Providing students with multimodal content can increase student learning, but creating such material can be costly and time consuming. In this episode, Oliver Steinbock joins us to discuss how he has used AI tools to generate songs and lyrics to support student learning. Oliver is the Cottrell Family Professor in the Department of Chemistry and Biochemistry at Florida State University, who specializes in research in physical chemistry and materials science.
Show Notes
- SUNO playlist of 16 songs on thermodynamics
- SUNO
- Tom Lehrer, The Elements Song (video)
- Neil Garg, Garg Lab Music Video Hall of Fame (2010-2015)
- Dirk Mateer, Using Media in Class
- Dirk Mateer, Dirk’s Media Library
- Mateer, Dirk (2024). Economics in the Movies. Cengage
- Kent Kauffman, (2026). Intellectual Property. Tea for Teaching podcast. Episode 457. August 5.
Transcript
John: Providing students with multimodal content can increase student learning, but creating such material can be costly and time consuming. In this episode, we examine how AI tools can be used to generate songs and lyrics to support student learning.
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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.
Rebecca: This podcast series is hosted by John Kane, an economist…
John: …and Rebecca Mushtare, a graphic designer…
Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.
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Rebecca: Our guest today is Oliver Steinbock. Oliver is the Cottrell Family Professor in the Department of Chemistry and Biochemistry at Florida State University, who specializes in research in physical chemistry and materials science. Welcome, Oliver.
Oliver: Thanks for the invitation.
John: Today’s teas are:… Oliver, are you drinking tea with us today?
Oliver: Yes, but I must admit that I’m usually a coffee drinker, but for this special occasion, I brought some ginger lemon tea.
Rebecca: Oh, that’s a great choice!
John: It is.
Rebecca: I have blue sapphire today, John, because I am recording from home, so I was able to make a pot.
John: …which you haven’t had in quite a while on the podcast. It was one of your old favorites.
Rebecca: I’m excited to have some today. [LAUGHTER]
John: And I am drinking an Earl Grey tea today.
Oliver: Good choice.
Rebecca: Good classic choice. We’ve invited you here today, Oliver, to discuss your recent experiments with using AI to generate songs that explain thermodynamic concepts. Can you talk about how you decided on this approach?
Oliver: Yeah, I’m very interested in AI and integrating it into my research and teaching. So I try to keep up a little bit with generative AI. So, AI methods that can generate text, music, pictures, and you see these fantastic videos of fake songs, [LAUGHTER] fake music videos. So I thought, okay, I want to play a little bit with the music generation part. And so in the beginning, I kind of just took abstracts of my papers, very technical text, and turned it into Korean pop songs, K-pop, [LAUGHTER] and I was kind of amazed how well it worked. I mean, of course, ridiculous, [LAUGHTER] but you can take very technical text, and it works surprisingly well. So, okay, that was maybe day one, and I shared it with some colleagues who also played a little bit around with it, and I’m teaching a lot of physical chemistry upper-level undergraduate classes, and so I thought I can take just an equation or some concept and see whether I can turn it into some kind of catchy song, and so I started playing around with that, and that’s how the whole thing started.
John: And we will share a link to the playlist that has those songs in it, so they will be in the show notes.
Oliver: Yeah, please listen to them. [LAUGHTER]
John: What was the process of creating these songs? What tools did you use to create the lyrics and then to create the actual music and the singing?
Oliver: Yeah, so there are many different ways how you can do this today. But what I did kind of converge to was that I used ChatGPT or a similar large language model, and I had an idea, let’s say I wanted to do the first law of thermodynamics or some kind of fundamental thing,
and/or an equation. And so I said to ChatGPT, “I have this idea. I would like to turn this into a song. Can you suggest some lyrics?” And I gave a little bit of guidance because part of the problem is you have to find the right balance between scientific rigor and technical rigor and making it kind of a little fun. I mean, whatever fun means to different people. So you walk that line a little bit. And so, I prompted it for lyrics, and it’s just amazing. These large language models-they are so supportive, let’s say. [LAUGHTER] They’re like, “Yes, the first law of thermodynamic is an amazing idea, and I think it would work well as a country song.” [LAUGHTER] And I think it doesn’t make any sense really. So, I generated the text and some mild instructions in there, like chorus lines and stuff, and then you transfer it over to a AI music web page. I used SUNO without advertising for it, and so you essentially just copy paste the text in there. You give some guidance on the music genre, let’s say K-pop. You can pick whether you want a female voice or leave it up to them, or a female-male duo, and then you have essentially only two things that you can select. It’s like how closely it adheres to the style and how weird it is. [LAUGHTER]
Oliver: The weirdness is interesting because I wouldn’t say that I fully understand, but it gets more interesting, but sometimes slightly awkward if you dial this up. And then it generates, really within a few seconds, two songs for you. And if you’re in the free level, these are limited to a minute or so, I don’t really remember. And if you have a fairly inexpensive subscription, you can get longer songs, and then you listen to it and you can play with it again. And so that’s how I converged eventually to my playlist. [LAUGHTER]
Rebecca: I love the weirdness is another line.
