The Human Advantage: Why Liberal Education Matters More in the Age of AI
Good afternoon, everyone, and welcome to The Why Liberal Education Matters More in the Age of AI. I'm Lynn Pascarella, President of the American Association of Colleges and Universities. And we're so thrilled to have you with us today. I want to thank Instructure for sponsoring today's webinar. Their generous support allows everyone to have access to this. And I encourage you to be an active participant, to submit your questions through the hour using our Q and A function in Zoom.
So And let's get started. This conversation about AI in higher education has changed remarkably quickly. Not long ago, we were talking about whether students should be allowed to use ChatGPT to write an essay. And then we began talking about how AI might transform teaching and learn AI enabled workplace. Now the questions are much bigger.
What happens when AI can perform a growing number of the cognitive tasks that we've traditionally associated with college level work? What happens when some of the entry level tasks through which young people have learned a profession can now be automated? And are the systems becoming increasingly capable of doing the kind of work we assumed was central to the preparation that we were providing students for work citizenship and life? In the end, it's about what decisions and responsibilities should remain human. Certainly students need AI fluency. They need to understand these technologies, to use them effectively and efficiently, to recognize their limitations and evaluate what they produce. But that's not enough. Students also need the knowledge and judgment to distinguish what is credible from what's merely plausible, to ask good questions and evaluate evidence, to imagine alternatives, to understand the consequences for other people and to take responsibility for their decisions.
None of this is new. These capacities have long been central to liberal education. What AI may be doing is making their importance impossible to ignore. The challenge then is not to choose between technological capabilities and human ones. It's a more fundamental question.
What do we want education to prepare and cultivate in human beings in an age of increasingly capable machines? So that's the question that we're going to be exploring today with three extraordinary panelists. I'm pleased to welcome Rachel Bauser, who is a professor of English and vice president for academic affairs and dean of the college at Agnes Scott College. Peter Felton is the assistant professor for teaching and learning and executive director of the Center for Engaged Learning and professor of history at Elon University. And Ryan Lufkin is vice president of global academic strategy for Instructure. So Rachel, Peter, Ryan, thank you so much for being here.
Rachel, let's begin with you. Does the AI era require us to redefine liberal education or to understand its enduring purposes in a new context? Thank you, Lynn, for having us all here, and thanks to all of you for being here with us today. So I think it's a great way to start a question about sort of the definition or the way that we understand liberal education. And I think that we can probably take as a shared value, right, a statement that you have already made and grounded us in, which is that the skills and the capacities that we so associate with liberal education, the critical thinking and the reading and the problem solving and the collaboration, etcetera, that those are those are enduring because they are, in fact, enduring. Right? So I don't and I don't imagine that any of us would really argue that we need to abandon any of those skills.
I wonder, though, if this is, like so many moments we've had in the last sort of twenty years, an opportunity for us to think about how we talk about the work and how the terms that we use to describe the work predispose people to understand it or value it in the right way. And so, you know, this webinar is framed in terms of, and the question is framed in terms of liberal education, which probably to everyone on this call has the resonance that we wanted to have in terms of civic engagement and the portability of skills outside of the classroom, etcetera, etcetera. There are some branding challenges with that term broadly, obviously. And you know, a thing that I have thought about a lot over the last ten years is the way liberal arts as a term has become a shorthand for humanities, which is always sort of frustrating the way that the central nature of the STEM fields often get written out of that term whenever folks are wanting to be dismissive of liberal arts. And so when I think about those terms, I think about, in particular, having said humanities, when I think about phrases that we don't as often use anymore when we describe the work in this context, things like humanistic education and humanistic pedagogies, humanistic development.
And so I think then about what is the case that we want to emphasize in terms of what is enduring about this work and how it positions people better. And I I do think we have some language already around humanistic inquiry, around built environments that we deliver it in in so many of our spaces that are designed around human engagement and interaction. And frankly, I think, you know, I'm an English teacher so I'm a little biased in this way, but I think it's time for the humanities to say the humanities have a very unique role to play in this engagement because of the human centeredness being central in the conversation in a way that maybe it hasn't been with a different level of urgency, but at any rate. Thank you so much. So Ryan, building on what Rachel has said in terms of the critical thinking skills around humanistic identification, moral imagination, long serving as a foundation for preparing students for work citizenship and life.
What's the strongest case that we can make for that enduring vision? And does the age of AI require us to rethink it, or does it make its underlying purposes more urgent than ever? I I think that the latter. Right? The this is not new, as I think you and Rachel both pointed out. This conversation has been going on for some time. What's interesting is, you know, a few years ago, the prognosticators were saying the the computer science degree was the the, you know, hottest degree, and then it was prompt literate you know, prompt writing, prompt engineering. And now more recently, it's philosophy.
Right? Specifically because those those skills those those more ethereal skills, to train these tools and think about how we're gonna use these tools, ethically and efficiently, are are incredibly important. I I also think the industry kind of shapes this. Right? A few years ago, we saw a lot of headlines around, tech companies removing the requirement for a degree, for hiring. Right? And then over the course of several years, those were added back in. And and what they found was many of the engineers they were hiring without the the more liberal arts degree, didn't have some of the skills that you mentioned, the the basic communication skills, the collaboration, the empathy.
Right? Those those skills that are core to who we are as humans and and core to what we teach in in the humanities programs, sometimes, are are necessary for the workplace. And they're not always I think I think Rachel made a really great point. We don't always call them out. And, unfortunately, the the degree that they're kind of lumped into that degree and assume that those are taught in that degree program, and in many cases there. But we have an opportunity to move towards that more skill based, outlining of of, how we talk about not just the, you know, a lot of people when you start talking about skills programs, they say, you know, that that's that's more of a vocational aspect.
It's not. Right? We can we can quantify those skills. The the soft skills, as they're sometimes called, those human skills as we're talking about here, And we can do a better job of articulating students' preparedness with those skills for the workplace. Thank you. Peter, does ready access to AI generated information diminish the importance of disciplinary knowledge or make deep knowledge even more essential to judgment, interpretation? Yeah, thanks for the question, Lynn.
You froze for a second for me, but I think I get the question. And I'm going to pick up right where Rachel and Ryan left off saying, you know, aren't new questions, like what's the role of knowledge in higher education and liberal education? And when the Internet came around, it really accelerated things. As a historian, people started saying like, why do I need to know anything? Because I can just Google it. And if you read about AI in higher education right now or AI in work, there's the phrases come up all the time. The end of knowledge, the collapse of knowledge, and things like this.
In fact, I was thinking about this yesterday, and I asked Claude to summarize for me disciplinary knowledge, AI in the future of higher education based on what's out there right now. And Claude wrote this in part, quote, Mastering a discipline's body of knowledge used to be the goal. Now it's increasingly the thing you use to catch AI when it's wrong. And I just wanna point out that that comment, and I think a lot of the AI and education discourse implies what Paulo Freire called a banking knowledge of, or banking conception of education, that knowledge is something a student acquires and possesses, like money or some other commodity. And that's not really a helpful way to think about education.
I'm going talk about that more in a second. Just one quick sideline because I was just maybe bashing AI companies and technology writers a little bit. I think a we in higher ed have contributed to this problem too. And there's smart people writing about how we flattened knowledge in how we talk with students about things. So sometimes we imply or even state that it doesn't matter what you major in, you're going get the same skills no matter what you do at my university.
It's like, as if skills aren't tied to discipline or knowledge. John Warner, I think, has some really smart things to say about this in regards to writing in his recent book, More Than Words, where he says what students need is both writing proficiency and deep knowledge. Right? Because these things are inseparable. And I want to point people towards a new book. It came out a couple months ago.
Scott the lead author is a guy named Paul Ashwin, who's a higher ed scholar in The UK, and it's a really sophisticated study of students at six universities, US, UK, South Africa, for seven years tracking these students from when they matriculate to after they're in the workforce. And what Ashwin and friends say, demonstrate is the primary way students develop skills like writing, critical thinking, communication is by getting inside structured bodies of knowledge. You need to think like a chemist or like a chemical engineer or something like that to be able to develop these skills. And so their point is that it's not just about what students know, but how engaging with knowledge changes who the student is and how they're able to act in the world. So in the end, I think we sometimes mistake knowledge as an end for knowledge also as a means to the transformation we're seeking in education.
Thank you. That's fantastic. Rachel, I love the fact that Ryan gave a shout out to philosophers. I I did a paper a few years ago called, the ketchup's in the bag and the check is in the mail. It was a defense of humanities, but, you know, signified on the old trope of philosophers as asking, would you like fries with that? Now we're in great demand.
So this is all good. But I'm wondering what other fields, within the liberal arts might have distinctive contributions in this age of AI in terms of the different ways of knowing, creating, judging, and instruction on how humans should understand and use the technologies that we have available to us. So, I'm with you and Ryan on philosophy for sure. Not least of all, because I have a daughter who is a junior at a liberal arts college and is studying philosophy. And so I really hope that this moment, that everyone is right about philosophy having a moment, and she's made the right choice.
So that I think, and I also think, and this is obviously a biased response based on my own training, but I do really think English, English, English, English. Like, this is the moment for the English departments on campus to be leading the analysis. These are large language learning models. Like, our work is right there embedded in the name for the thing, in the shorthand, right? We are being called into doing this work, and I think, you know, a lot of us, as you've noted at the beginning, in the sort of early phases of institutional responses to this, it was English departments that kind of carried the burden of helping people to think about how are we going to mitigate the erosion of academic integrity in this context, right, and and how do we still teach students to write, etcetera. So I think we were on the forefront of that space, and now I think we're on the forefront of the analysis space.
You know, I the output there are a lot of ways that we can talk about people using agentic AI to manage their workflows and things that are important when we think about the future of work in lots of sector specific ways. It is also the case that for our students, for the majority of our undergraduates, what's happening with their engagement with AI is a pathway to knowledge, maybe an extraction of things structured bodies of knowledge, to Peter's point, right, and summaries of texts and solutions to problems, etc. Output of those LLMs is text, and it is text that does not live in a vacuum, it is not created in a vacuum, it is an amalgam of representational biases and persuasive techniques and rhetorical positioning, this is where the English departments can really ask students to look at the output in a way that I think brings to bear what all these decades of rhetorical analysis and work and representational analysis have brought us. It is not, this feels important to me, I think that we do spend a lot of time asking students to think about how their education positions them to assess whether an AI output is credible or is it merely plausible or is it outright fake, and how only our students who go to have a certain kind of education or position to know when the AI output is wrong, etc. That's valuable.
