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Addressing Cheating in the AI Era: Moving Academic Integrity Upstream
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- [00:03]
Ann Jones
This session was recorded live at the
- [00:05]
Moderator
2026 ASU GSV summit in San Diego. I want to welcome you this morning to the panel addressing cheating in the AI. Moving academic integrity upstream. Hopefully today we will make you think a little bit and try to really embrace what are some tough things about. How are we making sure that our students are learning in our educational environments and really being challenged even in the era of AI. So we have a pretty diverse panel here today that hopefully will be able to solve all the world's problems throughout today. But I've noticed as we're on our last day of this conference that even the panels that I've been to, it's been interesting. There's been a lot of discussion about we need to embrace artificial intelligence. We need to have our faculty members use artificial intelligence in the classroom. We need our students to use artificial intelligence. Yet there are some students that are going to choose to use this inappropriately. And what do we do in that case? Are there some proactive measures that we might be able to follow? How can we help our students? And so I have not heard that much about the academic integrity side of making sure that our students are the degrees that our universities are conferring, that they are actually degrees of integrity. That's what this panel is really going to discuss and try to think about some of those questions that are going on, especially in this age of AI. So before we really start, the panel really wanted to know who all was in the audience. So I'm going to ask some quick questions. If you'll raise your hand, you can answer as many times as you would like because you might fall into different categories, but it'll allow us to know who's all in the audience. So who all would, if you are representing higher education, if you could raise your hand? Well, a good number of higher education. What about K12? A number of K12 as well. Investors. So ed tech companies are investing? Few investors, yes. Any faculty members, even if you're administrator, but also in faculty role. So a few faculty administrators, especially in higher education and then ed tech companies. So we have a very diverse audience. As y' all can know.
- [02:39]
David
People might have four or five jobs. Looks like.
- [02:41]
Moderator
So yes, yes, I saw a lot of people, they didn't want to be cut off that they were telling the wrong answer. They're like, I'll just answer all of them. So we have a diverse group in this room, a diverse panel with publishing ed tech companies, student facing administrators. My background is in academic integrity. And so one thing that we wanted to start off with is that even though we're a very diverse panel, we wanted to make sure that you all understand there are several things that we all agree upon. So if you were coming here to see a fist fight, probably not going to see that we do agree upon. One, artificial intelligence is not bad. Two, academic integrity is very important. And three, the most important thing is the connection of where what I will say, AI and AI, academic integrity and artificial intelligence, how that comes together, and especially focusing and examining how authentic learning and the productive struggle can be part of a student's experience within higher education within K12, even in the age of artificial intelligence. So with that, I'm probably not going to be talking as much. I'm going to let our panel talk, but I've got some questions that we're going to guide our discussion. So first I want to ask, and I'm going to have Ann start first with this question as think about how has academic integrity changed in the past five years with the enhancement and the development of artificial intelligence? And kind of asking again, where do you foresee it in the next five years?
- [04:17]
Ann Jones
Well, good morning. My name is Ann Jones. I'm ASU's Vice Provost for Undergraduate Education and I'm a professor of chemistry. I forgive all of you who cringed when I said chemistry. I think that AI has accelerated conversations about academic integrity that were already happening and caused everyone to dig in just a little bit deeper into whatever their position was. And what do I mean by that? I think academia has often had a culture, a fist fight culture of policemen and marshmallows and a really different philosophical divide around how they feel about academic integrity. And AI has brought that to the fore. But perhaps more importantly, what it brought to the fore, or must bring to the fore, is a conversation about what we teach that has value because that's what's really at the core of the academic integrity conversation. Right? There's only integrity if there's value. And AI tools have, I think, demonstrated for many students and faculty that much of what they learn does not obviously have value to them. And so we are in a position of, I think, cops and robbers or catch up in many cases, of trying to justify things when we really ought to be having a different conversation about what we're teaching and why. So my hope is that in five years we are teaching very different things. You promised we would not solve all the world's problems on this panel, but I'd like to think that education could solve all the world's problems. Right. And that educating People differently would lead to solving different problems. And we are potentially at a moment to do that.
