EPISODE 619: The Science of Learning Meets AI

Bonni Stachowiak [00:00:00]:

Today, on episode number 619 of the Teaching in Higher Ed podcast, The Science of Learning Meets AI, with Lew Ludwig and Todd Zakrajsek.

Bonni Stachowiak [00:00:14]:

Production Credit: Produced by Innovate Learning, Maximizing Human Potential. 

Bonni Stachowiak [00:00:23]:

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. Today, I’m excited for a wide-ranging conversation, which draws on decades of experience in learning, science, and faculty development, and looks at the ways that we can rethink how artificial intelligence might be able to deepen student learning, by enhancing some time-tested frameworks, such as TILT, UDL, and others. And I’m joined by the two co-authors of the book. Lew Ludwig is a mathematics professor and former director of Denison University’s Center for Teaching and Learning. And Todd Zakrajsek was probably a familiar name to many in the Teaching in Higher Ed community.

Bonni Stachowiak [00:01:44]:

He is an adjunct professor in the Department of Family Medicine at UNC Chapel Hill, and director of the IT LC Lilly Conferences, on Evidence-Based Teaching and Learning. He’s also been a many-time guest here on Teaching in Higher Ed. Todd Zakrajsek, welcome back to Teaching in Higher Ed. And Lew Ludwig, welcome for the first time to the podcast.

Lew Ludwig [00:02:10]:

Thank you so much. Thanks for having us.

Todd Zakrajsek [00:02:12]:

Great to be back again, Bonni. I’m really looking forward to it.

Bonni Stachowiak [00:02:15]:

We’re going to start in a little bit of a depressing, at least for some of us, a depressing way. Lew, can you tell us, we’ve just experienced all the emotions, the fatigue, the resentment, sometimes, the uncertainty, none of us asked for this. Or at least, maybe there was just one guy who asked for this, but the rest of us did not ask for this. Would you start just by reflecting on some of the challenges that you’ve been experiencing these past few years with regard to artificial intelligence?

Lew Ludwig [00:02:46]:

Sure, be happy to. Yeah, so there’s definitely been some fatigue. I mean, if you think about it, kind of where we were in the calendar, we had just come out of COVID and all the trials and tribulations that we had with that, and all the loss. And we’re just basically trying to get our feet back under us, and then here, November 30, 2022 hits, and this new little app was just released, and within five days, we had a million users using this thing. The fatigue part, it’s one of the things that didn’t quite land with me. So I appreciate you bringing this up so that you can kind of learn from my mistake. So like I said, it landed in November.

Lew Ludwig [00:03:18]:

By August of the following year, we had a pretty large campus-wide convening of faculty and staff, and different things about AI and what was going on with it. And as the teaching center director at that time, I was kind of in charge of like, “hey, here’s, you know, what AI is, here’s what it can do, here’s what it’s capable of”. I won’t necessarily call myself a cheerleader, I was just trying to be pragmatic about this new technology. But I think some folks really, they were kind of in that place of angst, and I don’t need this, and this is a cheating machine, and the copyright issues. And they were really in a, not a very good spot. So they kind of viewed me as like, oh, he’s the pro AI guy. So I all of a sudden was wearing like this scarlet letter for some folks that said AI on it. And I really had to step back and kind of learn from that.

Lew Ludwig [00:03:59]:

So you kind of mentioned this a little bit. But yeah, from now on, all of my workshops, I start off with, I always have- the second slide says this: You didn’t ask for this. You didn’t request an unregulated, untested, and rapidly evolving technology to suddenly upend education and nearly every other sector of our society. So I think, if you’re working in this space, you have to be really, really mindful of that. No matter where you land on the scale is that, people are in just many different spaces with this, but kind of give them the space, for lack of better words, to kind of grieve what’s going on with us right now, because it is, it’s a big challenge, and we need to be mindful of that. But I think three years out, we’re now kind of at the point where, you know, probably time to roll up the sleeves and see what we can do with this thing, whatever that means.

Bonni Stachowiak [00:04:41]:

I so concur that it can be so healthy, and I would even argue vital to name things. And another really big word concept idea for me, Todd. You end the book this way. You end with a word that I love. It’s the word becoming. You end by asking us, what kind of educator are you becoming in response? Why do you find that to be such an essential question for us to be asking ourselves at a time such as this?