That accuracy testing is valuable. And when they find that it is wrong, it does not mean that they ought, in my view, sort of discard the utility of that output as a piece of knowledge or as an object of learning. In my courses, I tend to try to think about this. I ask the students to think about their high school English classes when they were, taught about unreliable narration and how unreliable narration was understood to be a feature of a novel and not a bug. You didn't throw Gatsby away because Nick Caraway is an unreliable narrator, but it becomes an integral part of how you understand what the work is and what the text is trying to say.
And so what if we bring to bear that same kind of framework to AI outputs? Like, okay, it's wrong, or okay, it's always already unreliable. It's unreliable in what ways in response to what things, and what does that tell you about the training paradigm that produced it or about the users that are engaging it. Right? I think, this is the the English teachers of the world have taught some lesson plans that I think can be, with a couple tweaks here and there, pivoted and brought to bear here pretty productively. Thank you. Ryan, I'm gonna turn to some of the questions that we're getting through our Q and A.
The first is how can we get our admissions staff and administrators to hear that practical degrees are becoming less desirable. They're still selling degrees like accounting, data science, and others, and and many institutions are cutting the humanities, and faculty lines rather than supporting general education requirements. So how do we make the case? Yeah. I mean, I think it's interesting because we're we are I like to remind people that we're four years into this evolution. Right? November 30 will mark the fourth the four year anniversary of of the launch of of ChetchiPT by OpenAI, which really kicked all of this off.
And we spent the first year really talking about, banning you know, it seems so long ago. Many of us don't even remember. First year, we banned, OpenAI or the AI tools, language model tools on campus for the most part. The second year was very focused on academic integrity and how do we how do we look at, identifying cheaters, with finger quotes. And then we over the last year and almost now two years, we've gotten to a more productive phase where we're signing enterprise wide deals with, you know, one of the big players, to offer, these tools for, most of our faculty members, and in some cases, students.
But we haven't really moved into that that next phase where we start digging into these individual tools. And I think the programs have been slow to evolve with that. Right? Our perceptions have been slowed because of that. And so so to the question in the chat by the way, can't wait to review the the I'm watching some of these pop up, and I can't wait to actually dig deeper into the the chat transcripts. I think they're incredibly interesting questions.
It's it's we're in the middle of this massive transformation, and it's very hard to, you know, predict where we're gonna come out on the other end. And so, any major change where you're throwing out programs on either end of the spectrum is probably not a great idea at this point. Right? Actually, looking at what, the outcomes might be, There's some really great data on what jobs are going to be replaced, by AI directly from Anthropic, and it's a very interesting graph. I've shared it on my LinkedIn if if you wanna go find it, but it's it's out there. And it's really interesting to actually look at their prediction for which job fields are gonna be impacted and which fields are actually already being impacted based on the data that they have.
Interesting prediction. But we have to actually take that into, the broader context. We're seeing a lot of AI washing in headlines right now. Right? I was in Australia, and the the, head of, AI for Microsoft came out and said, you know, all, white collar jobs or 90% of white collar jobs are gonna be automated within the next eighteen months. And I had a couple 100 people in front me.
Said, is your job gonna go away in the next year and a half? And they laughed. And I said, those headlines are really great for bolstering the valuation of AI companies. They're very great at manipulating the markets. They instill fear in our students. They fee they they establish uncertainty in our programs.
And so to me, I you know, that's kind of a long winded answer to say, I think we need to take a breath and not jump at shadows right now. We need to actually watch this transformation come through before we make those decisions on, entire programs being eliminated or or or being, you know, less focused on. Thanks. Peter, another question from our audience. A lot of people talk about how science is valued over the humanities, how the humanities are particularly useful now.
But what about the role of social sciences? What role do they play in this emerging context? Well, I love the question. I'm a historian, so I don't know whether that counts as a social scientist where you are or not. But I wanna I wanna point to to two things, I guess, on this. One is if you read social science, and I'll mention someone, Jill Vapore, brilliant scholar, historian at Harvard. She says one of the things that's fascinating about the way we talk about AI right now and the way AI companies talk about themselves is their history lists.
They are trying to not let us recognize where they come from, because almost all of them come under the defense and intelligence industries. And they don't want that to be seen as their origin story. They want to be these. You can consider those industries however you'd like. So I think it's really important.
Jill has a great book called If Then about and they came back in the book was written before AI, right before AI. But it's a really interesting book about social the the attempt in the 1950s, computers to solve social problems by sort of bad social science merging with technology. So one thing we should do is study history. The other thing I want to, I want to give a shout out to someone who does social science and thinking about something we haven't talked much about yet, which is the role of civic education and sort of community learning in higher education, because that's a crucial part of liberal education. And I I don't know about you.
I'm fairly depressed about where we are civically and culturally right now. And I don't think AI generally is helping us with that yet. Late last spring, I saw a talk by this brilliant scholar. I never encountered her before. Her name is Beth Simone Novak.
I'll put a link to her. She's got a brand new book came out in July. She's a scholar at Northeastern. The book is called AI and the Race to Save Democracy. And she starts by capturing lots of the fears I have, maybe you have, but then she makes the case that we should use AI to quote reinforce our democracy, mend the tears, and strengthen the bonds.
And her book is just full of examples from all over the world about how AI can support participatory democracy, can support effective governance, can help people build meaningful communities despite or across differences. Right. So she's not writing about higher education. She's writing about governance and social social science in action. But I think her work could be really inspiring to a lot of what we do.
Because, you know, one of the things I think about a lot, and thanks to ACU and the work you all have done is, you know, community based learning is a high impact practice. It's amazing. I use it in my own teaching. It's really, really hard to scale. There are too many students.
I'm too constrained of needs, you know, developmentally and if it's anyway, it's time and all this sort of stuff. So what do we do about that? Well, maybe one thing we can do is use or develop some really sophisticated AI tools to help students ourselves practice difficult things like developing empathy, developing some deeper awareness of their own assumptions and biases, practicing skills and all this, not to replace community based work, but to prepare students so that they're really ready when they are doing community based work. And then after they do that work, could we have some AI tools that complement humans to reflect on what students are learning and help them make meaning or help them process that? So that we're we're not putting as much stress on our communities by asking them to help us educate our students. But we're still getting the benefits and our students are getting the benefits of that community based work. That's such a great point in terms of equitable access to AI and high impact practices.
Thank you. Rachel, building on something Peter has said, it has to do with fears. What are the most common anxieties you hear on campus about AI from students, from faculty, from the administration? So, you know, I think there is some of the fear about workplace transformation that Ryan mentioned and that I think, you know, those of us who are trying to take a sober look at things think is maybe a bit overblown, the immediacy of that paradigmatic shift. But I do think there is a worry there. I think on the part of my teams, when I think about it more, we do it's harder to ensure that we know, as people whose work is inside this institution, that we know what is happening in the sectors that we're sending students to.
It's a little bit more urgent that we sort of are making contact and going out, etcetera. And so I think there's a little bit more burden on the preparedness of your coaching teams on staff, of your career teams, etcetera. I think from students, what I hear is some of that, but that kind of gets swept up in the overall, like, they're living in a very destabilized moment, destabilized economy. But, you know, I don't think it's one thing. I think it's all the things.
Right? I've been struck by a number of student conversations here about the way their fear about the way AI sort of destabilizes the kind of meritocratic approach that they hope that they can count on from their college experience. Right? They don't want to use it to shortcut their learning. I mean, really, enormous majority of the students that we have on campus will talk very earnestly and very candidly about how they don't wanna rely on AI. And they don't they have a whole host of ethical concerns about it in terms of labor displacement, environmental impact, data security, etcetera, that they they have strongly held opinions about. But they also don't wanna use it for reasons that they think are bad for their that are detrimental to their learning.
They they are keenly aware of that. And, also, they're quite worried that if their peers are using it and if their other students are using it and if somebody if everybody is running their cover letters through AI and generating them that way to begin with, and everybody's cover letters, sooner or later, sound exactly like McKinsey documents. How am I gonna fare when mine doesn't sound that way? And so I and I think that's a really fair worry when we're telling them to be really guarded about this technology and they wonder about the extent to which that guardedness disadvantages them in a less discerning audience, right, who who where they're not gonna get a chance to say, oh, of course, this isn't quite as polished, but I worked on it myself, right? And I didn't get any help. So I worry about that for them because I don't think, you know, nobody's ever really promised them a clean meritocracy here, don't think, and I think our students are all sort of smart enough here in 2026 to know all the ways that things are unfair. But I do think this is a kind of urgent question we're asking them to ask about holding themselves accountable.
And the thing about that is that you can hold yourself accountable, but you still have to be in the same pool as a whole bunch of other people who aren't. And so I find that that comes up often with our student body. Yeah, it's interesting. We're hearing the same concerns from faculty who are worried about being assessed against people who are frequently using AI to produce books and peer reviewed articles, but they're not. So does that put them at a disadvantage and what do you do? That kind of moral dilemma.
Ryan, we have a question here from a participant who wonders, despite the support that we all have for liberal education, are employers really valuing that or are they continuing hire people from the world of business or those insights into that? Yeah. I mean, I think the the one of the biggest disconnects we have right now is that many programs aren't really modeling, for students how they will use AI in the workplace. Right? So these students, one of the first questions they're asked when they go out is, how are you using AI to make yourself more efficient? Right? And if they've been you know, to Rachel's point, if they've been in programs where they've they've said, I'm not going to use it because I feel like that's detrimental or, you know, they've been using it heavily, but they're not really using it in the way that they, would apply it to to work or their their intended career, that's not really helpful for them. They're not we're not equipping them with those answers. And so I I I feel like, especially in, programs that have, you know, more of a connection to, you know, the the the workplace.
