[00:00:00] Bonni Stachowiak: Today on Episode Number 452 of the Teaching in Higher Ed Podcast, ChatGPT and good intentions in higher ed with Autumm Caines.

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[00:00:16] Bonni: 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’m so pleased today to be welcoming back to the show Autumm Caines, and I love her bio so much.

I’ve invited her to hear me read it [laughs] because she shares a word that I learned from her for the first time in August of 2016, Episode 115, which I’ll have in the show notes, but I laugh because this word certainly has come up for me many times in my life and in my career and today, as of the year 2023, is coming up in new ways as you’ll hear us discuss. Autumm, thank you for being here, and I’m about to read to you.

[00:01:21] Autumm Caines: Thank you.

[00:01:23] Bonni: Autumm Caines is a liminal space, and liminal space, by the way, is the phrase/word that Autumm taught me all those years ago. Autumm is part technologist, part artist, part manager, part synthesizer, and she aspires to be mostly educator. You will find Autumm at the place where different disciplines and fields intersect, always on the threshold and trying to learn something new.

Autumm currently works full-time as an instructional designer at the University of Michigan-Dearborn and part-time as instructional faculty at College Unbound, where she teaches courses in digital citizenship as well as web and digital portfolio. Autumm, welcome back to the show.

[00:02:11] Autumm: It’s a real pleasure to be here, Bonni. Thank you so much for having me back.

[00:02:14] Bonni: I recently did an episode about creating a digital author speaker media kit, and I feel like you could just teach the class on how to write a good bio because I could just literally talk to you about it, just the bio, for the rest of the show. Thank you for all the beauty and richness and insight that is just in those very, very few words. We are going to spend a little bit of time here up top talking about what a liminal space is in the small chance there’s anyone listening that doesn’t know what that means.

I’m embarrassed to admit that in 2016 I didn’t know that word, but boy, do I ever know it now. Anyway, what is a liminal space, and what has this meant to you? Why is it in your bio?

[00:02:55] Autumm: Yes, so if you look up the word liminal, it’s about thresholds. It’s an anthropological word about coming of age or ritual spaces, that kind of in-between space, the threshold of moving from one space to another space. I embraced the word because I found that I am often in these in-between places, right? I forgot my first start in educational technology.

That’s two whole fields right there: education and technology, right? I’m always in these in-between places, on the threshold between one place and another place. Yes, I just embrace it. I also think that, and I think I talk about this in one of the blog posts that you’re going to link to a little bit here, I’m fascinated by the intersection between who we are in our identity and the environments that we inhabit, and how those two impact one another. Of course, because I do so much with educational technology, I think about that in terms of physical spaces, but I also think about that in terms of digital spaces as well.

[00:04:04] Bonni: I am not a psychologist, [laughs] but I felt a little bit like I heard something in the way that you just said those words that may or may not be there. This could be a projection on my behalf, and it certainly resonates with me and my whole being. I felt a sense of intimacy as you said the words and described why they mean so much to you that they show up so early in your bio.

I wanted to just share with people listening today that we truly are in a liminal space when it comes to artificial intelligence broadly when it comes to large language models a little bit more specifically, and then you’ll hear us saying ChatGPT a lot in this episode. We’re doing big-picture and then medium-picture and then down to that specific emerging kind of technology.

I think both of us really come into today’s conversation with a sense of– because we’re going to be talking about things that have to do with our values and where our values are intersecting with technology, and this conversation to me feels rather intimate. I’m so grateful, Autumm, that you are here to have this conversation. It also feels liminal that when I might listen back to some of my words, even just even weeks from now, I think that I’m going to continue to be thinking differently.

This is not a set of issues that I have rather defined rigid beliefs about that I am quite open to five years from now, I will think differently about. There are some things that are also going to come up in this conversation that I think I’m probably going to be a little bit more rooted in, such as things like cheating. My thinking on that has really evolved, and I have some pretty deep roots because I’ve done a lot of thinking and reading, et cetera.

Anyway, I just wanted to acknowledge that. I could keep going, but I want to acknowledge that and then invite you, Autumm, to set for us any sort of thoughts that you want to be having in our minds and in our hearts as we listen to you today.

[00:06:16] Autumm: I’ll echo what you said there. This is moving very fast. I think by the time this airs, I might have changed my mind on some things, right? At the same time that it’s moving fast, I feel like it’s been around for a while, right? I guess this stuff has been really taking off in the last two years but gosh, it feels like it just took off in the last two months for a lot of us who are starting to pay attention to it.

