The Opposite of Cheating

Student use of generative AI tools as  a substitute for learning has led to increased concerns about academic dishonesty.  In this episode, we discuss why students might use these tools and strategies instructors can  use to encourage academic integrity. Thanks for joining us for Tea for Teaching, an  informal discussion of innovative and effective practices in teaching and learning. This podcast series is hosted by

John Kane, an economist... ...and Rebecca Mushtare, a graphic designer... ...and features guests doing important research  and advocacy work to make higher education more inclusive and supportive of all learners. Our guests today are Tricia Bertram Gallant and David A. Rettinger. Tricia is the Director of  the Academic Integrity Office at UC San Diego and

Board Emeritus for the International Center for  Academic Integrity. David is an Applied Professor and Undergraduate Program Director in the  Psychology Department at the University of Tulsa. He is a Professor Emeritus at the University  of Mary Washington, where he directed Academic Integrity Programs and the Center for Honor,  Leadership, and Service. David is also President Emeritus of the International Center for Academic  Integrity. Tricia and David are the authors or

co-authors of numerous articles, books, and book  chapters on academic integrity. Their most recent book, The Opposite of Cheating: Teaching for  Integrity in the Age of AI, was recently released as the 4th volume in the Teaching, Engaging, and  Thriving in Higher Ed series at the University of Oklahoma Press, edited by Jim Lang and  Michelle Miller. Welcome, Tricia and David. Thank you. Thank you. Our teas today are;... Tricia,  are your drinking tea?

I am, my standard tea of  peppermint after lunch. Very good. It's one of my favorites. And David? I'm a coffee drinker, I have to admit. But it's a really good Sumatran. Excellent. Good choice. And Rebecca? I have Lady Grey today, John. And I have an Irish Breakfast tea today. Nice. So, we invited you here today to discuss  the Opposite of Cheating. Could you tell us a

little bit about the origin story of the book? Sure. Well, Trish and I have been doing this work for a long time, both separately and together, and  so as AI has really started to become a big issue, but even before that, when the pandemic hit,  a lot of our colleagues have been asking us, what should we do? Oh, my goodness, our students  are cheating, and Trish and I both sort of smiled separately and said, “Yeah, they've kind of  already been cheating. You're just kind of

deciding to worry about it now.” This has been  an ongoing and persistent problem for probably as far back as Plato’s Academy, if not further,  because people are people and it’s a fundamental problem. So when Jim and Michelle came to us  and said, “Would you consider writing a book for faculty that's practical, that's positive,  and that really takes a keen eye on academic

integrity?” we said “We'd be happy to.” So we  wrote this book. We were kind of proud of it, and it was ready to go to the publisher pretty much  exactly the same week the ChatGPT came out. Nicely timed. Yeah. Yeah, couldn't have done it better if  we tried. So the result of which being, we sent it to the publishers and to the  editors, and instead of a two-week turnaround,

there was about a six-week turnaround. And  Jim broke the news to us and said, “You know, you're probably going to have to rewrite the  book,” which is, I'm sure, his favorite sentence to tell an author. But we did. We wrote the book,  and as we did, AI grew faster and better and more accessible, and we learned more and more about  it, and now I think we've got a book that can really be useful in an evergreen sort of way,  because the principles we're applying are not

based on the tools that you see today. They're  based on the people who are using those tools, and the people really haven't changed at all. And  so we really hope this book will be evergreen. And as you noted, students have always  cheated to some extent, but there was a bit of an increase in that during the pandemic  based on self-reported results from students in terms of academic integrity issues. Why did  it increase so much during the pandemic?