Right? We we're seeing more and more of that kind of, education industry partnerships where they're saying, look. We're gonna try to figure out an experiential pathway to make sure students have those skills and are prepared with how they're going to apply those in the workplace. I think we've seen an explosion of that because, we've also seen that skills gap widen. Right? You know, the the entry level jobs that many of us did as we entered into the career are automated by AI. They were they were they were simple jobs to to test your skills, test your metal, see if you'd show up every day, and do those basic things.
And then it was those human skills that you're talking about, those the connections they were able to able to make, the culture that they were able to contribute to, the communication. Those were what got them the job, beyond that. And so to me, it's not necessarily our employees still valuing them. Our the question is, are are we providing opportunities for students to demonstrate those skills, right, and and let themselves be picked for that, right? We shouldn't just be using those those, you know, the different tests that they they do that can be gained by AI or writing samples or things like that. Those are not the metric that employers should be using.
They should be finding ways to truly engage and measure these skills, that we're talking about today. And that's that's a challenge, you know, when you're you're hiring remotely, you're hiring, employees that that may work from home. I, you know, I saw a really interesting interview process where the company had shipped out a mask, a mirrored mask, so that when they were on the call, the student couldn't have, an additional screen up or a phone up. And I was like, that seems like you're trying to measure the wrong thing if it is it's so easily gamed. Right? And so we've gotta find new ways.
We've gotta evolve how we're thinking about not just these kind of strange stop gaps or try to try to, shoehorn the existing processes into this new world, we've got to fundamentally rethink these processes and how we're measuring this stuff. Thank you. Well, since 2007, AAC and U has been doing employer surveys and we can confirm that employers do indeed and hiring managers look for skills in students graduates that are the ones that we've outlined. And so check it out. Our latest one was done two years ago, the Engility Imperative.
And it's not only a reinforcement of critical thinking, creativity, but it's also speaking across differences. It is that moral imagination, narrative imagination component, and developing not only the skills, but the mindsets and dispositions necessary to thrive not only as you enter the workforce, but as you progress through it. That's the biggest challenge that employers point to. Peter, this next question seems perfectly suited to you given your work on relationship rich education. How can technology instructors and STEM professors more broadly encourage students to focus on human skills and what does that pedagogy look like? Yeah, well, I get to that.
I just wanna say one yes and to Ryan's thing, which is I think in liberal education, we also need to be preparing students to answer that question about how are you using AI in ways that critique efficiency and then demonstrate that they have discernment and judgment. Right? So it's like, it's not just I use it for everything. I make intentional informed choices about how to use this. And I would hope that actually distinguishes those people from someone who could just say, here's 62 tools I use. Yeah.
I mean, they should be asking questions like, you know, tell us an instance where you were skeptical of the output of an AI tool, you know, and and we're able to to provide a better, a better answer. Right? That those are we've gotta create those opportunities. I I like what you're saying there. Yeah. And and then to this issue of relationships and STEM and other fields.
I mean, first, I would say the research in general is just so clear that students learn more, they persist more, their well-being is higher when they have strong relationships with faculty, staff and peers. That just is. And if you look at the research in STEM specifically, much all of the STEM reform work around active learning and equity belonging, this is all about cultivating relationships at its root so that students are closely connected to their peers as they're working together on hard problems. Students feel connected to and valued by their professors. Now that doesn't mean this work has to be one on one.
Right? In fact, if we think about relational work as requiring one on one, we're gonna lose. Right? But if we say students are infinitely scalable, and so how do we help to build educationally purposeful relationships? How do we help students build educationally purposeful relationships with their peers That helps them learn, helps their well-being. And then the other thing I hope I think that we need to claim more of in higher education is that prepares them for work and life after they graduate. And you can think about, you know, Allison Pugh's book from 2024, the last human job that says fundamentally, work is going to be about human connections and rooted in human connections. But if you don't even want to go there, I think there's just this remarkable book that everybody in higher ed should be talking about from 2023.
It's called The Good Life. It's a summary of the the current state of the Harvard project on American lifespan or something like this, which has been going since 1938. And what that book says is if you want to do one thing to be healthy and happy in your life, you should cultivate warm relationships. I said that is the science based, social science based thing that we can do. So if we're saying we're preparing students to work, we're saying we're preparing students for civic life, we're preparing students to thrive.
Relationships are going to help them in college and the skills, habits, capacities, values that they develop to build those relationships are going to help them thrive after college. So we need to, I think. Thank you for coming to my TED talk. But, we really need to center this relational work in all the disciplines, not simply in some. Thank you.
I love that book, Peter. One of my favorite passages from it is the data that show that those people who are on a train and say they would be happier if they didn't talk to the person next to them. There are overwhelming majorities and yet they find that when they do, they're much happier. So we have our own predispositions about what makes us happy and then those are thwarted by the realities of the experiences and that human connection is what makes the difference. Rachel, there's been a lot of discussion about this being a moment of existential crisis for higher education and our democracy.
Is this moment really different, do you think? So I do think probably it's different here, but I think that in the humanities, I think we are in liberal arts education and in higher education, we're not we're not strangers to a crisis, and we are, you know, sort of on a repeating cycle of defending the the humanities and the end of, you know, the the investment in these fields and the arts, etcetera. You know, I, like everybody else, passed around that piece from the Atlantic last month about the age of reading being over. And so there are some familiar tropes around it. Right? And often, the crises that come at us, sometimes they're they're sort of mechanical or industrial in nature and new technology. So a lot of the time they're political, we're weathering political attacks.
Here's what I think is different about this moment is think Peter talked helpfully a little bit ago about the extent to which we can be optimistic and proactively positive about the way that AI technology can, for example, expand participation in civic democracy. I think there are real positives there in terms of access. I think that we have talked in this call about so many of the declarations that are overblown, but we can see why we get there around this era have been that the age of knowledge is over. All knowledge is equally available right now. And I do like that point about flattening the disciplines and the disservice we've done in that.
Think the thing about those pieces that makes this moment different is that to the extent that we are willing to admit it or not, this sector has really organized itself around gatekeeping and still understands itself to be hierarchized in terms of selectivity and around limiting access. There are no one person could change it, and it's no one institution could change it. And there are lots of reasons that drive it. But it continues to be the case that the fewer your percentage, the smaller your selectivity is, the higher your selectivity is, depending on how you frame that, the better product you're construed to be providing. And this is a tool that is fundamentally everything positive that can happen with it is about access expansion and about expanding people's access to information, to processes, to time, to relationships, to all kinds of things.
It is paradigmatically different, and I think for us to get through it, we really have to interrogate how much of what we value and how much we assess ourselves. It's based on gatekeeping. Think it is really a paradigm shift for how institutions like us need to think. Thanks so much, Ryan. This is a related question.
It really builds on that. So one of our participants asks, how have the esoteric and inherently classist aspects of higher education led to this perceived existential crisis. And when it comes to higher education, reconciling what's happening with AI, how much of the issues are a direct result of our own historical development? Yeah. I mean, I I loved Rachel's point. I'm gonna I'm gonna go back to that a little bit.
If you go back to something like three forty eight BC, three seventy BC, something like that, Socrates actually criticized writing as as the end of of critical thinking. Right? So we've been debating this for twenty five hundred years, and somehow it has not become, like, the existential crisis that we always think it's going to be. It's it's constant evolution. And I and I think that, this is a this is a a major shift, you know, to Peter's point about history. I I I'm a I consider myself a novice historian as well, but I love finding those patterns in history.
And when you look at, you know, the age of the printing press and you look at the age of the Internet, and, like, we've been through this before. The calculator is the so many things have been, kind of held up as the death of critical thinking. And instead of we kind of wrap ourselves around that instead of evolving to support that. And and, you know, to this is a great question. I've got a feels like a potential minefield.
Gotta be a little careful talking about, but the the the classes aspect of education, you know, when people talk about over the last, you know, prior to the last two years, we had over a decade of declining enrollment in higher education that peaked in about And a lot of people consider that the, you know, we've heard about the enrollment cliff. The enrollment cliff isn't real. It was a democratic dip that is not presenting itself as reality. And we we tend to find these team things to catastrophize, right? And and if we can actually stop doing that and look at the the the possibilities, we do we do need to look at things like, access to the models, right, being not not widening an accessibility gap. Right? We can't have third world countries that are using sure.
They've got AI, but they're using outdated models that are substantially more likely to hallucinate or provide confidently incorrect answers while, you know, you know, top tier universities are using the most advanced models that are are seamless. Right? Those are the more concerning aspects of this. And so how do we how do we make sure that we, to some level, democratize access to these tools and ensure access for everyone to the same the same quality of tools. That's very difficult when you've got, industry partnerships with individual institutions that are that are learning to experiment with the the most advanced models. Right? And and not that, you know, University of Florida and their partnership with NVIDIA sets them up to be truly elitist, but they they do have access to, tools that that other institutions don't.
And so I to me, it's it's it's about ensuring that we maintain some level of of equitability when it comes to these tools. I think, one of the biggest challenges of kind of the skills based hiring that we or the skills based, you know, demon demonstration of learning that we were talking about earlier is that it you will never get Harvard to agree that their English 101 course is equivalent to, California Community College's, you know, English one zero one. And that's the the kind of bias. We will never have a unified skills taxonomy that everybody says, yeah, we all agree that this is this is equal. That's just never gonna happen.
The best thing that we can do is actually try to move towards some semblance of those basic underlying skills, some some, agreement to what those definitions look like so that employers have confidence that the you know, that that translates into to real, productivity so that universities can have confidence that they that's created a foundation for learning that they you can build, you know, additional learning on top of. And so it it is a it's an incredibly transformational time. The one other thing too I do wanna point out to with as we were looking at the the obviously, these chats are popping up. One of the things I find really interesting is this idea that, if my if my educator is using AI to grade my work, then somehow that that devalues the work. And the it ignores the idea that this technology is actually being trained on the educator's work, on the educator's rubric, and it's doing a first pass with human in the loop, access if designed correctly.
