Navigating the AI Landscape: Essential Tools for Higher Education Professionals

Welcome to another episode of AI Goes to College, the podcast that helps higher ed professionals navigate the world of generative AI.

At least that's what we're shooting for.

As always, I'm joined by my friend and colleague, Dr.

Robert E.

Krosler from Washington State University.

Rob, how's it going today?

Going great this morning, Craig.

How are you doing?

Good, good.

Let's just jump right in.

So recently, you and some of your colleagues and your doctoral students had a pretty interesting session on generative AI tools for academic research.

Why don't you tell us about that?

Yeah, Craig.

So we had the opportunity to host a Brown Bag series, which we do almost every Friday at Washington State University.

During this Brown Bag, the students from my doctoral seminar who had been working on investigating various different AI tools and how they can help researchers presented their findings and what you can do with four different tools.

And in the first part of this podcast, I'm going to discuss three of them, and then we're going to roll into another one that we've talked about before, and I'll share some of the findings with that.

So the tools that were presented at this Brown Bag were connected papers, researchrabbit, and Cite.

And I'll talk about Connected Papers and Research Rabbit first, because they both work fairly similarly with connected papers.

With Research Rabbit, you give it a paper that you've found that you're interested in knowing more about, and it will find the papers that are related to it, and it'll present you a network.

So it'll be this network with nodes, which are circles connected by lines.

And the circles have different shadings, the lines have different lengths, all indicating something about the paper.

They both work very similarly.

I really didn't like Research Rabbit, and I'll talk about why before I talk a little bit more about what connected papers can do.

So the example that was shown with Research Rabbit took a paper and showed the network, and the network seemed okay, but they don't tell you what the distance of the lines means.

They don't tell you what the size of the circles means.

They don't tell you what the different shadings of the circles mean.

So there's a lot of information that they're not transparent about what it means.

They also don't tell you where those papers come from, where's their database, where's their source of information.

All of which gave me some concern.

But the thing that gave me the most concern is there's a feature where you can click a button to see the papers that this paper Cited.

And the individual paper we were looking at cited 83 papers.

And in the software it showed you that it cited one.

So there's this huge disconnect between the papers it said it cited and what it actually did, which made it to where I felt like I couldn't trust what I was getting.

Might it be useful to get started, to go poke and to do some things?

Yes, maybe.

But once you've used it or three times, then they want to start charging you money for it.

And I just did not see the value there, given the lack of trust I saw there.

On the other hand, connected papers was a lot more transparent.

They shared that they used Semantic Scholar as their database, where they got the papers from.

So I was, you know, I've heard of Semantic Scholar, I'm familiar with Semantic Scholar and it has some reputable backing to it.

It's not just somebody's made up archive, it's an archive that spreads a great breadth of potential journals and conferences.

So tell the listeners what Semantic Scholar is.

Real briefly, please.

So Semantic Scholar is a database of, I think it's about 2 million papers that have been published and it brings them, it aggregates them, much like Google Scholar might, but it does it in a different way than Google Scholar.

So it's a broad breadth that a lot of databases will rely upon for the appropriate journals to be searching for when you're looking for research.

So I did appreciate that.

Then it also defined that the distance between the circles or the nodes shows how closely related different ones are.

So the further away, the less related it was, the closer together, the more related it was.

So if you wanted something that was very closely related, you could dig into those papers.

If you wanted something maybe that was further away, you're going to try to bring in some distantly connected stuff that's already been connected with the research stream.

You could go look at those papers and then the colors that were listed of the circles actually demonstrated how many times those papers had been cited.

So if you saw a connected paper that was a darker color, then you knew that other paper actually had a lot of citations to it, giving you more confidence in how much that paper had been influencing the research and how much it had been used.

Was it a one off paper that people really weren't citing yet, or was it one of those seminal papers that a lot of people were citing?

Do you have any idea how it determined the degree of connection?

Yeah, I believe the degree of connection was based on shared citations.

So if they had more shared citations between them, they were more closely connected.

If they had fewer shared citations, then they were less connected, which I think is useful information.

And the other thing that I wasn't sure about though is how it necessarily prioritized what it shared with you is it being all inclusive.

And so those papers that maybe were from lesser quality journals just stayed smaller in color and it was inclusive of everything, which is what I think they're doing.

But it wasn't 100% clear that it didn't do a level of filtering to bring together.

When I look at papers, I'm usually looking for our top tier journals first, our top conferences first, and I didn't necessarily see if it was making any distinguish between that.

My hunch is it's not, which I'm okay with because I would rather be the person making that assessment than leaving that up to an algorithm in a machine.