A simple reason. As David said, students are  humans, and so if you think about a couple of things that were going on during the pandemic, if  you were a student. One is, like everybody else, you were operating at Maslow's bottom hierarchy of  needs: survival, safety, security, that's all that mattered at that time. Like integrity would  be way, way, way at the top of that pyramid, so that was the first thing that was going on.  The second thing was increase in temptations

and opportunities. All the assessments went  remote, and where before, there were proctors walking around the room, maybe checking your ID,  maybe checking the authorized age you brought in, seeing if you brought something else in, all of  a sudden, there was none of that. If you take our traditional age college student of 17- to 21-year  old, putting them in that kind of situation, or putting any of the younger kids in that  kind of situation, of course they're going

to cheat. It's a lot to ask a student  to resist those temptations, especially under times of great stress and pressure.  So two things created a perfect storm. Can you talk a little bit about, you’ve hinted at  this already, but why students cheat in the first place, or maybe why they act without the integrity  that we're hoping they would act with? Well, it’s a how long have you got sort of an  answer? You can break it down into two parts,

though, and Tricia, pretty much already did.  It's the situation that they find themselves in, and the individual differences are the nature  of who we are as people, and those are going to interact to create the situation where students  cheat. Well, I think to take a step backward. The first thing to say is that almost all students,  just like almost all people, if you put them in the right circumstances, will make a decision  that they're not proud of later. So it's not as

if we can just weed out the few bad apples and  solve the cheating problem. Having said that, there are a few bad apples, probably about eight  or 10% who use what we would call cheating as a dominant strategy throughout their higher  education and probably throughout their life. Those are students, yeah, we really do want to  try to weed those out. But for the other 80%-ish,

some percent, I'm going to guess 20%, would never  do something dishonest knowingly. Some of them still will cheat because bringing us to reason  one, which is they don't really understand what we expect of them, and that's a situation that's  growing more and more as higher education becomes more accessible to people who don't have the same  background, values, cultures and understanding. This is a good thing, that higher education is  more plural and more diverse, but it does change

what our strategies need to be in order to help  everybody be successful. And then you have the other situational reasons, things like it's a  lot easier to cheat in a high-tech environment in a lot of cases, so students are more able  to do it. They feel like they're less likely to be caught in those situations. And then you get  into the interpersonal ones that I think are the most interesting, but I'm a psychologist, so of  course I do, these are things like number one on

the hit parade is peer pressure. Students are very  sensitive to peer pressure and to peer motivators. If students think that cheating is acceptable,  they'll be more likely to do it. If they think it's unacceptable to their peers, they'll be less  likely to do it. If they think their peers are using it as a strategy, students don't want to  be left behind, and so they'll be more likely to try to cheat to keep up with the Joneses. So peer  pressure, peer motivation is a big deal. Then you

can add in things like, how wrong do they think  it is? And different kinds of behavior are quite varied. This is where AI comes into play, because  we're all in a new frontier, and none of us really understand what AI is doing. I shouldn't say  none of us, but most students don't really understand how AI works, and so they don't really  understand whether using it is inappropriate,

and almost none of our students understand why  we're asking them to do the assignments. If they knew at a deep and intuitive level why they  are doing what they're doing in the way that we do when we create the assignments, then they  could make a better evaluation about whether the tools they're using are appropriate or not, but  because they don't know, we haven't told them, and not all students are coming from a context  where it's automatically expected that they know.

We're left in a situation where students sometimes  don't think what they're doing is actually wrong, although as often as not they do and they're  doing it because of things like time pressure, stress, desire to be successful. And  there is more, but that's a lot. In other words, I'll sum up what David just  said. They cheat ‘cause they're human.

And there's multiple reasons. And one of the  things you stated in the book is that there isn't one solution to addressing these academic  integrity issues, but there's a right approach. Could you provide just an overview of an approach  to try to address this collection of challenges that may lead to academic integrity issues, I think we would call it a teaching and learning

approach, rather than a crime and punishment  approach. So a teaching and learning approach means that we're centering the faculty and  the students in the conversation, and we are centering learning and what we can do to best  facilitate, which is one job of the educator, and assess learning, which is the second job of the  educator. And we often forget that second part. We like to focus on helping students learn, and  we forget that assessing learning is a huge reason

