Bonni Stachowiak [00:00:00]:
Today on episode number 528 of the Teaching in Higher Ed podcast, assessment reform for the age of artificial intelligence with Jason Lodge. Production Credit: Produced by Innovate Learning, maximizing human potential.

Bonni Stachowiak [00:00:22]:
Welcome to this episode of Teaching in Higher Ed. I'm Bonni Stachowiak, and this is the space where we explore the art and science of being more effective at facilitating learning. We also share ways to improve our productivity approaches, so we can have more peace in our lives and be even more present for our students. I am so grateful for today's conversation with Jason Lodge. He's an associate professor of educational psychology and director of the Learning, Instruction, and Teaching Lab in the School of Education and is a deputy associate dean in the faculty of humanities, arts, and social sciences at The University of Queensland. Jason has published over a 130 refereed articles and is a national award winning educator. Jason's research with his lab focuses on the cognitive, metacognitive, and emotional mechanisms of learning, primarily in a postsecondary setting and in digital learning environments. Jason Lodge, welcome to Teaching in Higher Ed.

Jason Lodge [00:01:40]:
Thanks, Bonni. Great to be here with you.

Bonni Stachowiak [00:01:42]:
So glad to be having this conversation after a quite stimulating week of many AI conversations. So much of what many of us have been discussing is the ways in which there are so many different tensions that come up and tensions related to our identity, tensions related to our teaching. And I'd like you to take us to one of your memorable moments when you first remember viscerally feeling the tension toward the need to rethink assessment because of the hyperemurgence of artificial intelligence in your own teaching.

Jason Lodge [00:02:23]:
Thanks, Bonni. When CATGPT first emerged, it was really interesting when I thought about what that meant for my own students who are going to be future teachers in primary and secondary settings. There are some things that it was immediately obvious that there was enormous potential in these new tools to be able to help my students in their future careers. 1 in particular is lesson planning. So it was pretty obvious early on that ChatJPT was really great at producing pretty good lesson plans that they could kind of pick up and use in their classes. The problem, of course, is that lesson planning was also a major part of what our assessment tasks were built around. So we immediately have a tension here that something that we asked them to produce that is supposed to be authentic to the kind of work that they will do in the future was something that machines could already do a great job with. And it really made us think about what is it that we really want our students to be able to do and how do we need to be able to see that beyond what is represented in, the kind of basic artifact that is a lesson plan itself? So we needed to think about what that looked like in terms of how a teacher executes that lesson plan in a classroom, where there are 25 or 30 children or adolescents in there and what kinds of judgments and decision making needs to go on to make that lesson work effectively.

Jason Lodge [00:03:59]:
So it was pretty obvious that the artefact that we were basing our assessment on was something, again, that machines could do very easily. So we needed to shift more towards really emphasizing the human component of what it means to be a teacher around that and think about ways that we might capture that through our assessment.

Bonni Stachowiak [00:04:18]:
That's such a helpful story. And as people will have heard in your bio, you're coming from a context from a school of education. And, understandably, many, if not all, of the schools of education I've ever been familiar with, they didn't come into artificial intelligence not having really done lesson planning before. But what I'm seeing in so many other disciplines, maybe maybe people might come from a department where assessment is somewhat on track, but many times, lesson planning just isn't something that higher education has really done in as disciplined or intentional of a way. And your story reminded me a little bit, and I hope I can connect this analogy for you, let alone everyone who's listening. But it reminded me of when people would get into all these arguments about whether online teaching is good or bad, and in a classroom teaching is good or bad, but so much of the weight of evaluating that goodness or badness is disproportionately weighted toward the online. And then we go, well, you keep asking, well, it's not good enough. What's the quality like? But are you actually doing that for your on campus classes with the same level of scrutiny or intensity? So as you were talking about lesson planning, I loved you say like you said, yeah.

