Recent Talk: Looking All AI-round

Seeking the human collective in the age of the machine collective...

You know when things click into place and you realize that you’re finding your rhythm—I think that’s where I am with engaging folks around generative AI. A big shift in doing these talks has been a move to trying to be more interactive—even when the time feels limited and the folks bringing me in want a more traditional talk.

Last week, I have the pleasure to be the guest speaker at a day-long event at Georgia Institute of Technology. It seemed to go really well as I mixed in a lot more interaction and conversation. I also centered in on and expanded a point that I’ve done in many of these talks. I emphasized the room. I want folks to understand that the wisdom and insight they are looking for is really in the room. And while I can come in and give them some ideas, it’s really about unearthing what might already be doing that could be powerful and useful. So here’s that talk and of course, the annotated slide deck.

Bing Image Creator with prompt: “a robot emerging from a large computer mainframe as if being birthed from it. Style: Michelangelo”

Looking All AIround: A Retrospective & Forward-Looking Discussion of Generative AI

I’m Lance Eaton and I guess I’m here to do a bit of retrospection and projection about the role of AI in education Right? I think that’s what I’m doing.

I hope this is going to be a little bit different from the conversations I’ve been doing and maybe the conversations you’ve been having. Because I also know as we approach a year on this subject, we’re definitely growing tired of all the talk, the changes, and the hype.

But, let me introduce myself a lil more before we get started. Today’s talk is an extension of work I’ve been doing for 15 years in education and technology. I’ve had the privilege and desire to explore this space in many ways, learning about both its possibility and of course, its problems and challenges.

During the past decade, I’ve worked on different projects such as exploring hybrid flexible course design in the mid-2010s, well before the pandemic. I’ve examined how digital technologies can change how we think about and perform service learning. I’ve considered the power and importance of student agency and how open educational practices such as open pedagogy can improve student learning and meaning-making. I’ve also grown concern about how surveillance and data practices can be another form of power-hoarding over student learning and agency.

Often, I’m trying to figure out where is the balance of technology as a means of improving opportunity and agency for students while recognizing that the tools themselves are not neutral and come with trade-offs that can be problematic.

All of which is to say that when generative AI was propelled onto the scene last November, I had lots of different feels and thoughts about it and those continue to evolve through today.

Let’s get started!

Before get into the agenda, at the bottom and going into the chat is a link to what I call the Annotated Slidedeck. This Document contains much of what I’m saying today as well as resources and prompts for you to try. It’s covered with a Creative Commons licenses which means you’re welcome to share with others and repurpose.

So what is the conversation going to be today?

Where we have been? Where haven’t we been?

We’re gonna talk about the last year and highlight what has happened. But what also means we will consider what didn’t happen. In that, we may gain some clarity and comfort. And it’s only through reflecting upon the last year that we can be prepared for now and what’s to come.

And don’t worry–what’s to come is not the robot apocalypse. At least, not yet.

Where are we now? Where aren’t we now?

We’ll do a pulse check on where we are in this moment and hopefully, realise a little tension. We’ll also get to see and learn from some of you about what you are doing. Because wherever we’re going, whatever comes next, it’s not going to involve me but your colleagues and friends.

Seriously, I can provide the slide deck for you to use and explore more but if you want to truly learn more and do more–it’s going to be your colleagues that you’re going to do it with. We’ll take a look at what y’all are doing and gain strength from the amazing talent in this room.

Where are we going? Where aren’t we going?

As we look to the second year of this world of generative AI, what is possible and what are we pretty sure we can hold off or not thinking about.

How might we get there? How we’re not getting there?

What might be the strategies and practices that keep us moving forward in all of this–some of which I’ve already hinted at. And what’s not going to get us moving forward?

Sound like a plan? I hope so because I don’t have a back up slide deck to work through. Ok, I do, but it’s so much less fun!

First, let's acknowledge that so much has been going on or at least so much feels like it has been going on with generative AI that it can feel like you missed the train and you're stranded somewhere.

That's why I love this quote from Laura Dumin, a colleague who has also been doing a lot of work in generative AI and education in the last year. And she's one among many that I could point to and would point to encourage you to follow.

Others include: Bryan Alexander, Maha Bali, Amanda Bickerstaff, Autumm Caines, Sarah Elaine Eaton, Anna Mills and many others. And don't worry--that list is in the slides.

The core of this message is that there's lots out there and if you're feeling behind or lost, there's lots of us doing public work and making sure y'all can catch the latest train.