why students come to us. They need that degree.  That degree is supposed to be a culmination of the assessments of learning that represents what  they now know and can do. And so the approach is: focus on teaching, focus on learning, and part  of that is focusing on good assessment design. So that's the right approach. It does a couple  of things. One, it's less emotionally draining for faculty when they feel like they have to be  police officers to catch cheating and to report

it. That doesn't sound like fun, and it doesn't  sound like what they signed themselves up for. But if you have them focus on learning, that's  like, “Oh, well, yeah, that's what I signed up for. “So it feels better for the faculty, and  it's also, of course, better for the students, because they do want to learn. Yes, there are  some, as our colleagues in Australia call them, enrolled persons in our school that do not want  to learn. Put them aside. Like David said for a

second, the rest of the students do want to learn.  We're humans. We love learning. We loved learning as kids before we got into school. And it could be  that school maybe dampened that love of learning a little bit, but people love to learn, and so  having that focus says we're in this together. And you know what, I am going to closely observe  you to make sure that you actually learned what

I think you learned or what you were supposed to  learn. But it's not to catch and punish you, but it's to validate your learning and to ensure that  you are making progress that you want to make. You hinted at this already a bit about a  misalignment between faculty understanding of what acting with integrity might be  versus what a student might think. There's a discrepancy there. Can you talk a little  bit about why there might be this discrepancy,

and then also how we might address it? So we've already talked a little bit about the discrepancy in knowledge between faculty and  students, and that we are not teaching mini me's. We are an unusual subset of university  students who go on to be professors, and we sometimes forget that they are not us.  It's also the case that the student population has changed in the time that a lot of us have  been doing this. So we shouldn't teach the

students we were nor the students we had. We need  to teach the students who are in front of us, and sometimes that means matching our expectations to  theirs. It doesn't mean changing our expectations, but communicating what ours are in a way that  they can understand. So some of it is knowledge, but some of it is also goals, as Tricia alluded  to. They are there, and I've asked them, to get a degree, to get a diploma, to get a good  job, to make money, to have a place in society,

and learning is a path to do that. And so we need  to leverage their desire to do these things in an authentic and meaningful way….and yes, they do  have that desire, most of them… by communicating the value to them of the learning that we're  asking them to do and the assessments that we're asking them to do. None of this involves  watering them down or dumbing them down or

simplifying nor justifying your existence to your  students. It involves helping them bridge the gap between their experience and yours, which is, of  course, learning, such that they're doing these things in a way that will prepare them best to be  successful in the future, whether as professionals or as humans or as family members or as voters,  whatever. And if we can persuade them that what we're doing is worth doing, we're going to at  least give them the opportunity to act in a way

that's in accordance with our expectations. Sounds very aligned with the TILT or the transparency and learning  and teaching approach. We're not inventing anything new with respect  to pedagogy or teaching. And in fact, I think, going back to the origin story question, from  my perspective, our first concern when we wrote

this book is, is there any audience for it?  Because we're not writing anything new. But then it kind of occurred to me, and I think to  Tricia as well, we're writing about teaching and learning for academic integrity folks, and we're  writing about academic integrity for teaching and learning folks. And so we hope to introduce  some of the core concepts in these fields that we bridge to folks who maybe haven't had the  opportunity to bridge it in the same way yet.

It sounds as if one of the strategies you're  recommending is to help students see that education is valuable because of the skills  they're developing, and that it's more than just getting that certificate at the end that  matters, that if they don't acquire skills, they're not going to really be all that more  valuable after they graduate, and just helping to align their values with the values that we hold  concerning education. Would that be part of the

message we're trying to get across to students? Yeah, I think so, as I've been telling students of late, look, no employer is going to want  to hire you if the extent of your skills is you pushed a button on ChatGPT and it gave you  output and you handed it in, they don't need you for that. These things are becoming agentic, and  they can operate without any human intervention.

