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Minding Machines is a podcast dedicated to fostering thoughtful, open dialogue about artificial intelligence. The show brings together educators, students, and practitioners to explore how these tools are changing the way we teach, work, and think. Our conversations focus on practical experiences, emerging questions, and diverse perspectives across disciplines rather than establishing rigid "right or wrong" answers or settling debates about AI.
Minding Machines is an independent podcast produced at Lawrence University, Appleton, Wisconsin. The views expressed are those of the participants and do not represent the university.
Background music - Relaxing Light Background (short version) by AudioCoffee (https://freesound.org/s/725615/)
[00:00:00] (music)
[00:00:03] Abhishek: Hello and welcome to the very first episode of Minding Machines, a podcast dedicated to fostering thoughtful, open dialog about artificial intelligence. The show brings together educators, students, and practitioners to explore how these tools are changing the way we teach, work, and think. Our conversations focus on practical experiences, emerging questions, and diverse perspectives across disciplines rather than establishing rigid right or wrong answers or settling debates about AI.
[00:00:36] Abhishek: Thank you for listening. I am your host, Abhishek Chakraborty, and I am an associate professor of statistics at Lawrence University. To get us started on this podcast journey, we have with us today, the co-directors of the AI at LU Task Force, Mark Phelan and Mike Clayville. Welcome, Mark and Mike. It's great to have you on the show, and thank you so much for taking the time to chat with us.
[00:01:04] Abhishek: Before we begin, could you please share your individual roles at Lawrence? Apart from being the co-directors and the focus of your work at Lawrence?
[00:01:15] Mike: Sure thing. So I'm Mike Clayville. I am a professor of practice in the business and entrepreneurship program. My background is in music, so I am also a performing musician and a trombonist. Maybe not all the things that fit directly with an AI task force, but I think it speaks to the fact that AI is affecting everybody, and we all need to grapple with its effects.
[00:01:38] Mark: I'm Mark Phelan, I'm a professor of philosophy here at Lawrence. I'm also the director of the program in cognitive science, and the co-director of the AI Task force.
[00:01:53] Abhishek: All right then. Thank you. Mark and Mike. Let's dive straight into our conversation. The first question is a pretty obvious one. What is the AI at LU Task Force? Could you please tell us a little bit about how the task force came about, and if you have the previous history of where AI was at Lawrence, that would be helpful for our listeners too.
[00:02:18] Mark: Sure. So the AI task force at Lawrence is, of course, a follow on to the development of AI in the past several years. If you recall, ChatGPT premiered a customer interface or a user interface that was open to the public back in around 2023.
[00:02:42] Mark: And since then, higher education in particular has had to do a lot of thinking, a lot of soul searching about how to engage with AI and how to structure higher-ed education in an era with generative artificial intelligence. So the following year, a group of faculty at Lawrence participated in a year-long educational introduction to artificial intelligence.
[00:03:18] Mark: The year after that, so that would have been 2025 to 2026, so last year there was some more informal work done on a campus. And then in the spring of last year, spring 2026, spring term 2026, it was decided that it made the most sense to convene a faculty task force to organize projects and conversations around AI at Lawrence. All of this is also attached to a generous gift given by the class of 1974, on their 50th anniversary from graduating from Lawrence, so they made donations to support the development of different AI activities at Lawrence. And part of our task on the AI task force is thinking about what to do with that generous gift.
[00:04:14] Abhishek: All right. Thank you so much. Could you please tell us a little bit more about the mission or the goals of this task force?
[00:04:24] Mike: Sure thing. So we have two main items in our charge. The first is to increase institutional understanding of gen AI. This is the convening work that we're going to do. This is the bringing together a people to have conversations. We're going to have speakers come to campus to foster that conversation, to develop things, to talk about. And we're also planning some community reads so that we can have some things to discuss so that people can understand what they're arguing about. So there's a lot of fear out there. There's a lot of concerns about what Gen AI is, and we want to have everybody have a fundamental, foundational understanding to bring to these conversations.