- [06:19]
David
Yeah, I'll go. I would boil down the impact of AI on academic integrity in the last five years in three zones. One, primarily, I think AI has created a definitional problem for academic integrity. We don't know what it is. We're going to try to answer that for you today a little bit. But it's created a definitional problem. I think in the past five years it was a little more binary or polar, meaning you were either academically honest or academically dishonest. AI has now created a diffusion and more nuance. And so we have to go deeper there in terms of getting at the definition. Secondly, I think it's been a cultural shift. Like this is the emotional side of AI's impact on academic integrity. We have all the feels about academic integrity relative to AI, what it means to be human, what is human intelligence. And I think that's created a cultural impact and probably fueled some of the conversation you're talking about, Ann. And then third, it's created a more competitive landscape. Just the people in this room were competing to figure out what it is, how we support it, and we're all doing business while we do that. By the way, in the next five years, I think what we'll see is some predictions really quick. We're gonna have to redesign assessment from the ground up. If you think about AI augmenting human intelligence, then how are we going to. Hey, I got a smile already. How are we going to assess human intelligence? Secondly, I think skill erosion is going to accelerate as. As AI augments human intelligence and we learn new skills, we're going to start to learn which skills we need and which ones we don't. And then that will also impact, thirdly how we remap what. What is an academic requirement and the value of a degree. So those are three predictions. Quickly, what would you guys say?
- [08:14]
Faculty Member
Yeah, I like that. You started with the definition. I decided to go back and look at the definition of integrity, and there are actually two definitions of integrity. One is the quality of being honest. This is a short version of it. And the second is the quality of being whole, not divided, meaning there's a wholeness that integrity, it retains its shape. And so when you think about. Both are really important when you think about learning integrity. Right. It's not so much about academic integrity, but it's more so about. Yes, there has to be a quality of representing your work because that's what we do for academic integrity. And that's important. But second, and this is more of the, you know, how that's going to change on the spectrum of, yes, many students are using AI and they want to use AI productively, but we don't know exactly what that is. Let's be honest. We do not know the impact in certain ways of using it and how to use it. And so that second piece is we need to come together to figure out what does that take? How do you keep the learning whole and the individual development whole while moving forward with this technology? And so I think that the academic integrity hasn't changed as a definition, but where we want to focus in which one of those two definitions, I think that more time spent on the second, perhaps, than the first of the binary choices. So that's how we're thinking about it.
- [09:45]
Jenny Maxwell
Yeah. I'm Jenny Maxwell and I lead the global education team at Superhuman, the company that owns a little writing tool called Grammarly that you may have heard of. We've been thinking a lot about this in the last several years. I would say, you know, our position is this. For 17 years, we have been the trusted writing partner for close to a billion students and millions more professionals who depend on our trusted, contextually aware AI to serve them in their flows. And one of the things that came about in the last several years was AI really wherever we were in November 2022 when AI came and commoditized writing. And writing has always been the biggest indicator of learning and education since the dawn of time. And so why we're here and why this room is packed on the third day is that we're still really struggling with what does it mean to learn? How are we going to measure learning our position at Superhuman? Because we have incredible partners. One of them is sitting right here is a huge opportunity for AI to really live up to the promise of uniquely personalized learning experiences, where we are now at the cusp of being able to quantify incredible durability skills like curiosity, literacy, prompt, you know, prompt elevation, those. Those types of things, grit and persistence. And that is what Superhuman is, you know, really focusing on in the next several months in partnership with asu. So we're thinking about how do we create an environment where it isn't binary anymore and AI becomes an extension of teaching in a way that students find the value of the experience and immediately can then take that into the world of work and be successful.
- [11:44]
Moderator
So with a diverse group and then also all the diversity within the room, you're in different types of institutions and what might work for One of you might not work for another person. And Annie, I appreciate your comment about the definition of integrity. The International center for Academic Integrity really incorporates six fundamental values of academic integrity. Honesty, trust, fairness, respect, responsibility and courage. And the goal is that if an institution can maintain those six values, then those are the values that are going to create that culture of integrity. And so my question for you all is, as if we're looking at the discussion that there's not going to be a one size fit all model in terms of academic integrity and artificial intelligence in your current position. Or you can call out another position maybe if you want to say these other people need to be doing something in your current position. What is moving academic integrity upstream? What does that look like? Anybody?
