On Tech Ethics Podcast – AI Ethics in Teaching

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1. Episode Introduction (00:00:04) Daniel opens the episode with a disclaimer, introduces Tiffany Bourelle, and frames the conversation around AI ethics in teaching.
2. Tiffany Bourelle’s Background and Responsible AI Work (00:00:59) Tiffany discusses her work at the University of New Mexico, her research on effective teaching practices, her graduate-level AI ethics course, and her upcoming Fulbright research on the EU AI Act.
3. How Educators Are Currently Using AI in the Classroom (00:03:15) Tiffany describes the range of educator responses to AI and shares examples of students using AI for brainstorming, research support, outlines, article summaries, and learning reflections.
4. The Gap Between AI Hype and Practical Classroom Use (00:06:13) The conversation turns to AI literacy, cognitive offloading, the limits of AI-generated outputs, and the need for students and educators to critically evaluate whether AI use truly benefits learning.
5. AI Literacy, Ethics, and Critical Evaluation (00:09:39) Tiffany explains AI literacy through functional, rhetorical, and critical lenses, emphasizing bias, hallucinations, invisible labor, power dynamics, and environmental impacts.
6. Practical Steps for Responsible AI Use in Courses (00:15:15) Tiffany recommends using the EU AI Act as a starting point, distinguishing between low-risk and higher-risk AI uses, developing course policies, and involving students in an evolving AI policy conversation.
7. Mid-Episode Message About CITI Program’s On Campus Podcast (00:20:24) Ed Butch briefly promotes CITI Program’s On Campus podcast and invites listeners to subscribe before the episode resumes.
8. The Role of Schools, Districts, and Governments in AI Policy (00:21:34) Tiffany discusses the need for formal AI policies, greater transparency, student involvement, and small audits of AI systems when institutional guidance is limited.
9. Recommended Resources for AI Ethics and Teaching (00:25:22) Tiffany recommends resources including Mark Coeckelbergh’s AI Ethics, Stuart Selber’s Multiliteracies for a Digital Age and related work, and Teaching with AI by Watson and Bowen.
10. Final Advice for Educators Navigating AI in Teaching (00:26:41) Tiffany encourages educators to acknowledge anxiety around AI, use available resources, move beyond plagiarism-focused discussions, and approach AI as an evolving responsibility.
11. Episode Closing and Tech Ethics Training Promotion (00:28:56) Daniel closes the episode by encouraging listeners to explore CITI Program’s other podcasts, courses, and webinars, including its Tech Ethics training solution, and thanks the production team.

Daniel Smith: Welcome to On Tech Ethics with CITI Program. Today I’m going to speak with Tiffany Bourelle, who is a professor at the University of New Mexico, an instructional strategist and an AI ethics scholar with more than a decade of experience designing high impact educational programs. In our conversation, we are going to discuss AI ethics in teaching. Before we get started, I want to quickly note that this podcast is for educational purposes only. It is not designed to provide legal advice or legal guidance. You should consult with your organization’s attorneys if you have questions or concerns about the relevant laws, regulations, and guidance that may be discussed in this podcast. In addition, the views expressed in this podcast are solely those of our guest. And on that note, welcome to the podcast, Tiffany.

Tiffany Bourelle: Thanks for having me.

Daniel Smith: It’s great to have you. So, I very briefly introduced you, but can you just start by telling us more about yourself and your work in advancing responsible AI practices?

Tiffany Bourelle: Sure. So, as you mentioned, I’m a professor at the University of New Mexico. And my research over the last, I would say 15, 20 years maybe, has been with finding effective practices for teaching in the 21st century. So, researching them, reporting on it. So, naturally AI seemed like the next step. It’s really permeating the workplace and education, as we know, as we’re going to talk about today. So, my research naturally made that shift about a year ago. So, I taught an AI ethics and theory class at the graduate level at the University of New Mexico. And as I was teaching that class, I started to research the EU AI Act. And I became fascinated with that because that’s the gold standard. It’s one of the first policies that came out that gave practical ways that organizations could approach ethical AI use. And I think that educators can also use those policies too, and I’ll talk a little bit about that later too.

So I became interested in that, brought that into my own teaching, my own policies. And then, I’ll be studying the EU AI Act actually in France next year on a Fulbright fellowship. So, I’ll be looking at how people are using the EU AI Act to develop ethical AI software, but I’ll also be looking at how educators can help their students think about ethical uses of AI. And if they go on to use AI in the workplace, how they can approach that with the principles in mind for the EU AI Act of privacy, transparency, bias, and things like that.