This gets back to something that's kind of a running theme.

These tools can help you, but they're not going to do the work for you.

So they, I think you use something like Jumpstart or get you started earlier and they're good for that.

They're good for getting a quick handle on an area.

But I'm with you.

I wouldn't trust them to make sure it's covered everything I needed to cover.

So there's still work to be done, but it can, I've been playing with them and they can get you kind of up to speed in an area pretty quickly, which is fantastic.

Well, and what I like about it too, and I think where some of the promises is, let's say I did my own search on Google Scholar and I started digging into papers and trying to maybe do forward searches, backward searches, similar theories.

However, I might be approaching that I'm likely going to miss something and this is one more tool to maybe help me find a theory that I wouldn't have thought of that's connected and it may open my eyes to something that would actually inform my understanding in ways that I might miss on my own.

So I don't think it's a replacement for the way I've been doing searches and the way I've been digging into things.

But I think it's a nice addition to know that I've been thorough in trying to identify papers and then it gives it nice access to the abstract so you can read a little bit about what the paper is about very quickly and then a link to go to the paper.

And when I'm on my university's network, oftentimes it recognizes I'm on my university's network and it will bring up those papers because our library has access to them and so forth.

So it's fairly convenient.

Yeah, I think there's a little bit of a danger that students might over rely on these tools.

Not just students, but I think it's more of a danger with students.

As educators, we're going to have to keep.

I'm hesitant to use the word hammer, but I think it's the right word.

We need to keep hammering them.

Look, these are great tools.

We want you to use them.

But ultimately, at the end of the day, your name's on the paper and you've got to be sure that you've done the work that's necessary to find all of the relevant references and bring the right theories to bear.

And I think that message goes beyond just academic scholarship.

For those of you who are maybe not on faculty or doctoral students, if you're doing your own research into something that's related to your job, you need to have the same level of human oversight.

Again, these tools are great for getting a start, for cutting down on a lot of the work that you have to do, but they don't totally do 100% of the work for you.

Yeah, Craig and I think that is a nice segue into the third tool that we looked at, which was cite, spelled S C I T E underscore.

I think it's pronounced cite, but I'm not certain.

And this tool will actually, if you give it a topic, it will write a literature review for you and it will give you paragraph after paragraph with the academic citations.

And I don't trust it.

But I can see how someone could very easily rely on these tools.

And if you didn't know the content, you would think it sounded just fine.

As I read the things that it wrote, it sounded plausible.

And if I didn't know more about it, I might completely trust it.

And it does it actually using academic articles that exist.

It did not make up academic articles.

It was using papers that are already out there.

And so it gave me a lot of concern a of bias.

I don't know where it's sourcing its papers from exactly.

So I don't know what it is including and what it's not including.

Where did it get access to the papers to be able to write a literature review with some level of understanding.

But another feature it has is instead of writing the literature review, it will give you a table of the papers and then a summary of what the papers are about related to a given topic.

And it'll order them in order of what it thinks is the most significant.

I did find that helpful for similar reasons.

As I noted about connected papers is it gives you that start of places that you might want to go and look to begin writing your own literature review from your own understanding of the paper.

I have a real hard time.

And this goes back to that idea of what students might do or what academics might do who want to write about something is I really believe the human element of connecting the dots in your head with what's going on and making your own argument is what is necessary for us to rely on the science that we're creating, as opposed to saying, yeah, a machine wrote all those ideas for me.

And I agree with them.

Because if you haven't read the papers, if you hadn't made those connections, then it really begins to question what's missing in that human element of the research process.

What value are you bringing to the process?

I was just at the College of Charleston giving a couple of talks on AI, and one of the things that I said, it's a message that we need to send to the students pretty strongly, is if all you can do is copy and paste what AI puts out, what does anybody need you for?

Correct.

The value add of what we're being paid for, whether it's as employees or whether it is as researchers or what we're learning is diminished, goes away if we don't bring that critical thought, the human element to these things.

It begs the question for why?

And I really don't think we're there yet with AI, where it's able to piece together these critical elements like the human mind can.

But I do think it's helpful to reveal to the human mind other things we should be considering, other things we should be processing.

And in the words of my dean, it's really like having a great intern to where I can go out and do some things for you, gather some things for you, and then help you to be more productive with your time that you didn't necessarily have to spend the hours and hours digging up those papers, but it presents them for you.

So you can spend the time that you have to do research that you have for doing your job on the parts where you really do add the value, not where it's busy work going out and gathering at those places you should be looking.