- [12:45]
Jenny Maxwell
I have a unique perspective here. So we work with thousands of universities all over the world and thousands of K12 partners. And one of the things that I have seen work very well in the last year, which is when the leaders in these institutions actually demonstrate the productive struggle that they are claiming they want their students to have in their workflows. The productive struggle of leaders, of everyone in this room means how are you challenging your thinking? How are you challenging the ways that you've always ever done your job and how you've always ever delivered your learning experiences to students? It is not on students to fix this world of AI and navigate in old antiquated pedagogical systems. We are the adults in the room and it is our responsibility to lean into these human durable skills to model them. I have the luxury of having a 16 year old daughter who tells me every day where I fail. If those of you have teenagers, we can have a little trauma session later on Wednesdays. Yeah, yeah, yeah. So I will just say where we're seeing incredible opportunities to innovate and reimagine what this could be with AI. It comes from those, those leaders globally who say, I need more productive struggle. I need to be thinking about how to deliver organic chemistry in a way that supports learning alongside AI. So that's my take.
- [14:07]
Faculty Member
Yeah. The way you think about moving the learning upstream is really not new. I think it's been good teaching for a number of years, but it's difficult to scale. So you think about moving away more from science summative to more formative, smaller assessments throughout that learning process to see how they're scaffolding their learning. But that requires somebody to grade a lot, right? And that's been difficult. But now with opportunities in AI, AI doesn't get tired. AI doesn't need a Coffee. And so you can think about opportunities in the future. But how do we break that learning down more, but also give instructors and teachers and faculty insight into the entire process of writing, not just the end? So how did they think about forming a thesis? How did they challenge that thesis? How did they think about what evidence should I collect, what sources should I use? And many teachers do this today, especially in K12. They'll create kind of those different multi part assessments that allow the teacher more window into that creation and into the critical thinking that goes along the way. And so we think about offering insight into the process and also insight into how the student is using AI. One of the problems we have is that students are using AI outside of the teacher's guidance or teacher's knowledge. So it's very difficult for them to understand what are the uses and what were good uses and what are bad uses. There's nothing bad, but what are uses that aren't productive. And so for us it's important to show the teacher how that AI is being used and also making sure that the AI is, is talking to the student or assisting the student in an educational forward way.
- [15:54]
Jenny Maxwell
Right?
- [15:55]
Faculty Member
Not giving the answer, asking additional questions. And so it's incorporating it in that way for the teacher to give that guidance.
- [16:05]
David
I would offer this, I mean in terms of the spirit of moving upstream, which I think we, you know, we all agree on this. You don't want to wait to the end of the learning journey on the output and say where was academic integrity? We do have to go upstream. I would offer this definition that academic integrity in the age of AI is a multidimensional co constructed commitment to honest and meaningful learning. Then if you take those components and think about the whole learning journey at your institution, if you want to think about it that way, multidimensional, something happened back here. Hope everybody's okay. Multidimensional, you have to think about each individual part of the ecosystem working together to produce a learning outcome. And by every part, I'm talking about the platform features, I'm talking about how data is handled, policies, teaching methods, culturally, how it's handled at the institution. Think about all of those pieces. Every single piece doesn't own academic integrity. They do in concert co constructed. Every one of those parts contribute something. And if you're not thinking deliberately about those individual contributions, you're going to have a difficult time thinking about it upstream. And, and then the commitment piece. How do we handle responsibility and accountability in that? When each individual part or dimension contributes something, I think we have to get deliberate about that at each institution and think about it. I also think we have a, I call it a proficiency dilemma, which is that if you look at entry level jobs right now, you'll read phrases in the job descriptions that are akin to
- [17:45]
Jenny Maxwell
say, what is it like an AI expert?