Todd Zakrajsek [00:05:12]:

That’s interesting. I just don’t think we’ve faced anything like this, at least in my teaching history, it was about 40 years of teaching is that, I think, fundamentally higher education’s got to change, education’s got to change. And in some respect, I think we haven’t figured that out yet, but it’s going to happen. That we can’t keep teaching the way we’ve always taught, and there’s always going to be this feeling of loss. Like I wish I could, but we don’t know what’s out there. I mean, not sure what it’s going to be, and it could be so much better. But this concept of scoring points, that’s just not going to work anymore in terms of you’re just going to earn a number of points.

Todd Zakrajsek [00:05:45]:

And when you get a certain number of points, you get an A, because that just proliferates the cheating on it that everybody’s so concerned about. So I thought from the beginning, one way out of this, or at least a way to look at it, is ungrading. When you ungrade, you’re not really looking for points anymore. And so it’s really about the learning, and if it really make it about the learning, then AI could be used to help you. But it doesn’t make sense for it to just take over. Spec’s grade is the same type of thing, just getting away from the points.

Todd Zakrajsek [00:06:09]:

And the other thing that I think is really going to change is, and some people might argue that UDL has already become a big thing, but I don’t think it still has. At the Lilly Conferences, I’ve always had UDL sessions in there, but they’ve always been low-attended sessions, because people love the concept, it’s just really hard to do it. And I think one of the things to keep in mind is with UDL, you don’t design to meet a need. You design so that that need is no longer relevant. So I always use curb cuts in the street. You don’t cut the curbs in the street so that people in wheelchairs can get across the street easily. You put the curb cuts in, so it doesn’t matter if you’re in a wheelchair.

Todd Zakrajsek [00:06:44]:

It’s a very different mindset. And I think AI has the potential for doing this is. Is that we could actually create an educational system. Not so that it deals with the problems we have with AI, but those problems are no longer relevant. We don’t really care if students are trying to game the system and have AI write their paper, if that doesn’t matter anymore. And so I do think that we’re going to move in a new direction. I think it’s going to change away from this point-based system and it’s going to help us to rethink what we’re doing. I think it’s good. And Lew mentioned something, we were talking the other day about standards-based grading.

Lew Ludwig [00:07:18]:

So I actually, after 30 years of teaching, I’m actually jumping into standards based grading. I kind of agree with Todd. By March of 23, I was like, I think we’re going to need a book, something like Ungrading the Age of AI. And I don’t know if that book exists yet. But here I am three years out, this semester I will take my linear algebra course, and I’m going to do standards-based grading. This is a course I’ve taught for years and I had notes and tests and all this stuff and you know, I thought I was doing right by the students, but. But how was I to know that? So what I’m doing is I’m kind of converting this to a standards-based grading system. I have 13 standards now.

Lew Ludwig [00:07:53]:

It’s very clear to my students. But the thing that was like overwhelming was, how do I even get to those 13 standards? Well, AI helped me figure that out, right? It helped with that organizational stuff. And then the other thing is, I’m doing the idea where they’re going to be able to take the same standard more than once. So how do you test the same thing three times over the semester and get all that mapping out? I couldn’t even fathom that before. Now with AI, I have a nice schedule planned out. It’s the most planned I’ve been for the whole year. So I’m really excited to see how this new adventure into the world of alternative grading goes for me.

Lew Ludwig [00:08:24]:

But again, wouldn’t have happened without AI.

Bonni Stachowiak [00:08:26]:

I think another thing, it wouldn’t have happened. Lew and I appreciate, I saw you sharing a little bit about this new adventure for you on social media. I think for the three of us, if we didn’t focus on who we’re becoming as educators, as long as we’ve been at this, that sense of continually renewing who we are, and really how that facilitates change. Another really big tension that you write and speak about. Understandably, so many of us felt our assessment methods, like Lew just described, be disrupted and challenged. And a lot of these conversations started with, how do I AI-proof my assignments? Help me make it so they can’t cheat. And again, totally understand the concerns there. We want degrees to mean something. Most of us got into this because we believe in the kind of transformation that’s possible through higher education.

Bonni Stachowiak [00:09:26]:

But you talk about a shift from how do I AI-proof my assignments? To what do I want my students to be able to do? What is it that makes that hard? And why is it so important to press on anyway?