And it also ignores the potential for eliminating, grading fatigue, grading bias, a number of those things that we know impact educators as they as they do that work. And the fact that there are times when educators just can't give you timely feedback before your next assignment that can help shape that assignment, I would rather have the feedback, you know, even if it's, been it'd been, AI, assisted, because it should never just be AI generated. I would rather have that feedback in a timely, timely, you know, time frame, so that it could affect my future work than have no feedback. And and I think that's what we get kind of confused about. Was at Clark County School District in Nevada, and they were saying, Give me AI first pass grading today because I've got too many students.
I can't give them feedback fast enough. But then when within the university space, they're saying, no. That somehow diminishes my my, passing of knowledge to you. And I I think we've gotta get over that kind of strange bias that exists there. I remember a student in a large intro class saying once, I get frustrated when you write so many comments on my paper.
Well, don't know if it would worse now. Peter, this is an interesting question. I think about this a lot. AI can generate responses that sound supportive, understanding, and even compassionate, but it doesn't experience human emotions. As students increasingly interact with AI, how do we help them understand the difference between simulated empathy and genuine human empathy and what role that human connection can continue to play in education along with the tools? What a great question.
And do you have any easier questions, Lynn? Now and and I'm resisting the temptation to get into a longer conversation with Ryan about AI and grading because I actually think this is one of the in some ways, this is one of the issues. I think it's one thing when the AI says, Peter, this is good work. And it's another thing when a teacher I trust says, Peter, this is good work. Now the thing is, we've got to have institutions where teachers can build those kinds of relationships with their students. Because if teachers are teaching huge numbers of students in really anonymous ways, then saying AI is so much worse than a human, that's that's not real.
Right? But that's a structural problem more than an AI problem. I think a couple of things in response to this really good question, Lynn. One is we have to be really, really transparent about our AI use as faculty. You know, if AI is if we're having AI assisted human grading, to use Ryan's phrase, we need students need to know that. The students need to know how that's working and why we're doing it.
Right? Because I think we're if we're misleading students about who's giving them feedback and how the feedback is being done, I think that's really dangerous, both because students may not Maybe they'll trust the feedback, maybe they won't, but they're not gonna trust us anymore because they thought it was coming from us. So that's thing one. Thing two, I think I I believe, actually, this is like a fundamental challenge with AI that we need to be helping students navigate, which is AI is super powerful and AI is super disempowering at the same time. Right? That it can help us do things, and it can also contribute to us being passive receivers and passive users of knowledge. And and, you know, we're talking about the liberal arts.
That's what liberal arts is about is these kinds of paradoxes that I don't know that you can completely resolve. But what we need to do is be transparent about that. So help students discern when and how is this feedback helping me learn. When and how is this feedback not? Or when and how is AI supporting my capacity development? And when is it hindering that? And we can make all the rules we want about this and have all the policies we want. But fundamentally, we have students for a short period of time, and they've got a long life ahead of them.
And so we've got to be developing the capacities in them to make those discernments for themselves. And I actually think this is one of the fundamental challenges of liberal education in the time of AI is to develop to develop that discernment in students. And some of them will choose to use AI in different ways. But we can't develop it just by creating virtual worlds. We gotta develop it by having them be part of the process of thinking about when and how to use AI, when and how not, what sense to make of the feedback AI is giving.
Let me ask you a follow-up question, and this is from someone who wonders about how those teaching philosophy can apply AI and lead practically. Well, I want to be clear. I don't think we all have to use AI everything we do. Right. So I think we should be discerning to.
I do think there are ways where we can use any tool with the limitations, awareness of the limitations and taking advantage of the capacity. So I think in philosophy, for example, there are possibilities. I don't teach philosophy, although I studied it as an undergraduate double major, yay me, on the philosophy. But I think there's ways that you could have students with maybe well trained bots having certain discussions or being prompted to think in different or to question their own assumptions to come up with alternative explanations and things like this. But one of the things I want to emphasize here is a my sense is philosophy, and like many of our disciplines, is very process oriented.
And one of the mistakes we've made in higher education is we focus so much on products. So in my own disappointment, it's about the paper students can rather than thinking that goes on to create those papers. And we've got to help put the focus back on process. So then the question is, can AI help students do the some of the processes of thinking like a philosopher in really critical ways rather than can AI help or hinder a student producing a proxy for thinking like a paper? That's excellent. Yes.
I mean, I can think of all sorts of ways in in teaching philosophy since I was teaching medical ethics and philosophy of law in addition to metaphysics, how that AI could help generate the kind of problem based learning when I was teaching in medical schools. In talking about the Lindsey Clancy case, say, bring up the rulings in the Yoder case or the Susan Smith case, how are they different? So you ask students to engage in the material, but you have the material at your hands much more readily than in the past. Here's a question about assessment and general education. What does assessment mean? How do you best assess work at both the graduate and undergraduate level in this current environment? Rachel? I mean, I think that Peter is exactly right about process. And so we have to be able to think about learning outcomes that involve not a demonstration of not produce x or demonstrate x mastery, right, but show facility at an iterative, etcetera, etcetera.
Right? I I do think that we have to try to think about what's the workflow, what's the process, and not what's the proof of that. In some ways, it's what's the patients and how do we see the patients for engaging through a process. And think that the human piece, again, critical thinking, I think we have all of us at our institutions decades worth of learning outcomes that are kind of wrapped around the concepts of critical thinking, critical reading, collaborative work, etcetera. We might need to really interrogate those terms to sort of say, how are those demonstrated in human centered ways? In a human environment or in a human centered environment, what might the demonstration be of critical thinking or problem solving, right? I think back to something Ryan was saying earlier, it is the case that an employer may say, right, how have you used AI to become more efficient? Right? I think the thing and I think it's right that not necessarily a great question, etcetera, but the thing that I would want our students to understand is that that is essentially a question of how have you solved a problem about your own ability as a learner? How have you solved? How have you made yourself a more effective participant in your own life? Like, what has been the creative problem solving that you have deployed to do something in a way that you more like doing it? Right? They can think about those as problem solving questions and not, I think to Peter's point, efficiency questions. Right? We don't we can help them think beyond that particular fetish and move towards the idea that you're being asked to think creatively about what problems face you and how these tools can help you.
And, you know, my philosophy department right now is putting on a lecture series about AI and human flourishing. And as, you know, all of the the the talks are oriented towards the idea that there are ways to solve problems with this technology that are not about productivity, but are instead about pathways to happiness and pathways to flourishing. And what would it mean to sort of ask those questions persistently instead of only the productivity questions persistently? Thank you. Ryan, we touched on this a bit earlier, but this interesting question. With all of this talk about AI in the classroom, what's being done about students' right to refusal, that's that are continuing to surface these days? Yeah.
I mean, I think Rachel talked about this a little bit where, you know, how do you if I choose to refuse to use these tools, how does that position me against my fellow students who are using these tools? Right? And how do we you know? It I I actually had a a coworker compare it to the the the Olympics versus the unlimited Olympics or whatever. They're really like, you know, use whatever, you know, drugs or or prosthetics make you faster and stronger. Go with that. You know? And then you've got the Olympics where we really keep it to human capabilities. Right? We we're not in a world where we can do that, and so we need to make sure that we are being consistent with how we apply these rules across, you know, across the thing.
There were it was really interesting, though. I think we're we're to to Rachel's point as well around assessment, it's really interesting because I do think we're seeing a backlash. There were some stats on, how much something like 80% of the posts on LinkedIn are AI slop. Right? They're just AI generated content. And and I think Robin in the chat posted something about her communication students are actually offended by some of the content that's put out by these companies that are using AI, tools to, you know, create this generic garbage.
And I spent some time in advertising. I think the same thing's happening. This pushback on AI generated images, AI generated content. I I think even within the the grading or assessment space, we're starting to recognize these patterns, and we're starting to see what what is AI generated and what's not. There was another comment about, somebody that said, you know, in grading work, I'm I'm scared to praise, some assignments that are too good because they might be AI.
Right? That that's unfortunate. Right? I've heard students I've talked to students that said, dumbed down my papers because I don't want it to sound too good or it might be flagged as AI. Right? So, I think if we're in this, like, tumultuous middle middle ground where we don't have the solid answers for that, I think we will see more clarity as we get coming out the end, but but we have to work together. And there's been a lot of great comments in the in the chat that were basically like, love this conversation. We need to have more of these conversations.
And this is how we start building some consensus around how we come out the other side in a in a coherent way. And, yeah, I I don't know that we have a lot of solid answers at this point, for this, but but we'll get there. Yes. Then the questions are sometimes just as important as the answers. Yeah.
I wanna give you an opportunity to close with any comments you think it's important for folks to hear. Peter, what what advice do you have? Well, just speaking right back to what Ryan said, which I think is so helpful, is we need to find ways to bring joy and pleasure and excitement and curiosity into our classrooms. And and I actually think students bring that with them, and then we often iron it out. Because to this point, Ryan, you're talking about a student saying, I'm trying not to do work that I care about well because I don't want it to be predicted, or a faculty member saying, I don't wanna be excited about and proud of my students' work because maybe I'm being misled. That that's, like, damaging our hearts.
It's damaging our educational tasks. Because none of us went into higher education because it was the most efficient thing to do. Right? We went into it because we love our disciplines. Think they help solve problems. They help us understand the world or ourselves.
And so how do we center that? And then can AI help us find more joy in what we're doing in the classroom? Maybe. I don't know. But let's try to center not efficiency and time the way the AI companies want us to, but center the human work and the human emotion of that work. Thank you. Rachel? I didn't prove on that.
I think I I second. More joy, and more more poetry. I think those are probably the two best approaches to this moment and to our in these politics and these classrooms. More joy, more poems. Can't argue with that, Ryan.
No. I mean, honestly, I don't I don't know how you build on that. I just think that is such a joyous, so core to the human experience, and it's intrinsic in everything that that, you know, we we can't lose that because of of, you know, any sort of technology. I think that is that is core to who we are. And I love Rich's point about poetry as well.
You know, it is one of those things where, yeah, yeah, can write a haiku, but it you know, I hate when poets say, you know, there's no value in what I do anymore. There absolutely is. There's we're seeing I think we'll come full circle, and we'll see, you know, the the attachment, the value of the human the human production, the human value, the human talent is is resurfacing. It's part of this cycle. Thank you so much for sharing your insights.