[00:05:04] Mike: Our other part of our charge to develop university academic response with faculty perspectives on responsible use, and this is the more challenging one. We don't have a mandate to write policy, we have a-, nor is this a mandate to promote the technology. We're not here to be boosters for AI. We're here merely to try to get us to think about it, and to see how it might affect our work, and to develop strategies to handle those effects.
[00:05:34] Abhishek: All right. Thank you. Mike. I also wanted to ask about your individual perspectives on what do you think success would look like from a year from now?
[00:05:47] Mike: Yeah. I think success at the lowest bar is having these conversations. It is being able to take different points of view on AI and do it respectfully, and to do it in a way that we can further our knowledge and further our understanding of our community and our feelings about what the effects are going to be. A higher bar would be to have a more, maybe concerted point of view about it, but that's aspirational and something I'm not sure will come out of our work.
[00:06:19] Mike: Our charge is for a year, and so it's a lot of work to do within one year's time. So I think even reaching, having the conversation stage is going to be a success for us.
[00:06:29] Mark: Yeah. It's probably also worth mentioning that the task force itself has two kinds of areas that it's concerned with. So one of these, as Michael mentioned, is focused on faculty conversations and faculty opportunities around AI.
[00:06:45] Mark: The other is looking at student programming and opportunities concerning AI. So Mike is the head of the faculty conversations component of the task force, and I'm in charge of the student programming component. For the student programing component, you know, we're thinking about considering different student faculty collaborations around generative AI, considering the adoption of a gen AI credential for students. And some of our living work includes thinking about whether it makes sense to build up in-house LLM systems within Lawrence, and thinking about what are certain vanguard areas across the curriculum where we would like to develop different resources related to the instruction, instruction and artificial intelligence.
[00:07:42] Abhishek: All right. Thank you. I wanted to follow up on one thing you mentioned, Mark. You have it seems like there are two subgroups within within the task force. One is focusing on the student side of things. Are there any plans or do you do you have current students as part of the task force or is it, is it mainly faculty based?
[00:08:08] Mark: So right now we are primarily faculty based. The committee or the task force got going at the beginning of spring term last year. So we are very interested in bringing students into this work and we're actively looking now. So if you're listening to this podcast, you're a student at Lawrence and you're interested in working with faculty on considerations about AI, those may be positive or negative or on developing resources around AI instruction, either ethical, the ethical implications of AI, actual hands-on work with AI, whatever your interests may be. Please reach out to Mike or me, and we'd love to talk to you.
[00:08:51] Abhishek: And we will put Mark and Mike's email on our show notes so you can reach out to them. The next thing I was thinking about, have you guys got has the task force gathered any data about the perception of AI at Lawrence? And, you know, as an as an educational institution, we have students, we have faculty, and we have administrators, so if you could tell us about what data you have in terms of the sentiment about AI at Lawrence?
[00:09:27] Mark: So I think sentiment about AI at Lawrence is mixed, and I think largely reflective of a sentiment about AI in the wider United States. So I think a lot of people now are very concerned about the environmental impacts of AI, particularly AI data centers on utilities, on water, on electrical generation, and so on. There's also a kind of concern about the way in which these tools will impact different jobs going forward, particularly jobs in the creative space, I think.
[00:10:11] Mark: And certainly amongst the faculty at Lawrence, there's a real concern about the dangers of cognitive offloading that arise from the presence of AI. Faculty are thinking a lot about ways to redesign their classes to encourage students to engage with the material when you have a system that's capable of generating, for example, answers to homework in very sophisticated ways.
[00:10:40] Mark: So those are some concerns that both faculty and staff have, I think faculty and students have. I think, you know, the AI task force to some extent has its work cut out for it because I would say probably the balance of opinions about AI at Lawrence are on the negative side. So we're interested in fostering conversations regardless of what your point of view is, and encouraging communication between folks who are interested in AI, folks who are anti-AI and hoping to find more common ground.