Bonni Stachowiak [00:05:42]:
So lesson plans and then we look at them and we decide these are really good. And I think so many people who might come into artificial intelligence might never have done lesson planning the way that you and I, Jason, think about it in the first place, and then that kinda changes the dynamic. It's kind of the difference between a novice and an expert coming into AI and then being able to quickly assess whether or not what the AI produced is good or not. How did my analogy land? Because I I

Jason Lodge [00:06:13]:
think you're spot on, Bonni. I I fully agree. And it's it's something that I've grappled with a lot because I think as somebody who's been teaching for a while, both online and on you know, in a campus based environment, it would drive me nuts if I had to write out a full lesson plan for everything that I do because you develop a certain level of expertise, and the the planning process is something that you do on a much more dynamic sort of interactive way with the students. It it's almost like a co creation cycle that's happening in in real time, but it's taken me years years of practice and experience to get to the point where I can do that across different environments. To your point about novices, I think this is AI provides such a powerful tool to help provide some guidance about how that lesson planning plays out. I guess part of the issue that we've got is that where does the capability of AI end and where does the impact of the teacher start? And we tried this out. So I have a big group all at once. I have 250 students in a large space, and we work together in active learning type scenarios.

Jason Lodge [00:07:23]:
And I have demonstrators who work with me. And we thought, okay. Let's let's see what the capabilities of this thing are. So we got ChatGPT to do a lesson plan for us. I gave it a big prompt, told us all about the class and all about how it fits into the program and all of the background information. And we taught the lesson to the letter as Chat GPT told us to teach it. What was really interesting, and this is something that we talked about with the students, is that in doing so, it was one of the hardest lessons I've taught in a long time because what we found, and this was both for my demonstrators and I, is that our tendencies as teachers and the way that we wanted to teach was clashing with the way that the lesson plan had been structured by chat GPT. So even though I gave it all of this information about the context, it still didn't quite fit with how we identified ourselves as teachers and how we had learned to navigate that teaching environment in a way that worked for us and worked for our students.

Jason Lodge [00:08:24]:
And that was such a powerful lesson for our students in that they could then see where the potential of AI was to give them ideas, but also see where the limits of that were because we, as experienced teachers, were struggling with how do we get this to work for us in the way that we want it to work. So I think it was such a powerful demonstration of both the capability, but the limitations of this technology for that particular task of lesson learning.

Bonni Stachowiak [00:08:50]:
That is such a helpful set of examples. I as you were talking, many people out there in this space that are thinking about artificial intelligence and how it's impacting our teaching and our learning, we're starting to talk a lot about metaphors. I'm sure you have too as well. And one metaphor that Jon Ippolito, who was on an episode of Teaching in Higher Ed, pushes back against is the metaphor of guardrails. And in my conversation with him, I kept telling him, I know guardrails isn't it, but I can't find a better word than guardrails. And so he started talking about the knowledge, and this is back to you saying about the novice versus the expert. The expert might know the right vehicle to take for the particular terrain that we're about to embark on, but the novice might think that guardrails will help them when they're entirely in the wrong vehicle. So you're talking about these 250 students all at once and and you creating this learning experience, and then you're able to cocreate it with artificial intelligence while also utilizing the others who are able to help you facilitate those experiences.

Bonni Stachowiak [00:10:05]:
And you were just reminding me, I was nodding my head as you're talking. I'm thinking he's not talking guardrails. He's talking choosing the right vehicle for the journey and the terrain. And speaking of vehicles, I guess I'm gonna totally mix metaphors. No. I'm not. You know, there's all these expressions about getting the right people on the bus. I'm sure you've heard the overused analogies in business.

Bonni Stachowiak [00:10:30]:
I'd like you to take us to a bus, a bus that you've been traveling around with a group of people. Tell us about some of your collaborators that this group that you've been working with to shape this, rethinking of assessment.

Jason Lodge [00:10:45]:
Yeah. Metaphor's everywhere, isn't there?

Bonni Stachowiak [00:10:47]:
Can you stick with the boss? I mean, you can change your metaphor. Jon changed his metaphor with me. Feel free to pick a different one, a campfire. I don't know. You pick.

Jason Lodge [00:10:56]:
We did actually have one. So this is a group of people in the Australian context. For anybody who's not familiar, we have 43 universities and 206 higher education providers all up. And the the group that, I have been working with over the last year, and it was really intense work around August where we did most of the heavy lifting on this, was to try and figure out, okay, we need to rethink what we're doing in in assessment in particular. There is a a risk here. There is a risk to academic integrity that we need to be mindful of. There's been a lot of discussion about the possibility of using generative AI to cheat. And the reason I mentioned the Australian context is that we have one overarching regulator called the Tertiary Education Quality and Standards Agency who is responsible for maintaining the quality of the entire sector and does a lot of work around regulation.

