April 9, 2025 • 41 mins
Student use of generative AI tools as a substitute for learning has led to increased concerns about academic dishonesty. In this episode, Tricia Bertram Gallant and David A. Rettinger join us to discuss why students might use these tools and strategies instructors can use to encourage academic integrity.
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 James Lang and Michelle Miller.
A transcript of this episode and show notes may be found at http://teaforteaching.com.
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(00:00):
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.
(00:22):
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
(00:55):
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
(01:16):
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 (01:32):
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.
(01:56):
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
(02:19):
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
(02:40):
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
(03:05):
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,
(03:27):
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
(03:48):
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
(04:08):
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,
(04:30):
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,
(04:55):
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.
(05:17):
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
(05:39):
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
(06:03):
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
(06:24):
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
(06:45):
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
(07:06):
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
(07:28):
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
(07:51):
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
(08:13):
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.
(08:37):
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
(08:58):
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
(09:21):
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:
(09:41):
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.
(10:02):
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
(10:26):
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.
(10:50):
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
(11:11):
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
(11:31):
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,
(11:55):
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
(12:19):
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
(12:42):
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,
(13:06):
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
(13:29):
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
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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.
(14:17):
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
(14:39):
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’
(15:04):
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
(15:29):
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
(15:54):
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
(16:15):
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
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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
(17:01):
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,
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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,
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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
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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,
(18:28):
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
(18:53):
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
(19:17):
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
(19:41):
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,
(20:03):
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
(20:24):
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
(20:46):
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
(21:09):
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
(21:33):
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,
(21:58):
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
(22:21):
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,
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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
(23:10):
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
(23:34):
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
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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,
(24:22):
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
(24:46):
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.
(25:10):
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
(25:35):
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
(25:56):
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,
(26:22):
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
(26:45):
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
(27:09):
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
(27:32):
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
(27:58):
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.
(28:18):
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
(28:41):
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
(29:07):
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
(29:31):
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?
(29:57):
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
(30:22):
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
(30:47):
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
(31:11):
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
(31:34):
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
(31:55):
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
(32:19):
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,
(32:45):
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
(33:08):
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
(33:29):
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
(33:52):
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
(34:12):
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
(34:34):
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
(34:56):
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
(35:18):
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,
(35:39):
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
(36:03):
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
(36:28):
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
(36:49):
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
(37:13):
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
(37:38):
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
(37:59):
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,
(38:20):
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
(38:44):
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
asking (38:54):
“What's next?” That’s a big question.
What's next is change, that I can promise you. We're at an inflection point, and this change
has been coming for a while. COVID, we thought, was going to be the impetus for that change,
and maybe it would have been without Gen AI, but there's no doubt that Gen AI is going to
(39:16):
be the impetus for a huge amount of change in almost any part of our lives. But I think that
education is going to be the pointy end of the spear with respect to that, and you can think
of it from just one little example, which is the intellectual power of writing. Writing,
until now, or rhetoric, maybe more broadly, has been the way that we've honed our ideas as
(39:39):
humans. It's the way that the late, great Frank Yates, who taught me undergraduate psychology,
said writing is nature's way of telling you how unclear your ideas are. And that's true of all
rhetoric, and it's actually a fundamental fact about our lives as intellectuals up until now,
but suddenly it's possible to create a coherently and sometimes even well written
(40:00):
document without understanding what you're writing. That's what Gen AI does. Is this
a good thing or a bad thing? Well, hard to say, but it's a new thing,
and it is what's next. And so the question is, what is the next generation of intellectuals
look like when they don't have to write to think? I don't know what it's going to be, but it's going
(40:20):
to be a bumpy ride getting there.It's a good thing. You said you
were leaning into flexibility, huh? It's really the only choice. It's amazing
what you can do when you have no choice. I have nothing to add there. That's a
great answer, and I think I'm just gonna upvote what David said.
Well, thank you for joining us. We strongly recommend that people take a look at your book,
(40:41):
because everyone in higher ed is addressing these issues, and it's a really nice resource that's
very timely. And thank you for joining us. Yeah, thank you so much.
You're welcome. Thanks for having us. Yeah, thank you.