[00:11:16] Mike: Yeah, I would only add that the sentiments that we hold here at Lawrence, if they are generally negative, which I feel they are as well, are held within a larger environment within this United States ecosystem. And regardless of whether we appreciate it or not or we accept it or not, when we leave these walls, the broader population is more engaged with AI than we might be willing to do at the university. And I think that's part of the responsibility of this group, is to show how it's being utilized within business, around the country. And so it's a bit of a disservice to try to not use it whatsoever, not expose people to it, and give insight into the way it might affect them when they leave Lawrence University.
[00:12:01] Mark: Yeah, I totally agree with that. I think I would just add to Mike's comment that certainly it is becoming more and more important in industry and business. Also, I think that these devices are changing the way in people in which people do research across a variety of fields. So I think, you know, it's not only limited to people who are interested in careers moving forward in the business world. I think it's going to impact many different ways of life.
[00:12:30] Mike: Yeah. And another point on this topic is that: something that's been happening more frequently is that people utilize AI, but are worried about talking about it. And so whether or not the perception is negative, people are using the technology and then still expressing that they don't, which is also a dangerous place to be. And so I think it's better for us to be able to have these conversations and show how it is actually being used and not have to hide those facts.
[00:12:58] Abhishek: Thank you, Mark and Mike, for putting that into like perspective for our listeners. I wanted to circle back on something, Mike, you had initially mentioned that it seems like and correct me if I'm wrong, one of the charges of the task force was to think about whether there needs to be an institutional policy. The reason why I say that because we have had examples of many institutions which have either drafted an all-inclusive AI policy or probably lesser in number, who have banned AI from their campuses. So I was wondering if you could speak a little about whether there has been any discussions within within the task force and what direction are you guys heading towards, if there is any direction about the institutional policy statement?
[00:13:54] Mike: Yeah, I would have to say that any policy that came would have to express the the general community that exists here. And as Mark pointed out right now, the the sentiment is split and there is no one overriding feeling toward AI. And our current policy is just one that every faculty member needs to have a statement in their syllabus about AI, and that could be a red light policy of no AI whatsoever. It could be green light in that students are allowed to use it, or a yellow light in that there are certain guardrails put around the use of it.
[00:14:32] Mike: And so we do have a structure. We're discussing whether there needs to be a larger institutional policy. I am hard pressed to imagine what that might be at this point. And I think that's kind of the sentiment within the group, is that it's going to be really hard to speak for everyone and create that.
[00:14:49] Mark: Yes. I think one thing we're trying to promote is faculty thinking through these issues for their specific classes, what makes sense for them there. And we certainly want to encourage every faculty member to have a statement concerning AI on their syllabus. When it comes to blanket university statements, I at least am a little skeptical of those to some extent. I think, especially if some colleges, for example, the University of Chicago, have tried to issue large blanket bans on AI. I'm not sure how those work.
[00:15:26] Mark: We don't ban students at Lawrence from accessing websites, so it's just difficult to see how you would implement such a policy. I know there are tools that where certain schools are making students log in to certain websites to complete their homework, or using tools that will identify if a- if an assignment has been completed by AI. But there are workarounds for these. Each of those systems has been shown to fail under different conditions. And so it seems like a dangerous precedence to try to ban AI entirely from the university.
[00:16:02] Mark: I think also there's a distinction to be made between using AI to complete assignments, maybe where you're required to use AI to complete assignments, and studying AI as a subject of interest. Within my own area philosophy of mind, the study of artificial intelligence has long been an important aspect of thinking about how human minds work. And certainly the research now is abuzz with people really thinking about these new tools and using these new tools to figure out how they engage in quote unquote, “inference.” So I think we we want to leave open the possibility that people can engage with these tools when it's important for their own research programs in their own areas of study.
[00:16:55] Abhishek: I would definitely say that personally, I'm glad to hear that you guys and the task force are not thinking about banning AI, because I think this is a tool, we need to interact with it more to learn more about it and to use it effectively and ethically.
[00:17:09] Abhishek: I wanted to ask you next about your personal perspectives on a few things. I would start by asking, what what were your personal motivations to serve as the co-directors of this task force? What what made you take this thing on?