Lew Ludwig [00:09:41]:

Yeah. So there’s actually two questions. Whenever I do a workshop, I get one of two questions. So I get, how do I AI-proof my assignment? But the other one I get is, how can I use AI in my classes? People get really excited. And you know what? The thing is, both of those are the wrong question, right? They’re both starting in the wrong place. One group is panicking about this new cheating machine and trying to keep it out of the classroom. The other is also excited about the shiny new tool, and they’re looking at ways to jam it in their classroom.

Lew Ludwig [00:10:08]:

But neither group is really asking that fundamental question: What do I actually want my students to learn by the end of this course? So that’s- I think that’s really the shift that’s hard for us. We get distracted either by fear or by excitement, but we lose track of what is actually important, and that’s student learning. And, I mean, really what this gets down to is backward design. Right? I mean, we start with the learning goals. We should figure out how to assess them, and then decide if AI fits in that or not. But it’s amazing how quickly we kind of forget these things when something new and shiny, or scary, is kind of thrown in front of us, I think.

Todd Zakrajsek [00:10:40]:

There’s another part that I think is important here. Is that it’s funny, when AI came out, and what you said, Bonni, is like, the very first thing out of the gate was, oh, no, the students are going to cheat on this. And how do I assign my paper? And they turn in a paper that’s written by AI. Again, those were never great assignments to begin with. But the other part is, when I was reading a lot of stuff that was talking about this, very few people mentioned, like Don McCabe and all the research that was done on cheating in the late 1990s and early 2000s. There’s a whole body of literature out there that we’re not drawing on, and people are acting. And I don’t want to sound dismissive of this, but a lot of folks were acting as if nobody ever cheated before. And now we have a problem.

Todd Zakrajsek [00:11:18]:

We had a huge problem before, and some of that research showed that a huge proportion of the students would cheat. But they ended up coming up with these concepts. Don did, and a few others that, for instance, students were more likely to cheat on an assignment if it wasn’t meaningful to them. They were more likely to cheat if they were backed into a corner, if they were going to flunk it anyway, or if it was the last-minute thing, or they were assigned something that was kind of over their head, and they didn’t understand it, or there wasn’t resources to help them. When I started reading that, I thought, check, check, check. These are all exactly the same thing. So I do think we would benefit from looking at the literature that’s out there in a related field. But yeah, I think that we had to deal with it before, and I think it’s something we do have to talk about still.

Bonni Stachowiak [00:11:59]:

You’ve mentioned a few frameworks, so you talked about backward design, you mentioned universal design for learning, and one that hasn’t come up yet is TILT. So I’ll allow you to explain that one in just a moment. But let’s start with just what surprised you most about how these tried and true frameworks for many of us hold up in an age of artificial intelligence.

Todd Zakrajsek [00:12:26]:

Oh, that’s, I’m going to take this one first because the book is “The Science of Learning Meets AI”. So, yeah, when we started looking at this, and Lew and I are both in agreement, we started looking at the things that just were there, like backward design, UDL, and TILT. But also spaced learning, repetition, automaticity, those types of things, interleaving, those have always been really solid. And as AI comes into it and you start looking at different ways of doing these assignments, it’s still really solid. Attention! If you don’t have students attention, we’ve known this long before AI; if you don’t have students attention, they can’t learn because if you don’t attend to something, you can’t learn it. So it’s easy for students to be daydreaming or thinking about bacon.

Todd Zakrajsek [00:13:07]:

I mean, like, why wouldn’t you be? And so the idea there was, how do you get their attention? I know, I shouldn’t have ever said that. Bonni, I have a friend who said, I’ve never been at a presentation you’ve done Todd, without mentioning the word bacon. So now I have to slide it in there. But the concept of if we just like right there, see, we got a lot of people listening, thought, I’m going to go make a BLT. But if we don’t have their attention, then we’re going to lose them. Well, now we’ve got AI that we can use. So for instance, I might have a class of 200 students, and I can have each one of them think about, they have to learn this concept. But they could go to AI and say, I like rock climbing, and I need to learn the concept of metacognition.

Todd Zakrajsek [00:13:42]:

You might come in and say, I like skydiving and I have to learn this term. So now all of a sudden, because we’re talking about something we really like, we have a lot of attention. The fundamentals of learning don’t change in that situation. So it’s really neat to see how they layered in there very easily. And Lew did a whole lot more on TILT than I did. And I think he had some great experiences. Lew, I don’t know if you want to mention that real quickly.

Bonni Stachowiak [00:14:01]:

Yeah. And make sure we tell us too, what TILT is for those who may not be as familiar.