Oliver: The whole thing is very weird. [LAUGHTER]
Rebecca: So, be careful. [LAUGHTER] But songs themselves are not an unusual thing to find in a learning environment. Maybe not as common at the college level. Can you talk a little bit about the history of song in learning?
Oliver: I feel like there’s something to music and especially rhythm that helps people memorize things. And when we learn the alphabet, no matter what country, that’s usually an alphabet song, and there’re jingles. Maybe they’re a little bit less popular nowadays, but they can be very catchy, although they’re actually annoying. [LAUGHTER] So I would say, in the context of science and chemistry, it’s not frequently used, but probably the early classic is Tom Lehrer’s song about the periodic table, where he, at extreme speed, recites all the elements in the periodic table. It’s real fun, but it’s from the 60s, there’re really cool YouTube videos of it. There’s actually even a version where he shortens it to the four classic elements, and the song becomes very, very short: earth, air, water, and so on, fire. And so that was probably one of the memorable and remembered chemistry songs, but then more recently people played with it on social media. Usually, it’s students performing a song or lip-syncing or changing the text, but you keep the music. A person who did a lot of this and quite successfully, I think, is Neil Garg. He used to be at UCLA. He’s now at Baylor, and he used it in teaching organic chemistry, and to produce great videos. You can see the students really love it, and yeah I would say these are maybe it’s a little bit the context of where all of this work kind of sits.
Rebecca: And although jingles can be annoying, you do remember them.
Oliver: You do remember them, and you know the way I see it is many people, unfortunately, have bad memories of their chemistry classes, and they’re just like negative emotions, to be really honest. There’s not even like there’s this one particular thing that I remember but don’t like. So to maybe make it a little bit more approachable and maybe a little bit more fun, at least to some people, could be a valuable tool. So I’m not thinking of doing every lecture in some format, though, right? But to use it as a small tool and sprinkle a little bit of it in can be fun, maybe.
John: Many years ago, I had listened to Dirk Mateer, an economist, talk about using music and video clips in classes, and one of the things he had done, along with some of his students and colleagues, was creating short little videos that had the songs with the lyrics on them and some connections to economics in it, which I used to play at the start of each of my classes. He also worked on creating this economics in the movies platform, which provided information on short clips from movies that illustrated economic concepts, and I used to use those fairly regularly in my classes. With the songs, I’d start my class with them… I teach a class that back then generally had 350 to 420 students in it, and they’d come in and they’d sit down and they’d be quiet and they’d be listening intently. And I’d start the class by saying, “What economic concepts did you see here, and how might this relate to what we’re doing for that day’s class?” And that worked really well. The only problem is I got really concerned about copyright issues, so I stopped doing that. But what I really liked about this approach is that because the material is generated by AI, it’s not subject to copyright, so there is no concern about copyright infringement with using it. In fact, we had a podcast on that that was released on August 5 that discussed that very issue. So that’s a really nice feature of this that you can create these songs and share them freely with your students without worrying about infringing on any musician’s or author’s or producers’ rights.