[00:17:32] Mike: Yeah, I personally have a deep interest in the effects that artificial intelligence is going to have on, specifically the industries that I'm working within. So music as a creative art, you know, AI, Gen AI has come and positioned itself as a replacement.
[00:17:50] Mike: And I'm curious why it has any legs. I'm curious why people would consider listening to artificially generated music. But the truth and the fact of the matter is that it is gaining legs and that people are absorbing this art, if you want to call it that. And I'm concerned about the future of the industry for that reason, and I'm concerned for our students, I'm concerned for myself and my own work, and I want to understand more about it. And I feel like just putting my head in the sand and trying to ignore it is not going to be an effective move in this case. It's better to have the knowledge to be able to push back and press and find other areas.
[00:18:29] Mike: Within Marketing, it also is impacting our life in every sense, in every way, in every second. And I am very curious about the effects there. It has never been easier to target individuals with materials that are made and crafted directly for them. Artificial intelligence has this promise that at scale, it can create things that are made uniquely for each individual, and that is both promising and that things can be highly individualized, but also scary too, in that that leaves open the door for greater manipulation and greater, more effective marketing activities, which is good and bad.
[00:19:10] Mike: And so I have a lot of curiosity about it. I'm curious about what it means to the future, and to the education, and to the way that we express ourselves and the way that we think. Is this a new tool that's going to change the way that we solve problems, and what does that mean for us as a community as well? So I have so many questions, and that's what has brought me to this position, that I'm just seeking some sort of clarity and some answer in a group of people who are like-minded.
[00:19:38] Mark: Yes. I think for me, my initial interest in AI came from the kind of research perspective. As I mentioned before, the area of philosophy of mind has long thought about artificial-, what artificial systems would suggest about how human thought, human cognition works. I think originally within computer science, the artificial intelligence program was mainly thought to be, the thought was originally that it would be implemented through some kind of symbolic processes. So using explicit rules that allow systems to make decisions to emulate human thought. In the last 25 to 30 years, I think that is really shifted to this machine learning program where we're feeding these systems just tons and tons of raw data and giving them some, some sort of attention algorithms that they can use to sort of train themselves to think. That's very different from how humans learn about the world.
[00:20:46] Mark: Famously, humans need very few examples to learn concepts about the world. These systems need, and thousands of examples to learn something about the world. So how are these kinds of intelligent behaviors-, how are they able to arise from very different processes on the front end? So that was kind of the theoretical questions that I was interested in going into it.
[00:21:12] Mark: Since playing around with these systems as a kind of theoretical target, however, I've discovered how very useful they are at performing lots of tasks and really streamlining my own research and thinking. And, you know, I think if people feel that these systems are useless, they really haven't engaged with them substantially. There's a lot that's really good here that frees up a lot of intellectual space to think more deeply about the problems that I'm really interested in. So it was theory first, and then I've come around more to the practical applications of these systems, and I am hoping to promote both of those ideas through this work on the task force.
[00:21:55] Abhishek: Thank you for sharing your individual perspectives. I also wanted to know as co-directors, where do you think AI is heading in the in the future? I mean, we have seen, I would want to say, significant development from the first time ChatGPT was launched to what what we have right now, and it's making inroads into literally every field, every discipline. So what do you think? What is next in the world of AI?
[00:22:26] Mike: That is the $700 billion question and literal, if not more? I can't predict the future. I truly can't say. And I wish I had an answer. And that's part of the scary proposition for me right now is that, and I think it's where a lot of people are coming from, we're not sure how this technology is going to impact us. I can look at it on the microscopic level and think about Gen AI and the way that it's producing written work and musical work, and the powers continue to grow, and it continues to get better at the things that it's doing. It's more natural language prompting systems.
[00:23:00] Mike: Whereas just a year ago, you were you had this idea that people would need to be specialized in prompting and understand how to work with the systems. But now, the systems, they can infer quite a bit from what you write, and so they don't need nearly as much context. They don't need nearly as much, thought put into the prompt to get exactly what you're trying to get out of it.