Jason Lodge [00:11:49]:
And a big part of their role is to help facilitate management of various different risks as they emerge. And some fantastic colleagues in there, particularly the director of integrity within the regulator, partnered with us to do some work to figure out, okay, okay, what is it that we're really dealing with here and what does rethinking assessment look like? The metaphor that we had, because we all love to have one, is that there is so much uncertainty about the development of AI and where it's going. And we look at the people who are in Silicon Valley who seem to have deep disagreement about whether or not we're going to have fully sentient AI next week or never. There's too much uncertainty there to map the future. And that's why I tend to agree that the idea of guardrails may not be quite right because you need a road and we don't know where the road is headed. We felt we couldn't come up with a map, let alone a road. But what we could potentially do is as a group of experts, national experts in assessment and educational technology and higher education, that we could get in a room together and really nut this out over a couple of days and develop something that might look more like a compass. We don't know where we're headed, but at least we can have a sense of what the direction might be.

Jason Lodge [00:12:58]:
And the conversations that we had over that 2 day period allowed us to, I think, give the sector here in Australia and and the guidance that we developed is being used around the world now, which is fantastic. The point was to say, here are the things that we know that are important. And from this basis, that gives us a sense of where we might head no matter what the technology develops into or changes over time. Part of that is about what our values are in higher education, what is really important to us. But part of it's also about what do we know about good assessment design, what is still true about good assessment design that has always been true, and what is the appropriate role of really secure assessment where we know that the student sitting in front of us who's doing pen and paper exam or some sort of oral assessment or a presentation is the student who's done the work and is meeting the outcomes that we're we're looking for. So there's a bit in the guidance that we've been working on and developing. But the idea is that it provides a sense of direction that allows enough room for all of the different discipline areas that we're in to be able to take the propositions and principles that we've developed and and think about what that means in in each of those contexts.

Bonni Stachowiak [00:14:12]:
So, listeners, that's exactly where Jason and I I are gonna head next. We'll be looking I'll be asking him to explain 2 guiding principles to us that they came up with in their collaboration, and then these propositions that he mentioned, what should assessment emphasize. But before I even ask you those questions, Jason, you mentioned you bring a group of people together, and they're gonna come up with a metaphor. Oh, we've gotta have an acronym, and then it's gonna be really hard to remember what the acronym stands for. So did you come up with one of those, or or, did you decide to to skip the acronym part that so many in higher ed tend to want that?

Jason Lodge [00:14:50]:
For once for once we decided to skip that, there were there are enough acronyms in the mix that we thought that the the metaphor of the compass and not the map was was enough, and there's enough complexity, I think, in the problems that we're dealing with to to not add further acronyms and

Bonni Stachowiak [00:15:06]:
Oh, I

Jason Lodge [00:15:06]:
wonder into the mix.

Bonni Stachowiak [00:15:08]:
I just wanna embrace all of you from across oceans. That's amazing. So let's start with your guiding principles then. What were tell us about these 2 guiding principles that surfaced out of those conversations and collaboration.

Jason Lodge [00:15:22]:
So the first principle is really to say, this is here. This is a technology unlike any that we have dealt with in the past. And whether we like it or not, one of our roles as educators is to help our students to participate actively and ethically in a society where this technology is pervasive. And partly this was to just say, look, there have been technologies that have come along in the past that there has been a huge hype around and that hype hasn't necessarily played out. We felt that this was a little bit more of a fundamental game changer. I'm not entirely convinced by that term, but I think you know what I mean. It's just this is a fundamental change in the way that we interact with knowledge and information, and we we need to take that seriously and think about how we help our students to navigate the world in which these technologies are now in in existence. So that was the first principle.

Jason Lodge [00:16:19]:
Then the second one is really starting to get towards 2 factors. Firstly, is that idea of security and making sure that we can have a certain level of confidence that the students are actually meeting the outcomes that we're looking for. And part of that is that we need to make sure that we're not just thinking about assessment as one individual task in one particular module or unit, but it is about a regime of tasks and activities that exist across an entire degree program. And how do those different tasks and activities connect with each other to tell us about what is essentially a developmental process that students go through from the beginning of their degree through to the end? I think what tends to happen, and that's certainly true in this context that I know in other contexts around the world, is that individuals have their particular part of the programme, whether that's a unit or a module or a subject or a course or whatever it might be called. And that's our kind of bit that we own. And we do our little bit in there, but it's not necessarily students' trajectories are through those programmes as a result of that. Students' trajectories are through those programs as a result of that. So the second principle is really that we've got to be more systemic about the way that we assess and connect these things together to get a real sense of how students are progressing.