So you have to bring more value to the employer  than that, and so that helps to explain to them, one the importance of developing skills, but also  of why we don't allow them to use AI for every single assignment or every single activity.  We still teach students basic arithmetic, even though there's calculators, because  there's a belief or knowledge that learning that is helpful in order to better understand more  complicated math you're doing later, or just to

understand math. And so we have to figure out  the same thing for students. And then second, it's related to one of the causes of cheating that  we haven't mentioned yet, which is the extrinsic motivation, right? If I'm only motivated by the  grades and the degree, I'm more likely to cheat, and if I don't see the point of the assignment  in helping me get there. And so raising students’ intrinsic motivations means we've got to  really seriously rethink what we do in

college and university. Are these learning  objectives still relevant in the age of AI, and if they're not, what needs to be updated?  Are my assessments still relevant? So many courses require students to demonstrate  their knowledge in writing. Why? Is writing really something that every single  class needs to use as an objective measure

of a student's knowledge and abilities? Probably  not. And so we really need to rethink some of those things to make everything more authentic  and meaningful to students’ lives today, which will increase their intrinsic motivation and help  reduce the likelihood that they might cheat. We want to make it clear that learning objectives  are where things should begin. It's that classic backwards-design approach, and so Tricia says  we don't want anyone to think that Tricia is

saying that writing is not important. The  question is not writing is not important, the question is, is writing important to what you  do? And that's what for instructors to determine. Sometimes it's critically important. Sometimes  it's just really useful. But at any rate, if you design your courses from the objectives  back, then you can be really thoughtful about what you're asking your students to do in a way  that can incorporate AI when it's appropriate,

ban it when it's appropriate, and be agnostic when  it's appropriate. But to focus on the learning… It doesn't matter what you're doing, focusing on  those learning goals and updating what you do is maybe the other key theme. It’s not all about  student motivation. You can't design cheating out of a class, as our friend Kath Ellis likes to say,  you can only design it in, so that's one, and very important component, is motivation. Another is  assessment, as Tricia said, and communication.

This emphasis on learning is certainly aligned  with one of the strategies that you suggest around growth mindset. Can you talk a little  bit about how to help students develop that growth mindset in this kind of a context? Well, we mentioned that in the book because

it's one of the pieces, as David said, we're not  introducing anything new. And so our book really isn't about so much of how do you create growth  mindset is thinking about, if we have a growth mindset about our students, that will change the  ways in which we tackle the problem of cheating, and if we have a growth mindset not just of  students in my content that I'm trying to teach, but in their ethical decision making. So a lot  of faculty may start off thinking, well, these

students are already adults. They're 18 years old.  They should know better. My job is not to educate people on ethics and morals, and so they think  of them as having fixed mindsets. If they cheat,

that must mean they're a cheater, and therefore  they need to be punished. And our point about the growth mindset is, in both of those areas,  consider that your student still has a lot of room to grow in making ethical decisions,  especially when under stress and pressure, especially when given temptations and  opportunities, and consider that they

still have room to grow in skills and knowledge  that is related to your classroom as well. So it's not so much that we present strategies  to help them do that as here's another way of framing your approach to the cheating problem  that you might find helpful and enlightening and

empowering rather than depressing, I suppose. Sometimes that seems tied to this idea of an assumption that students need to  come in with certain skill sets, rather than meeting students where they're at  and recognizing where they might be at around this particular idea of academic integrity. Growth mindset really is bound up with another really important psychological concept, which is  self efficacy. Students are much less likely to

cheat if they think they can do the assignment  to their standard in an honest way. If they think it's impossible or too time consuming  or unlikely, then they're more likely to take a risky or a dishonest approach. And of course,  if they come into a class thinking they can't do it, and they can't ever do it, then you have low  self efficacy and a fixed mindset. And those are students who are either going to fail or cheat  because they don't see any other choice.