[00:23:24] Mike: So I think that's a direction that it will continue to head. It will understand the user better and continue to infer more and more about the way they use language. I can imagine that its ability to create more and varied types of art will also improve. On a larger level, on the the national and international level, I, I cannot predict where AI is headed. We're in a position where it's unregulated and where it's just a race to get somewhere, and I'm not sure we know where the goal is quite yet.
[00:23:55] Mark: Yes, I agree with that as well. Talking first from the angle of how we engage with these systems. So I have taught this workshop at Lawrence for professors who are interested in using AI, and in its first version, we really focused a lot on delimiting different aspects of prompts that you might want to feed these systems that help them run a lot better.
[00:24:21] Mark: I feel like much of that is obsolete now. The systems within the last year have gotten to a point where their understanding of what you're asking when you asked them to perform a task is like the understanding of human assistant would have if you, if you put that task forward to you then. So I think in the current context, AI is looking more like it's approaching something like what Ethan Mollick, the professor of education, refers to as a “Co-Intelligence.”
[00:24:51] Mark: It's almost as though it provides everyone with an assistant who can help them complete tasks surrounding their work, and hopefully free up more opportunities to think critically, to think carefully about the different research that they're performing. I expect that to continue with the development of the tools over the next few years. The agentic aspect of AI is really growing a lot right now.
[00:25:19] Mark: On the sort of global perspective, I don't know where this ends or what ultimately the implications are going to be. It feels like there's a lot of fear within the United States in general about a potential AI arms race involving the United States and China. And I do worry that that kind of fearful stance is causing us to set aside certain safety considerations that we should be thinking a lot more about. And I certainly hope that the US government can engage with more regulation of AI corporations in the years to come.
[00:26:03] Abhishek: Thank you for for sharing with us your your individual perspectives on where AI is heading. I know this is a loaded question, but I just wanted to, you know, see what you guys are thinking. I want to come back to your role as co-directors of the AI at LU Task Force. And I wanted to ask, as in your roles, because you mentioned AI is changing so fast, how do you-, and the task force, I believe, is currently supposed to be there for a year, how do you balance the the the urgency of AI changing this quick and to restrict from moving into any conclusions, coming to any quickfire decisions?
[00:26:54] Mike: Yeah. Mark may have a better ability at doing this than me. As a philosopher, he deals with open ended questions much more than I do. I would love to have a conclusion, but I am doing everything in my power to understand that we are humans, working with a technology unlike anything that we have come across before. Most of the time, humans have had the ability to evolve with the technology that that's placed in front of them.
[00:27:17] Mike: So with computers, we learned how to work with them, with the internet, we learned how to work with it and how to develop alongside it. AI has come at us in a place that we were not ready to to be, and I think we still need to give ourselves the human timeline in this sense. We need to be able to work through the emotions that we are having as we face this technology.
[00:27:37] Mike: And I think that's where I keep falling back to. And that's why I would love to have conversations with our community about this and spend the time having these hard conversations, regardless of the fact that the technology itself is not waiting for us. So my hope is that we can accomplish quite a bit this term or this year. Overall, as long as we show up, as long as we're present and ready to have the talks together.
[00:28:03] Mark: Yes, I agree with that as well. I would just add that while the task force is planned now to run through the 2026-2027 year only, that doesn't mean that it will cease to exist at the end of this year. I think there's likely to be reconsideration of our charge and an extension of the work the task force is doing in some form at Lawrence over the next few years.
[00:28:32] Abhishek: And Mark, since you mentioned about the timeline, I wanted to ask you, are there any plans to have the AI workshop with faculty that you mentioned earlier? Are there any plans to run it this year as well?
[00:28:46] Mark: Yes. We are also planning to run another version of the workshop in the Winter Term. So for any faculty that are listening to this, if you're interested in learning about how to use AI in your courses, how to use AI as a subject of discussion in your courses, I would encourage you to look at the call that will be coming out shortly, and consider applying to our group.