Bonni Stachowiak [00:17:39]:
And what you just shared to me keeps bringing me back to wishing I could have been in the room with you all as you were having these conversations because what what I I I appreciated your hesitation at wanting to say this is a game changer, that that almost seems a little bit false to you, but fundamental changes. And yet assessment and these kinds of tensions that you're talking about of wanting to be able to see across not just a single assignment, not just a single class, but actual entire programs and curriculum. But but it's very hard to that's hard to do even without artificial intelligence coming into the mix. So what a what a what a interesting set of people to bring together, and I'm glad that this work is being drawn from and wrestled with, you know, around the world. That's exciting. I'm gonna read the 5 things that are under the propositions for listeners. So these are what things that assessment should emphasize. 1st off, assessment should emphasize appropriate, authentic engagement with artificial intelligence.

Bonni Stachowiak [00:18:52]:
Secondarily, assessment should emphasize a systemic approach to program assessment aligned with disciplines and or some types of qualifications. Oh, number 3 is good. Assessment should emphasize the process of learning. That one struck a chord with me, just this idea we're never done as someone who loves learning, I will be learning until the day I die. The process of learning versus the destinations. And, number 4 not maybe not versus, but in addition to destinations. Number 4, assessment should emphasize opportunities for students to work appropriately with each other and with artificial intelligence. The emphasis is mine, by the way, readers, listeners.

Bonni Stachowiak [00:19:40]:
And finally, assessment should emphasize security at meaningful points across a program to inform decisions about progression and completion. And, Jason, you already did touch on that earlier. But what did you not touch on earlier that you really think listeners need to hear about that was either surprising or really difficult to come to consensus

Jason Lodge [00:20:02]:
on? Thanks, Bonni. The first thing I would say is that I think your point about being in that room with all of those people for 2 days to just really focus on this task was such an enormous privilege. And it was just so exciting. And it reminded, I think, many of us who are in the room about why we love what we do. So your point about that being a fantastic thing wasn't. For the regulators' involvement in this, I mean, I feel very thankful that we had the opportunity to to do this work. But for me, all of it's difficult. I think there are aspects of this that are complicated by many different factors.

Jason Lodge [00:20:36]:
The 2 that jump out to me, and this is partly my bias because I research learning, is is the one that you pointed out about us shifting from this idea that assessment is about some sort of outcome. And, yes, outcomes are important, but learning is both an outcome and a process, and the process is often hidden from us because many assessment tasks require the production of some sort of artefact, whether that's an essay or an exam script or a report of some kind. And, really, what we were trying to get at with this proposition is that we need more sophisticated ways of understanding the process that students went through to get to that endpoint. And that's hard. That's going to be very difficult. In some instances, we can see it. So for for those of us who have research students, PhD students, we work very closely with them. So seeing that process happens because we've got a one to one relationship over time.

Jason Lodge [00:21:34]:
But in one of one of my courses, I have 250 students. There's there's no way I can have that same insight into how they're learning over time. So can we come up with more sophisticated ways of getting some insight into that? And that's going to be tricky and it's going to take time. The other one that you touched on, which is absolutely important as you've highlighted, is that this is not just a situation where students are taking an assessment task, putting it into Claude or chat TPT or whatever it is, getting an output and submitting it. We're talking about a very complex environment here where our students navigate multiple different tools. They interact with us. They interact with their peers. They interact with various different things online.

Jason Lodge [00:22:15]:
And I think thinking about this as a factor that is kind of somehow removed from that complex, as a colleague who was involved in this, Tim Fonds, would say, entangled kind of environment that students really live and learn in is not really going to do justice to the way in which these technologies are impacting on learning and on education. So we really need to think about how does generative AI, other AI tools, other technologies interface with peers and online spaces and physical spaces, and where does that interact with the material that we produce as educators and us and the role that we play to to support students? And it's messy. And I think that we we need to be able to embrace that messiness to a degree because the only way we're really going to be able to figure out how to use these technologies well and integrate them into what we're doing is to embrace all of those components and think carefully about how they how they interact.