Lew Ludwig [00:14:06]:

Yes, sure. So your original question was, what surprised you most about these frameworks? What surprised me was how few people actually know about these frameworks. So TILT, Transparency in Learning and Teaching by Mary-Anne Winkelmes. This has been out 10, 15 years now. If I want a guaranteed win with an AI suspicious faculty member, here’s what I do. I have them take one of their existing exercises and ask their favorite AI to TILT it for them. Just that simple.

Lew Ludwig [00:14:33]:

Here’s my assignment. You upload it and then say, apply the TILT pedagogical framework. And then what it does is it builds out what that framework looks like for the faculty. So then I stand back and just watch them, kind of be amazed. So it just makes it easier. And what it does is it takes that- the AI, takes the assignment, and it makes it something that’s student-facing and student-friendly. It clarifies the purpose. That’s part one.

Lew Ludwig [00:14:54]:

It explains the task clearly, that’s part two. And then it lays out the assessment criteria. And this has been shown to work for all students, but it’s especially good for students who are underrepresented and first-gen students. It really kind of helps raise their test scores and things like that. So suddenly, the faculty can see where their students have been struggling all along. But here’s the important thing. The framework, it isn’t new.

Lew Ludwig [00:15:14]:

It’s been around for years, AI just makes it easier. But the thing you have to keep in mind is that as a new faculty member, or, sorry, as a new AI user, as you’re looking into this, keep in mind that you’re the expert. This is your assignment. You know what you’re doing, you know the content, so then you can judge what AI gives you, what works, and what still may need some work. So keep that in mind that you are the expert, you’re in control here. You kind of have the final word of what it’s trying to help you with.

Todd Zakrajsek [00:15:39]:

Actually, before we leave this question, because you said that Lew, I want to mention real quickly is, that was one of the, also one of the most fundamental things about learning. The more you know about something, the easier it is to learn about that topic more. And so, that what Lew just mentioned is, that if you’re the expert and you’re working at this, keep in mind first of all that you pick up stuff very quickly because you know a lot about it that your students won’t. But it comes back to that concept as well, that I can get a lot out of AI for a certain topic that I already know a lot about, that somebody else may not. And so as you’re starting to build your teaching materials and stuff, it’s a different experience for you than it is for someone else. So I think it’s- we can’t assume, assume that just because we’re doing it, anybody else could just plug it in.

Todd Zakrajsek [00:16:19]:

Our students can’t just plug in what we’re doing and just get the same thing.

Bonni Stachowiak [00:16:23]:

Sometimes stress can be bad, and sometimes stress can be good. And I’d love for either of you to reflect on how generative AI has acted or can act as a stress test, good or bad for pedagogy, whether or not these frameworks that should be tried and true. But Lew, you’re telling us may be new for some of us, were ever actually implemented the way we intended.

Lew Ludwig [00:16:52]:

Well, you know that standards-based grading that I was talking about before, right? Now that I’ve enacted that, I finally got clear about what I was actually trying to evaluate, what was important for the course. Before I had a vague sense, the big ideas, how they fit. I thought I was doing a good job communicating to that my students. But you know, how did I really know? Now it’s crystal clear what matters in the course, what students need to know to be successful. It’s visible to them, and it’s visible to me through these standards I’ve laid out, and with the help of AI. And that’s what the stress test, I think really revealed. When I had to get really clear about what matters my course, I realized something. I’ve kind of been fooling myself before, right? I thought I had a clear learning goals.

Lew Ludwig [00:17:32]:

I thought students understood what mattered, but I never actually implemented those frameworks in such a way that I could actually test and see that. So that’s what’s happened. When you’ve been teaching the same course for years, you have to be kind of clear. It might be clear in your own head, but clarity in your head doesn’t necessarily mean it’s clear for your students. So that, that stress test there really kind of helped me focus on what I was trying to assess, and what was important for the students, so that they could then affect their learning.

