to the blog posts by Leon Furz that we referenced in our conversation today, as well as links to some of the studies and resources that Leon mentioned.
00:43:41.779 --> 00:43:47.860
If you like a podcast episode that gives you lots to read after you listen, this is the episode for you.
00:43:47.860 --> 00:43:53.460
And if you liked what you heard from Leon here today, please do visit his website for lots of ways to keep up with his work.
00:43:53.460 --> 00:43:58.500
He's got newsletters and podcasts and LinkedIn connections to make.
00:43:58.500 --> 00:44:02.980
He really is a prolific thinker and writer about AI and learning.
00:44:02.980 --> 00:44:10.099
Intentional Teaching is sponsored by UPSIA, the Online and Professional Education Association.
00:44:10.099 --> 00:44:18.500
In the show notes, you'll find a link to the UPSIA website where you can find out about their research, networking opportunities, and professional development offerings.
00:44:18.500 --> 00:44:22.980
This episode of Intentional Teaching was produced and edited by me, Derek Bruff.
00:44:22.980 --> 00:44:31.059
See the show notes for links to my website, the Intentional Teaching newsletter, and my Patreon where you can help support the show for just a few bucks a month.
00:44:31.059 --> 00:44:36.739
If you found this or any episode of Intentional Teaching useful, would you consider sharing it with a colleague?
00:44:36.739 --> 00:44:37.699
That would mean a lot.
00:44:37.699 --> 00:44:39.779
As always, thanks for listening.