- [17:47]
David
Well, or at least that someone should be proficient in using AI to do some kind of skill. Not fluent, but proficient. So if you're an entry level person at a new firm, you are going to have to use AI in an Excel spreadsheet. If you're working at a news organization or something, you're probably going to have to create some kind of first draft using AI the first day on the job. So then the question becomes, where do you establish that proficiency? When does that happen? And I think that's the trick with academic integrity. How do we preserve academic integrity while helping students establish proficiency? If we had time to ask people in the room, I've been going around asking faculty, do you think it's your role to help students be proficient in AI in a skill domain? You get a lot of mixed answers there. I don't know. What do you all think?
- [18:37]
Ann Jones
Well, maybe that's a pass to me. My answer is yes. And I'll highlight my unique role on the panel as a faculty administrator for the moment to say my answer when I talk to faculty with my administrator hat on is that is your responsibility. And we need to talk about how curriculum needs to change so that that responsibility is being fulfilled. And so to the original question of what does it mean to move academic integrity upstream? From an individual faculty perspective, if you are reporting a violation in your role as a policeman, it's too late. You already missed the boat.
- [19:13]
Faculty Member
Right.
- [19:14]
Ann Jones
And so I think about upstream as two things. Right. From my perspective as a faculty member and an administrator, there's two conversations I need to have in both of those roles. The first is what are we teaching and why do we care about it? And it's got to include AI. For decades now, when we survey students, why are students in university or college to improve their job prospects. If we give the same survey to faculty and ask them, why are students in college? The answer is to find themselves, to expand their horizons, to become better people. That's the disconnect and bridging. Of course all of those things have value, but helping everyone in that co created experience see themselves in the experience is really important. So those conversations, do we really need to teach? I'll pick my own subject. Balancing chemical equations. It's been decades that computers could do that better than people. And so we need to articulate who needs to know that, when and why? Because in my professional experience, that is never a skill I have used so that I spend a lot of time thinking about the things that I do in my research lab that I never teach to undergrads and why there's such a big disconnect. I think the other half of the conversation we have to have with faculty, and I think, David, you were alluding to this, is you got to get better at using AI too, because it's your responsibility.
- [20:57]
David
How do they do that? That's the trick.
- [20:59]
Ann Jones
And so as institutions, we have to provide opportunities for our faculty, our staff, everyone associated with the institution, recent alumni, students. We need deliberate conversations about what AI proficiency looks like in our disciplines and materials for fostering that.
- [21:18]
Jenny Maxwell
We hear this all the time. We work with a third of the Fortune 500 and they us all the time. Like, you know, Jenny, what's happening in these institutions? How can we hire domain experts who in many ways are the like blue check marked AI literate, not fluent, not completely proficient because this is an ever evolving tech soup. And so we, we are thinking about that too. Like what are, what are the ways that faculty can lean in and become coaches again instead of the sage on the stage of I'm going to transfer this knowledge from you and I will bless you as a bachelor of Science from asu, it's how can those ASU graduates come into the world of work? So all of these employers go, every time I hire someone out of asu, not only can they bring all of this expertise that came out of that experience, but they are leaning in. They are helping our various other colleagues in their work spaces lean in to try, adapt, to fail often to iterate and to bring this sort of again, human durability of confidence in their own skill set and problem solving to work. So the faster we can create an environment where faculty can maybe, hopefully not have to teach chemical balancing, chemical equations, yuck. And start teaching, you know, what type of creative, you know, experiences should they be exposed to as they are becoming chemists.