To all the participants for joining us, we hope to continue the conversation. Let us know what we can do to support your work. And thank you for all that you do every single day to advance our mission at AAC Unu, which is to promote the democratic purposes of higher education through equity, innovation, and excellence in liberal education. Thanks again. Enjoy the day.
So And let's get started. This conversation about AI in higher education has changed remarkably quickly. Not long ago, we were talking about whether students should be allowed to use ChatGPT to write an essay. And then we began talking about how AI might transform teaching and learn AI enabled workplace. Now the questions are much bigger.
What happens when AI can perform a growing number of the cognitive tasks that we've traditionally associated with college level work? What happens when some of the entry level tasks through which young people have learned a profession can now be automated? And are the systems becoming increasingly capable of doing the kind of work we assumed was central to the preparation that we were providing students for work citizenship and life? In the end, it's about what decisions and responsibilities should remain human. Certainly students need AI fluency. They need to understand these technologies, to use them effectively and efficiently, to recognize their limitations and evaluate what they produce. But that's not enough. Students also need the knowledge and judgment to distinguish what is credible from what's merely plausible, to ask good questions and evaluate evidence, to imagine alternatives, to understand the consequences for other people and to take responsibility for their decisions.
None of this is new. These capacities have long been central to liberal education. What AI may be doing is making their importance impossible to ignore. The challenge then is not to choose between technological capabilities and human ones. It's a more fundamental question.
What do we want education to prepare and cultivate in human beings in an age of increasingly capable machines? So that's the question that we're going to be exploring today with three extraordinary panelists. I'm pleased to welcome Rachel Bauser, who is a professor of English and vice president for academic affairs and dean of the college at Agnes Scott College. Peter Felton is the assistant professor for teaching and learning and executive director of the Center for Engaged Learning and professor of history at Elon University. And Ryan Lufkin is vice president of global academic strategy for Instructure. So Rachel, Peter, Ryan, thank you so much for being here.
Rachel, let's begin with you. Does the AI era require us to redefine liberal education or to understand its enduring purposes in a new context? Thank you, Lynn, for having us all here, and thanks to all of you for being here with us today. So I think it's a great way to start a question about sort of the definition or the way that we understand liberal education. And I think that we can probably take as a shared value, right, a statement that you have already made and grounded us in, which is that the skills and the capacities that we so associate with liberal education, the critical thinking and the reading and the problem solving and the collaboration, etcetera, that those are those are enduring because they are, in fact, enduring. Right? So I don't and I don't imagine that any of us would really argue that we need to abandon any of those skills.
I wonder, though, if this is, like so many moments we've had in the last sort of twenty years, an opportunity for us to think about how we talk about the work and how the terms that we use to describe the work predispose people to understand it or value it in the right way. And so, you know, this webinar is framed in terms of, and the question is framed in terms of liberal education, which probably to everyone on this call has the resonance that we wanted to have in terms of civic engagement and the portability of skills outside of the classroom, etcetera, etcetera. There are some branding challenges with that term broadly, obviously. And you know, a thing that I have thought about a lot over the last ten years is the way liberal arts as a term has become a shorthand for humanities, which is always sort of frustrating the way that the central nature of the STEM fields often get written out of that term whenever folks are wanting to be dismissive of liberal arts. And so when I think about those terms, I think about, in particular, having said humanities, when I think about phrases that we don't as often use anymore when we describe the work in this context, things like humanistic education and humanistic pedagogies, humanistic development.
And so I think then about what is the case that we want to emphasize in terms of what is enduring about this work and how it positions people better. And I I do think we have some language already around humanistic inquiry, around built environments that we deliver it in in so many of our spaces that are designed around human engagement and interaction. And frankly, I think, you know, I'm an English teacher so I'm a little biased in this way, but I think it's time for the humanities to say the humanities have a very unique role to play in this engagement because of the human centeredness being central in the conversation in a way that maybe it hasn't been with a different level of urgency, but at any rate. Thank you so much. So Ryan, building on what Rachel has said in terms of the critical thinking skills around humanistic identification, moral imagination, long serving as a foundation for preparing students for work citizenship and life.
What's the strongest case that we can make for that enduring vision? And does the age of AI require us to rethink it, or does it make its underlying purposes more urgent than ever? I I think that the latter. Right? The this is not new, as I think you and Rachel both pointed out. This conversation has been going on for some time. What's interesting is, you know, a few years ago, the prognosticators were saying the the computer science degree was the the, you know, hottest degree, and then it was prompt literate you know, prompt writing, prompt engineering. And now more recently, it's philosophy.
Right? Specifically because those those skills those those more ethereal skills, to train these tools and think about how we're gonna use these tools, ethically and efficiently, are are incredibly important. I I also think the industry kind of shapes this. Right? A few years ago, we saw a lot of headlines around, tech companies removing the requirement for a degree, for hiring. Right? And then over the course of several years, those were added back in. And and what they found was many of the engineers they were hiring without the the more liberal arts degree, didn't have some of the skills that you mentioned, the the basic communication skills, the collaboration, the empathy.
Right? Those those skills that are core to who we are as humans and and core to what we teach in in the humanities programs, sometimes, are are necessary for the workplace. And they're not always I think I think Rachel made a really great point. We don't always call them out. And, unfortunately, the the degree that they're kind of lumped into that degree and assume that those are taught in that degree program, and in many cases there. But we have an opportunity to move towards that more skill based, outlining of of, how we talk about not just the, you know, a lot of people when you start talking about skills programs, they say, you know, that that's that's more of a vocational aspect.
It's not. Right? We can we can quantify those skills. The the soft skills, as they're sometimes called, those human skills as we're talking about here, And we can do a better job of articulating students' preparedness with those skills for the workplace. Thank you. Peter, does ready access to AI generated information diminish the importance of disciplinary knowledge or make deep knowledge even more essential to judgment, interpretation? Yeah, thanks for the question, Lynn.
You froze for a second for me, but I think I get the question. And I'm going to pick up right where Rachel and Ryan left off saying, you know, aren't new questions, like what's the role of knowledge in higher education and liberal education? And when the Internet came around, it really accelerated things. As a historian, people started saying like, why do I need to know anything? Because I can just Google it. And if you read about AI in higher education right now or AI in work, there's the phrases come up all the time. The end of knowledge, the collapse of knowledge, and things like this.
In fact, I was thinking about this yesterday, and I asked Claude to summarize for me disciplinary knowledge, AI in the future of higher education based on what's out there right now. And Claude wrote this in part, quote, Mastering a discipline's body of knowledge used to be the goal. Now it's increasingly the thing you use to catch AI when it's wrong. And I just wanna point out that that comment, and I think a lot of the AI and education discourse implies what Paulo Freire called a banking knowledge of, or banking conception of education, that knowledge is something a student acquires and possesses, like money or some other commodity. And that's not really a helpful way to think about education.
I'm going talk about that more in a second. Just one quick sideline because I was just maybe bashing AI companies and technology writers a little bit. I think a we in higher ed have contributed to this problem too. And there's smart people writing about how we flattened knowledge in how we talk with students about things. So sometimes we imply or even state that it doesn't matter what you major in, you're going get the same skills no matter what you do at my university.
It's like, as if skills aren't tied to discipline or knowledge. John Warner, I think, has some really smart things to say about this in regards to writing in his recent book, More Than Words, where he says what students need is both writing proficiency and deep knowledge. Right? Because these things are inseparable. And I want to point people towards a new book. It came out a couple months ago.
Scott the lead author is a guy named Paul Ashwin, who's a higher ed scholar in The UK, and it's a really sophisticated study of students at six universities, US, UK, South Africa, for seven years tracking these students from when they matriculate to after they're in the workforce. And what Ashwin and friends say, demonstrate is the primary way students develop skills like writing, critical thinking, communication is by getting inside structured bodies of knowledge. You need to think like a chemist or like a chemical engineer or something like that to be able to develop these skills. And so their point is that it's not just about what students know, but how engaging with knowledge changes who the student is and how they're able to act in the world. So in the end, I think we sometimes mistake knowledge as an end for knowledge also as a means to the transformation we're seeking in education.
Thank you. That's fantastic. Rachel, I love the fact that Ryan gave a shout out to philosophers. I I did a paper a few years ago called, the ketchup's in the bag and the check is in the mail. It was a defense of humanities, but, you know, signified on the old trope of philosophers as asking, would you like fries with that? Now we're in great demand.
So this is all good. But I'm wondering what other fields, within the liberal arts might have distinctive contributions in this age of AI in terms of the different ways of knowing, creating, judging, and instruction on how humans should understand and use the technologies that we have available to us. So, I'm with you and Ryan on philosophy for sure. Not least of all, because I have a daughter who is a junior at a liberal arts college and is studying philosophy. And so I really hope that this moment, that everyone is right about philosophy having a moment, and she's made the right choice.
So that I think, and I also think, and this is obviously a biased response based on my own training, but I do really think English, English, English, English. Like, this is the moment for the English departments on campus to be leading the analysis. These are large language learning models. Like, our work is right there embedded in the name for the thing, in the shorthand, right? We are being called into doing this work, and I think, you know, a lot of us, as you've noted at the beginning, in the sort of early phases of institutional responses to this, it was English departments that kind of carried the burden of helping people to think about how are we going to mitigate the erosion of academic integrity in this context, right, and and how do we still teach students to write, etcetera. So I think we were on the forefront of that space, and now I think we're on the forefront of the analysis space.
You know, I the output there are a lot of ways that we can talk about people using agentic AI to manage their workflows and things that are important when we think about the future of work in lots of sector specific ways. It is also the case that for our students, for the majority of our undergraduates, what's happening with their engagement with AI is a pathway to knowledge, maybe an extraction of things structured bodies of knowledge, to Peter's point, right, and summaries of texts and solutions to problems, etc. Output of those LLMs is text, and it is text that does not live in a vacuum, it is not created in a vacuum, it is an amalgam of representational biases and persuasive techniques and rhetorical positioning, this is where the English departments can really ask students to look at the output in a way that I think brings to bear what all these decades of rhetorical analysis and work and representational analysis have brought us. It is not, this feels important to me, I think that we do spend a lot of time asking students to think about how their education positions them to assess whether an AI output is credible or is it merely plausible or is it outright fake, and how only our students who go to have a certain kind of education or position to know when the AI output is wrong, etc. That's valuable.