Bonni Stachowiak [00:23:09]:
As I reflect back on in November of 2022, of course, artificial intelligence didn't get invented then, it just got talked about a lot more than it had been in prior decades, I think about we kind of instantly went to understandably understandably went straight to trying to police it, to try trying to stop it, and and it's because it was very disruptive to our sense of meaning making and significance and what we do in our work. It's understandable why so many would have had that reactions. As I have considered, I've been so excited about preparing for today's conversation and looking at so many of the materials that you sent over. I'm kind of wanting you to reflect just for a few minutes on this idea of choosing the right vehicle for the terrain and what sorts of things come to mind for you off of the discussions that you've had and also in your own experience. So have you been able to retrain your brain, or was it never there in the first place to avoid the traps of guardrails and avoid the traps of do not enter and the policing, that type of a thing. And instead, trusting your students, trusting yourself, trusting the process to help people be able to then discern for themselves eventually, how do I choose the right vehicle for the terrain, for my values, etcetera. Is anything coming to mind for you, Jason, in your teaching or in those conversations around choosing making those choices?

Jason Lodge [00:24:44]:
Absolutely. So another colleague who was involved in this in this work, Kath Ellis, who's an expert in academic integrity, I think she sums this up really well that we have to get to the point where we stop looking for evidence that students are using these tools to cheat and shift our emphasis to looking for evidence that learning has occurred. So if I think about the kinds of vehicles that are going to help us and help our students get towards whatever the future might look like, there's an interesting tension there, I think, between how much we focus on the tools and whether they've been used or not and how they're evolving. And, look, there are really important questions about that. But I think on the other side is that we can also say there are really important questions we have that are fundamentally about learning, the learning process, and how we really get at how students are progressing through the way that they understand and use knowledge meaningfully in whatever their future profession might be. The other reason I say that is that we've been doing a lot of work and interviewing students, very long interviews, you know, 40 minutes to an hour to really get into the nuts and bolts of how they're using these technologies because we know that students pick these things up and started using them fairly early on. Not all students, about a third of them, we found, are quite quite into it, a third not so much, and then a third haven't touched it. So it's a classic sort of adoption curve situation.

Jason Lodge [00:26:13]:
But of the students who who are using it, it's been really interesting to hear their stories because a a trend that's emerging is that the capacity to be able to use these tools is not necessarily so much about the technology itself. They need to have some sense of how it works and how to prompt it, of course. But it's not as though they're world class prompt engineers. What seems to be the real differentiating factor is that students who are getting quite a lot out of using these technologies understand themselves. They understand their learning processes. And as a result of that, they've got a pretty good grasp on how the network of resources and people around them and how to use that to to help them progress their learning. And they've they've take they're able to take these new tools and slot them into that that complex network that they have around them. So it's less about the technology and more about the human, the human and how we learn and how we understand ourselves.

Jason Lodge [00:27:11]:
And for me, I think if if we're going to think about a vehicle going forward, it's easy to get excited about, oh, look what I I could do this week, and here comes version whatever of this of this particular large language model. But I think the key characteristic that allows us to all of us to deal with uncertainty into the future is knowing ourselves and and having that strong foundation of being able to navigate difficulty and complexity over time. So for me, I would hope that that's the vehicle that we're focusing on, learning, understanding ourselves, learning how to learn, and that gives us such a strong foundation, I think, to be able to adapt to whatever changes we might see in the future.

Bonni Stachowiak [00:27:49]:
Before we get to the recommendations segment, I'd love to have you give us 2 pieces of advice. I am a huge fan of James Lang's book, small teaching, because these things can really start to feel overwhelming. And they by the way, Jason, I'm I'm I'm overwhelmed, I think, in a good way as I'm hearing you. I I it's challenging me, and I'm so appreciating your words, but I'm way deep in AI. So so I could imagine listeners you you were mentioning the typical adoption curve. The 1 third of students have used it intensely, 1 third not as much, and 1 third never. Well, I'm in the, you know, high, high, high, early, early adoption as it relates to this this most recent hyperemergence of AI. So I like small teaching because it just helps remind us we can make it small.