Let’s talk about the rules of the game

Whenever there’s a question, you are welcomed and encouraged to put it into the chat–I will engage with them.

Ok, let's talk about the rules real quick for today. For many parts of this conversation, it's gonna be interactive. I'm going to be asking to you do certain things. Sometimes, that's going to be interact in the chat. Other times, I'm going to ask you to use the getures feature. Other times, I may just ask you to stop and take a breath.

I encourage everyone to be as fully engaged as you can. I know, it's very easy on a Monday morning in the middle of the semester to be doing all the other things--I mean, I can hear the emails piling up. But let's try to be here and engaged.

Along those lines, I would love for more folks to have their camera on. I know folks might not be able to--I respect whatever context makes that hard. But if you just have it off--I wonder if I can ask you to turn it on.

Thank you! Let's get started!

Where have we been?

Here’s our first opportunity for interaction. Into the chat–what do you think? How long has artificial intelligence been around?

Lots of good answers and also, it’s a bit of a trick question, because the goal posts keep moving. Even now, we’re talking about “generative AI”, we’ve want to distinguish it from AI in the past and some folks are arguing that this is the pathway to real AI.

As we continue to build towards whatever we mean with AI, we realize that it’s incredibly complex and once we achieve something, we recognize that it’s not quite what we meant. I say this collective “we” here and I mostly mean that there are of course researchers working on this deeply and understand the incremental progress that we’ve made, but how that gets translated through the research-to-general population pipeline is another story. It gets mangled and twisted.

We have lots of examples from storytelling of artificial intelligence going back 1000s of years but actual examples–real examples and not necessarily frauds, are limited to the 20th century. Probably the most known instance of AI to make a mark on culture is ELIZA in the 1960s, a program that automatically responded to text with reflective questions and answers that led people to believe that it was a real person.

Spoiler alert: it wasn’t. But it did give us a new term, the ELIZA effect–wherein we attribute human traits to machines because we’re anthropomorphizing them.

Show of hands, how many folks have referred to generative AI as a he or she?

How many have said that generative AI lies or hallucinates?

While it is providing false information, lying or hallucinating are human traits.

How about this one? How long has generative AI been around?

Much of what we’re seeing in the last year is actually the work of the last 15 years and yes, we’d also need a more defined sense of what is generative AI, because if you haven’t seen it yet, there’s an awful lot of products trying to ride the generative AI wave in the same way that egg packaging rallies to the fact that it is gluten free.

Now, the hard question. How long has it been since…

The death of the college essay. What do we think?

I remember the funeral. It was quite touching and eloquent.

What about when did “The end of education as we know it happened.” I think it happened or was supposed to happen–especially by now…I know read that in some sci-fi novel somewhere.

And we all remember being wiped out by the takeover of AI, right? Or am I in the wrong universe?

I’m not saying this to dismiss these ideas–well, ok, maybe the last one, but in earnest, to remind us of two things.

One: we’ve been constantly telling us these things over the past century or longer.

Two: Education has actually been changing, but gradually. I teach in a class that is different from the classes that I learned in which is different from the classes at the time I was born. 3 Generations and there are differences in teaching and learning.

And that’s something I want us all to sit with. Teaching and learning continues to change and adapt; we’re not stagnant as society would like us to think.

Where are we now?

Ok, now. In this last year. What have we learned? I mean, we learn lots of things. Yesterday, I learned that I can successfully grow and harvest sweet potatoes. But I mean what have we learned in the last year around generative AI, teaching and learning.

[Lift some up and talk about them]

Good--these are some great answers.

Pay attention to these answers; save this chat. See who your peers are and what they are learning. These are future conversations to have!

Has anything significantly changed in how we teach and learn in the classroom in the last year?

We’re more suspect–yes. We’re thinking differently a bit about assessment. We may even be using generative AI.

But let me ask this differently. How different is what you are doing in the classroom right now in this semester than it was last time this year? AND how different from is what you were doing in the classroom in say, April 2020?

Right? Right! One of these things is not like the other. And we’re going to talk about that.

But first, we gotta do a lil unpacking. And yes, that is my cat–because I’m a professional and include my pets in my slides.

Also, though, I feel like she may embody the way some of us are feeling–and that’s real!

What are your feelings about generative AI?

Like honest feelings about generative AI–don’t worry, I assure you, I'm just a large-language model--I do not have feelings.

[Call out specific feelings that show up]

That’s a lot of different feelings, right? And honestly, this stream of thoughts is about the range of feelings I have about generative AI at least once a day–seriously, I just go through all the feelings like I’m in some movie montage!