- [22:41]
David
It's so hard. I mean, the job of a faculty member is so hard right now. I mean, I'm just thinking about, I mean, in my opinion, I just feel like not only to use chemistry as a faculty member, not only do you need to update your own methods about how to be a PhD level chemist using AI, then you have to update your teaching methods about how to teach students leveraging AI. You may have not even gone to school to know how to teach. You went to school to be a chemist and here you are. I think it's just very, very difficult. I'm curious of the either faculty members, instructors and or administrators in the room. How many of your institutions have a robust enterprise wide effort or program to upskill faculty? Three hands, four hands. So I think that's one of the challenges. We have a skill gap, a huge need and not a lot of effort there to solve it. Maybe not effort, resources, I guess so
- [23:34]
Faculty Member
I struggle with the phrase students need to have AI skills for the workforce. And I tell you why, because what do we mean by that? And I'll give you very clean examples. We're having a conversation. Be honest with you. It's getting a little bit dated in the sense that when we say AI in this room, a lot of us are assuming that means an LLM who's helping us do something. Okay, well that's been the AI that we've seen for the past three years. That is not the AI that's happening right now. Nor is it the most important AI skill to have. Listen, student will figure out how to right click to polish without a course on it. Right? We can pretty much assume that they're going to know basic AI skills in how to write and mechanics and adjust their emails as they need the real AI skills that I'd like to see us talk about. And the conversations are around, do we need to teach students how to vibe code so maybe they can create a molecule without having to learn to code and show somebody the vision of a three dimensional molecule? That to me is fascinating AI skills. And that's an AI skill that they will use in the workforce and be rewarded for other AI skills that are really interesting are around. Like how would you work with an AI agent? How do you think about integrating multiple vectors? Or how do you structure data in a way that an agent can then use that data? And you kind of go back to the beginnings of information science, which we really haven't talked about that much in years. So many are so many. As I get my grammar wrong, there are so many can help you with that. Yeah. There are so many exciting ways to use AI and we're too narrowly focused on kind of the minutiae of writing skills. And I just want to make sure we broaden that conversation and we get more specific when we say students need to know AI to be successful in the workforce.
- [25:42]
David
I agree with your broadened definition. What I was posing is, so who teaches the entry level accountant agent at ki Right. Like, I mean, is that in the curriculum right now? Maybe in some places it is, and in other places it isn't. I think that's where we're at. It's like that expectation to be able to do that work, I think are great examples. Where do they acquire that? Some of it on their own, but we need to work it into programs as well.
- [26:06]
Jenny Maxwell
It's almost like you needed something that was ubiquitous, that lived everywhere you worked, that could serve you contextually aware agents in your workflow very easily. Superhuman.
- [26:20]
Moderator
And I go back to what Dave asked, the question of how many had a kind of a planning program for your institution? And if you look at the teaching and learning centers, you probably have teaching and learning centers that are doing great work, but who attends those? It's the same people who are already doing great things. It's not the people who actually needed to be there. It's not that faculty member who's been at the institution for 45 years and still using the same test from 45 years ago and still the same assignments and wondering why their students are cheating. And so I think that's the question of how do you reach. And if y' all have ideas of how do you reach those faculty members in particular? But then also we do have some students who are adamantly opposed to artificial intelligence. I think that's something that there's been a generalized consensus of, oh, all students want to use it, not all students want to use AI, and not all faculty members want to use AI. And so how do you balance going off script? This is not one of their pre. Pre known questions. Yeah, we're already off script, but so how do you think that institutions should balance that discussion of teaching when you have some people who just. Do you force them to use it? Do you force the faculty members? Do you force. What would be the response if you're in our institution?
- [27:37]
Jenny Maxwell
I think we're humans, right? So this is where the human behaviorists. I'm sure there's credible psychologists in the room, but this is around motivation, Right. We actually haven't seen any rewards significant enough with AI adoption for the faculty to be running as fast as they can to spend the time to reimagine what they need. We're still in early days, even though it feels like we're not. And so I think we're getting very close. And I do think that it is. It's becoming of the leaders at these institutions to be courageous and to be bold and to. And to take these calculated risks of where can we Divest if it comes down to a resourcing issue, like, there are lots of things because AI exists that you may not need to spend money on anymore. And so where do you divest so that you can reinvest in the human behavior mechanisms that will unlock the 6,000 faculty members at ASU to go in, run into the opportunity to change the courses so that these students recognize the value and have lots of opportunities to create molecules for their chemistry portfolio or whatever. But it does come down to pure motivation. We just talked about this this morning, where you're like, God, I'm so beaten down with the life of a faculty member right now. We have done no justice to faculty members being able to get to that North Star with AI. It's like there's a moral imperative to teach your expertise and make sure that everyone is graduating with exposure, but they've not had a moment to even think about what would be meaningful. Casey Evans, I will quote her. She's incredible. She's the COO at edplus at asu. She believes strongly. She'll say this on almost every stage, so maybe she'll be okay that I'm saying it for her. But she's like, we have to burn it down and build it back up the way we think our students in 2035 are going to need to operate. And it has to start literally today or soon. So I think it's around galvanizing faculty, but giving them the motivation that comes from. Are the students going to be rewarded in the way that aligns with academic integrity and the integrity of our degrees at our incredible institution?