I also feel like there’s a lot of that weirdness of liminality, that weirdness of in-betweenness happening because, for those of us who are paying attention to it, there’s a lot of talk, and there’s a lot of feelings, and there’s a lot of stuff going on, but then there’s another whole group. I don’t know if this is happening for you, Bonni, but there’s another whole group who aren’t even paying attention to it at all.

[laughs] When I go, they are like, “What are you talking about?” It’s so big and so important to this one group who’s just learning about it and then, of course, there’s the folks who’ve been paying attention to it and working on it, who I’m trying to read and learn from, and all of that for several years now. Then I go into work for a staff meeting, and they’re just like, “Okay, well, let’s talk about the blog post,” or “Let’s talk about our workshop that we’re going to put on that has nothing to do with any of this.”

It’s a little weird almost because it seems like such a big thing. There is a lot of hype around it, too. There is a lot of– and this happens with new technologies all the time. We’re all familiar with the hype cycle, right? Trying to figure out where the hype is and where you can find something a little bit more solid to stand on can be a little disorienting as well.

Yes, I just want to emphasize what you said about everybody being gentle with themselves around these things. I’m going to advocate for us maybe slowing down with some of these technologies, thinking about them, reading about them before we actually dive in and start using them, but we’ll get into that. We’ll get into that in just a little bit here.

[00:08:22] Bonni: You mentioned a tiny bit about some of the people and things that have contributed to your percolating thoughts about it. Tell us about how you first became interested in this stuff, and by stuff, I specifically am referring to large language models. Actually, maybe you could say what a large language model is as you’re telling us how you got interested in it.

[00:08:43] Autumm: I’ll try. Yes, I will try. I guess a large language model is my really rudimentary understanding of them is when you just take a lot of language, you take a lot of text, and you train it through machine learning to understand, statistically, a particular word, what is the most likely next word in a given context? It does a really good job of sounding human.

It does a really good job of sounding confident as well. I’m not sure if maybe that’s because of what it’s trained on because, well, we’ll get into that later, but especially ChatGPT, which is the only large language model that I have actual hands-on experience with. I think that’s the truth for many of us, right? There are other ones out there, though. There’s BERT, there’s LaMDA, there’s all these other ones.

ChatGPT was released to the world on November 30th, right before finals in higher education [laughs] by a company named OpenAI, and that’s where a lot of us have dove in, but that’s not where I first heard about large language models. I first were paying attention to them in 2021 with the upset with Timnit Gebru and Margaret Mitchell, and others at Google who were being let go/resigning/being fired for wanting to publish a paper that was criticizing these large language models.

That would, of course, get my attention right away because I work in educational technology advice faculty. I’m an instructional designer right now, but I flip-flop back and forth between the IT department and the teaching center. That’s been my career, right? I find myself in a situation when I have to advise on educational technology of either having to pay attention to the sales rhetoric in the people who use this stuff on a regular basis.

That puts you in a really precarious situation, though, because there’s always some problems with technology. There’s always harms that can come from them. If you don’t listen to the critics, those harms sneak up on you, and they end up biting you. [laughs] It’s just not good, right? I’m always interested, especially big corporations trying to silence these researches. Of course, I think it ended up being more of a … effect for them than anything else, right?

The paper finally comes out. It’s actually co-first-authored between a linguist named Emily Bender and Timnit Gebru, as well as Angelina McMillan-Major, and Margaret Mitchell, who’s listed on the paper as Shmargaret Shmitchell because I think that she had to change her name because of some of the stuff that was going on with Google. I don’t know that for sure, but I think that’s what was going on there.

I have heard them speak and say that there are other researchers who contributed to the paper who couldn’t put their names on the paper, so there’s other researchers in there as well. The paper’s called On the Dangers of Stochastic Parrots, and it points out several problems with these large language models, including– because takes an incredible amount of computing power to create them and to run them, so that translates into an incredible amount of electricity, which has impacts on the climate and the carbon footprint that it takes to run them.

There’s also incredible financial costs involved in running them. Then because they’re so large, there is a documentation debt that they talk about in a paper where there is so much going on in the training data that is not being understood. The input coming in is biased in terms of race and gender and all kinds of other things because it’s trained on huge data sets like all of English Wikipedia, like all of Reddit.