Bonni Stachowiak [00:28:39]:
It's not as hard of a mountain to climb because we could just start with that first step. So for people who want to take a first step at trying to retrain their brains, their hands, their hearts for not trying to as as your colleague, Kath Ellis, said, it's not about, looking for evidence they cheated or used AI, but looking for evidence that learning has occurred. What would be a small step someone might take who maybe isn't in the earlier adoption curve of artificial intelligence?

Jason Lodge [00:29:18]:
I mean, the the most obvious one is have a go and have a go at different at different models. But I think and that that, I think, is fairly self evident. But there's something else about James Lang's work that I really like, and I think that the the small teaching and the small moves thing, I think, is really important. But the other part of what he talks about that resonates with me is that there is a stable foundation underneath that. That is things that we've known for a long time about what good learning looks like. And I think that there's a really important message in that. And for some of the things, like, we've done a lot of work on confusion, for example, being confused doesn't mean that you that you're silly or that you don't understand or that you're not good enough. It just means that you might want to try something different.

Jason Lodge [00:30:00]:
You know? You're engaged enough to be confused in the first place, so maybe there's something in that. You know, try a different strategy, and maybe you'll get through it. So as as things change, I think the things that remain stable provide such a great foundation to be able to build from. And that is where I think the learning sciences really come into this. How does learning really work? And many of those things are not gonna change no matter what happens with the technology because our brains aren't changing at the rate certainly nowhere near the rate that AI is changing. The other thing that I think is is really important, and this project work that we've been doing, I think, is a really good example of this, is that for any of us, this is a such a big complex set of problems that we're dealing with. I'm not a technical expert in AI, but I have fantastic, brilliant colleagues who I work with and I talk to about what this means from the technical side or about other aspects of learning and education that I'm not an expert in. And we try to find a common language so that we can talk about what these issues are and and grapple with it in different ways.

Jason Lodge [00:31:05]:
And that has been such a powerful learning experience for me is to just hear about these issues from those different perspectives and see that, oh, actually, you're somebody who's working with this technical aspect of AI, and you're kind of struggling with the same problem that I am. Isn't that really interesting? And how do we understand each from those different perspectives? And I found that enormously helpful. So, again, it might seem fairly self evident, but the more we can talk to our colleagues who bring different perspectives and expertise to the table, I think the better off we are. Nobody's an expert in all of this, certainly not me, and I think the only way that we're gonna be able to progress the conversation forward is to do it together.

Bonni Stachowiak [00:31:43]:
Alright. Well, this is almost the time when we get to share our recommendations, but can I quick ask you about just any advice you have for the bigger classes? Dynamic. And maybe or maybe we put a bookmark in that and say, no. That's too big of a thing. I think And maybe or maybe we put a bookmark in that and say, no. That's too big of a thing to give you a tip for.

Jason Lodge [00:32:08]:
It it it is complicated. The way that I've tried to do it is that I've tried to use every tool at my disposal to get to know my students. And that means using things like learning analytics. It means using things like retrieval practice to try and remember students' names. I think that that's a really important aspect of of managing large classes. I have got to the point where I can usually get most most of my students' names in a 250 student class by about the 5th week, But that takes a lot of effort. And again, it's a small teaching thing where I'm using retrieval practice to to really train myself to kind of do that memory work to make that happen. And And I think that helps, but it's not there's no silver bullet there.

Jason Lodge [00:32:50]:
I think I try to use a range of different tools to do that. And even then, sometimes it's not it's not effective. That is a really challenging environment. I think anybody who's taught in large large class environments knows that, but technology can certainly help, I think.

Bonni Stachowiak [00:33:04]:
I don't know that in all these years of podcasting, I have heard someone say with that confidence and boldness and commitment about learning 250 names. And now I have to, after we stop recording, go look up because Michelle Miller is either coming out with or already has come out with a book about learning names. It is absolutely possible to learn 250 names, but, yes, you would need to work at it. Our son and I are having so many fun conversations because he's really fascinated with and motivated to learn more about Rubik's cubes and solving those puzzles. So he's watching all these YouTube videos and learning the algorithms and memorizing them to help cut seconds off his time. And so we're talking about memory. There's a wonderful book called Moonwalking with Einstein that also talks about memory too. And so, yes, retrieval practice, learning the names.