If you've enjoyed this podcast, please subscribe and leave a review on iTunes
(41:03):
or your favorite podcast service. To continue the conversation, join
us on our Tea for Teaching Facebook page. You can find show notes, transcripts and other
materials on teaforteaching.com. Music by Michael Gary Brewer.
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.
(00:22):
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
(00:55):
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
(01:16):
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 (01:32):
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.
(01:56):
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
(02:19):
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
(02:40):
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
(03:05):
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,
(03:27):
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
(03:48):
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
(04:08):
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,
(04:30):
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,
(04:55):
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.
(05:17):
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
(05:39):
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
(06:03):
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
(06:24):
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
(06:45):
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
(07:06):
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
(07:28):
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
(07:51):
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
(08:13):
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.
(08:37):
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
(08:58):
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
(09:21):
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:
(09:41):
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.
(10:02):
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
(10:26):
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.
(10:50):
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
(11:11):
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
(11:31):
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,
(11:55):
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
(12:19):
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
(12:42):
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,
(13:06):
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
(13:29):
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
(13:53):
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.
(14:17):
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
(14:39):
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’
(15:04):
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
(15:29):
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
(15:54):
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
(16:15):
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
(16:40):
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
(17:01):
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,
(17:22):
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,
(17:45):
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
(18:07):
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,
(18:28):
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
(18:53):
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
(19:17):
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
(19:41):
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,
(20:03):
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
(20:24):
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
(20:46):
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
(21:09):
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
(21:33):
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,
(21:58):
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
(22:21):
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,
(22:45):
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
(23:10):
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
(23:34):
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
(23:57):
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,
(24:22):
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
(24:46):
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.
(25:10):
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
(25:35):
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
(25:56):
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,
(26:22):
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
(26:45):
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
(27:09):
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
(27:32):
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
(27:58):
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.
(28:18):
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
(28:41):
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
(29:07):
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
(29:31):
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?
(29:57):
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
(30:22):
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
(30:47):
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
(31:11):
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
(31:34):
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
(31:55):
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
(32:19):
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,
(32:45):
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
(33:08):
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
(33:29):
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
(33:52):
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
(34:12):
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
(34:34):
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
(34:56):
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
(35:18):
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,
(35:39):
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
(36:03):
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
(36:28):
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
(36:49):
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
(37:13):
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
(37:38):
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
(37:59):
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,
(38:20):
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
(38:44):
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
asking (38:54):
“What's next?” That’s a big question.
What's next is change, that I can promise you. We're at an inflection point, and this change
has been coming for a while. COVID, we thought, was going to be the impetus for that change,
and maybe it would have been without Gen AI, but there's no doubt that Gen AI is going to
(39:16):
be the impetus for a huge amount of change in almost any part of our lives. But I think that
education is going to be the pointy end of the spear with respect to that, and you can think
of it from just one little example, which is the intellectual power of writing. Writing,
until now, or rhetoric, maybe more broadly, has been the way that we've honed our ideas as
(39:39):
humans. It's the way that the late, great Frank Yates, who taught me undergraduate psychology,
said writing is nature's way of telling you how unclear your ideas are. And that's true of all
rhetoric, and it's actually a fundamental fact about our lives as intellectuals up until now,
but suddenly it's possible to create a coherently and sometimes even well written
(40:00):
document without understanding what you're writing. That's what Gen AI does. Is this
a good thing or a bad thing? Well, hard to say, but it's a new thing,
and it is what's next. And so the question is, what is the next generation of intellectuals
look like when they don't have to write to think? I don't know what it's going to be, but it's going
(40:20):
to be a bumpy ride getting there.It's a good thing. You said you
were leaning into flexibility, huh? It's really the only choice. It's amazing
what you can do when you have no choice. I have nothing to add there. That's a
great answer, and I think I'm just gonna upvote what David said.
Well, thank you for joining us. We strongly recommend that people take a look at your book,
(40:41):
because everyone in higher ed is addressing these issues, and it's a really nice resource that's
very timely. And thank you for joining us. Yeah, thank you so much.
You're welcome. Thanks for having us. Yeah, thank you.
If you've enjoyed this podcast, please subscribe and leave a review on iTunes
(41:03):
or your favorite podcast service. To continue the conversation, join
us on our Tea for Teaching Facebook page. You can find show notes, transcripts and other
materials on teaforteaching.com. Music by Michael Gary Brewer.
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