Oliver: I would say, first of all, this is really awesome and inspiring to some extent because I will also use it a little bit more in the next semester. But I would say that there are, of course, very serious concerns about copyright infringement that occurred during the training of this generative AI, and many people very rightly point this out, and some AI companies were hit with pretty hard financial punishments. I’m not a lawyer, but for the user point of view, if you’re okay with this or the way it was settled, I would say that my understanding is that this material cannot really truly be copyrighted, and you’re the owner of the song for whatever that is worth, and that’s pretty much how much I know. But one thing I would like to add is that this copyright thing is, of course, a very important issue. But there’s also, of course, the technical musical ability to do this, right? So in a certain way, this generative AI approach democratizes the approach to it. Most people don’t have the musical skills, knowledge, the time, the money, the recording equipment to really do this, and while it’s a major shortcut, and the product is of course different from human-generated music, it is kind of opening up this to more people and more crazy ideas.
Rebecca: I think the interesting piece of this is if it’s used for learning, and that’s the point… the point is not making a good piece of music. It’s helping students [LAUGHTER] learn an idea and remember it, and maybe enjoy that learning experience. So the way music would be measured in those different scenarios would be different.
Oliver: Exactly. I don’t think that if you listen to the songs, I like them, but they’re not really trying to compete with human-generated music.
John: They’re not going to make it to the top 100 list in any genre, most likely.
Oliver: Unfortunately. [LAUGHTER
John: As you noted earlier, there is a long history of using music for teaching, these songs tend to stick with people for extended periods. You had mentioned the alphabet song, which is how many people learned the alphabet, and I know many people have said that when they’re trying to alphabetize lists, they often hear that song in their head as they’re doing so.
Oliver: I think that’s true for me too. Not always, but depending on my mental state. [LAUGHTER]
John: And I remember when my kids were very young, they learned other songs in school, including ones about the days of the week and the months in the year that helped them make some early connections that stuck with them. Songs have often been useful in creating long-term memories for many individuals.
Rebecca: Yeah, we hear a lot of those songs in my house, John. [LAUGHTER]
Oliver: I must admit that I sometimes wake up in the morning… I listen to these songs a lot, unfortunately, maybe… [LAUGHTER] and I have a song stuck in my head, which is good and bad at the same time. [LAUGHTER]
John: Two years ago, Rebecca’s daughter was learning how to play guitar, and I found some music to share with her. What were the songs that I shared the chords with you to give to her?
Rebecca: Oh, my favorite was “It’s a Small World.”
Oliver: Nice.
Rebecca: It’s painful.
John: One of those little earworms [LAUGHTER] that just sticks in your head if you’ve ever heard it.
Rebecca: Yeah. Oh, yeah. And then she thought that was really funny, very funny. [LAUGHTER] How have your students responded to the music?
Oliver: So I think the first response is surprise, people don’t see it coming, [LAUGHTER] and so it’s a combination of an onslaught of: I have to adjust to the idea “this is AI-generated,” and then “oh my God, on top of it, it’s physical chemistry.” [LAUGHTER] And then I would say that people started to respond very differently. Some started to speculate on what artists could be copied here. I honestly think it’s not like that. This is not how AI works. But sometimes maybe. But there’s also negative response, to be really honest. People, for example, I think a valid criticism is that people can kind of say this undermines the value of human music or makes fun of it. This is clearly not the intention, but people respond very differently. Overall, I would say positive, but there’s also some people who really don’t like it. Just to keep it real. [LAUGHTER]
Rebecca: Do you think it’s helped with their learning?
Oliver: It’s very hard to measure, right? Of course, I want to believe it. [LAUGHTER] I think of it mainly as some kind of tool to break up the monotony of a typical lecture with something that is a bit surprising and different. One could also turn it, and I will try to do this next semester, into like an exercise or homework, where you don’t use a large language model to generate the lyrics, but well, as long as the students play long, you try to ask them to write the lyrics themselves. And so you can also one-shot them. But I think that’s the least interesting thing.
John: And that is another thing I have done in my classes. I’ve allowed students to replace their lowest grade on an exam with a song or a video project or something else. And I’ve got a nice collection of songs that students had written over the years and produced as videos and shared with the class, and that again was another way of getting around the issue of copyright because the students owned the work that they were sharing. And that was a fun project. And students really appreciated hearing their colleagues do that. But I never required that. I always gave them multiple options. It was basically an unessay-type project.
Oliver: Yeah, I think one has to be careful. Both some people might not want to do it. Some students might be willing to do it but don’t want to share. So one has to be kind of considerate of this.
Rebecca: And having those options, having the song as an option as a way to learn or remember something, is a good way to implement UDL, universal design for learning, or different formats for learning.