Fail, cheat, or drop out. We're going  to lose them one way or another. So the question is, are we educators, or  are we cops? And if we're educators or are we gatekeepers? And if we're educators, then  our job is to figure out how to get them to at least engage with the material enough to have  one success experience and hopefully let that

build to a second success. So one might  argue, yeah, our job is not to make our students feel good about themselves, and I would  actually agree with that, both halves of it, but it is our job to give students tasks that  they have a shot at completing, at least to

start out. Because we're not gatekeepers,  at least not mostly, we're educators, Because our book is focused on instructors, and  when we talk about it, can sometimes feel like we're piling on instructors, that we're giving  them all sorts of advice, and there's so much on

their shoulders. And I want to point out that  in our book, we also make sure we say, “Hey, institutional leaders, if we're going to expect  faculty to do all this, to make these changes, to adapt their courses and assessments to the age  of AI, they need to be given the time, training, and support to do so.” We were not taught in our  PhDs how to teach. Like K through 12 teachers were

taught how to teach. We were not taught how  to design valid assessments. And yes, a lot of us will work at some place where there'll be a  teaching and learning center that will have maybe two instructional designers or maybe one person,  and they're often focused on helping faculty create online courses, as if teaching in person  is easier and everybody just knows how to do it. So as we're giving all this advice to instructors,  we're saying here's a menu of things we want you

to think about. Choose what gives you agency, what  empowers you now to tackle this problem? Choose something that you might do next year after you  do some more research and institutional leaders, you need to give your faculty, time, training,  and support to deliver on this new teaching and learning imperative for the age of AI. We've done several podcasts with Elizabeth Canning, and she's done a fair amount of work on  the importance of both instructors having a growth

mindset and in building that growth mindset for  students, not only does that tend to benefit all students, it tends to also reduce some of those  equity gaps. So we'll include a link to those podcasts and some of the resources there in the  show notes as well. With the introduction of AI, a lot of faculty panicked, but I don't  think, from what I've seen of surveys, student cheating continued, but it didn't seem  to really increase. The surveys that I've seen

have indicated that was mostly a shift in the type  of cheating. Instead of subscribing to Chegg or other services, students were perhaps a little bit  more likely to rely on AI to do some of that, and faculty, though, seemed to be much more concerned  about this type of academic integrity issue than with previous ones. What can faculty do to address  the issues with AI? You mentioned before that one issue is, some things can be done by AI. Some  things can't. What can we do to help faculty,

perhaps, discourage inappropriate use of AI? I think it's all the things we've already talked about. Start by making sure you understand as  a faculty member why you're banning AI or what the rules of engagement are. Is it a knee-jerk  reaction. Is it fear and ignorance, or is it a

principled approach based on the learning goals  of your class? Then, once you've done that, and if you're confident that your approach to AI is  based in the learning objectives of your course, make it clear… to start within the syllabus, but  I would say with each assignment that you write create some sort of meaningful engagement with AI  for your students. I don't mean have them use it, but tell them what the rules of engagement are for  every assessment and every activity in the course,

and why it is what it is. And I don't mean a  defensive “why,” because we ban AI when we're asking students to do fundamental skill-building  tasks that will ultimately benefit them in the future, as they try to do more complex tasks, as  a general rule, and we allow AI when that AI is

enabling them to demonstrate the knowledge that  they have in other ways more clearly. And so I think figuring out why you're doing what you're  doing, telling students when it's appropriate and when it's not and when it is appropriate,  I think it's incumbent upon us to learn how to use these tools appropriately and teach our  students how to use them appropriately as

well. There's a number of really useful research  tools out there, for example, that I don't think anyone would question the ethicality of using  if you use a Google search, you would probably should also be okay with using an AI-aided  Google search as an example. So we should start with thinking about it and then talking  about it and then doing it, I would say. We encourage, really in the book, communicating  with your students. So that means asking them,

what tools are you using? How are you using  them? How are you finding them helpful? How are you not finding them helpful? Hey, you know how  faculty always complain that students don't read the syllabus right? And they complain that they  don't look ahead, they don't plan ahead for all the assessments in their class. So let's look at  the syllabus together. Here's all the assessments or activities we've got coming up. When do you  think it would be ethical to use AI or in what

ways? And when would you think it not be ethical?  When students hear each other talk about this, whether it's asynchronously on a Google Doc or in  class, again, pure norms… they start to say, “Oh, so I've got my peers saying I shouldn't write my  first draft of my paper with AI, interesting.” That lands different than it does when the  professor just says, “Don't do it.” And then