[00:29:12] Abhishek: Thank you. I also wanted to ask something that relates more to our community at Lawrence. Lawrence is a small residential liberal arts college and we have distinct values of liberal arts education. I wanted to know your thoughts about or in what ways do you think AI will affect the distinct values of of a liberal arts education?
[00:29:39] Mike: I think it has the potential to highlight what we are already offering and to show how valuable it truly is. As a residential institution, we have an opportunity for people to come together in physical space and spend time together, having conversations in physical space. And what people are seeing in other places through AI is that people are isolating more and that they are withdrawing and using it as a source of information and no longer relying on their community as much.
[00:30:08] Mike: And I think if we lean into the things that are distinctly Lawrentian, things that are distinctly part of our community already and make them even more part of our values, then we can move forward in a way that most effectively deals with this technology. I stand by the fact that there are there are four types of activities that I don't think we should ever give up to artificial intelligence. Those being: creativity, leadership, judgment and empathy. And those are all things that you find within a community. You can be creative together. You must enact your empathy when you are working with others and feel their their struggles and their trials and their tribulations. And I want for us to come together for those things.
[00:30:52] Mark: Yeah. So the question is, how will I make us reflect on our distinct values as a residential liberal arts institution? And I think it will do so in essentially every way. I think, you know, for one example, when we're presented with a machine that can write informative pieces on anything, it forces us to ask the question, well, what is the point of us writing? What's the point of me, a human being, writing my own work that responds to questions that I have about the world?
[00:31:24] Mark: In other words, why do we write? That's something-, that's a question that has already always been there, but I think that AI puts it in sharp relief, causes us to really focus on it right now. And certainly this proliferates, right? Why do we create? Why do we bother thinking at all? You know, all of these are core values of the liberal arts in some sense, the ability to write clearly, the ability to think clearly, the capacity to create human creations. So I think AI will cause us to really reconsider and think again more deeply about each of those questions. And my hope is that AI will provide us opportunities to engage in more of these essential human aspects of the mind.
[00:32:17] Abhishek: Thank you, Mark. I would like to end with a question for both of you: What would be your message? Or it can be an invitation to to our colleagues at Lawrence.
[00:32:31] Mike: Yeah, my message is an invitation. It's an invitation to have conversation rather than reacting out of the fear that comes from not knowing. Let's come together and understand as much as we can, so that we can have conversations that are based on reality rather than on our feelings as much as we are feeling those.
[00:32:50] Mark: Yeah, I think I'd like to reiterate what Mike said. I think when we were putting together this group, we opened the invitation to all faculty, and we tried to make sure that their voices across the spectrum of people who think of AI more positively and AI people who think of AI more negatively. And I just want to encourage the faculty, the students at large, to engage with us on this work.
[00:33:18] Mark: Even if you feel like you're dead set against AI, you know, you wish Sam Altman had never been born, you want to eliminate all data centers across the United States, there's a place for you in these discussions as well, and we really need to hear from you. So please reach out. Please come to the events that we have planned to discuss AI over the course of this year. Please make your voices known.
[00:33:48] Abhishek: Thank you. Mark and Mike, thank you so much for your time and for sharing your ideas and perspectives. I would want to reiterate that the podcast is also my effort to bring people together, to have discussions, and discussions are probably the best way to head forward with AI. And we welcome all perspectives.
[00:34:12] Abhishek: Thank you to our listeners for tuning in to Minding Machines, full show notes, resources mentioned in this episode, and a complete written transcript will be available on our podcast page. If you have an idea for a future topic, a question you would like us to explore, or if you have suggestions for guests, whether they're educator colleagues or students, please reach out through our feedback form linked in the show notes. Until next time, stay curious and keep prompting thoughtful questions.
[00:34:41] Abhishek: Minding Machines is an independent podcast produced at Lawrence University. The views expressed are those of the participants and do not represent the university. This podcast is also separate from my teaching and research roles at Lawrence University.