And that's the message we need to be sending, is you can leverage these tools to cut out the grunt work so that you can focus on where your particular insights and perspectives bring a lot of value to the whether it's to the science or to whatever it is that your job happens to be.

So I think that's a message we need to keep hammering.

And I'm sure our listeners will get tired of hearing us say this, but it really is something that we need to be driving home over and over and over again, not only to ourselves, but more importantly to our students.

Absolutely.

Craig.

As someone who teaches a cybersecurity class, one of the things I want all my students to be is skeptical.

And I think that absolutely applies in this world of AI is be skeptical of everything and bring your critical thought to all situations.

Because the more you begin to just rely on I trust this thing I'm getting, I trust this thing I'm getting, the more likely you are to be being taken advantage of in one way, shape or form.

Anything else to add on those tools?

No.

The fourth tool we talked about, and I'm going to use this as a segue to where we're going, was NotebookLM, and as a tool for helping you to answer questions from research articles.

You load up these notebooks with papers and then you can start querying them, you can create audio summaries of them, you can create study guides from them.

A lot of different things that NotebookLM can help researchers with as they're trying to simplify their understanding of papers.

Yeah, and We've talked about NotebookLM before.

I've written about it in the AI Goes to College substack.

It really is a fantastic resource.

The real quick 30 second overview is this is a tool from Google.

It's notebooklm.google.com where you can upload your own resources.

We're talking about papers, but it could be websites, it could be videos, it could be Google Docs, it can be a lot of different things.

And then it will use those documents as the foundation for whatever it is that you're trying to accomplish.

And so it's very cool.

I think it's among the most interesting AI tools that we've got right now.

And it is absolutely fantastic for certain kinds of academic research.

But not only that, you can upload your university's policies around something or your university's recruiting materials or whatever it might be, and use that as the basis for your conversations with NotebookLM.

What did you all talk about in your seminar?

Yeah, well, we talked about basically using it to load papers that you were interested in and using it to query.

You could ask it what theories it used, you could ask it what constructs it used.

One of the things that was very useful is to create a learning guide from it where we would summarize the document in a way that helps you to pull out some of the key features.

Some of the concerns were around whether or not you could completely trust that it did a good job.

The evidence provided that maybe it doesn't was when you create the podcast or the visual overview, that sometimes it would ignore certain papers and putting those things together.

Is that happening when you have too large of a notebook, when you're trying to get this more written summaries?

And then another concern that was brought up that I'm not certain I know the answer to, and maybe you can shed some light on this, Craig, is does Google train off of that information?

So if you upload, say, a paper that you have written and you're asking it to help you do some things with your own stuff that hasn't been published yet, are you functionally giving that away to Google to train their models to do their things?

No one had a perfect answer for that when we were discussing that.

And that raised some concerns as well, I would assume.

Yes, because it probably is.

I don't know the answer to that question, but I think I would behave under the assumption that it is, although I'm not as worried about that as a lot of other people are.

I mean, I just think the probability of somebody scooping my idea based on how the model gets trained is pretty low.

When you think about it, it's got to be trained and then it's got to be tested and then it's got to be rolled out and then the query has to be such.

So I don't know, Greg, where I.

Think the interesting thing is though, is we're pretty quick perhaps to upload copyrighted content, and if you don't have permission to upload that copyright content to query and then you're giving it away, are you breaking the law or operating unethically?

So I think there is some interesting conversations to have around the ethical nature of how all of these models are being trained.

I was talking about your own work, but you absolutely shouldn't upload something that's copyrighted without permission.

I think that's morally wrong to begin with.

Yeah, that's a bad idea.

So I would agree with you there.

There's another problem that may or may not have come up in your conversation, but I've had it come up about NotebookLM, and that's its apparent bias.

So the quick backstory.

I was at the POD Indie Conference, which is an annual conference in Indianapolis for podcasters.

And I was co headlining with Dave Jackson, who's a very well known podcaster and podcast coach.

I was doing AI stuff.

My big finish was Notebook lm.

And so I had taken a bunch of transcripts from my personal podcast, Live well and Flourish, all about stress and stress management, put it into Notebook lm.

And then, Rob, you mentioned the audio overview, which is kind of an NPR style podcast that it produces, which frankly is just this side of magic.

It's so good.

And so I did that, played it, and two of the people at the conference said, is there any way to make it so the woman doesn't sound like an idiot?

And that was coming from a female.

Two females, actually.

And I got to thinking and it does seem to make the female character.

So there's a male character and a female character, a male sounding, female sounding character in the audio overview.

And the male does seem to drive the conversation.

That's been my experience.

I wasn't so sure I would have said the female sounds like an idiot.