the second thing is, if you're going to say, don't  do it, you better secure that assessment. So if you are on a diet and you're trying to not eat  any sugar, you do not put sugar in your kitchen. You secure, you lock that baby down, right?  And you say, like, that cupboard has no sugar in it. And yet again, we tell students not to use  something. And then we say, now go take your exam unproctored at home on your computer , and it's a  dereliction of duty. And so we really do have to

decide. If we're going to ban it, it needs to be a  secure assessment so we can actually enforce that, and so students don't feel like you told  us not to use it, but everybody's doing it anyway. So this is not fair. So those are  two things I would add to what David said. One of the things that we often find students  focusing on is grades and maybe not always

learning. And obviously this conversation is  focused a lot on the learning aspect, and even with the idea of assessments, kind of talking  about some of the values of those assessments. Are there other approaches, like alternative  grading, or other things that you might recommend that might help shift that perspective? No…. just kidding. I've tried them all. I haven't tried ungrading, but I've done all of them,  and I love some parts of all of the different

alternative grading schemes, and really don't  like some other parts. So I'm loathe to recommend anything to anybody else beyond stay open-minded  and then ask what grades are really for in your context and institution. At elite institutions,  I think that they really do play a different

role than they do at open admissions, broadly  serving institutions. And so I'm not going to sit here in my office and tell anybody how to do  anything in their classes, particularly grading, but I will suggest that you rethink how important  grades are in your courses, and how important you communicate about grades in your courses. When  students are pitted against each other for points, when faculty talk about the points and the  grades and the numbers instead of the learning

and the outcomes and the processes, students  internalize that, they absolutely do. I for one, have shifted away from rigidity in my courses to  flexibility with all sorts of things with respect to grades. So a simple example of this is the  redo. I'd call it a mulligan, but I don't think any of my students would really understand what  that means. So in one of my project-based classes, they get two redos, plus they can earn one by  actually reading the syllabus and responding

to an Easter egg in it. So they get three to  start. And then if I need to give extra credit, I can give an opportunity to earn more redos,  rather than just giving away points. Those redos do a million different things, but one of them is  they build in the possibility to fail. They allow students to try something they're not sure they  can do in a way that completely crashes and burns. And I tell them, please crash and burn at least  once. It means you got out of your comfort zone,

and it's free, it costs you nothing. And that  gives those students who maybe are concerned about engaging and concerned that they can't be perfect  to their own standard, a chance to give it a shot. And of course, more often than not, they're better  than they think they are, and they often don't even need those redos by the end, but they took  the chance. And you can also use a redo in my class, by the way, to get an auto extension so you  don't even need to ask for it. You just can use

a redo for that. And then the flexibility means  that you have that student who might be failing a class after the first assignment suddenly is  still in a position to get an A as long as they don't do it again, and it just opens up a world of  learning opportunities for your students, and at the same time, reduces their desperation levels.  And desperation levels are completely correlated

cheating. Those kinds of flexibility, whether it  be in the structure of assignments like redos, or whether it be something like specs grading,  where you can take a look at bundles of points and give students the opportunity to demonstrate  their learning in ways that make sense to them. Ungrading has its challenges as well as a  wholesale change. And I say, go for it if you have the time and the motivation to do it. But  whatever you do, think about flexibility and think

about ways to lower that student desperation. And David's answer there, just reminds me of something that might be important to say in  terms of our book. This is what our book is. Our book presents you with multiple options.  So as David said, we don't tell you you must do ungrading and that will solve the cheating  problem. We say, “Think about grading. Here's all the options.” And we use a lot of storytelling  from both of our experiences and the experiences

of others in the book to illustrate, to provide  that anecdotal data. And that's what this whole teaching and engaging and thriving series in  higher ed is about, is these practical books full of research-backed, theory backed-strategies,  along with personal stories. And it really is just a menu of things. I think we'd be really firm  on a couple of things, like you have to secure assessments if you're going to ban AI and you  really need to communicate with your students.