Bonni Stachowiak [00:17:57]:

Something I think is really important to bring up, as you’re describing this, Lew, I realize I said it earlier, but I just want to say it again. Lew is not describing a finished state on his part. And anything that I ever may share on this podcast is never me describing, I am done. Like, I’ve figured it out and now- and I, I’m thinking as we’re having this conversation today, I very clearly have someone enrolled in a class, who Tricia and David, who wrote the book The Opposite of Cheating, would refer to as an enrolled student. And we’re just seeing how an AI agent and, and for listeners who this is a new concept for,

Bonni Stachowiak [00:18:40]:

the difference between an agent that can act on behalf of a user, versus what many of us have been accustomed to navigating, where someone can copy and paste, for example, instructions from our assignments or grab a screenshot of them and put them into a chatbot. And I just, so I just wanted to mention to anyone listening who feels overwhelmed, I guess I’m going all the way back to the beginning of our conversation, friends, is that this is hard! We didn’t ask for this, but we’re never really done. We’re not, we’re, we’re not just like tie a bow, and now we’ve figured it all out, and everything’s better. But I do, I just want to applaud you really for taking this huge step. Because it is a fundamental change in how we think about assessment that goes well beyond simplicity.

Bonni Stachowiak [00:19:30]:

But on the other side of it, as someone who does make use of standards-based grading, alternative grading, there’s a lot of hope there, but it takes some time. And I know, Todd, you’ve got some experience with, kind of rethink this, are we ever done? Have we made it, Todd? Have we reached the pinnacle, or are we on the peak of the mountain?

Todd Zakrajsek [00:19:48]:

You know, I always remember when I first started in faculty development, there was a faculty member who come by and said, you know, Todd, I want to come to some of your workshops and really support, you know, I’ve been teaching for 20 years, but I want to be there and just support you. And I thought, oh my word, you think you’re done, you think you’re as good as you’re going to be. And Bonni just did a phenomenal closing keynote, by the way, at the Lilly Conference in San Diego. Really nice done, using games and stuff, it was fabulous. But at that conference, I love to ask how many of you taught for at least a year, at least two years? People put their hands in the air. Half of the audience had taught like 15 years or more.

Todd Zakrajsek [00:20:22]:

And so people are teaching for, you know, they’re at this conference because they want to get better. And the one thing I love about AI with respect to this, is what Lew mentioned too is, I mean, I used to spend four or five hours to put together material, so I could give a 50-minute presentation in a class or a lecture, or active learning kind of things. Now I can just try so many different things. If I’m going to teach distribution of sample means in a stats class, I can try a ton of different things, find the one I like, and still not spend that much time. So I do think it gives us a lot of opportunities. Like social science, we like to test things in a lot of different ways. It allows us to do that.

Bonni Stachowiak [00:20:56]:

One of the concepts that you at least introduced to me, I don’t know if you coined the term, but it felt new to me, is the idea of generative AI intuition. You introduced this idea of us developing Gen AI intuition. How do you see that as different from maybe thinking of it as technical skills or prompt expertise? And how do you see that changing or developing over time?

Lew Ludwig [00:21:23]:

Okay, so when I first started using AI pretty regularly, that was probably the spring of 23, I’d been out for a few months, and I was constantly frustrated. Right? The results were lacking, or just it didn’t give me what I was asking for, especially pictures, Oh my Lord! And I asked my son, who was a computer science student at the time, and he’d been using it for a while, and he kept on saying, just ask it what you want. What does that mean? What do you think I’m doing? But you know, three years later, my son Bjorn, yeah, he was right. So what, what I think it means is building generative AI intuition.

Lew Ludwig [00:21:54]:

It isn’t about like the technical skills or knowing the perfect prompt. It’s about the skills you already have as a teacher. Right? What are teachers good at? We’re good at asking questions. If a student doesn’t get the right answer the first time, we, you know, dismiss them out of hand, we rephrase it, right? We think about, oh, let me ask it in a slightly different way. We give it a different angle. We look for what they’re missing and try to adjust that approach.

Lew Ludwig [00:22:15]:

And if you do the same with AI, that’s what my son was saying, just ask it what you want. It just, it’s going to give you- if it doesn’t give you what you want, you can just ask it something differently. You can kind of iterate. You can clarify and not just rephrase the question, but think about how you would work with a student, and kind of thinking about how good teaching would apply in that context. So I think it’s not any kind of magic prompt, but just to converse with the thing as if it were a student that was eager and trying to please you, but you were trying to kind of help it through to get to the solution that you were looking for. And a lot of times, the student can’t look in your mind at what you’re thinking, and you have to share those ideas with it first before you can start asking the questions.

Lew Ludwig [00:22:52]:

So, yeah, the- I’m not much on it, like I said, prompt engineering, but I talk to my AI quite a bit, like, just kind of converse with it.