Oliver: I agree, and there’s another dimension to it. I really think it’s very important to integrate artificial intelligence education into all kinds of college and maybe even high school education, and you can do this in a very technical, mathematical way, or you can do it in a more playful way with generative AI, maybe.
Rebecca: Yeah, I like that approach for sure. Have you had some other ideas about how you might use AI-generated music to assist learning?
Oliver: So a little bit before last summer, I developed… it’s not music, but I developed… what I call Thermo Tutor. So it’s a program where I generated 400 questions and answers and tips, and then I use generative AI… ChatGPT at the end of the day… through an API to grade and give feedback on the students’ answers. So you bring up a question that kind of matches roughly the outline of the course, so you can follow along, and it gives you a question. You write in the answer. The AI grades it. If you are doubtful of the AI, you can look at my certified answer if you want so. If you have a question about this, you can ask the AI the question. And so I think that’s a kind of interesting way of doing it because the problem is if I go to ChatGPT and I say, “Hey, I’m taking physical chemistry in college. Give me a question,” it will generate all kinds of questions, but they will probably not line up with the order of the class and the level and maybe the expectations of the instructor and so this is maybe another way to introduce it. Where I see this whole thing going is a little bit; these ideas will grow with what AI gives us. When you look at social media, you see, admittedly many very bad, but sometimes very funny, [LAUGHTER] AI-generated videos, and I think in the moment it’s a little tricky and requires some knowledge and a little bit of money… not fortunes… but maybe hundreds of dollars to generate this. But soon it will probably become much cheaper and less expensive. And so, what is the song today could be a music video in the near future, and maybe even an instructor can make small video games to kind of complement and add to the more traditional teaching approach.
Rebecca: So, do you have video games and videos in the works? [LAUGHTER]
Oliver: I try very hard to make good videos. I fail very miserably, [LAUGHTER] so I will try again in half a year. [LAUGHTER]
Rebecca: Yeah, you wait about six months, then you have a whole new set of tools, right?
Oliver: It’s true.
John: You mentioned having students create songs. Were you thinking of having them do it on their own, or were you thinking of them doing a similar process where they would use an AI tool to generate lyrics and then create a song from there?
Oliver: It’s an interesting question because it goes to this: How far can an instructor or professor really control whether the student is using AI or not? So my instinct on it is to leave it to the students. You can say, “I really encourage you to do it yourself, write the lyrics yourself, and play around with it, and learn some physical chemistry in the process. Spot some errors and mistakes, but if a student is less interested in doing this, it will be very hard to spot the LLM use anyway, right? So my personal opinion is that it’s kind of a very difficult path to fully enforce this, and maybe also a path that will look kind of outdated in a few years.
John: I was very impressed with the quality of the music that was provided. We’re recording this in early August. I just saw last night that there’s a new release of SUNO, so I imagine the quality is only getting better.
Oliver: I haven’t tried it myself, the latest release, but if there’s one, I really didn’t go to the web page back again. I was busy with other things, but yeah, this stuff will only get better. It will never get worse again. It will never disappear again, I think, unless it’s completely outdated by some or overwritten by some other technologies. Yeah, it’s kind of astonishing. There is a weird dimension to this, that things that we think of very creative at some lower level, seem to be automatable, where you can automate the process, and will not reach good levels or top levels. We don’t know where this will evolve. I don’t know whether you’ve ever tried to write little poems with ChatGPT. They fake it pretty well, I think. [LAUGHTER] I’m not saying that an English professor or an expert or really someone who’s deep into this wouldn’t be able to spot the difference. But it’s kind of shocking that so many people, including myself, can be tricked by it.
John: Musicians and poets learn from what they observed in the past, the techniques and the practices of others, and in a sense, this is automating that same sort of process, and it’s learning from its mistakes along the way, and as people do. I suspect it’s only going to get harder and harder to detect what’s created by AI and what’s created by humans.
Oliver: It’s fascinating how this absorption of information and styles is kind of similar in the net effect, but the processes are a little similar, but already quite different, and then you get very different outcomes out of it. And it boils down to the question: What is really human creativity, and how creative can these systems really be? But creative is not a zero-one situation. I think it’s some kind of gradual thing. You can be a little bit creative, and you can be amazingly creative. [LAUGHTER] You can be creative by combining things that already exist. This is very common in science, and it’s usually a very, very nice accomplishment. If you take a result from one field and you apply it to solve a problem in another field, that was a very routine way of being creative in science, and that is something large language models can and will be able to do. But then there’s another type of creativity. We really create something original, something qualitatively new. I believe, and that is something we will have to see whether AI can ever do this.