That accuracy testing is valuable. And when they find that it is wrong, it does not mean that they ought, in my view, sort of discard the utility of that output as a piece of knowledge or as an object of learning. In my courses, I tend to try to think about this. I ask the students to think about their high school English classes when they were, taught about unreliable narration and how unreliable narration was understood to be a feature of a novel and not a bug. You didn't throw Gatsby away because Nick Caraway is an unreliable narrator, but it becomes an integral part of how you understand what the work is and what the text is trying to say.
And so what if we bring to bear that same kind of framework to AI outputs? Like, okay, it's wrong, or okay, it's always already unreliable. It's unreliable in what ways in response to what things, and what does that tell you about the training paradigm that produced it or about the users that are engaging it. Right? I think, this is the the English teachers of the world have taught some lesson plans that I think can be, with a couple tweaks here and there, pivoted and brought to bear here pretty productively. Thank you. Ryan, I'm gonna turn to some of the questions that we're getting through our Q and A.
The first is how can we get our admissions staff and administrators to hear that practical degrees are becoming less desirable. They're still selling degrees like accounting, data science, and others, and and many institutions are cutting the humanities, and faculty lines rather than supporting general education requirements. So how do we make the case? Yeah. I mean, I think it's interesting because we're we are I like to remind people that we're four years into this evolution. Right? November 30 will mark the fourth the four year anniversary of of the launch of of ChetchiPT by OpenAI, which really kicked all of this off.
And we spent the first year really talking about, banning you know, it seems so long ago. Many of us don't even remember. First year, we banned, OpenAI or the AI tools, language model tools on campus for the most part. The second year was very focused on academic integrity and how do we how do we look at, identifying cheaters, with finger quotes. And then we over the last year and almost now two years, we've gotten to a more productive phase where we're signing enterprise wide deals with, you know, one of the big players, to offer, these tools for, most of our faculty members, and in some cases, students.
But we haven't really moved into that that next phase where we start digging into these individual tools. And I think the programs have been slow to evolve with that. Right? Our perceptions have been slowed because of that. And so so to the question in the chat by the way, can't wait to review the the I'm watching some of these pop up, and I can't wait to actually dig deeper into the the chat transcripts. I think they're incredibly interesting questions.
It's it's we're in the middle of this massive transformation, and it's very hard to, you know, predict where we're gonna come out on the other end. And so, any major change where you're throwing out programs on either end of the spectrum is probably not a great idea at this point. Right? Actually, looking at what, the outcomes might be, There's some really great data on what jobs are going to be replaced, by AI directly from Anthropic, and it's a very interesting graph. I've shared it on my LinkedIn if if you wanna go find it, but it's it's out there. And it's really interesting to actually look at their prediction for which job fields are gonna be impacted and which fields are actually already being impacted based on the data that they have.
Interesting prediction. But we have to actually take that into, the broader context. We're seeing a lot of AI washing in headlines right now. Right? I was in Australia, and the the, head of, AI for Microsoft came out and said, you know, all, white collar jobs or 90% of white collar jobs are gonna be automated within the next eighteen months. And I had a couple 100 people in front me.
Said, is your job gonna go away in the next year and a half? And they laughed. And I said, those headlines are really great for bolstering the valuation of AI companies. They're very great at manipulating the markets. They instill fear in our students. They fee they they establish uncertainty in our programs.
And so to me, I you know, that's kind of a long winded answer to say, I think we need to take a breath and not jump at shadows right now. We need to actually watch this transformation come through before we make those decisions on, entire programs being eliminated or or or being, you know, less focused on. Thanks. Peter, another question from our audience. A lot of people talk about how science is valued over the humanities, how the humanities are particularly useful now.
But what about the role of social sciences? What role do they play in this emerging context? Well, I love the question. I'm a historian, so I don't know whether that counts as a social scientist where you are or not. But I wanna I wanna point to to two things, I guess, on this. One is if you read social science, and I'll mention someone, Jill Vapore, brilliant scholar, historian at Harvard. She says one of the things that's fascinating about the way we talk about AI right now and the way AI companies talk about themselves is their history lists.
They are trying to not let us recognize where they come from, because almost all of them come under the defense and intelligence industries. And they don't want that to be seen as their origin story. They want to be these. You can consider those industries however you'd like. So I think it's really important.
Jill has a great book called If Then about and they came back in the book was written before AI, right before AI. But it's a really interesting book about social the the attempt in the 1950s, computers to solve social problems by sort of bad social science merging with technology. So one thing we should do is study history. The other thing I want to, I want to give a shout out to someone who does social science and thinking about something we haven't talked much about yet, which is the role of civic education and sort of community learning in higher education, because that's a crucial part of liberal education. And I I don't know about you.
I'm fairly depressed about where we are civically and culturally right now. And I don't think AI generally is helping us with that yet. Late last spring, I saw a talk by this brilliant scholar. I never encountered her before. Her name is Beth Simone Novak.
I'll put a link to her. She's got a brand new book came out in July. She's a scholar at Northeastern. The book is called AI and the Race to Save Democracy. And she starts by capturing lots of the fears I have, maybe you have, but then she makes the case that we should use AI to quote reinforce our democracy, mend the tears, and strengthen the bonds.
And her book is just full of examples from all over the world about how AI can support participatory democracy, can support effective governance, can help people build meaningful communities despite or across differences. Right. So she's not writing about higher education. She's writing about governance and social social science in action. But I think her work could be really inspiring to a lot of what we do.
Because, you know, one of the things I think about a lot, and thanks to ACU and the work you all have done is, you know, community based learning is a high impact practice. It's amazing. I use it in my own teaching. It's really, really hard to scale. There are too many students.
I'm too constrained of needs, you know, developmentally and if it's anyway, it's time and all this sort of stuff. So what do we do about that? Well, maybe one thing we can do is use or develop some really sophisticated AI tools to help students ourselves practice difficult things like developing empathy, developing some deeper awareness of their own assumptions and biases, practicing skills and all this, not to replace community based work, but to prepare students so that they're really ready when they are doing community based work. And then after they do that work, could we have some AI tools that complement humans to reflect on what students are learning and help them make meaning or help them process that? So that we're we're not putting as much stress on our communities by asking them to help us educate our students. But we're still getting the benefits and our students are getting the benefits of that community based work. That's such a great point in terms of equitable access to AI and high impact practices.
Thank you. Rachel, building on something Peter has said, it has to do with fears. What are the most common anxieties you hear on campus about AI from students, from faculty, from the administration? So, you know, I think there is some of the fear about workplace transformation that Ryan mentioned and that I think, you know, those of us who are trying to take a sober look at things think is maybe a bit overblown, the immediacy of that paradigmatic shift. But I do think there is a worry there. I think on the part of my teams, when I think about it more, we do it's harder to ensure that we know, as people whose work is inside this institution, that we know what is happening in the sectors that we're sending students to.
It's a little bit more urgent that we sort of are making contact and going out, etcetera. And so I think there's a little bit more burden on the preparedness of your coaching teams on staff, of your career teams, etcetera. I think from students, what I hear is some of that, but that kind of gets swept up in the overall, like, they're living in a very destabilized moment, destabilized economy. But, you know, I don't think it's one thing. I think it's all the things.
Right? I've been struck by a number of student conversations here about the way their fear about the way AI sort of destabilizes the kind of meritocratic approach that they hope that they can count on from their college experience. Right? They don't want to use it to shortcut their learning. I mean, really, enormous majority of the students that we have on campus will talk very earnestly and very candidly about how they don't wanna rely on AI. And they don't they have a whole host of ethical concerns about it in terms of labor displacement, environmental impact, data security, etcetera, that they they have strongly held opinions about. But they also don't wanna use it for reasons that they think are bad for their that are detrimental to their learning.
They they are keenly aware of that. And, also, they're quite worried that if their peers are using it and if their other students are using it and if somebody if everybody is running their cover letters through AI and generating them that way to begin with, and everybody's cover letters, sooner or later, sound exactly like McKinsey documents. How am I gonna fare when mine doesn't sound that way? And so I and I think that's a really fair worry when we're telling them to be really guarded about this technology and they wonder about the extent to which that guardedness disadvantages them in a less discerning audience, right, who who where they're not gonna get a chance to say, oh, of course, this isn't quite as polished, but I worked on it myself, right? And I didn't get any help. So I worry about that for them because I don't think, you know, nobody's ever really promised them a clean meritocracy here, don't think, and I think our students are all sort of smart enough here in 2026 to know all the ways that things are unfair. But I do think this is a kind of urgent question we're asking them to ask about holding themselves accountable.
And the thing about that is that you can hold yourself accountable, but you still have to be in the same pool as a whole bunch of other people who aren't. And so I find that that comes up often with our student body. Yeah, it's interesting. We're hearing the same concerns from faculty who are worried about being assessed against people who are frequently using AI to produce books and peer reviewed articles, but they're not. So does that put them at a disadvantage and what do you do? That kind of moral dilemma.
Ryan, we have a question here from a participant who wonders, despite the support that we all have for liberal education, are employers really valuing that or are they continuing hire people from the world of business or those insights into that? Yeah. I mean, I think the the one of the biggest disconnects we have right now is that many programs aren't really modeling, for students how they will use AI in the workplace. Right? So these students, one of the first questions they're asked when they go out is, how are you using AI to make yourself more efficient? Right? And if they've been you know, to Rachel's point, if they've been in programs where they've they've said, I'm not going to use it because I feel like that's detrimental or, you know, they've been using it heavily, but they're not really using it in the way that they, would apply it to to work or their their intended career, that's not really helpful for them. They're not we're not equipping them with those answers. And so I I I feel like, especially in, programs that have, you know, more of a connection to, you know, the the the workplace.
Right? We we're seeing more and more of that kind of, education industry partnerships where they're saying, look. We're gonna try to figure out an experiential pathway to make sure students have those skills and are prepared with how they're going to apply those in the workplace. I think we've seen an explosion of that because, we've also seen that skills gap widen. Right? You know, the the entry level jobs that many of us did as we entered into the career are automated by AI. They were they were they were simple jobs to to test your skills, test your metal, see if you'd show up every day, and do those basic things.