Todd Zakrajsek [00:22:59]:

So, yeah, and I think this was pointed out, something else that Lew and I were talking about this the other day, too, is we’re not all great communicators. Some of us are. We’re not as good of a communicator as we think we are. And I’ll just say very quickly, as when I used to teach research methods, one of my favorite things, too is, you bring in a jar of peanut butter and jelly and a loaf of bread and a knife, and you say this. You get one person in front of the room and it’s like, okay, we’re going to tell you how to make a peanut butter and jelly sandwich. But you have to exactly what you hear. Or I might do that part, and students might say, okay, put the peanut butter on the bread.

Todd Zakrajsek [00:23:27]:

And so you take the jar of peanut butter and set it on the bread. I say, no, no, no, you want the peanut butter out of the jar. So then I tip it and say, it won’t come out. And it’s like, well, use the knife and then put it on the bread. Okay, put a scoop of peanut butter on the knife, and then you lay the knife on the bread. And the point I was trying to get across to my students is they’re not as clear with their statements as they think they are. So what we worked on operationally defining it is, how do you make these clear? And when Lew and I were talking, I thought about those days, because I think there’s some faculty members who are confusing their students, and now they’ll use AI, and they’ll say, these answers are confusing. And so one of the things that I think Lew actually helped me with is you start asking AI how you should ask it, a question like, ” How should I ask you this thing?” And then that helps out.

Todd Zakrajsek [00:24:07]:

But that concept of working through that, I think is really important.

Bonni Stachowiak [00:24:11]:

One of the things that you both emphasize, that I’d like to close this part of today’s episode with is just the hope, the belief, the confidence that generative AI should enhance human cognition, not replace it. Todd, what gets lost when we get tempted and follow through on those temptations to have it replace our- try to replace our brains, our beautiful, beautiful human brains?

Lew Ludwig [00:24:37]:

Yeah.

Todd Zakrajsek [00:24:38]:

And this is one that’s concerning me, so I’ll tell everybody out there is like, this is going to be the tricky spot. Number one, learning is not easy. The brain, the body is built so that we do things, And then our body, brain, and everything, we know that we need that, so we work at it. When we- We have to lift stuff a lot, then all of a sudden we develop muscles. If we don’t lift things, we don’t develop muscles. If we learn something by repeating it over and over and over again, then it comes to mind really easily, because our neurons, we got a system that figured out if that neuronal path is going to fire easily.

Todd Zakrajsek [00:25:09]:

It doesn’t take as much energy. So if you’re going to start the car every morning, you just do it. So, point is, learning takes some work. AI sometimes is really easy. So the danger here is that you just have AI do the work for you instead of doing the work. Now, the danger here is that we can just say, ” Oh, well, we can tell the students, you know, if you don’t do the work, you’re not going to do the learning, so you need to do it”. Terry Doyle had an old quote he used in one of his books, “The one who does the work, does the learning”.

Todd Zakrajsek [00:25:35]:

He never really thought about AI being maybe that’s the one that does the work. But here’s what it comes down to: In the end, a lot of us would. Would love to exercise more. In fact, we claim we’re going to exercise more, but we don’t go running for a reason. We don’t go to lift weights for a reason. It’d be great if we did, but if we kind of take that information and think, I’m not going to use AI just to kind of skirt the issues, you know, if we get backed into a corner for ourselves or our students do, they’re going to do it. So the real danger here, or the real thing we have to work on is, humans have to work at this stuff, and they have to put the energy in.

Todd Zakrajsek [00:26:04]:

However we design that, however we reward people for doing it, that’s going to become a critical part of all of this AI stuff.

Bonni Stachowiak [00:26:12]:

This is the time in the show where we each get to share our recommendations. And I wanted to revisit something that has been recommended before on the podcast that was part of our holiday gifts at the end of 2025, and continues to be a gift to us today. On episode 602, Eddie Watson recommended Caraway pans. And so I decided that was going to be one of our family gifts to ourselves, and it is the gift that keeps on giving. Just the other day, Dave was finished having cooked something, and he asked me, says, have you washed out the pans yet and experienced that? Yes, yes, I have. It is incredible to have a really, really good pan like that and to experience where you just wash it and it gets clean.

Bonni Stachowiak [00:27:01]:

It’s absolutely incredible. And we did a fair amount of research as to like, do these pans last forever? Does any pan out there last forever? I think the answer is no. But we’ve had our pans for a long time before we got the caraway pans. And I’m telling you, life is different. And Lew is just laughing and laughing and laughing at me. Perhaps because he too understands the power of a pan. No, we are not sponsored by Caraway pans.