Maybe not all of Reddit. I’ve heard that it’s all of a Reddit that has three uploads or more, but because there’s so much garbage coming in, of course, there’s garbage coming out. Now, those of us who’ve played with ChatGPT will know that when you go in there, one of the first things, there’s a big sign right there that says, “You may get some bias output from this.”

Also, if we played with it, if you’ve asked questions about race or gender, you may notice that it’s very careful, and that’s because those guardrails have been put in place. The product that they have created called ChatGPT, which is trained on a precursor that is GPT-3, has some of those guardrails that have been put on it. It’s not perfect because they still have that disclaimer right there on the front page as soon as you come in.

The other big problem that they spell out in the paper that’s also a big sign when you first go into ChatGPT is that truthfulness. The factual right nature of what the system gives you is always up for grabs because it doesn’t really understand what it’s giving you. It’s just statistically looking at what’s the next word that should come in this particular context to make it sounds very human.

Often, it does have good information because, again, it has the extra layers of training, and probably the more they train it, the better that it’s going to get. Right now, there is lots of problems with bias, as well as misinformation that can come from it. I’ve been talking for a long time, so I’m going to take a little breath there.

[00:14:33] Bonni: Thank you so much. Oh my gosh. You’ve been talking, but you’re encapsulating a lot in what you’ve shared because that did take place before I think this most recent, seemingly, explosion if we were to listen to just what’s showing up in the discourse and in the media, so thank you for that. We’ve looked at what the conversations are a little bit, what got us interested, but I’d love to have you talk a little bit contrasting what you’re hearing a lot versus what you’d like us to be having more conversations about.

[00:15:07] Autumm: In my blog post, I do express some frustration with so much of the conversation being around cheating. Here on the podcast, I guess I want to take a little bit of a breath there because I do think that cheating is something. I can totally understand why people would be concerned about it, right? I do completely understand why people would be worried about cheating.

I think ultimately, faculty members use writing as a way to get inside their students’ heads and figure out what they’re thinking, right? We hear that all the time, writing is thinking, and the process of writing is thinking. We want our students to learn how to think through the act of writing, and the idea that they could throw a question into a piece of software and then get all this text that they could just copy and paste, I can see.

I can totally see how that is threatening. That is threatening from a perspective of meaning-making, and that’s really the conversation that I want to have. I want to have a conversation that’s more about meaning-making because I get a little frustrated also when people compare this tool to a calculator because I can see it, but I also feel like there’s a different type of meaning-making happening with numbers than there are with words and that the meaning-making making that comes from words is–

I guess maybe I’m just being biased because it’s really close to my heart, right? I’m a writer. [laughs] I’m one of those people who have a narrator in my head. When I’m going through my life and I’m just going about my day, I have that narrator in my head who’s thinking through things all the time. I think I live through words in a lot of ways. I do think that there is a bit of a separation between those who are concerned about cheating from a meaning-making perspective and those who want to police cheating from an academic surveillance perspective.

I’m very worried about that conversation because I’ve done previous work, which maybe we can link to in the show notes about academic surveillance and remote proctoring, and those things. Bringing cameras and machine learning software for eye tracking and all of those things into students’ homes really worries me and really bothers me in a lot of ways.

[00:17:38] Bonni: Oh, I just love that distinction that you made. Talking about your own identity as an author and a thinker and then wanting to have students experience what it means to have an idea, to formulate that idea, to wrestle with it through the process of writing is such a beautiful thing. Then to distinguish wanting others to have an experience of meaning-making from wanting to police others, particularly, when we discover, at least in my evolution of thinking, that we are policing them when they don’t have an appreciation for what they’re doing.

Most traditional systems have some sort of measure that says, if you were aware that what you were doing was wrong, that’s different than if you did something wrong, but you weren’t aware. I was just thinking. I was on Liz Norell’s– She did this amazing tweet thread and then linked to some of her resources for her class, and one of her assigned readings is Bryan Stevens.

Of course, Bryan Stevens is floating around in my head this morning. Bryan Stevens, for people who may not know, has done a lot of work on justice and his advocacy in the criminal justice system for– I can’t even do justice for his work, but it’s just making me think about– we treat it as if when they cheat, they know exactly what they’re doing, and it’s for all the wrong reasons.

We get carried away sometime because that’s sometimes what the systems and policies, and mindsets reinforce. I hearkened back as you were talking, Autumm, to the work of people such as Kevin Gannon or Jesse Stommel, who their words float in my mind, “Trust students.” Start there. Liz’s tweet thread, who was talking about– and again, I’m not doing her words justice at this moment.