John: The best musicians, the best poets. It’s hard to imagine AI getting to that level.
Rebecca: Same with visual art and design. I’m just saying.
Oliver: I agree. [LAUGHTER] And the other dimension to it is: Are humans really interested in it? I think there would be no market for two super chess computers playing chess. Maybe there’s a niche market, but it definitely would get boring. But two mediocre chess players, watching them on YouTube playing against, that can be quite a bit more interesting. So there’s also that component to the story.
John: Because the AI systems are basically mimicking what people have already done, and the question is, can they ever get beyond that point by introducing a little bit more randomness and other components? It’s an interesting question, and I think we’ll be learning [LAUGHTER] the answer to that fairly soon at the rate things are going.
Oliver: I think so too.
Rebecca: In some ways, humans are already doing that because they’re bringing whatever their interesting question is and putting it into the system and challenging the system to start combining things that maybe wouldn’t have been combined otherwise too.
Oliver: And it might sound intimidating or maybe even threatening, but at the end of the day, it’s giving us an amazing tool. Maybe the tool is a little too good, [LAUGHTER] but it can also become a new way of being creative. Photography is maybe not a great example, but an example. I mean, it’s a very technical thing, and it somewhat competed with certain types of paintings, family portraits, and stuff like that. But it also allowed for new types of art and creativity to many.
Rebecca: And it didn’t get rid of portraiture and painting. It evolved both of those spaces.
Oliver: Absolutely, you know, of course, emphasis shifts.
Rebecca: Yeah.
Oliver: That’s I think progress and just history.
Rebecca: So I know that you’ve been doing some other interesting things with AI too. I think we heard something about a boot camp. Do you want to talk about that?
Oliver: So this is an idea that we want to allow students to more actively explore AI tools, especially in chemistry for data analysis, protein structure prediction, or what have you, drug discovery. Obviously, there’s a large range. Some people are extremely AI literate. Others have been very hesitant to even touch it, which is fine. But it is a tool that you probably will not be able to completely ignore, or only at a high price. So we thought together with the college of organizing an AI bootcamp for our department, and it’s for the whole college, but it is kind of focused on science and chemistry in particular. So it’s a 30-hour boot camp, and all of our incoming new graduate students, PhD students, are attending. But it’s open to everyone in the college, also undergrads and retired faculty, everyone who wants to come, existing graduate students. And so we have a lot of guest speakers from industry, from academic research, covering the copyright issues, ethical issues, mental health issues, but also some technical issues, without going too much into math or programming, and we try to do everything very hands-on. So there are lectures, but there are also always activities where students hopefully will be immediately putting to use what they’ve learned in the last lecture, and so this is something that I think Florida State University really wants to kind of develop in the next years. Introduce more of that in different forms into the teaching to position people better to do better research, position them to get better jobs, and we’re excited about doing it. Now, no one really knows how to really do it. It’s not like we have years of experience on how to integrate AI into teaching, and there’s a lot of institutional friction as it comes to grading and all kinds of concerns. So I think our society, we as professors or everyone, essentially teachers, students will have to figure out how to really do this effectively, and so this is one big step into this. I am sure that if we repeat the bootcamp next year, we will have lots of experience, and things will have changed, and maybe we’ll have completely new ideas about how to do it. But we have to experiment with it carefully to integrate it. I think ignoring it is, from my personal point of view not a very helpful thing for the students, and yeah, you could probably isolate yourself completely from it. But I don’t know whether that’s really a fair way of preparing students for the future.
Rebecca: Sounds like a really nice immersive launch into the new academic year.
Oliver: I’m looking forward to it. I’m excited.
John: How are your colleagues reacting to the use of AI in the classroom. I know here we have people with a fairly wide mix of views. Some people are really excited and enthused about it. Others want to resist it at all costs.