- [29:44]
Ann Jones
And what are.
- [29:44]
Moderator
I'm going to call you out. What are you all doing as you're seeing those faculty on a daily basis?
- [29:48]
Ann Jones
Yeah, I appreciate the question of.
- [29:54]
Moderator
I don't know if it's.
- [29:55]
Ann Jones
I think it's.
- [29:55]
Moderator
It.
- [29:59]
Ann Jones
I. I appreciate the question of what does it mean to use AI? Because we have consistently created groups of faculty from all of our colleges to actually ask that question. What is a baseline? What do we mean by proficient use of AI? And I think it. It comes to several of the things that my colleagues have said. You have to understand what AI is, whether you're an English faculty, an English student, a chemist. It can't be magic. You got to know a little bit about models. You got to know a little bit about capabilities. In fact, it's humanities faculty members who are, I think, the loudest in saying our students need to understand what this is, to be thinking about the human implications, to be thinking about the Philosophical implications. You need to be able to do discipline effectively things. Whether that is vibe coding. Yeah, we have that class actually. Whether it is handling data in entirely new ways to ask questions that you couldn't ask in the past. Whether it's creating art in a different way. Right. You need to be able to do something of relevance to your discipline using AI in some meaningful way. And you need to be able to evaluate whether a model is a good choice for you, whether an AI tool is appropriate, whether you should be doing something else entirely.
- [31:23]
David
Right.
- [31:23]
Ann Jones
We need to be equipping people with these three things. As an institution, we've asked every college to ensure that that training is available to every student in their college in some way. We're not forcing them to take it yet, but it needs to be available. And we created an analogous program for faculty to be able to get to those skills. And then it's, I mean, early adopters jump right in. And then you try to encourage more and more people through faculty meetings. And my friend did it. It wasn't that painful. Right. All the things you do with faculty members. Incentives, money. Right. All the works. It is also incredibly important and I think we're at the leading edge of those conversations to have honest conversations with all of our faculty groups about what they are teaching, what will need to come out and what needs to come back in. Because you can't just keep accreting stuff. Right. These are, they're emotional conversations because everything has value. But we have now successfully, in a few contexts, been able to say to some academic unit via teams, you have to take curriculum out of the hands of an individual and turn this into a co creation conversation to say, what do we need to have here as opposed to all the things that historically came here.
- [32:49]
Moderator
We actually only have about seven minutes left, which is. I knew this panel would not have problems discussing for 40 minutes, but I want to kind of finish us up and I want to make sure each person has a chance to share what they want, to make sure that you all know to take back to your institutions or your classroom. But I'd also like to throw out again, off script. If we're talking about AI and the future of academic integrity with AI, where does agentic AI fall into that? And I know we probably should have started that about two hours ago to have that conversation, but you can feel free to talk about that. But what do you, what in your position, what do you want these folks to go home with?
- [33:31]
Jenny Maxwell
I'm really excited that we're not talking about AI being Synonymous with an LLM. I always have said that from the jump that if that's your strategy to buy XYZ LLM, then that's not a strategy. I think agentic. What we're seeing at Superhuman, certainly with our agentic bench for students, is the diversification of partners for you in your workflows and the way that it can criticize your thinking in a non emotional, unbiased way. The way that it can test your thinking and put you into experiences that normally you wouldn't be able to do. I get very excited about the deep customization that a student can both create for themselves on their agentic bench or in partnership with their institution in order to get the maximized value out of whatever degree programs they're hoping to attain. I think the specificity of learning and the depersonalization is going to be completely unlocked with this world of agentic AI. I'm very optimistic about it.
- [34:32]
David
I would give you this to take away in terms of back into the classroom. And as you think about, I was looking at our title over here. We didn't really even talk about the word cheating at all.
- [34:43]
Jenny Maxwell
Thank God.
- [34:44]
David
Yeah. Well, I'm gonna fix that right now. No, I'm kidding.