If you’re going to err on a side here and you’re worried about cheating and I don’t blame you for worrying about cheating, I don’t, but if you’re going to err on a side, start with erring on the side of assuming that people want to learn, that they want to have their educational experiences be meaningful and worthwhile to them. When they don’t behave that way, sometimes it is that they don’t know, but sometimes it’s that they’ve just given up along the way because of all the crap they’ve had to put up with and a combination of people who have treated them as if they are not trustworthy and as if their words don’t matter.

All of that, thank you for inviting us now into this conversation about some of the ethical concerns. I wanted to have you talk a little bit specifically because I just think, for those of us that haven’t been there, so let’s talk specifically about ChatGTP for just a moment and OpenAI because you talked about OpenAI earlier. I’d like you to describe if we were to ever because some people are thinking like, “Well, let’s fully embrace this. Let’s have an assignment where all students have to go create an account and then do this thing.”

It comes from a good place, doesn’t it, because rather than running away from it and trying to police it, I don’t know, Autumm, what you think. I think it comes from a good place, but tell us what that would look like and what we are inadvertently probably asking students to do when we run the other direction.

[00:21:09] Autumm: Yes. In my blog post, I think I actually called it a go-to approach from humanistic educators. Let’s not be afraid of this thing. Let’s embrace it, let’s figure out what we can do with it from an educational perspective because it wasn’t released on the world as an educational technology. They didn’t create a company and come into schools and talk to schools about it and say, “Hey, we think that this could be used pedagogically. Let’s have a conversation about it, and let’s bring it into a classroom.”

No, they just dropped it on the world. They were just like, “Here you go.” Now you have all of these educators figuring out how to turn it into an educational technology. The idea of crafting a question and refining a question, what in this world they call prompt engineering, is something that has a lot of value in a lot of ways. I do think that there can be educational benefit to these things.

I guess what I talked about in my blog post was the fact that if you’re going to have students use this directly, what you’re going to be asking them to do is make an OpenAI account, and to get an OpenAI account, you have to give an email address. I’ve heard some people say that they have to give a phone number. I don’t remember giving a phone number, but I think I actually attached it to my Google account, so maybe if you attach it to your Google account, then you don’t have to give a phone number because I hear people say all the time that you have to give a phone number.

Anyway, you have to have an identity. That’s what that is, and educators do it all the time. “Hey, we’re going to teach the students how to use LinkedIn. Everybody, create a LinkedIn account.” Do we read the terms of service for LinkedIn? Do we read the privacy policy before we do that? Probably not. It’s a slippery slope because I end up on the other side of this conversation when I talk to administrators sometimes who want to say, “Well, we have to talk to the faculty and ban it so that they can’t do that. We have to tell them that they can’t do that anymore, especially in higher education.”

In higher education, I have to remind folks, “Well, but people can have consent.” Students can consent to the terms of service and to the privacy policy, and they can make that decision that they want to do that, but I find that often, what happens is, you’re not going to question that if you’re a student and the faculty member is asking you to create this account. You’re just going to create the account.

You’re not going to take the time to read these things. You’re not going to take the time to think about what it could potentially mean. You are giving them the information that you have to give them to set up the account. Some people are like, “What’s the big deal? It’s just an email address. What’s a big deal? It’s just an email address. It’s just a phone number,” but I also feel like we’ve learned from the past.

The Cambridge Analytica scandal, all of these data breaches that we’ve seen, the scandals around targeted advertising that have influenced misinformation campaigns and had political consequences, all of these things, that little bits of information can be gathered together and that, in aggregate, they mean a lot. You give OpenAI your email address as well as your phone number or any kind of identifying information, but then they also know every single question that you’re asking it and the different ways that you’re approaching your prompts.

I have never built a large language model, so I don’t even know what you could do with all of that information, but my imagination can go to some places that aren’t so great.

[00:25:01] Bonni: Something that I discovered recently in terms of just playing around, I mentioned I believe on a recent show about having– I have a new note-taking app that I’m totally geeking out on called Craft. I had done an assignment, a little experiment that Todd … had talked about in the POD list. .. for those who don’t know, is a professional organization for people who are in the field of faculty development.