Oliver: Yeah, I think it’s the same at Florida State University. I think it depends a little bit on the department and college, and I completely understand that if you are a creative writing professor or in the English department, the reality on the ground looks very different from the reality in chemistry, where we are like, “Okay, we are teaching this material. Here’s a new way of teaching it. Let’s integrate it.” Probably the biggest thing at the root of it is evaluation. You can think of all kinds of clever and not so clever ways of integrating AI into education, but evaluation becomes a real problem. Where you could before give a long homework, a take-home homework, or ask people to write an essay over two weeks. Those things have gotten very difficult as evaluation tools. Same with college admission, I would say. The classical essay that people wrote, I don’t know how much value it still has. So these are things that we all have to kind of find ways to deal with it and adjust to it and make good use of it. It’s not like we are downgrading things… maybe in the short term… but I think I’m very optimistic about the future, that this will be an empowering tool for us to learn better, to study better, to teach better, to give people access in places where they maybe don’t have access to education. It’s a time where you have a 24/7 pretty smart, maybe not perfect, but pretty smart mentor and tutor at home. That’s like stuff from science fiction, and you might have it for free or for very little money.
Rebecca: I’ve been using it for running coaching. [LAUGHTER] You know, like when you need an expert, but you don’t have an expert, there’s ways that you can kind of integrate it into different ways of learning about new things.
John: So we always end by asking, “What’s next? With AI, that’s something we’re always asking pretty much every day.
Oliver: I think it’s probably always difficult to predict the future, in particular. It is even more difficult to predict the AI future and how it affects us, how it will technically develop, how it will be regulated or not. I think we all sense that is happening very fast, and this makes it different from earlier changes like the internet happened also fast, also led into bubbles, but it’s hear, right? And it’s our life nowadays, and we are communicating via the internet in the moment. And so it’s hard to predict. But I think in the short term, and that’s maybe one thing I’ve actually learned, when you start thinking about it, it’s very difficult to think about how will it look like next year, in five years, in 50 years. It’s very tempting to immediately smear everything together and paint out a very positive or very negative future. I think realistically, next year will look very similar to this year for most people, for most students, for most professors, and so we should just not sleep through it, and we should try to really integrate it carefully with a little bit of experimentation, but not too much. Listen to feedback from students. For my specific project, I really, like I said earlier, would love to integrate, see whether I can generate videos. A short teaching video could be very fun. I don’t want to change my whole lecture to songs and videos, but to have that as one component in the whole process, something that was impossible before. I can make an animated PowerPoint slide, but a four-minute video on my research would be, or on my teaching topic, would be difficult and very time-consuming, and so I think these things might be possible next year or the following year, and most of the other things remain to be seen.
Rebecca: It’s exciting to think about improvements or things that you want to integrate into your class that you’ve been thinking about for a long time, but just can’t take on because of the time or whatever, and that they’re only a fingertip away, perhaps.
Oliver: And it’s so playful. I mean, it’s not like I don’t have to read six books and spend a whole summer on trying to get into something new. You can open up the thing and say, “So what can I do with you?” Right? [LAUGHTER] It’s remarkable. The answers might be bad. [LAUGHTER]
John: Experimentation can be fun.
Rebecca: That always helps.
John: One nice thing about this is it provides more options for multimodal learning, and we know that when students see the same content in different modalities, it tends to reinforce learning, and it tends to make the learning a little bit deeper and to stick a little bit better. So it just gives students one more way of learning these things and making connections, and that’s pretty much always a good practice.
Oliver: I think so too. I’m really very traditional in my teaching. I write on the board. I don’t even use PowerPoint slides mostly, unless there’s something really I want to show, like a photo or a little animation or something like that. But I do know that while that is the traditional approach in math-heavy subjects, not every student necessarily responds well to it. Not because they are different; it’s just like a matter of taste. And these songs, I think, they have a good chance to stick also in general introductory general chemistry classes where we interact with the students maybe only for a semester or two, and then they go off and do something very different… really, kind of leave some positive, hopefully, impressions and memories, and maybe even some concepts that they remember.
Rebecca: Definitely. Well, thanks so much for sharing your experiments with us.
Oliver: Yeah, that was fun. We’ll see how it evolves. [LAUGHTER]
John: Yeah, thank you. This is a really fascinating experiment, and it’s nice to hear about it in more detail.
Oliver: Thanks again for the invitation. It was real fun.
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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.
Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.
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