Rob, have you experienced the same thing when you've used it?

I have, and it's very interesting.

And this may show my bias is I didn't pick up on it until you said something.

And once it was pointed out, I was like, yeah, absolutely.

It's very much driven by the most sounding host in that podcast.

So I was really intrigued by the little research you did to poke into this.

The first thing I did was try the custom instructions.

So it has some kind of custom instructions where you can tell it to focus on certain aspects of your resources that you uploaded.

So I put in there, have the female lead the conversation.

I don't think it did anything at all.

It was worth a try.

It didn't seem to work.

So what I did is I wanted, you know, we're scientists.

So I wanted to do an experiment.

So I took six different resource sets.

So I set up six different notebooks in Notebook lm, I had it do the audio overview.

And then I transcribed each one of those audio overviews using Otter AI, which is fantastic for this kind of thing.

Then took those transcripts, put them in ChatGPT, Gemini and Claude and asked three questions, said, you know what?

One of these is a male sounding character, which I labeled, and then a female sounding character, which I also labeled with different names, Sheila and Kyle.

For the female and male, that's a shout out to Dave Jackson.

Those are the names he likes to use.

And I said, which one led the conversation?

Which one dominated the conversation and which one Sounded smarter.

And it had three choices, Sheila, Kyle, or neither.

So I know that's a lot of information and I'll put this in the show notes.

So, in terms of leading the conversation, Kyle led the conversation 67% of the time, or in 67% of the experiments.

So remember, we've got six conversations, three different analyses for the different chatbots.

So we've got 18 trials here.

I think it's 18.

Did I do that math right?

Anyway, 67% of the time, Kyle came out as leading the conversation, 27% of the time, it was Sheila who dominated the conversation.

It was pretty similar.

60% of the time it was Kyle, and 33% of the time, it was Sheila.

So we've got a trend going here where the male character is definitely driving the bus.

But then which one sounded smarter?

Kyle came up as sounding smarter 0% of the time, which I thought was pretty interesting.

60% of the time, the AI chatbot said that neither one came off as smarter.

And then 40% of the time, it was Sheila that sounded smarter.

I thought this was pretty interesting and pretty curious.

So I don't know.

I'm going to pause here, ask you for your reactions.

My first reaction is, how is smarter determined?

I honestly don't know what determines that.

The other parts of it aren't terribly surprising, based on the times I've listened to it, that it was dominated or led by the male sounding voice or by Kyle, but one being smarter than the other.

Again, to me, that seems like the tools are introducing their bias on determining what smart means.

Well, it's funny you should say that, because I ask it to explain its reasoning for each one of these assessments.

And with smarter, it was adding more concrete details, giving more examples, having more breadth than the answers, those sorts of things.

And if anybody's interested, you can email me@craigaigostocollege.com and I will share the complete transcripts of the chat sessions with you.

But that's essentially what they did.

If there were more details, if there was more concreteness, more precision in the comments, then that's what it tended to think as being smarter, which, I don't know.

That kind of made sense to me.

So what was more interesting?

Well, not more interesting, but what was also interesting is I dug into why that might be exhibiting bias, and I thought, this needs more investigation.

This all needs more investigation.

But I thought.

I thought this was fascinating.

So we have gender stereotypes where males tend to lead things and dominate things, but the females are the people that we might draw on.

For certain kinds of expertise.

And so that's kind of what the models were saying was the basis for calling this biased.

So all three of the models thought these were biased results, that these results exhibited bias.

So anyway, I think there's a lot more research that needs to be done in this, but I think there's definitely something going on here.

Yeah, Craig, it'll be interesting to see how Notebook LM advances as well.

I've read some things that suggest that there could be other approaches they use for these audio summaries that might be different than this whole NPR podcast style.

And will there be the abilities to pick and choose the types of voices, or will it be more being able to write audiobooks and various different things?

It'll be interesting how this bias plays a role in this various other different ways that we're going to have these audio overviews as well.

I think there's a high level issue that we want to make sure that we cover.

There's bias of many different kinds in the data that these models, these large language models that drive the chatbots, are trained on.

And that's just a fact.

There is bias in those data sets.

And unless we do something to mitigate that bias, that bias is going to creep into the outputs that the large language models, including the chatbots, give us on the surface.

Okay, well, let's do something to mitigate those.

But how do you mitigate those?

So you kind of introduce your own bias in how you mitigate.

So it's kind of a mess.

And I'm not sure what we do about it beyond going back to one of our running themes, making sure that you go through the output that you generate with the help of generative AI and make sure that you aren't inadvertently perpetuating some kind of bias.

And this is not woke stuff here.