Daniel Smith: That’s really interesting. And I’d love to hear more about applying the EU AI Act and its risk-based framework to teaching and things like that. So, I look forward to that part of our conversation. But to lay some groundwork, you talked about your teaching about AI. How are you seeing educators using AI in classrooms today? And as part of that, can you share a practical example or two of what you’re seeing?

Tiffany Bourelle: Sure. At the University of New Mexico, I teach a course that trains educators to teach in the 21st century. So, we just call it the effective practices of teaching in multiple formats, including online, hybrid, and face-to-face. So, one thing we do talk about is AI use, and I think that we’re seeing a broad spectrum. We have instructors who absolutely do not want to use it. We have people who are a little hesitant and have reservations, and then we have others who are just fully in and committed and want to prepare their students for the future as well. So, there is this broad spectrum. So, I kind of see a little bit from each of those areas that I just talked about.

Some of the ways that instructors are using it are things like brainstorming. They’re having students use generative AI, so large language models, to think about their essays before they get started. So they can brainstorm, they can ask it for peer-reviewed research, they can generate an outline, things like that. They can also ask the LLM for research and to summarize an article, with the idea that they’re going to go and read that article to make sure that the summary was correct, of course.

And then, also, I was just talking with colleagues two days ago about having students write reflections of their learning. A lot of them are already starting to use AI to help them with that, which sounds counterintuitive, right? It’s not really metacognition if someone else is doing the work for you. But we were talking about how reflections of your learning are the hardest things sometimes for students to grasp. So using it as a first draft, and then going back to what the AI wrote for the reflection and saying, “Okay, here’s where in my project I feel like I did X, Y, and Z.” So, really being able to use it as a starting point but not the end all be all.

I see educators starting to use AI a little bit more in various ways that they thought it couldn’t be used for, I.E. reflection. And really taking it to the next level as an assisted learning tool and teaching students to use it that way instead of, “Oh, I’m going to generate this through AI and pass it off as my own.” So that’s kind of what I’m seeing currently.

Daniel Smith: This sounds like some really good practical uses of AI currently. Obviously, there’s also a lot of hype around what AI could be used for. And I think, given that hype, a lot of anxieties around what that will mean for the future of education for people all the way from K to 12 and beyond. So, where do you see a gap between how AI is currently used in teaching versus how it’s often promoted or imagined of how it will be used, and just your thoughts on the impacts of that?

Tiffany Bourelle: Yeah. So, that’s an interesting question in itself. Because I think that what we’re seeing is that the workplace is starting to want people who are skilled in AI, but I would say that it’s AI literate. And I can talk more about that to my definition of AI literacy, because I think that AI literacy also comes with ethics. So, it’s not just being AI literate, “Oh, I know how to input and create prompts that will generate X, Y, and Z,” but it’s looking at things like, “Okay, let’s talk more about how the output could be biased, hallucinations.” And then, I think also the important thing that I feel like… I can’t speak for all educators, but I feel like in the conversations a lot of this is missing too is students are using it for cognitive offloading.

And I just read this article and it was about how students tend to pick up AI when they’re fatigued. And we’re hit with so many things in society today. We’ve got websites, we’ve got TikTok videos, we’ve got social media. So students tend to turn toward AI because they want to offload their cognitive processing. But the thing is that sometimes what I think is missing is this discussion of, does it actually benefit you? So at the same time, the NIH had a study that talked about how it’s hindering our memory loss, our creativity, and just critical thinking in general. So, I think there’s this myth that it’s helping more than it actually is.

So ,if you were to look at some of the research, again, I’ll just keep quoting some of these things. But the BBC, and I think the European Broadcasting Union came out with recent research in 2025 that said something along the lines of 45% of the outputs from AI LLMs is erroneous. So just teaching students that it can offset cognitive load in some ways, but you always have to go back and check. And you’re not just checking output, but you have to evaluate what is being gained and what is being lost, I.E. cognitive functioning, memory, critical thought, things like that. And I think the gap is there for the workforce too, because I think that they’re starting to look at things like this as well. Meaning, okay, where does it actually help us and where is it requiring our employees to do more work? Where is it actually not a cost benefit to us?

So, I think there’s this push thinking one thing, that it’s going to be helping. But in reality we’re seeing if we’re using it ethically, “correctly”, is it really benefiting us? Is it really providing a cost benefit in the workplace? I think that gap, to sum up what I’m saying, is not discussed. Right? It’s not discussed with students what is lost and what they need to be doing in terms of looking at the outputs a little more critically.

Daniel Smith: That highlights the importance of AI literacy across different domains. So, can you talk some more about how you think about AI literacy and how ethics fit into the broader AI literacy picture?

