Hi, I'm Ethan Malik, a professor at Wharton and the co-director of the Generative AI Lab
here at Penn.
Hi, I'm Lina Hmalik, and I'm co-director of the Generative AI Lab at Penn, and I have
been working on interactive pedagogy for the Wharton School.
So in this short class, we're going to be talking about how to use AI in education from
the teacher perspective.
And this covers all kinds of instruction, college level, but also K-12.
And we're going to focus on open AI tools, specifically using chat GPT and various GPT
models to solve problems.
But there are other AI solutions out there.
We'll be focusing on open AIs, though, in this particular course.
It's important to realize that AI, even though it's a really advanced technology, is not
inherently technical.
And we don't expect you to have a technical knowledge.
In fact, we don't think technology helps you very much outside of a very tiny technical
discussion about how AI works.
So we'll cover key concepts in generative AI, benefits and risks of using it in education,
ethical concerns, and talk about some policies you should use in this class.
Then in future classes, we'll be covering how to do prompting and how to actually deploy
AI assignments.
So for a little bit of context, AI is already impacting education.
We know that a lot of you have already had some experience with AI or are actively using
AI.
In a recent Wharton Family Foundation survey, half of the teachers survey say they use tools
like ChatGPT at least once a week.
And most of those educators who use ChatGPT have a favorable opinion of the tool, as do
your students.
Educators who use the tool say that it saves them hours of work a week.
But although it's a powerful tool, there is just no instruction manual.
And the way that you use it, the way that it's helpful for you, might not be helpful
for someone else.
And so what all these educators have in common, what you might have in common with them, is
that there is a need for more guidance.
In this course, we want to try to help give you some guidance, give you tools and use
cases to show how these systems operate and what you can do to help you teach and help
your students learn.
So in order to talk about AI in education, we first need to talk about AI.
And it's a very imprecise term that might mean anything from science fictional robots
to the large language models we use today.
For a long time, when people talked about AI, what they talked about was the idea that
you could train machine learning systems to predict sequences of numbers.
And the predictor is used for all sorts of things.
So when you watch a Netflix movie, the algorithm is predicting what other movie you might want
to watch.
When you're scrolling through TikTok, the machine learning algorithm, based on all the
things that you've liked before and other people have liked, is guessing what you might
want to see next.
And so these systems are really good at making those kinds of predictions.
And AI has been booming for that reason.
But these forms of AI were not really good at human language.
Because human language isn't as predictable.
If I end a sentence with the word filed, am I filing my taxes or filing my nails?
The AI didn't know.
So a new kind of AI was invented in the late 2010s called the large language model based
on the transformer mechanism with attention that allowed the AI to pay attention to not
just the last word in the sentence, but the entire sentence, the entire context around
it, and so on.
And these new approaches to AI we call large language models.
And what they are are word prediction machines.
So just like those other forms of AI were about predicting the next number in a sequence,
what the AI does is predict the next word or token in a sequence.
So if I start a sentence with the best pet for anyone to have is a, the AI would use
all of the knowledge it's learned from training on all of human language to predict the next
word in the sentence, which might be 60% chance that word's dog, 30% chance it's cat, 10%
it's parrot.
And it will most likely pick the most likely word.
Sometimes it picks the second most likely or the third most likely.
So there's a little bit of randomness built into this.
But the AI is just very fancy autocomplete.
That doesn't actually help us understand how to use it, but it's useful to know what these
things are doing because it also helps tell us some of the downsides of AI.
One of those is the fact that AIs hallucinate.
I told you that AIs predict the next word in the sentence.
They're not looking at a database of every sentence ever written.
They've learned statistical patterns in language and they're reproducing it.
That means that what the AI produces seems realistic and real, but it may not always
be.
So when what the AI tells you a sentence is, is different than what the reality is, we
call that a hallucination.
And all AIs do is hallucinate.
They always are making up the next word in the sentence.
The fact that they're accurate so often is actually somewhat surprising.
They are accurate a lot.
We find they outperform humans in almost every sort of standardized test for what that's
worth.
They're quite accurate, but they still make things up that are very plausible.
And we call this hallucinations.
It's a way of getting misled with AI.
We'll talk a little bit more about that later in the course.
But it's not the only risk that you face as an educator using these systems.
So we know that students are using these systems and because they have the power and the capacity
to write essays and help solve problems and talk to students through solutions and in
fact give students the answers, these systems, it's important to remember, don't teach
out of the box.