I've talked to hundreds of students now about Gen  AI misuse, and a lot of them just said, “I just wish our professors would be more clear on what's  allowed and what isn't. I just wish they'd be more clear.” So those might be the two things that  we're really only very firm about that you really should do everything else is a menu of options. You mentioned securing assessments. The share of students taking classes remotely has  grown pretty rapidly since the pandemic,

which raises a lot more concerns with this.  And there's also concerns about proctoring services in terms of Fourth Amendment issues and  legal issues associated with that. What sort of advice would you provide for people teaching  online classes in addressing the use of AI? Yeah, I've said a few times now on LinkedIn and  social media, online learning can continue. I don't know that online assessments can continue,  at least not if they have to be secure. There's

those proctoring companies that have their own  concerns. We've got deep fake. We used to say, “Oh, just have Avixa, have an oral meeting  with the student where they tell you what they know” …they could deep fake it. Or Derek Newton  just showed us at the International Center for Academic Integrity conference in his keynote that  there's just even software to make it look like I'm looking at you taking my test, but I'm really  looking up here at my notes. And so even doing

those kinds of checks on knowledge are going to be  super difficult. One thing that we're doing here in the University of California system is we're  looking at computer-based testing facilities. Now this is not our idea. University of Illinois,  Urbana Champaign started this a while ago. Faculty

created an assessment platform called Prairie  Learn. So the great thing about Prairie Learn is it does allow mastery-based assessment because  it helps you individualize the assessments, and so students can take them over and over again  until they master the learning objectives to the

level they hope or that we want them to. But  also, in a computer-based testing facility, we take tests or assessment administration out of  the hands of faculty and TAs who, frankly, their time could be better well spent engaging with  students, teaching students, coaching students, tutoring students. And the students come in,  we make sure it's the student who is enrolled in the class is the person actually taking the  test, and then they have no access to anything

they're not supposed to have access to. And we're  looking at building those out across the system, and then having an agreement with the Cal State  system and the community college system so that any of our, at least California, students who  are taking online classes could easily still accessibly transport themselves to a local  in-person assessment. Now, the other thing we wanted to make sure when we talk about this  is something that another Australian colleague,

Phil Dawson, talks about, is probably not every  assessment in every course needs to be secure. And we got to start maybe thinking at a programmatic  basis, what do students need to know and be able to do by the time they finish this program? At  what points do we need secure assessments to make sure they're hitting those benchmarks? And  then, if they're not hitting those benchmarks, how can we help them catch up? And so I think we  just need to rethink how we deliver curriculum

and how we assess whether students have met  those learning objectives. Another Australian colleague…. we keep always citing our Australian  colleagues because they're brilliant… but Kane Murdoch says that's like putting a bandaid on  an amputated limb. It's just not going to work if we just try and tinker around the edges. To cite another one of our Australian colleagues, you want to take a Swiss cheese approach to  academic integrity, because no one thing will

work. So start by building a basis of integrity,  by communicating integrity, building dynamic, interesting, and relevant courses for your  students, just like we've been talking about. But then some assessments that you give them will  need to be secured, as Trish said, not all of them do. You can think of it as sort of a division  between formative and summative, but it doesn't

quite necessarily have to break down exactly  that way. But when you're getting to those big summative, high-stakes assessments where you're  making sure people can actually be effective doctors or engineers or even psychologists or  teachers, it doesn't really matter what it is, everybody should be able to demonstrate those  skills and knowledge. That's when you have to

start using series of other options. So that might  mean a technological option, especially in online courses, technology is going to have to play  some sort of role, but today, you can probably secure an online assessment at the margins, but  still, anybody really motivated can probably work

around it. In five years, most elementary school  students will be able to work around it. So yeah, I think even if we can secure online assessments  today, and that's an if, we will not be able to secure them long term, and not even the new,  clever assessments that the technology allows for. Eventually, we'll get to a point where AI is  testing AI, and people won't be in there at all,