Tiffany Bourelle: Sure. That’s a great question. So, I come from the Stuart Selber school of thought, and he wrote an amazing book called Multiliteracies for a Digital Age, and it came out in 2004. But I’m citing this book because I follow this person. He’s also a professor. And he has jumped into the AI realm as well with podcasts to Ted Talks, YouTube videos. He’s got his own blog, he does workshops. So, the reason I am obsessed with Stuart Selber is because he has three literacies that he talks about, and I think they apply to AI. And he’s talking about this as well. And those three literacies are rhetorical literacy, functional literacy, and critical literacy.

And so, let’s start with functional. It would be basically how the system works. Is that you could have conversations with your students about the different types of AI that they might encounter, so machine learning versus generative AI. And just generally, how does this work? And then, rhetorical literacy could be things like, okay, let’s look at the outputs and who the audience is for this generative AI, let’s say, but also for the output. So, meaning, are you going to use this outline as your starting point? Are you going to use the output that it generates? Are you going to give attribution? For what context, for what purpose, for what audience? You’re also looking at the rhetorical aspects in terms of the generative AI tool as well. For what purpose, context? Why am I using this, right? So not just the output that you’re hoping it generates in terms of rhetorical literacy, but why you’re using the tool in the first place.

And then the critical literacy I think is the piece I always say is missing when we look at technology in the classroom. And that is, drum roll. Who would benefit the most from this? What are the power dynamics, right? I’ll just say out loud that some people are benefiting and profiting. And you can find research on this as well, people benefiting from these large language models. But at the cost of what? So, for instance, Time did come out with this article, an investigative report about people in the global south are often making very limited wages and under extreme conditions. So the AI system, in other words, is not “automated”. There’s always a human behind it who is reviewing or training the AI system. And then, who’s benefiting from this? And that goes to the critical piece I think that’s missing, the critical literacy piece.

So, if you were to look at this article called Anatomy of an AI System. I’ll give a shout-out, because one of my graduate students actually gave me this article and presented on it. It’s by Crawford and Joler. And it traces how an AI system comes about, and the resources that are used. Who’s doing the training? And then, who is sitting in the seat of power disseminating some of these systems and having the final say-so, and making decisions? For instance, whether or not they charge, and having the final say.

And if I could keep going down this thread, I’ll say too that I think the critical piece is also looking at some of the outputs for things that we know are occurring. For instance, bias. So, there’s been research about how different AI systems either lean left or lean right. For instance, we did this in my class. You put in things like “Give me influential thinkers of the last century.” Generally, more white males are going to pop up than people of color, women. So I think it’s important to look at those aspects too. And to have students recognize that AI literacy is bound up with not just those three literacies I talked about, but looking at the outputs for things like bias, hallucinations.

And I’ll say lastly, if you were to look at more scholarship beyond Anatomy of an AI System, what is it doing to the earth? What are the resources and what are the cost benefits of that too? So, we know that it’s taking up a ton of energy, cooling systems are contributing to climate change. So, I think it’s having students read some of that scholarship as well just to open their eyes to these different elements of AI. So I know that was a lot, and I’m happy to pull at whatever thread you want or talk more about anything that I just said.

Daniel Smith: I think you touched on a lot of really important pieces. And it’s an interesting conversation in the context of teaching because it requires them to think about these aspects that you’ve covered, but also how they’re helping educate their students about these, and how they’re using them in their work as well. So, with that in mind, what are some practical steps that you think individual instructors can take to use AI responsibly and ethically in their own courses, but also as part of that, teaching their students how to use AI responsibly and ethically in their work, whether that be learning and then also just beyond?

Tiffany Bourelle: Yeah. So, I think if I could go back to the EU AI Act, because a lot of people think it’s just for organizations, but I do see some crossover and some usefulness. So, I think it’s a good starting place for just a policy in general. So practical steps would be to think about your own policy. And I just wrote a piece with some colleagues in Ireland, and another in New York, about the fact that policies vary so widely across universities, across countries. We looked at the University of Limerick versus Hofstra University, versus the University of New Mexico, and the three were so vastly different, right? That I think educators have a hard time knowing where to turn. Their universities might not have policies. So I think looking at different university policies, but also looking at the EU AI Act as a gold standard to start from and thinking about your own policies that way.

And I’ll talk about the EU AI Act a little bit, because I think it’s a good place to start when you’re thinking about low risk versus higher risk. So, what are some low risks and high risk uses of AI? So, for instance, you might think about, okay, I want to stay with low risk. So low risk would be brainstorming. It would be generating discussion questions. And it could be creating study guides. Now, this is for the instructor standpoint, right? Now we’re talking about how the instructor can use AI in their classroom, not just teaching AI. It’s now using it for your own teaching to supplement.