They are not a teacher.
They just provide information.
And we know that just reading information is not, in fact, learning.
And we have some early evidence, in fact, that students can use AI as a crutch when
given access to the tool and feel like they're learning something.
So they have the illusion of understanding, but when confronted with a test, they actually
have not learned that topic or subject.
And AI cheating is really hard to stop.
You can't easily detect AI writing and you can't use AI detectors when you grade.
Not only do AI detectors not work, they're really easy to defeat.
And they also have a high false positive rate against non-native English speakers.
So they really can't be used.
Cheating is not the only concern you might want to think about when using AI systems.
Another is bias.
So AI systems have been trained on human writing and human writing contains lots of biases.
The types of writing that people select to use to train the models might have biases
themselves.
And the AI picks up this information as it goes along.
We know, for example, that there are subtle biases in AI work.
So if you ask the AI to write a letter of recommendation for someone and the person
that it's writing for is female, we know that some AI systems are likely to be biased and
talk about that woman being warm as opposed to if it's asked to write a letter of recommendation
for a man, in which case it's more likely to say they're competent, a fact we see in
real life letters of recommendation too that the AI repeats.
And so you want to be aware of these sorts of biases and risks.
There are some areas where AI is more biased than others.
In a lot of cases it's mitigated, but we're really just still learning about that.
So it's something you want to keep in mind.
I know people are also concerned about things like privacy issues.
And while those are a valid concern and you need to look at the legal and regulatory situation
that you're in as an instructor, there are now a lot of ways in which to ensure privacy
in these systems that we won't go into detail here.
But open AI offers a lot of options to create private chats and the other AI systems do
the same.
So that may not be as much of a concern.
And another thing that we think that people should be aware of is issues of access and
equity of access.
So the nice thing right now when we're recording this is that open AI has made available to
the world access to its most advanced models, which means that anyone with internet access
can get a limited number of interactions with those models for free.
So even if your students are not paying for chat GPT access, they should be able to access
the same advanced models that everybody else does.
Where that continues in the future, we can't tell you one way or another.
It kind of depends on the time, but we hope to see that continued devotion towards free
access to everybody.
So what does this mean for you in your classroom?
You can ban AI in your classroom.
Now banning AI in your classroom does not mean that students won't use it outside of
class, but it does mean that you'll be preventing students from using it inside of class.
That is a very legitimate option.
We know that students don't come to class as experts and that gaining expertise in any
topic takes a lot of work, a lot of friction, a lot of practice.
And in banning AI, we are really doubling down on what we know pedagogically works,
including low stakes testing, flipped classrooms, blue book exams, separating assessments from
homework.
So we can certainly, that is one option that you can certainly take on.
The second option is actually one we don't recommend anymore, which is taking a sort
of laissez-faire integration of AI into your class situation.
So originally when the first AI models came out that were widely used, like the CHAT GPT
3.5 that came out a couple of years ago, the systems were pretty good, but not amazing.
They made lots of mistakes.
And so it was relatively easy to tell your students, hey, just use the AI, acknowledge
that you're using it, and you're responsible for the errors in your assignment.
The problem is that GPT 4.0 and successor models are so good that they make far fewer
errors than your students do.
So you can't just simply say use AI and it'll work out, because then you're setting up yourself
a recipe for people to cheat either accidentally or on purpose, and you're in risk for learning
loss.
So I think we need to move away from a model of just casually using this and start to think
more seriously about how to integrate AI into classrooms in a transformative way.
So while it's certainly OK to, as we just discussed, ban usage and think about other
approaches to working with AI in class, we're going to talk a bit for the rest of this session
about transformation, about how you can use AI to create assignments, help you do work,
and what the future of education might look like, and how you can help shape that future.
We've been recording, as you probably guessed, this entire presentation so far.
So I'm actually going to give what we recorded so far to the AI and get some feedback on
what we might do better in our next video, which is coming up.
So given this script of our class so far, what should we do better in the second video?
OK.
Great.
OK.
So it's pretty complimentary so far, but its three main suggestions is that we show more
examples, which we will absolutely do in the next video, that we make sure to include links
and additional resources, and those will be available to you on the learning platform.
So that's excellent advice, I think, and we'll take that for our next video.
In the next video, we'll be sharing how you can use AI to help you in your teaching and
how to craft AI assignments for students.