This is all really complicated and strange. I appreciate the honesty and thought here.

And also, I also wanna give space for some levity.

What is generative AI? Wrong answers only…

Now, let’s go for it.

What is generative AI? BUT wrong answers only. What do you got?

[Call out specific answers that show up]

I love these and feel like I want to use them. This is what we all need on a Monday morning!

Now, we're going to go into something that might be a little uncomfortable but I also think it can be really powerful.

I've done similar things in other places but never quite like this. So I hope you will trust me enough to join me in this activity.

As I said before, I want to lift up you--the folks here who have way more to share going forward than I can. Therefore, I'm going to start to ask folks to make themselves known in some ways.

If you're uncomfortable and don't want to. I 1000% support that. But I hope that we can maybe do a little interaction here that proves fruitful going forward.

Ok, for those of you who have been using generative AI in some capacity in teaching and learning the last year, I want you to use the "Raise Hand" feature. I ask this specifically because if I know Zoom well enough, it should mean they should populate people's windows and also show up on the participant list.

[Look at the participants list to see the amount].

Look at all these folks. If you’re not raising your hand, please, please, please look through this list of folks and decide who you may reach out to in order to learn more.

Ok, I'm going to ask those folks who raised their hand to do 2 things right now.

The first is lower your hands. The second is to put into the chat what you have been doing--to the degree that you can or want to share. I really hope you can do that.

While they are doing that, I'm going to ask folks who didn't raise their hands to get ready to participate in this next part.

For you folks, I want you to raise your hand if you want to figure out how to use generative AI in your teaching and learning.

Here again, look at the participants list and determine--who do you want to work with? Who do you want to build a community of practice with?

Maybe just a raised hand isn't enough? That's fair. So now, I'm going to ask you to lower your hands and also put into the chat, share what you want to do or try out.

Now, if I've done this right and folks are following along, I can return to the chat and start to call out some of the things that people are doing here. Fingers crossed!

[Call out ideas from folks].

Now, this final group. The group that are resistant to engaging with and using generative AI in teaching and learning.

Are any folks willing to raise their hand? I know this might feel the hardest or most challenging to do now that we’ve looked at those who are for it or trying to find their way there.

If you don’t want to identify–no worries.

But I want to take a moment to allow them the same opportunity to be seen and heard about their legitimate concerns in navigating generative AI.

Thank you for raising your hands. And now, I’ll ask the same of you–share in the chat what your concerns are.

[Call out ideas from folks].

Where are and aren’t we going?

The reason I save those resistance for last is because, if we are going into this future we have to understand why we need to move but move thoughtfully & critically. There’s lots of possibility & also, some concerns to admit to..

The tool comes with a lot of baggage. I mean a lot. That baggage is different than other technologies in that we’re learning about the problems of the tool just as we’re learning about the tool itself. Generative AI contains all sorts of bias based upon the large language models that were used and also the bias that the companies themselves knowingly imbue into these tools.

In the last year, generative AI relies on exploited workers, environmental degradation, resource depletion, and significant energy use that’s likely to contribute to climate change. We have so many questions about misusing or abusing copyright of creators, authors, and artists. And the privacy challenges these tools represents doesn’t leave us particularly safe.

It raises our hackles because it’s another product from some Silicon Valley tech bro offering utopia in the form of something that promises to help so long as we don’t care about human rights, environmental preservation, or intellectual property.

It challenges our notions of work. It creates text, visuals, audio, and video with little expertise–it creates an opportunity for folks to wonder why they have to do certain types of work.

Like the calculator, folks are asking, why can’t I just use this. And we can come up with answers about the importance and ethic of writing as thinking or creating visuals as part of the process of learning or becoming.

What does it mean then to “do the work” or “show your work”? Some folks will reasonably challenge the real material value of “doing the work.”. We may buck at such challenges but folks will have different reasons for doing so that we should be mindful of.

I know, in academia, we cherish the process–we love to do the work because we know there is deep learning in there. But you’re teaching folks who are going out into a world where many are going to be overworked and underpaid. Folks who feel the tension of needing education, not being able to afford it, and knowing there’s a ticking clock of productivity that has been ticking even before they entered college.

In this world, the process doesn’t make sense–there’s no real time for it. The process is the path to burnout. They may choose to lean on shortcuts if only to maintain what is often an unmaintainable disposition.