Bonni Stachowiak [00:27:26]:

I just feel really strong that you got to get yourself a good pan, and you got to experience what it is like to cook with a good pan and what it is like to get to wash that pan and actually have it all come all clean and nice afterwards.

Lew Ludwig [00:27:37]:

I am laughing because this is so unfair. I always thought it was had to be some little tech advice or something related to teaching. And here I could, I could do all kinds of stuff like that.

Bonni Stachowiak [00:27:46]:

Welcome to your first episode, Lew is now your freedom. Just imagine you’ve been set, set free.

Lew Ludwig [00:27:55]:

Okay, how about this? I will go ahead and do my recommendation, and I, even though I use the heck out of AI and do a lot of stuff with that, I’m not really a big apps person. I always lose the password and the how to do. I just- I never have the patience to kind of figure something out. So, Bonni, I’m going back, and I’m going to jump in the Wayback Machine and go, an episode you had 536. See if she remembers this one, Oh, she does. So anyway, you had John Warner on, but this was in September of 24.

Lew Ludwig [00:28:24]:

And the interesting thing, you didn’t recommend a pan or an app, but you recommended a poem, Joy Comes Back, and it was read by Harry Baker. The frustrating part, it was on, like, Instagram. It was really hard to kind of like it, start right up. But anyway, it was a beautiful poem. Harry’s this British fellow, has a really great accent, and he delivered it. But what really stuck with me, again, it was called Joy Comes Back. It was the opening line. He says, when you finally realize that joy is less fireworks and more firefly.

Lew Ludwig [00:28:52]:

Okay, so what I do now is I actually start all my classes sometime in the first week or so, and I share that with them. I share that little thing. And what I ask is, every Monday when we start class, we begin each week, and I have the students turn to one another. I said, hey, share a firefly from last week, okay? Now, a firefly has to be something that’s unique to you. It can’t be like, you know, the org football team won or something like that. It’s something that you have to recognize, and you have to go out of your way. You know, somebody held the elevator for you, somebody the lunch person knows your name.

Lew Ludwig [00:29:24]:

Someone scooted over on a crowded bench for you. My wife folding down the sheets for us before we go to bed. It’s that little thing that you kind of have to go out of your way to recognize those. And, you know, I think if we start looking for those little things, we train ourselves to notice what’s good instead of what’s wrong. That changes, you know, it kind of moves through us throughout our day and helps us to become, I think, more grateful, more connected, and as the poem says, more joyful. So there you go.

Bonni Stachowiak [00:29:48]:

Oh, what I love about this is it’s so infectious. I’m over here smiling and starting to experience more gratitude just from even hearing you talk about it. And what I’ll do is I’ll go, I know that he is on YouTube. I’ve found his videos there, too. So I’ll maybe find an easier way to share that. And I’m so glad that you’re resurfacing that. I feel like today is like, resurface old recommendations. And now I’m waiting to see what Todd does next.

Bonni Stachowiak [00:30:12]:

What do you have for us? What would you like to share today, Todd?

Todd Zakrajsek [00:30:14]:

Well, first of all, I’m going to just say about the pans, Bonni, it’s not just the cleaning of them, because now I’m enthralled with this. The even distribution of heat is just fantastic. So they’re really good.

Bonni Stachowiak [00:30:25]:

Yes.

Todd Zakrajsek [00:30:26]:

Okay, let’s get her dreaming about her pans again. I’m going to go ahead and go with an app. I have had the hardest time through my life keeping track of when I travel. And so I have found this little app, Tripit, and I had to give it permission to look at my emails. But every time you book something or you reserve something, they send you a confirmation email. So now what I do is I will book my flight, and then I book my car, and I book the hotel, and I don’t touch anything, and Tripit always stays open on my phone, and I click the button, and it’s all just laid out there. It’s got confirmation numbers, what time you pick the car up, what time you’re supposed to drop it off. And so it’s just everything is there.

Todd Zakrajsek [00:31:02]:

And so Tripit just has taken care of all of that issue for me. And before I hand over the mic again, I just want to point out, Bonni, I think we’re in a point where we get all of this stuff going on out there. You know, we should come back to some good poetry at times.

Bonni Stachowiak [00:31:16]:

And is this the time where you have one that you’re ready to share?