He had talked about having it write an essay about him and what things it got right and what things it got wrong, so I wasn’t really ready to give my email address to OpenAI quite yet. It’s not that I won’t, by the way, speaking of things that could change literally tomorrow, but as of this recording, as of our conversation, I haven’t yet. I thought, “Oh, it’s just built into Craft AI,” so I had it do that, and then I didn’t play on this.

Maybe it’s a bad thing, but the topic came up during a class that I’m teaching this semester, it’s called business ethics. I basically have lesson plans that I’m keeping in an external thing from the LMS because it’s just better, so I have this lesson plan for business ethics, as the first day of class, and then I just, below that, had asked it to write an essay about me while I was sharing my screen.

The student’s reactions, by the way, again, I’m embarrassed to even say this because don’t do what I did because you’re going to need to really plan that conversation better than I did. Oh, boy, this is one of those “Don’t do what I did” conversations, but the reason I’m admitting this to all of you, listen, is that the essay that it wrote about me was in context because when I had asked it to write an essay about me because I got inspired by Todd … talking about it, it was on a note.

Again, I just had no idea until it happened. I have an internal note that I keep for all of the speaking engagements that I have, so it was on the Lilly Conference thing, so it got confused at the time, and thought that I managed somehow the Lilly Conferences, which I spoke at one but don’t manage them, and at the time, I assumed wrongly that it thought that because of the web presence.

There’s a public-facing website for the Lilly Conferences, which has the description for my talk, et cetera. I’m sure it’s a combination of perhaps both things although what I have read in Brian Kristensen’s stuff and some of the other stuff is like they’re large language models. It’s not the entire internet. It’s most likely that it was getting the great ability to provide the thinking that I run the Lilly Conferences because I’m asking it to write an essay on the same page or set of notes that have to do with that and then I’m in an entirely different space.

Then, all of a sudden, whatever AI tool it is that they use to create these notes thinks that I write and think and speak a lot about pay transparency. I don’t write and speak and think a lot about pay transparency, so I’m like, “Whoa.” I was just one of those like, “Holy cow, what just happened?” and then realizing that it is– Again, as you might imagine, Autumm, I will be reading now the privacy policy and the terms of service so that I can have a better appreciation for these things, but even I, who is the geekiest geek, geek, geek don’t read every single privacy policy as carefully. I just don’t.

[00:28:33] Autumm: It’s really hard to, right? There’s not a lot that I will give OpenAI props for, but I will give them props to the fact that their privacy policy and their terms of service is pretty readable. You get some of these, and they’re size-6 font, and there’s 30 pages. It’s just ridiculous, but their privacy policy and their terms of service I found to be pretty readable.

They also have an FAQ that I think brings some transparency around it. I had had some folks that I’d been talking with just online on Mastodon or Twitter or whatever who it seemed like they had read the privacy policy but were still saying, “Oh, well, don’t worry. The training of what you’re doing when you use the tool is not training,” that training is separate than that, but if you read the privacy policy, they say, “Well, we have the right to do this,” but then in the f FAQ, they’re very clear, “We will do this.”

It’s been released as a research preview. That’s what they’re getting out of this. It is costing an incredible amount of money to run this system. Some analysts are estimating it at $3 million a month to keep it open and free like this. That’s what they’re getting. They’re getting product development out of this, and they’re getting all the training data, so every time we use it, we’re basically providing a little bit of free labor back to OpenAI in doing this.

That’s another thing that I think that students need to be aware of because if you start reading some of the long-term implications of artificial intelligence and machine learning, in our world, there’s always labor consequences. Now, I don’t want to be doomsday. I don’t want to say that all the robots are coming for our jobs, and we’re not going to have any jobs left.

Some people do say that and then they have conversations about universal basic income out of necessity because nobody’s going to have any jobs left. It’s quite terrifying that stuff is out there. There could be jobs that are created by these technologies as well, but we don’t know that they’re going to be as well compensated as the jobs that we currently have. That’s such a great thought experiment, too, right?

Have students think about these things. Have them grapple with these bigger issues before you start throwing them into these systems, having them give their data away, a collective social annotation of the privacy policy, a collective social annotation of the terms of service. Those are great activities to do before you even start talking about creating accounts, having them read some of the reports on the potential economic impacts of machine learning and artificial intelligence in the world. Those are great things that we don’t ever have to touch the chatbot to have those conversations, and they’re rich and important conversations that we need to have.

[00:31:46] Bonni: This is perhaps one of the most awkward segues into the recommendations segment. I say that because I literally could talk to you for days and just be getting started. I want to point people specifically to the three blog posts that you mentioned, which are going to be prominently noted in the show notes. I want to give you an opportunity to share some related.