- [34:47]
Moderator
You have five minutes and 20 seconds.
- [34:48]
David
I know. I'm gonna be quick. I'm gonna be quick. I just wanted to raise up that. I think that as when you go back to your institutions, I would encourage you to depathologize AI and have more conversations about it. In particular, find some empathy for the workflow and the strategic decisions that students are making when and when not they use AI on their own. I don't think that a large percentage of students get up in the morning nefariously trying to use AI to circumvent all learning. I think along the way they are making strategic trade off decisions. They run out of time and solve that problem with AI. They run into a learning gap and solve that with AI. They decide just to spend more time on one task than another. Because AI can't help them with one task and it can with the other. So the rational human beings, I think the more we understand that experience, the better we can help and give them more productive options.
- [35:42]
Faculty Member
Yeah, I think it's okay to say that we're cheating and I think we need to have eyes wide open that there are absolutely students who are looking to circumvent learning and to pretend that that's not a subsection is not good for the value of the credentials we give them. So I think that we do need to address that. There are students doing that. We could have a whole other panel on what Agentic does for that and fast powers that that'll be. Next year's panel is what has Agentic done with cheating? And there's an entire industry trying to now help students cheat and avoid doing work. You can type in any Google search and you will get more answers than you could possibly imagine about buying a service to help you do your schoolwork. It's not good. So that's one takeaway is that hasn't gone away. The second is around critical thinking is still what we want students to have. Those skills will still make a student successful. More than anything is making sure they understand how to weigh risks and benefits and make a decision and understand how to assess information that remains the same. So how we get students there, I think evolves and I think making sure that we are cautiously optimistic. If a teacher says to me, I don't want to do AI in this assignment, I'm okay with that. I am okay with that. I think that that is something that to be supported and we can argue that, but I think that we need to be open about how AI is shaping and evolving student learning. And the only way to do that is for full transparency for the teacher to see how the AI is being used in a way that's helping them learn.
- [37:24]
Ann Jones
Yeah, I think I can pick up exactly that topic. To say every institution needs to create more opportunities for people to see productive use of AI. It has to be. I mean, I've had so many conversations with faculty members in which they go, wow, that's amazing. How did you do that? And I think I'm not actually that good. It just means you haven't seen anybody.
- [37:48]
Faculty Member
Right.
- [37:49]
Ann Jones
And everyone needs to be able to imagine doing something that was impossible for them without the kinds of technology that it's available today. Right. They need to be able to see that this creates opportunities to learn and work and create knowledge and create creative work in ways that they couldn't before. Because then we're going to develop millions and thousands and countless ways of using these tools differently, which is going to transform education.
- [38:18]
Moderator
And if you. Annie mentioned that there are some students who wake up every morning thinking, I'm going to cheat. Somehow in terms of academic integrity research, you kind of have a bell curve. You've got a group at the back end. They're going to cheat no matter what. They're going to do something. You have a group up in the front area. They are never going to do it. I mean, they could be about to lose their scholarship, be kicked out of their house. And they are still, morally, ethically, they're going to make the decision. I'm not going to take a shortcut. Then you have pretty much the middle half, if not more than half. If you say 25, 25 and 50, those are the students that we are able to interact with. And the way that we interact with them is going to help them make their decision. Do I take a shortcut and do I not? And so I think, as I mentioned at the very beginning, we all agree that academic integrity is very important. The productive struggle is very important. Building that culture of integrity, I would say, is one of the key components at your institution. You have to have that. The norm of your institution is that students are going to sometimes struggle with things. That human body, that five hour class that has a seems like 15 hour lab every week. It's going to be tough. It's not going to be an easy A for you. But the amount of skills that they learn with or without AI is the very important part. And I think it's very important that we are able to think about how AI can be a benefit to that. But how do we manage and make sure that the degrees that we're conferring at our institutions are still going to be worth something in terms of the future? Because that's going to help our recruitment, retention. I mean, it all goes back. If our institution is that case study of cheating, well, nobody's going to want to come to our institution as well. And I'm getting the red light now. So I appreciate, let's please thank our very dedicated panel. I appreciate it.
- [40:26]
Jenny Maxwell
Thank you.