Bonni Stachowiak [00:33:58]:
Jason, thank you for your students. Thank you for the world that you're committed to and understanding the importance of learning names and also modeling for your students about, you know, that learning is possible. And, yes, it is possible to learn that many names. And, thank you for your dedication to that. What a great challenge for those of us who who may teach large classes and feel like that's not something we could do.

Jason Lodge [00:34:19]:
Yeah. It's hard, but we we give it our best scope.

Bonni Stachowiak [00:34:22]:
Yeah. Alright. So this is the time in the show where we each get to give our recommendations. And for mine, it is a fun website, which I'm not even gonna recommend the whole website because I wanna save it up and be able to recommend many posts off of it, but it comes from a website called Moss and Fog, and they have and I'm I told Jason that once I started sharing it, I was gonna put it in the chat so he could see it right when I right when I started to describe it. So it's okay. It is AI generated images of retro kids games that are so bad, they're good. So these are things that do not exist in other than in someone's imagination and the prompts that they gave the AI. So the first one that I'm looking at, many of us grew up maybe with hamsters as pets, and you might know that hamsters travel remember my hamster growing up would be going all over our house in its little wheel.

Bonni Stachowiak [00:35:23]:
I remember my hamster growing up would be going all over our house in its little wheel. Well, this is like hamsters inside of those wheel looking things, but they're attached to robots with, like, lasers. So, essentially, these hamsters are look like they're fighting each other to their death. Robo hamster fight. And, of course, these toys don't really exist. They only exist in AI generated images. There's another fictitious children's toy called Zappy, and it is a toy doll, a boy in strapped to a chair that he can't get out of, and he's being electrocuted. And of course, many of us might hearken back to some of the psychological tests in the famous experiments that of course aren't given today.

Bonni Stachowiak [00:36:09]:
The next image is my friend Rufus, and it's another doll sitting on a picnic bench. And Rufus is smoking and so is the child smoking. They're enjoying a good smoke together. I could keep going on and on, but, oh gosh, I can't get enough goodness out of these hysterical AI generated images of of fictitious retro kids. So have fun going to check out that. And the second thing I wanted to recommend, which is perfect because we we didn't have time to go too much into this, but Leon Firs wrote an article which has what an assertion right in the title, don't use generative AI to grade student work. And if that starts to generate any feelings for you, either I agree with Leon or I don't agree with Leon or any combination in between, go click that link, read it. He makes a compelling case worthy of not just you rating it, but also discussing with your other colleagues.

Bonni Stachowiak [00:37:10]:
Leon is has been on the show before, really creates some, important ways to challenge us and how we're thinking about approaching artificial intelligence. So that's it for me. Jason, I'm gonna pass it over to you for whatever you have to recommend.

Jason Lodge [00:37:24]:
Thanks, Bonni. I I just had to mute my microphone because the moss and fog site is so funny. It's it is so funny. There are oh, some of those things are wildly inappropriate, but very funny.

Bonni Stachowiak [00:37:37]:
I should've I should've warned people. Right? Yeah. Not not some of it is wildly inappropriate. Yeah. I

Jason Lodge [00:37:44]:
love it. And I I I know Leon, quite well. I've done some work with Leon, and, yeah, his work is fantastic, and I think he's dead right about the the ways in which we might be using AI inappropriately in some of the teaching that we're doing. Maybe we'll be in a different environment sometime in the future, and, I guess, that's part the work that we've gotta do in the meantime. So love those recommendations. Thanks, Bonni. I've got something a little bit left to field, but bear with me. I used to be a chef, so I was a chef for 15 years.

Jason Lodge [00:38:13]:
So I did it for quite a long time. And it's interesting now when I cook that people are fascinated by how fast I can do everything. And I'd never really thought about it before because when you you've got 200 people, 300 people who are all hungry and wanna be fed straight away, you just get on with it. You you get into a flow state, and you just do it. And I sort of got interested in how I managed to become so efficient at that over time. And it's not as though I was taking any steps out of the equation because you still have to cut food and you still have to Cook it and you still have to clean up. And you can't skip any of those steps along the way. And I realized that a lot of it was really about doing all of these small things in a more efficient way using different kinds of tools.