Well, first of all, I just want to say my recommendation is, go read those three posts, start doing your own thinking, have conversations with colleagues, have conversations with yourself. See, when you have a visceral reaction to it, start thinking about, “Well why am I having a visceral reaction? What does this disrupt in me that is making me who I am as a teacher, as a writer, as a thinker, and then what I believe about students that I may get to teach and who they are as writers and thinkers and creators?”

All this to say, Autumm, I know you have something to recommend on a related topic, having to do with misinformation. Then I’ll, untraditional, which I usually start, I’m going to end the show with my recommendation.

[00:32:51] Autumm: Yes, I have some recommendations around misinformation and targeted advertising, that kind of stuff. I was invited to come out to Brown University for a sandbox that they ran. It was a couple of days long, and it was through their Information Futures Lab, and just the lab itself has some great resources. I’m going to give you a link to their website. Out there, we were working with HestiaLabs Digipower Academy.

This is a group that’s headed up by Paul-Olivier Dehaye, who’s famous for breaking open the Cambridge Analytica scandal and doing some work around that. What we did there was the Digipower Academy, what you can do is, they have you go out to these different websites. They have you go out to your social media sites, I should say, and download your data from those sites and then upload it into their Digipower Academy where you can look at different dashboards and really get a better understanding about why you’re getting targeted for different types of advertising.

I blogged about my experience of taking a look at my data and some insights that I had specifically around the advertising that I was getting around online gambling. I wrote a blog post about this with Michelle Ciccone called Reflecting on Data Power and Pedagogy. That’s published on the Civics of Technology website. I really want to give them a shout-out.

The Civics of Technology website has a curriculum around how to take a look at technology from a more ethical standpoint and has a bunch of resources that you can use to work with your students and help your students better understand how to look at technology from an ethical standpoint. Then finally, I have Bethany McGowan there, and she’s involved in a project called the Diplomacy Lab, and it’s Strategies for Identifying Mis and Disinformation.

They basically partnered with the US Department of State and course-sourced some materials. They’ve got some great videos as well as many other resources. I’m going to be providing you with some links to all of that so that you can put it in the show notes. Thanks so much, Bonni.

[00:35:08] Bonni: Oh my gosh. I’m going to have to pause the interview now and just go explore these. These sound fascinating. What a cool opportunity. Oh my gosh. Literally, if you had just emailed me things and it wasn’t going on a podcast, I would be so excited. I’m so excited that you shared these rich resources with people to think even more about these related topics.

Thank you so much because speaking of visceral reactions to stuff, I think probably less about the cheating aspects of it and more about the global cheating, which is to just continue to put out flagrantly untrue information with very little guardrails and all of that. Thank you for these really rich recommendations. We spoke early on and I would say throughout our conversation about issues having to do with identity, our own identities, the identities of the students we’re so privileged to get to walk alongside with in their learning journeys.

I was thinking back to probably almost 20 years ago, one of our faculty members did a tiny workshop about Howard Gardner’s multiple intelligences and included having us take an instrument. I will say that I’m aware that the theory of multiple intelligences has been criticized for things such as a lack of empirical evidence to it and subjectivity involved.

That being said, I still have just a fond memory from that experience, which was, I don’t know if it’s the instrument is intended to be a sorting instrument or a degree to which, but it doesn’t matter for what I’m about to share.

I showed up as my number one intelligence, 100% whatever, 100% means, in this case, of musical intelligence. This is according to the instrument, the area of intelligence with sensitivity to– and I’m reading from Wikipedia’s description of musical intelligence, sensitivity to the sounds, rhythms, and tones of music. People with musical intelligence normally have good pitch or might possess absolute pitch and are able to sing, play musical instruments, and compose music.

They have sensitivity to rhythm, pitch, meter, tone, melody, or timbre. All this to say, I discovered at the time that there were two of us. It’s a small university now, and it used to be even smaller. There were two of us in the faculty who had this 100% musical intelligence. What we discovered about each other is that we both, almost 100% of the time, have a soundtrack playing in our head.

I walk around with my imagination being filled with music, and I will associate songs with places and people, and memories to a degree that I now realize is not normal for most human beings. By the way, if you’re listening and this is something that you share, I would love to hear from you because I just don’t meet many people like me. I meet people that like music, but just to say that I like music is not an accurate description.