And then it was those human skills that you're talking about, those the connections they were able to able to make, the culture that they were able to contribute to, the communication. Those were what got them the job, beyond that. And so to me, it's not necessarily our employees still valuing them. Our the question is, are are we providing opportunities for students to demonstrate those skills, right, and and let themselves be picked for that, right? We shouldn't just be using those those, you know, the different tests that they they do that can be gained by AI or writing samples or things like that. Those are not the metric that employers should be using.
They should be finding ways to truly engage and measure these skills, that we're talking about today. And that's that's a challenge, you know, when you're you're hiring remotely, you're hiring, employees that that may work from home. I, you know, I saw a really interesting interview process where the company had shipped out a mask, a mirrored mask, so that when they were on the call, the student couldn't have, an additional screen up or a phone up. And I was like, that seems like you're trying to measure the wrong thing if it is it's so easily gamed. Right? And so we've gotta find new ways.
We've gotta evolve how we're thinking about not just these kind of strange stop gaps or try to try to, shoehorn the existing processes into this new world, we've got to fundamentally rethink these processes and how we're measuring this stuff. Thank you. Well, since 2007, AAC and U has been doing employer surveys and we can confirm that employers do indeed and hiring managers look for skills in students graduates that are the ones that we've outlined. And so check it out. Our latest one was done two years ago, the Engility Imperative.
And it's not only a reinforcement of critical thinking, creativity, but it's also speaking across differences. It is that moral imagination, narrative imagination component, and developing not only the skills, but the mindsets and dispositions necessary to thrive not only as you enter the workforce, but as you progress through it. That's the biggest challenge that employers point to. Peter, this next question seems perfectly suited to you given your work on relationship rich education. How can technology instructors and STEM professors more broadly encourage students to focus on human skills and what does that pedagogy look like? Yeah, well, I get to that.
I just wanna say one yes and to Ryan's thing, which is I think in liberal education, we also need to be preparing students to answer that question about how are you using AI in ways that critique efficiency and then demonstrate that they have discernment and judgment. Right? So it's like, it's not just I use it for everything. I make intentional informed choices about how to use this. And I would hope that actually distinguishes those people from someone who could just say, here's 62 tools I use. Yeah.
I mean, they should be asking questions like, you know, tell us an instance where you were skeptical of the output of an AI tool, you know, and and we're able to to provide a better, a better answer. Right? That those are we've gotta create those opportunities. I I like what you're saying there. Yeah. And and then to this issue of relationships and STEM and other fields.
I mean, first, I would say the research in general is just so clear that students learn more, they persist more, their well-being is higher when they have strong relationships with faculty, staff and peers. That just is. And if you look at the research in STEM specifically, much all of the STEM reform work around active learning and equity belonging, this is all about cultivating relationships at its root so that students are closely connected to their peers as they're working together on hard problems. Students feel connected to and valued by their professors. Now that doesn't mean this work has to be one on one.
Right? In fact, if we think about relational work as requiring one on one, we're gonna lose. Right? But if we say students are infinitely scalable, and so how do we help to build educationally purposeful relationships? How do we help students build educationally purposeful relationships with their peers That helps them learn, helps their well-being. And then the other thing I hope I think that we need to claim more of in higher education is that prepares them for work and life after they graduate. And you can think about, you know, Allison Pugh's book from 2024, the last human job that says fundamentally, work is going to be about human connections and rooted in human connections. But if you don't even want to go there, I think there's just this remarkable book that everybody in higher ed should be talking about from 2023.
It's called The Good Life. It's a summary of the the current state of the Harvard project on American lifespan or something like this, which has been going since 1938. And what that book says is if you want to do one thing to be healthy and happy in your life, you should cultivate warm relationships. I said that is the science based, social science based thing that we can do. So if we're saying we're preparing students to work, we're saying we're preparing students for civic life, we're preparing students to thrive.
Relationships are going to help them in college and the skills, habits, capacities, values that they develop to build those relationships are going to help them thrive after college. So we need to, I think. Thank you for coming to my TED talk. But, we really need to center this relational work in all the disciplines, not simply in some. Thank you.
I love that book, Peter. One of my favorite passages from it is the data that show that those people who are on a train and say they would be happier if they didn't talk to the person next to them. There are overwhelming majorities and yet they find that when they do, they're much happier. So we have our own predispositions about what makes us happy and then those are thwarted by the realities of the experiences and that human connection is what makes the difference. Rachel, there's been a lot of discussion about this being a moment of existential crisis for higher education and our democracy.
Is this moment really different, do you think? So I do think probably it's different here, but I think that in the humanities, I think we are in liberal arts education and in higher education, we're not we're not strangers to a crisis, and we are, you know, sort of on a repeating cycle of defending the the humanities and the end of, you know, the the investment in these fields and the arts, etcetera. You know, I, like everybody else, passed around that piece from the Atlantic last month about the age of reading being over. And so there are some familiar tropes around it. Right? And often, the crises that come at us, sometimes they're they're sort of mechanical or industrial in nature and new technology. So a lot of the time they're political, we're weathering political attacks.
Here's what I think is different about this moment is think Peter talked helpfully a little bit ago about the extent to which we can be optimistic and proactively positive about the way that AI technology can, for example, expand participation in civic democracy. I think there are real positives there in terms of access. I think that we have talked in this call about so many of the declarations that are overblown, but we can see why we get there around this era have been that the age of knowledge is over. All knowledge is equally available right now. And I do like that point about flattening the disciplines and the disservice we've done in that.
Think the thing about those pieces that makes this moment different is that to the extent that we are willing to admit it or not, this sector has really organized itself around gatekeeping and still understands itself to be hierarchized in terms of selectivity and around limiting access. There are no one person could change it, and it's no one institution could change it. And there are lots of reasons that drive it. But it continues to be the case that the fewer your percentage, the smaller your selectivity is, the higher your selectivity is, depending on how you frame that, the better product you're construed to be providing. And this is a tool that is fundamentally everything positive that can happen with it is about access expansion and about expanding people's access to information, to processes, to time, to relationships, to all kinds of things.
It is paradigmatically different, and I think for us to get through it, we really have to interrogate how much of what we value and how much we assess ourselves. It's based on gatekeeping. Think it is really a paradigm shift for how institutions like us need to think. Thanks so much, Ryan. This is a related question.
It really builds on that. So one of our participants asks, how have the esoteric and inherently classist aspects of higher education led to this perceived existential crisis. And when it comes to higher education, reconciling what's happening with AI, how much of the issues are a direct result of our own historical development? Yeah. I mean, I I loved Rachel's point. I'm gonna I'm gonna go back to that a little bit.
If you go back to something like three forty eight BC, three seventy BC, something like that, Socrates actually criticized writing as as the end of of critical thinking. Right? So we've been debating this for twenty five hundred years, and somehow it has not become, like, the existential crisis that we always think it's going to be. It's it's constant evolution. And I and I think that, this is a this is a a major shift, you know, to Peter's point about history. I I I'm a I consider myself a novice historian as well, but I love finding those patterns in history.
And when you look at, you know, the age of the printing press and you look at the age of the Internet, and, like, we've been through this before. The calculator is the so many things have been, kind of held up as the death of critical thinking. And instead of we kind of wrap ourselves around that instead of evolving to support that. And and, you know, to this is a great question. I've got a feels like a potential minefield.
Gotta be a little careful talking about, but the the the classes aspect of education, you know, when people talk about over the last, you know, prior to the last two years, we had over a decade of declining enrollment in higher education that peaked in about And a lot of people consider that the, you know, we've heard about the enrollment cliff. The enrollment cliff isn't real. It was a democratic dip that is not presenting itself as reality. And we we tend to find these team things to catastrophize, right? And and if we can actually stop doing that and look at the the the possibilities, we do we do need to look at things like, access to the models, right, being not not widening an accessibility gap. Right? We can't have third world countries that are using sure.
They've got AI, but they're using outdated models that are substantially more likely to hallucinate or provide confidently incorrect answers while, you know, you know, top tier universities are using the most advanced models that are are seamless. Right? Those are the more concerning aspects of this. And so how do we how do we make sure that we, to some level, democratize access to these tools and ensure access for everyone to the same the same quality of tools. That's very difficult when you've got, industry partnerships with individual institutions that are that are learning to experiment with the the most advanced models. Right? And and not that, you know, University of Florida and their partnership with NVIDIA sets them up to be truly elitist, but they they do have access to, tools that that other institutions don't.
And so I to me, it's it's it's about ensuring that we maintain some level of of equitability when it comes to these tools. I think, one of the biggest challenges of kind of the skills based hiring that we or the skills based, you know, demon demonstration of learning that we were talking about earlier is that it you will never get Harvard to agree that their English 101 course is equivalent to, California Community College's, you know, English one zero one. And that's the the kind of bias. We will never have a unified skills taxonomy that everybody says, yeah, we all agree that this is this is equal. That's just never gonna happen.
The best thing that we can do is actually try to move towards some semblance of those basic underlying skills, some some, agreement to what those definitions look like so that employers have confidence that the you know, that that translates into to real, productivity so that universities can have confidence that they that's created a foundation for learning that they you can build, you know, additional learning on top of. And so it it is a it's an incredibly transformational time. The one other thing too I do wanna point out to with as we were looking at the the obviously, these chats are popping up. One of the things I find really interesting is this idea that, if my if my educator is using AI to grade my work, then somehow that that devalues the work. And the it ignores the idea that this technology is actually being trained on the educator's work, on the educator's rubric, and it's doing a first pass with human in the loop, access if designed correctly.
And it also ignores the potential for eliminating, grading fatigue, grading bias, a number of those things that we know impact educators as they as they do that work. And the fact that there are times when educators just can't give you timely feedback before your next assignment that can help shape that assignment, I would rather have the feedback, you know, even if it's, been it'd been, AI, assisted, because it should never just be AI generated. I would rather have that feedback in a timely, timely, you know, time frame, so that it could affect my future work than have no feedback. And and I think that's what we get kind of confused about. Was at Clark County School District in Nevada, and they were saying, Give me AI first pass grading today because I've got too many students.