Todd Zakrajsek [00:31:19]:

Oh, for me, it was “The other side of the door”. It starts out with the other side of the door. On the other side of the door I can be a different me. And the whole poem is about the concept, and it was done by Teaching With Fire was the name of the book. And there’s these poems, and every page is a poem, and then on the opposite page of it is the teacher explains why they picked that poem. And this is one of the poems that was picked, and basically what it is is, you know, I can fly, I can go to all these places. It’s just like anything is possible on the other side of the door.

Todd Zakrajsek [00:31:49]:

And I always thought that was neat. And it was a teacher, a grade school teacher, who hung that on her door. And so that was fabulous.

Bonni Stachowiak [00:31:55]:

Oh, my gosh. We could have had a whole conversation just about that because it’s reminding me of the trust or the lack of trust. We were talking about friction earlier with regard to assessments, and we want learners to trust us. That the friction that we’ve introduced to the learning process actually has a worthy purpose. And I would say some of us, in some cases, even the three of us perhaps, sitting right here together today, aren’t 100% worthy of that trust. And that’s why the process that Lew was describing earlier, of just continually going back, what is the learning goal? You’ll never get finished asking that question, if you ask me. So the idea that on the other side of that door, so much is possible, but that it’s going to be a little bit difficult, there will be challenges to endure, but that they are worth it, because there’s something incredible on the other side of that door. And the more that we can be in solidarity to be worthy of the kind of trust that says, yeah, this is going to be hard, but you know what? It’s going to be worth it.

Bonni Stachowiak [00:32:58]:

It’s going to be worth it. One of our kids was just telling me the other day that she was so mad at the seventh graders. Oh, I guess I just gave away which kid it was, so mad at those darn seventh graders. So, “Mom, I’m so mad at those darn seventh graders”. What those darn seventh graders did was use AI to cheat on an assignment. So now all of the sixth graders have to handwrite every single assignment. And there’s apparently some assignment that is quite, quite difficult to do, handwritten versus being able to type it in. So it was just making me think.

Bonni Stachowiak [00:33:33]:

And I was trying to emphasize the positive side, which is that, you know, the friction to learn new vocabulary. I’m trying to remember what the learning goals were. There were definitely established learning goals, but I’m pretty sure learning new vocabulary is one of them. So I was trying to be on the positive side, like, hey, you know, the student, the teacher’s introducing this friction so that you can learn, and how wonderful it is when we, when we learn these words. But at the same time, I was thinking, man, was the learning goal to write more effectively, you know, handwrite or get a cramp in your hand, all the things. And I mean, it’s just so unfortunate the kinds of tensions that all of this that we’ve been talking about introduces to us.

Todd Zakrajsek [00:34:09]:

So one of the things, Bonni, when you just talked about handwritten and stuff, I think we also have to keep in mind we can lament this all we want, but if, if students are always using their devices, and then we ask them to write like a two page essay, that’s, that’s musculature that they don’t develop. And I was talking to my daughter about this the other day, and she said, you know, at the end of about a page, page and a half of writing, my hand starts to cramp up. And again, we could sit back and say, oh, everybody should. The point is, if you don’t use something, you lose it. And so we have to be careful when we just say, oh, I’ll just have them all handwrite it.

Bonni Stachowiak [00:34:38]:

Yeah, it is, it’s tough. And by the way, those of you that are deciding to implement this as part of your assessment, please don’t think that I’m like, full of judgment. There are just no easy answers. I’m so grateful we have each other to keep having conversations like the one we’re having today, so we can keep making each other better and sharing our struggles, because there’s just no easy answers with any of it, so. And I’m grateful for this book, and I’m grateful for this chance to get to have this conversation with you today. And what a delight.

Bonni Stachowiak [00:35:07]:

Until the next time, thank you so much for your generosity, and for everything you do to help make us better at what we do.

Todd Zakrajsek [00:35:13]:

Thank you.

Lew Ludwig [00:35:14]:

Thanks so much, Bonni.

Bonni Stachowiak [00:35:18]:

Thanks once again to Lew Ludwig and Todd Zakrajsek for being guests on today’s episode of Teaching in Higher Ed. Today’s episode of Teaching in Higher Ed was produced by me, Bonni Stachowiak. It was edited by the ever-talented Andrew Kroeger. Thank you so much for listening, and if it’s been a while and you have yet to sign up for the weekly updates, now is your time to head over to teachinginhighered.com/subscribe. You’ll receive all of the show notes, and also some other resources that extend beyond those resources. Thanks so much for listening, and I’ll see you next time on Teaching in Higher Ed.