unless we choose to make it so that we're in  there. And that means more human time for us as instructors and assessment creators, and  more human time for the students as well. And when we think about it, what do students  need us for? They need us for human-to-human interaction. They need us to develop skills and  knowledge experientially in solving problems. They do not need us to deliver information. And when  you think about it, online learning was really

a way to deliver knowledge to people. My master's  was in rural extension studies. How do we deliver knowledge out to everybody, whether they can  come to campus or not, and we're just not in the business of delivering knowledge anymore. We're in  the business of creating knowledge. We're in the

business of co-creating knowledge. We're in the  business of human-to-human interaction. So if we want students to give us money for something  other than a piece of paper they get in four years or three years, then we need to provide them  with something that AI can't and what is that one thing? It's interaction with other humans. It's  learning with other humans. And I thought maybe this is a good time. My tea is Yogi Tea. It comes  with quotes, and I just think it's so fitting that

this is the one tea bag I just pulled today. And  the quote is, “we can always start again.” And

really, at a higher education college, we need  to start again. And that sounds very painful, but we have to start again in thinking from  scratch of what are we offering, what's our value to students and to society in the age of AI. Part of that seems like we all start again as instructors as well, and make sure that we're  familiar with how AI can help us and help our students, instead of constantly fighting it, like  better understand what some of the possibilities

are so that we can do that redesign work. I don't think anyone said yet in this podcast, rearranging the deck chairs on the Titanic, but I  think it might be time to say that. You should buy our book and you should use the techniques that  we suggest, but unless post-secondary education makes some fundamental changes philosophically  and practically, that's what we're doing. We need

to recenter humans in what we do, and we need  to offload the parts that we can offload. AI is going to be an incredibly powerful tool for us  as teachers, just as it's becoming for assessment takers and hopefully learners at some point as  well. So we need to figure out how to deliver the

value that Trish is talking about. When I give workshops to faculty, I'll often remind them that we need to teach  students for their future, not for our past, things that worked for us in the past in the world  before, AI may not work so well, and there are a lot of people out there trying to find ways to  integrate AI into this, because students will

be working with AI in the future, and they have  to be able to add some value to that. And if we can somehow assess your ability to go beyond just  AI, perhaps that might be a direction that's worth exploring. And there's a lot of people trying to  work on that right now. Is that something that

might be a way around some of these issues? Yeah, I think it's exactly what we're advocating for, which is to build assessments that allow AI  to be used in a way that is fair and equitable and clear and that allows us to see what students are  bringing to the material over and above those same fairly used tools that everyone else. Having said  that, there's also going to be those assessments and those skills that you really need to be able  to use without AI, because those are the bedrock,

foundational skills, and also the things that  you simply don't want to trust AI for. Computers don't, and probably won't for a while, have  the ability to do intentionality. They don't understand that there's a physical, real world  out there. They are living in an electronic world,

and so that means you can't ultimately trust them  with ethical choices. So that's on us to make those ethical choices and to use machines in ways  that are ethical, and that's going to always be, at least for my lifetime, a big part of  what we're teaching our students to do. And I also think that when faculties play with the  tools, they have to remember that they are experts

in the discipline, probably in what they're asking  the AI tool to help them. And so the other thing I've heard, some mistaken conclusion is, “Oh  well, I found it very useful for helping me write, so I'm going to let students use it to help them  write.” They're novices. They're novice writers

compared to faculty, they're novice in your  discipline, they're novice in the content. They're novice at a lot of skills that you may have been  working on, and so how they should be using it, or how you need to scaffold or coach their use in  it, is very different than how you would use it. It's important for us to keep that in mind,  that we have to think again, like you said,

the future, right, for their future? Well, what  does the future look like? Because right now, we only know how AI is beneficial to people with  expertise who didn't have it and developed all these knowledge and skills without it. How do  we prepare them for that? If we just allow them to offload everything to AI, then will they be  able to use it in any way that's meaningful? That seems like a really good note to get to  our last question, which is we always end by