I can't give them feedback fast enough. But then when within the university space, they're saying, no. That somehow diminishes my my, passing of knowledge to you. And I I think we've gotta get over that kind of strange bias that exists there. I remember a student in a large intro class saying once, I get frustrated when you write so many comments on my paper.
Well, don't know if it would worse now. Peter, this is an interesting question. I think about this a lot. AI can generate responses that sound supportive, understanding, and even compassionate, but it doesn't experience human emotions. As students increasingly interact with AI, how do we help them understand the difference between simulated empathy and genuine human empathy and what role that human connection can continue to play in education along with the tools? What a great question.
And do you have any easier questions, Lynn? Now and and I'm resisting the temptation to get into a longer conversation with Ryan about AI and grading because I actually think this is one of the in some ways, this is one of the issues. I think it's one thing when the AI says, Peter, this is good work. And it's another thing when a teacher I trust says, Peter, this is good work. Now the thing is, we've got to have institutions where teachers can build those kinds of relationships with their students. Because if teachers are teaching huge numbers of students in really anonymous ways, then saying AI is so much worse than a human, that's that's not real.
Right? But that's a structural problem more than an AI problem. I think a couple of things in response to this really good question, Lynn. One is we have to be really, really transparent about our AI use as faculty. You know, if AI is if we're having AI assisted human grading, to use Ryan's phrase, we need students need to know that. The students need to know how that's working and why we're doing it.
Right? Because I think we're if we're misleading students about who's giving them feedback and how the feedback is being done, I think that's really dangerous, both because students may not Maybe they'll trust the feedback, maybe they won't, but they're not gonna trust us anymore because they thought it was coming from us. So that's thing one. Thing two, I think I I believe, actually, this is like a fundamental challenge with AI that we need to be helping students navigate, which is AI is super powerful and AI is super disempowering at the same time. Right? That it can help us do things, and it can also contribute to us being passive receivers and passive users of knowledge. And and, you know, we're talking about the liberal arts.
That's what liberal arts is about is these kinds of paradoxes that I don't know that you can completely resolve. But what we need to do is be transparent about that. So help students discern when and how is this feedback helping me learn. When and how is this feedback not? Or when and how is AI supporting my capacity development? And when is it hindering that? And we can make all the rules we want about this and have all the policies we want. But fundamentally, we have students for a short period of time, and they've got a long life ahead of them.
And so we've got to be developing the capacities in them to make those discernments for themselves. And I actually think this is one of the fundamental challenges of liberal education in the time of AI is to develop to develop that discernment in students. And some of them will choose to use AI in different ways. But we can't develop it just by creating virtual worlds. We gotta develop it by having them be part of the process of thinking about when and how to use AI, when and how not, what sense to make of the feedback AI is giving.
Let me ask you a follow-up question, and this is from someone who wonders about how those teaching philosophy can apply AI and lead practically. Well, I want to be clear. I don't think we all have to use AI everything we do. Right. So I think we should be discerning to.
I do think there are ways where we can use any tool with the limitations, awareness of the limitations and taking advantage of the capacity. So I think in philosophy, for example, there are possibilities. I don't teach philosophy, although I studied it as an undergraduate double major, yay me, on the philosophy. But I think there's ways that you could have students with maybe well trained bots having certain discussions or being prompted to think in different or to question their own assumptions to come up with alternative explanations and things like this. But one of the things I want to emphasize here is a my sense is philosophy, and like many of our disciplines, is very process oriented.
And one of the mistakes we've made in higher education is we focus so much on products. So in my own disappointment, it's about the paper students can rather than thinking that goes on to create those papers. And we've got to help put the focus back on process. So then the question is, can AI help students do the some of the processes of thinking like a philosopher in really critical ways rather than can AI help or hinder a student producing a proxy for thinking like a paper? That's excellent. Yes.
I mean, I can think of all sorts of ways in in teaching philosophy since I was teaching medical ethics and philosophy of law in addition to metaphysics, how that AI could help generate the kind of problem based learning when I was teaching in medical schools. In talking about the Lindsey Clancy case, say, bring up the rulings in the Yoder case or the Susan Smith case, how are they different? So you ask students to engage in the material, but you have the material at your hands much more readily than in the past. Here's a question about assessment and general education. What does assessment mean? How do you best assess work at both the graduate and undergraduate level in this current environment? Rachel? I mean, I think that Peter is exactly right about process. And so we have to be able to think about learning outcomes that involve not a demonstration of not produce x or demonstrate x mastery, right, but show facility at an iterative, etcetera, etcetera.
Right? I I do think that we have to try to think about what's the workflow, what's the process, and not what's the proof of that. In some ways, it's what's the patients and how do we see the patients for engaging through a process. And think that the human piece, again, critical thinking, I think we have all of us at our institutions decades worth of learning outcomes that are kind of wrapped around the concepts of critical thinking, critical reading, collaborative work, etcetera. We might need to really interrogate those terms to sort of say, how are those demonstrated in human centered ways? In a human environment or in a human centered environment, what might the demonstration be of critical thinking or problem solving, right? I think back to something Ryan was saying earlier, it is the case that an employer may say, right, how have you used AI to become more efficient? Right? I think the thing and I think it's right that not necessarily a great question, etcetera, but the thing that I would want our students to understand is that that is essentially a question of how have you solved a problem about your own ability as a learner? How have you solved? How have you made yourself a more effective participant in your own life? Like, what has been the creative problem solving that you have deployed to do something in a way that you more like doing it? Right? They can think about those as problem solving questions and not, I think to Peter's point, efficiency questions. Right? We don't we can help them think beyond that particular fetish and move towards the idea that you're being asked to think creatively about what problems face you and how these tools can help you.
And, you know, my philosophy department right now is putting on a lecture series about AI and human flourishing. And as, you know, all of the the the talks are oriented towards the idea that there are ways to solve problems with this technology that are not about productivity, but are instead about pathways to happiness and pathways to flourishing. And what would it mean to sort of ask those questions persistently instead of only the productivity questions persistently? Thank you. Ryan, we touched on this a bit earlier, but this interesting question. With all of this talk about AI in the classroom, what's being done about students' right to refusal, that's that are continuing to surface these days? Yeah.
I mean, I think Rachel talked about this a little bit where, you know, how do you if I choose to refuse to use these tools, how does that position me against my fellow students who are using these tools? Right? And how do we you know? It I I actually had a a coworker compare it to the the the Olympics versus the unlimited Olympics or whatever. They're really like, you know, use whatever, you know, drugs or or prosthetics make you faster and stronger. Go with that. You know? And then you've got the Olympics where we really keep it to human capabilities. Right? We we're not in a world where we can do that, and so we need to make sure that we are being consistent with how we apply these rules across, you know, across the thing.
There were it was really interesting, though. I think we're we're to to Rachel's point as well around assessment, it's really interesting because I do think we're seeing a backlash. There were some stats on, how much something like 80% of the posts on LinkedIn are AI slop. Right? They're just AI generated content. And and I think Robin in the chat posted something about her communication students are actually offended by some of the content that's put out by these companies that are using AI, tools to, you know, create this generic garbage.
And I spent some time in advertising. I think the same thing's happening. This pushback on AI generated images, AI generated content. I I think even within the the grading or assessment space, we're starting to recognize these patterns, and we're starting to see what what is AI generated and what's not. There was another comment about, somebody that said, you know, in grading work, I'm I'm scared to praise, some assignments that are too good because they might be AI.
Right? That that's unfortunate. Right? I've heard students I've talked to students that said, dumbed down my papers because I don't want it to sound too good or it might be flagged as AI. Right? So, I think if we're in this, like, tumultuous middle middle ground where we don't have the solid answers for that, I think we will see more clarity as we get coming out the end, but but we have to work together. And there's been a lot of great comments in the in the chat that were basically like, love this conversation. We need to have more of these conversations.
And this is how we start building some consensus around how we come out the other side in a in a coherent way. And, yeah, I I don't know that we have a lot of solid answers at this point, for this, but but we'll get there. Yes. Then the questions are sometimes just as important as the answers. Yeah.
I wanna give you an opportunity to close with any comments you think it's important for folks to hear. Peter, what what advice do you have? Well, just speaking right back to what Ryan said, which I think is so helpful, is we need to find ways to bring joy and pleasure and excitement and curiosity into our classrooms. And and I actually think students bring that with them, and then we often iron it out. Because to this point, Ryan, you're talking about a student saying, I'm trying not to do work that I care about well because I don't want it to be predicted, or a faculty member saying, I don't wanna be excited about and proud of my students' work because maybe I'm being misled. That that's, like, damaging our hearts.
It's damaging our educational tasks. Because none of us went into higher education because it was the most efficient thing to do. Right? We went into it because we love our disciplines. Think they help solve problems. They help us understand the world or ourselves.
And so how do we center that? And then can AI help us find more joy in what we're doing in the classroom? Maybe. I don't know. But let's try to center not efficiency and time the way the AI companies want us to, but center the human work and the human emotion of that work. Thank you. Rachel? I didn't prove on that.
I think I I second. More joy, and more more poetry. I think those are probably the two best approaches to this moment and to our in these politics and these classrooms. More joy, more poems. Can't argue with that, Ryan.
No. I mean, honestly, I don't I don't know how you build on that. I just think that is such a joyous, so core to the human experience, and it's intrinsic in everything that that, you know, we we can't lose that because of of, you know, any sort of technology. I think that is that is core to who we are. And I love Rich's point about poetry as well.
You know, it is one of those things where, yeah, yeah, can write a haiku, but it you know, I hate when poets say, you know, there's no value in what I do anymore. There absolutely is. There's we're seeing I think we'll come full circle, and we'll see, you know, the the attachment, the value of the human the human production, the human value, the human talent is is resurfacing. It's part of this cycle. Thank you so much for sharing your insights.
To all the participants for joining us, we hope to continue the conversation. Let us know what we can do to support your work. And thank you for all that you do every single day to advance our mission at AAC Unu, which is to promote the democratic purposes of higher education through equity, innovation, and excellence in liberal education. Thanks again. Enjoy the day.
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