Higher risk, obviously things like grading. I don’t support using AI for grading. Not just for FERPA laws, but for ethical reasons as well. So, think about things like that. What are some areas that you think might be a little bit riskier? And have conversations with your colleagues, have conversations with the university about the policies. Join some of these conversations that are, I’m certain, ongoing at many universities. It definitely is at the University of New Mexico. Have conversations about these policies, but also what might be lower risk, what might be higher risk.

But I think that policies are often written, even your own as an instructor, from an instructor standpoint. And so, I also advocate forming a democratic one with your students. I did that in the AI theory and ethics class I talked about at the beginning. I generated one with my students. And it was this living document, it wasn’t one and done. We talked about it in the beginning. We looked at other policies from other universities across the globe, the EU AI Act. And we came up with one together that shifted and evolved over the semester regarding all the things that I just talked about that we read about the bias, environment, invisible labor, all of it. So it was this living, breathing document that we came to together and we returned to throughout the semester and kind of said, “Okay, where do we think this fits in, what we’re doing now with the policies?” So I think there’s many ways, and I think that’s one place to start.

The next is to look at and have discussions with your students about bias, invisible labor, and environment. So bias is everything we just talked about, who’s being left out, who’s being promoted. And then invisible labor, not just the humans behind the system, but your own invisible labor and what’s lost there. How risky is that? Is it low risk, high risk? And then environmental ramifications, you can look at it through that high, middle, and low risk as well. But also things like transparency. Students need to have some guardrails about when AI can be used, report how it’s being used, and talk through what role it has in decision-making.

And then, also maintaining human oversight. So, that’s huge in the EU AI Act as well, is that there has to be some human oversight. And I think that’s the point of AI literacy that connects with ethics is it’s not replacing you and it’s not doing this perfect brilliant work like you might think it is. It has to have oversight. So, starting with a general policy, maybe the high and low risk with your students talking about those things. And talking about the elements that get lost, but remembering that it’s a conversation that has to be ongoing. It’s not just a conversation at the beginning of the semester. It’s ongoing, bring it into the classroom at many different points. If you start with that democratic AI policy, you can come back to that document and work on it together and continue to have these conversations. So, I know I hit you with a lot, but that’s where I would really start and just have these evolving conversations with students as they organically occur.

Ed Butch: I hope you are enjoying this episode of On Tech Ethics. If you’re interested in important and diverse topics, the latest trends and the ever-changing landscape of universities, join me, Ed Butch, for CITI Program’s original podcast On Campus. New episodes released monthly. Now, back to your episode.

Daniel Smith: I think that’s a lot of really good advice. And from another perspective, I’d like to hear your thoughts on the role of schools and school districts and government agencies in helping shape those policies. Because I think, similar to what you were saying about how it varies so much across universities, it also varies so much across university systems, or school systems if you go down to the K-12 level. And then state by state, as we see some states implementing AI laws, like in Colorado. So just, what is your thinking on what those oversight bodies, for lack of better term, should be doing to help shape ethical responsible AI policies for education?

Tiffany Bourelle: I think something has to be done. And I think it has to be done now because there’s a lot of promises that have been made with AI. And I think that one is, this is related to teaching I promise, but that AI can also help solve the climate crisis. But what if it doesn’t? To use that cliche, “We have no planet B.” So, I think that there’s a huge responsibility for government. The City of Albuquerque actually has a policy, an AI policy. That’s where I teach. And I think schools have to have a policy. I think that we can no longer bury our heads in the sand. We have to admit that we’re using AI, admit our anxieties around it, admit that students are using it, and it can no longer be from a plagiarism model the way that we approach it in teaching. So, because there are these loose guardrails, and everybody’s doing their own thing, I think that does fall on the educators to have their own policies.

And like I was saying, talk to students about those policies and have them shape it too. And I think that advocates for transfer into the workplace. Meaning, once students see, “Oh, I can have a hand in shaping the policies of AI that I’m using in the classroom,” the hope is that they’ll go into the workplace and say, “Let’s have a conversation about some of these policies and how we’re using AI in the workplace, when we’re going to use it, how it’s affecting the planet. What times can we use it? Under what circumstances? How can we have less impact?”