Convincing them not to do so feels like a really hard thing to do–on top of all the other things we have to do as educators.

Then, of course, AI can be really hard to distinguish. This creates two deep challenges.

The first means that there are no good automatic ways of catching it. There continues to be a lot of companies trying to get rich off our insecurity but AI generative detectors are not going to be useful in any way that makes sense. The only folks that are going to get caught with those are folks who are actually in need of help or false-positives of students. Those false-positives will also be directed more towards students who are multi-language learners.

And that is the most harm we can do–accuse an innocent student. Not just because it alienates them from the institution but also because there is no way to prove their innocence. These machines work from probability, not facts. Therefore, a student is going to have to defend themselves against a machine on probability. How? Exactly–it’s a stacked deck.

Yet the deeper challenge is that because AI is hard to distinguish, we are left wondering about our effectiveness to evaluate work. Many folks will claim a Spidey-sense or just “knowing” when generative AI has been used. I know I certainly at times think I know when a student’s writing is off. But we don’t really know and we won’t be able to really prove it. This leaves us to a vulnerable space where we know but don’t want to say that there are possibilities of students fooling us and passing our class without actually learning anything. And that idea challenges many of us as educators. It can make us feel inept or wondering what we are doing in this work.

And in this way, generative AI challenges power and the power of the learning space. The power of us as educators to know and hold knowledge in a particular way. What does it mean that students can choose to use this tool to challenge us or bypass us and our role as knowledge gatekeepers.

Now–I’m not saying that individually, we feel like we hold that power or we operate through that lens, but as representatives of a larger institution within higher education, that is, in fact who we are: Knowledge gatekeepers–deciding who goes forward with passing grades and who does not.

Generative AI leaves us wondering about our ability to hold this role which means it represents some level of power change that we’re not entirely comfortable with.

It feels very much like the vast majority of mental work that gets turned into tangible deliverables for evaluation in higher education are very quickly becoming possible to being generated by AI.

And this challenge comes at the end of a long train of technology, pedagogies, and world events that kept demanding complete overhaul of faculty practices.

Many have taught for years and developed a deep and rich practice and philosophy of teaching where our courses are interconnected webs. Everything comes together in an alignment that we work years to perfect. That alignment deeply interconnects with learning outputs by students that are directly thrown into question as a result of generative AI.

To pull on that thread, means to unravel all the other interconnected threads. I don’t know that everyone fully appreciates that depth of that fatigue, frustration, pain, and yeah, even sadness. It feels like we’re back at page 1 but that page 1 might have been written by an AI chatbot.

But teaching and how we show up to a class is so personal, so individual, and so deeply a part of our soul--that this isn’t just a pivot…it’s a paradigm shift.

And the lift to reinvent our approaches can be hard, scary, and exhausting. I wanted to name and acknowledge that and appreciate the folks who spoke to their resistance and the reasons they listed.

What are we to do as educators?

Ok, so let’s take a breath. Also, this is Bear the cat. She’s here to give you a smile…even though she always looks like she’s judging us.

What are we do to as educators? I’m not looking at the chat but I bet someone is going to respond to that rhetorical question with a reference to retiring.

Let’s talk about some of the ways we can use generative AI and what might be strategies to deploy in the coming year.

What can we do right now?

While it’s incredibly interesting and helpful, I think we’re far from it’s full potential and so these tend to be the ways that I see folks using it mostly right that feel effective and useful.

The next are just a example prompts to give you a sense of what it looks like. The annotated slide deck will have a lot of additional prompts and guidance on how to use it.

Collectively, I’m about to save you all (10 minutes X as many attendees). Seriously.

I use this prompt at the beginning of each semester and anyone needing to get a list of dates will adapt it. And now you can do.

We all do this. We try to get the list of dates of our classes and toggle back and forth between screens and such to get that list for the syllabus.

Here is an example of minimizing how long a task will take with generative AI. I asked ChatGPT to give me all the Tuesdays between the start and end of the semester. This saves me toggling back and forth between my syllabus and a calendar to get these days. I also asked it to include holidays and such so I can keep that in mind as well.

Here are the results that I got in less than 30 seconds. Now I can copy & paste that list into my syllabus. It also included Islam and Hindu holidays but you can find those in the annotated slide deck.

Generative AI can help with tedious tasks like this.

We can often figure things out on our own and we also know that our thinking can be improved through dialogue or having someone provide examples. In this instance, I asked ChatGPT to provide some examples of ways folks might should be able to use generative AI.