Music is almost my life, that is, I carry it with me. It is absorbed in my imagination. All this to say, and I warned Autumm that I was going to talk longer on this recommendation than usual, a friend of mine, who I don’t know if she wants to be named, so I’m going to leave it out of it now, I still have to talk to her, but she texted to our group chat this song that she said was going to be her theme song for 2023.

Instantly, when I started listening, I thought, “Okay, it’s mine, too. It’s mine, too.” The reason I am attempting to share a little bit more broadly about it is that if you first just listened to it and you’re not the kind of person that has music floating around in your minds, it may sound like I’m recommending something that’s actually really negative because the words are–

I’ll share some of them in just a moment. By the way, I’m not going to, on this podcast, share the word that is used because, otherwise, it gets marked as explicit. Also, you might be driving in the car with your kids, so don’t worry, I’m not about to do that. You may not want to listen, to people who you don’t want to hear the S-word. Anyway, it could sound really negative because I’ll just read some of the lyrics.

It starts out with, “Are you kidding me? I’m getting sick of the industry. I’ve had enough of the make-believe. Oh please, oh please, am I lost or found? I’m getting sick of the ups and downs. No need to give me the runaround. I’m out. I’m out.” Then it goes into, “I’m going to say stuff. The stuff’s going to kill me, but I won’t let it. I try to give them hell, but they don’t get it, so I tell myself when I sleep at night.”

This, by the way, is the refrain. It’s both the name of the song and why it means so much to me. “Don’t lose sight. Baby, don’t lose sight.” So much of the rest of the lyrics just resonates to so many of the experience about running in place. “Am I good enough? Does that even matter, or is it luck? I’m checking the prices on giving up. Now what? Now what?” Then just the refrain over and over again.

“Don’t lose sight. Don’t lose sight.” In at least the acoustic version, which is the one I prefer, although I love them both, it really ends. The last part of the song, the refrain, instead of saying, “It’s never going to change,” says, “I’m not going to change,” and why this resonates to me so much and then I’m almost done recommending here, [laughs] but why that resonates so much to me is we don’t have to give up ourselves to work in higher education.

There are forces and systems that are exerting a lot of energy to ask us to give up a lot of ourselves, being able to show up in the fullness of who we are. I love the idea that “I’m not going to change.” Of course, as Autumm and I talked, I’m a person who I want to change my mind, but I don’t want to change who I am to something that I don’t recognize or I don’t believe. This is absolutely my theme song for 2023.

Let me just say I have texted it to about a thousand people now who work in higher education. I’m exaggerating, of course, but it’s so meaningful because we don’t have to lose sight, and we don’t have to change to morph into something we’re not. I am so thankful to the friend who sent it to us, and I am so thankful to just the people that already wrote it back and went, “Yes, that’s it.”

Also, the artists are amazing. I accidentally sent the wrong link to the song to some of my colleagues, but they were like, “This one’s good, too.” Lots of good music. That’s it, as far as my recommendation. Autumm, I’m just so glad that you came back on Teaching in Higher Ed.

[00:42:10] Autumm: It’s really great to be back, Bonni. Thank you so much for having me.

[00:42:14] Bonni: This is going to be a continued conversation. I think this is your fifth time coming back. I look forward to the next time, and I look forward to the conversations we’ll have outside of this context. Thanks to each one of you for listening to today’s episode. If you have yet to sign up for the emails that I send once a week, the Teaching in Higher Ed updates, I would suggest you do so because this one, particularly, is going to have so many glorious links in it to find out more information.

Head on over to teachinginhighered.com/subscribe. Today’s episode was produced by me, Bonni Stachowiak. It was edited by the ever-talented Andrew Kroger. Podcast production support was provided by Sierra Smith, and I look forward to seeing you next time on Teaching in Higher Ed.

[music]

[00:43:07] [END OF AUDIO]

The transcript of this episode has been made possible through a financial contribution by the Association of College and University Educators (ACUE). ACUE is on a mission to ensure student success through quality instruction. In partnership with institutions of higher education nationwide, ACUE supports and credentials faculty members in the use of evidence-based teaching practices that drive student engagement, retention, and learning.

Teaching in Higher Ed transcripts are created using a combination of an automated transcription service and human beings. This text likely will not represent the precise, word-for-word conversation that was had. The accuracy of the transcripts will vary. The authoritative record of the Teaching in Higher Ed podcasts is contained in the audio file.