And then, I didn’t say this earlier, but I think too, we all have to be doing audits. So, in the absence of policies, we all have to be doing audits. Our students, teaching, everyone who’s using AI should be doing a small audit of the AI system. And you can find out some of this information just by doing research on the things that we’ve been talking about, bias, invisible labor, environment, some of the ethical standpoints, privacy, transparency. You can find out some of the information regarding those aspects by looking at the AI system itself and some of the transparency reports that they’re publishing. But also, looking at people are starting to do undercover research, finding out what’s going on behind the scenes.

A book I’m reading right now, I just started reading it, is the Empire of AI, by Karen Hao. And I think it’s fascinating, because she’s talking about some things like resource extraction, labor, data, the influence of AI. So, I think that doing the research outside of just looking at what the AI system is reporting itself, but looking at outside research to see where it matches and where it’s missing before you use that system. I think teachers need to do that, students need to do that. I think it’s especially important in the absence of these policies that you were asking about. So it’s really up to us, not to put too much pressure on everyone listening.

Daniel Smith: Absolutely. So, we’ve touched on a lot of really important things like hidden labor, the hidden costs, the environmental impact, bias, accountability, EU AI Act, so on. For our listeners who want to go deeper on these topics and learn more, do you have some resources that you would recommend? I know you’ve mentioned a few throughout the conversation today, but are there any others that you’d like to point out for our listeners?

Tiffany Bourelle: Sure. Another book I really like, and I’m using it for my own research, I think you can start here too, is AI Ethics by Mark Coeckelbergh. I mentioned Stuart Selber’s Multiliteracies for a Digital Age, and I think that is interesting from the rhetorical, functional, and critical literacy standpoint. But I also would highly recommend that teachers start there with his blog. Again, his name is Stuart Selber. I feel like I’m pitching Stuart Selber, but his work is just brilliant, especially if you’re lost at where to start. Those are for teaching AI and teaching students to use AI responsibly.

But also, there’s a book if you’re interested in learning how to use AI within your own teaching practices. And that is simply Teaching with AI, by Watson and Bowen. And I get nothing for supporting these people, so I just want to say I’m not paid by any of these people. These are just books and work that I have found to be influential. So, I would say starting there would be good.

Daniel Smith: And then my final question for you today is just, what advice would you leave our listeners with as they navigate AI in teaching? I know you’ve provided a lot today, but if you were to distill it down into a quick answer, what would your advice be?

Tiffany Bourelle: Yeah. To give my colleagues a shout-out to the ones I’m collaborating with in Ireland and New York. We are writing an opinion piece on anxiety with AI. And so, I just want to say to the listener, I may be a “expert” in AI ethics and using AI in the classroom, but I think we all have anxieties about using it. I think it’s natural. I think it’s natural to feel overwhelmed. It’s natural to feel like you might be left behind if you’re not using it. That’s a completely normal feeling. And know that there are resources out there to support you. I’ve named books, I’ve named articles, I’ve talked about policies. Generally, you can find these by looking on different university websites, especially if your university doesn’t have a policy.

But I would say that it’s normal to feel like you don’t know what you’re doing. And I think that admitting that to your students might also help them say, “Okay, we’re figuring it out as we go. And what can we do to ensure AI literacy skills are enhanced together throughout this semester?” So, I would say don’t be afraid to look at different ways of how to use AI beyond plagiarism in the classroom and your curricula, but also in your teaching, but thinking about it in ethical ways too. I think some of the things I’ve talked about, and the resources I’ve mentioned, will help you think about how to use those in responsible ways.

The last thing I’ll say is that my students and I often talk about how AI ethics might not exactly be the term. It’s more about AI responsibility. So, how can you use AI responsibly and with your students, but knowing that the target’s always moving, the conversations are always changing? It’s overwhelming, anxiety-inducing. You just have to be ready to be malleable and shift and grow your curriculum over time, and be open to new ways of teaching.

Daniel Smith: Well, I think that’s a wonderful place to leave our conversation for today. So, thank you again, Tiffany.

Tiffany Bourelle: Thank you. I appreciate being on the podcast.

Daniel Smith: If you enjoyed today’s conversation, I encourage you to check out CITI Program’s other podcasts, courses, and webinars. As technology evolves, so does the need for professionals who understand the ethical responsibilities of its development and use. That is why we developed our new Tech Ethics training solution. This new offering brings together practical, thoughtfully designed courses to help professionals navigate ethical and regulatory challenges with confidence. The courses cover responsible AI, software as a medical device and clinical decision support systems, big data and data science, data management, software development, and more. Check out the link in this episode’s description to learn more.

And I just want to give a last special thanks to our line producer, Evelyn Fornell, and production and distribution support provided by Raymond Longaray and Megan Stuart. And with that, I look forward to bringing you all more conversations on all things tech ethics.