I also framed the output in the form of a table with some added factors. I’m not just asking it to give answers but clarifying the response.

There’s more on the annotated slide deck but it provides a lot of ideas right out of the gate. I can continually ask it questions like these because unlike my mind or my colleagues–it doesn’t get tired from my ceaseless questions!

This tool can also be really helpful with 1st drafts. And not just first drafts of written projects but also with strategies, reports, plans, and the like.

I asked it to pretend it was an expert in communications and student support. I followed this with asking it to create a communications calendar to students about the different timely information and supports throughout the semester.

And now, I have the start of a plan. I could take it further an ask it to go deeper on one of the items and start to flesh out that part of the plan and in less than 30 minutes have a fully detailed first draft that the team can adjust and update as needed.

I find it help for looking at qualitative data and can use it to make sense of things quickly. I’ll still dive into the data for more insights but the high level analysis can help me move with more clarity.

Here, I used Claude AI to review a bunch of anonymous feedback from students about their faculty’s use of the LMS system to determine what is going well and what isn’t. This is qualitative data and often, we’re dealing with hundreds of responses to this question every 4 weeks during the semester.

From here, I can engage in further dialogue for recommendations, plans, what to do next and the like.

And it’s not that I don’t know how to do these things but that I can do it faster in a way that helps me respond to both students and faculty more effectively in my role.

Ok–so these are some examples of the ways we will be using them for now. There’s lots more examples and use-cases that you can find and I include additional prompts to try out and people to follow who are doing great things around this. And, of course, you have your colleagues–some of whom are doing really amazing things with generative AI in their work already!

How are and aren't we getting there?

Ok–then what should you do?

This is where I wanna call back to that moment when I asked about what has changed in the last year in terms of teaching and learning. Not a whole lot and this is where it’s different from the pandemic. The pandemic happened all at once to everyone.

Even though the hype cycle feels like the robot have already taken control. It’s not just not true. So what follows is the longer game and guidance to help you move to more deeper ways of engaging with generative AI as it continues to evolve and we continue to see it arise.

Play with it and its variations. Don’t wait–go and kick the tires. You’re not going to break it and there’s lots of support materials to get you started (again, see the annotated slide deck). Try different tools and see what’s right for you.

Always think about what you are putting into it and if you have the right to do so or are violating someone’s privacy. This particularly includes students!

Find your outlets to learn about it. You’re part of an institution of higher education–you’re part of an entity that whether we like it or not, are required to be lifelong learners if we want to continue to adapt to an every-changing and complex world. Put this on your personal agenda for learning and development.

Hone in on how the tools are being used in your field. Start to learn what that conversation is like and how folks are considering it. Along those lines, –find your people! People in your fields and areas of work are already doing things with this. And they are writing, making videos, doing podcasts–all the things! Find them–and if you need help, let me lift up our amazing librarians–the OG’s of knowledge search and creation–the folks who could teach Google a thing or two about effective searching!

Keep an eye out for the guardrails that your industry or field are communicating or establishing. Standards for generative AI use are going to be different for market than it will be for recruitment than it will be for alumni outreach.

Does your usage align with your mission? For instance, any institution that upholds supporting their community and antiracist practices in their mission opens up interesting quandaries. Does marketing save time and money by using generative AI image-creations of a “diverse student body” for brochures, social media, the website and such. OR do they spend the extra time hiring community photographers or getting permissions of individuals to build out their stock photos for visual materials?

As you start to learn about it and use it, have conversations within your departments and teams to figure out the norms and acceptable use. Make sure you’re all on the same page and learn from your industry about what should be on those pages!

Create your communities of practice–here, among your professional colleagues, among your friends–but start learning and sharing–you do not have to be the holder of all the knowledge.

Within that, determine among your community of practice how you are going to share. Some folks are collective creating prompt books, others are creating Youtube playlists of things they are doing or of what they find others doing. But find a way to capture and share that works for your group.

But as you learn more about these tools and find new ways of using generative AI–share them with colleagues. If generative AI is a collective of what already exists, then amplify its benefits by sharing with others.

So I’ve been here talking for a while and we’re turning it over for questions, but let me end on this.

Look at this room, look at that chat. There’s a lot of brilliance already here and so much you can learn from and lean on one another for. I won’t pretend navigating all of this is easy but also, you’ve got a lot of what you need right here and I encourage y’all to make use of it.

Thank you for allowing me to ramble here for the past hour and show you photos of my pets, including this one, my 37-year-old mud turtle, named MJ.