Jason Lodge [00:38:59]:
So, for example, I use what's called a Chinese vegetable knife as my main knife, which might sound a little bit weird. And if you haven't seen one of these, they're shaped sort of like a cleaver, but much thinner. And where that becomes a really efficient way to work is that it's it's a tool that serves multiple purposes all at once. So, obviously, it cuts. I mean, that's its main job. But because it's got quite a large surface area on the side of the with the blade itself, you can also crush things with it. So crush garlic cloves with it. You don't need to get a separate tool for that.

Jason Lodge [00:39:33]:
You can also then use that to pick the food up. And I think traditionally because what cooking is so fast, that that was a really big advantage is being able to cut and then use the side of the knife as a large surface area to pick that food then up and

Bonni Stachowiak [00:39:45]:
then

Jason Lodge [00:39:45]:
put it straight into the to the wok. I I raise this as an example because I think there are 2 things in terms of productivity that come to mind for me. The first is that I think we can learn so much from different cultures and ways of of thinking and seeing what we do in and of itself. Of course, that's an enormously beneficial thing. But in the ways that we appreciate what we do in life, including things like cooking and the way that we work, I think we can learn so much from non Western cultures and how we might blend some of the thinking and ways of seeing with some of the stuff that we do. So I I'm an Australian. I was trained in French cuisine. I use a knife that is from a traditional Chinese design that is made by a Japanese company using a combination a combination of Japanese and German steelmaking techniques.

Jason Lodge [00:40:31]:
It's kind of amazing that we're able to blend those things together. And a lot of the efficiencies that I find in my work, as I do now as an academic and as a researcher and an educator, similarly, I try to think about ways in which I can draw on different ways of knowing to do some of what I do more effectively, better and more efficiently. Small things can add up to make a huge difference. Text expanders or when I travel, I take an external portable monitor with me because I know that I can find extra efficiencies by having 2 screens in front of me. And I think sometimes being able to speed up some of those more mundane parts of what we do helps us to be able to spend more time on the things that I think are more valuable, like spending the time with our students rather than managing huge volumes of of email. So I know they're not very specific things or specific tools, but I guess the point was that what I've learned from my time being a chef is that sometimes it's not about the specific tools, but it's about the ways in which we might move the environment around to find small efficiencies based on different ways of thinking about what we do, and I've certainly found that that beneficial for me.

Bonni Stachowiak [00:41:42]:
I cannot wait until I mean, I was I was gonna say, I can't wait till this conversation is over. That is not at all true. I would like to talk to you for hours and hours and hours and hours. However, when we are done talking, I then cannot wait to instantly go try to find a, Chinese vegetable knife. You've expanded my imagination, this idea, Of course, choosing the right tool, we do hear that many times in terms of cooking, but yet I've never heard of this tool, and I'm very intrigued. But then also, yes, the idea of using that. And, of course, you've completely wrapped up this episode because how much have we been talking about not guardrails, but, the terrain. And I I love that you also then added in the metaphor of just then, yes, pick the tool, but then be able to use it perhaps in unexpected ways that somebody just with a little bit more expertise and potentially creativity could help us do in community and learning together.

Bonni Stachowiak [00:42:37]:
That's so fun. So fun. Jason. 

Jason Lodge [00:42:40]:
Thanks for the year. That's great.

Bonni Stachowiak [00:42:41]:
Yeah. Thank you so much for being a guest on today's episode, and I can't wait for the next one because you already made me a pinky swear promise. So, I can't wait till next time.

Jason Lodge [00:42:51]:
Thanks, buddy. It was great chatting with you, and, yeah, I look forward to being able to do it again.

Bonni Stachowiak [00:42:57]:
Thanks again to Jason Lodge for joining me for today's episode of Teaching in Higher Ed. Today's episode was produced by me, Bonni Stachowiak. It was edited by the ever talented Andrew Kroeger. Podcast production support was provided by the incredible Sierra Priest. If you've yet to sign up for the updates from Teaching in Higher Ed, head over to teachinginhighered.com/subscribe. You'll receive via email the most recent episodes show notes, and you'll also receive resources that don't show up anywhere else except in those email newsletters Thanks so much for listening and being a part of the teaching and higher ed community and I'll see you next time on Teaching in Higher Ed.