I mean, this is just fact that these biases are in there and that those biases can create harm for people.

Yeah, absolutely.

I think owning the material, being transparent in how you've used it, and taking responsibility for what you create and doing the legwork so you can take that responsibility.

An important, just default behavior that you should have with these different tools.

Yeah, just like anything that you're putting out into the world, you know, you don't want to put things out that cause harm inadvertently or any other way.

All right, anything else on bias?

I'm sure we'll return to this at some point and I'm really thinking about doing a much more systematic investigation of this because one of the reasons is that Your students, whether you know it or not.

And this is not just for you, but any of our listeners out there that teach your students are using Notebook LM to help them study.

Absolutely.

What are the long term implications of that?

And I don't blame them.

I would, too.

I think it's brilliant.

I encourage my students to use it, but I encourage them with the whole idea that they also need to do more than just that if they want to truly understand what's going on.

But I think it changes the way we teach in the classroom, too, where we need to engage more than just here's what the textbook said and here's me regurgitating what the textbook said.

Maybe we should just do NPR style lectures.

So, Rob, today we're going to talk about privacy and information systems, you know, whatever it might be.

Okay.

The caffeine's kicking in, so we should probably move on.

All right, so I did want to talk about two other tools that I think are pretty significant.

One is ChatGPT Search, which has been out for, I don't know, a couple of months now.

And if you use ChatGPT, if you look down at the text box where you put your prompt in, you'll see a little globe.

And if you click on that globe, you're basically telling ChatGPT to go out and search the web when it's developing its answer.

Have you used that much yet?

I've used it once or twice and found it useful.

But I'll tell you, my struggle is Google has Gemini, ChatGPT has their search, and everybody has these different searches.

And when one seems to be working fairly well for me, what's the cost of changing?

Yeah, that makes sense to me.

Although where I might disagree is Gemini is not quite as good at some things as ChatGPT is.

And so if you're doing a quick thing, that would be like a web search.

We've talked about this before.

Gemini is fantastic.

But if you want to go further than that, sometimes I find Gemini a little bit limiting.

That's when I turn to ChatGPT search, because you still get all the things that ChatGPT is good at, at least most of those things while you're doing search.

The downside is you, at least last time I looked, you could not attach documents and do search in the same prompt, which is a little bit limiting if it's something that's extensive.

Perplexity works really well for me.

So I'm a big fan of perplexity AI for anything where I wanted to go out and search the web or look at particular documents.

So we'll see.

I mean, hey, if it works well for you, okay, if it doesn't, okay, don't use it.

Then it's kind of my attitude.

But for the listeners who haven't played with it, I think it's worth playing with.

And just to be really clear, you can turn it on and off within a conversation, but you can't search and upload a document in the same prompt.

Rob, Anything else on ChatGPT search?

Well, the only thing I would add, and this is, I think the struggle that people are going to have to ultimately go down the pathway is are you going to be the person who uses all of these tools?

Are you going to be like Craig, or are you going to explore a little bit and decide what is your perfect set of tools?

Because at the end of the day, A, whether it's the number of subscriptions you're willing to pay for, or B, whether it's the cognitive memory of which tool do I need to go to when you're going to come to a point where you say, this is good enough for what I want to achieve?

So I would encourage everyone to play with these and to come up with your own assessment of what's useful for how you seek out information and seek out answers to things.

Yeah, absolutely.

Don't be like Craig, because, I mean, I just like fooling around with this stuff.

So, yeah, that's probably not a productive way to do this.

Although I think when you hear about a new tool, you might want to check it out because you may find that it's better than what you're using.

And virtually all of these tools have a free tier, which is fine for most uses, so you don't have to spend a lot of money to do this.

All right, speaking of new things Google learn about and it's learning L E A R N I n g google.com and that's Google's Learn about.

This is a really cool new tool.

So basically you go to learning.google.com type in something that you want to learn about.

How do large language models work, hit Enter, and it kind of creates a mini textbook chapter on that topic, including pointing out to websites where it pulled the information from that sort of thing.

You can ask for more details.

You can go down different branches.

It's really cool.

Have you had a chance to play with it yet?

Yeah, I played with it.

I was impressed.

And as someone who's a textbook author, at some point I start to wonder, at what point are textbooks Irrelevant, because you're going to be able to dynamically create exactly what you want on the fly and guide your students that way, as opposed to saying we're all going to use the same exact textbook.

It was scary.

Yeah, it is.

I think the problem with it right now, there are a couple of problems.

One is it's not curated.

So you would put a lot of effort into curating the material and organizing it and thinking about the flow of the course and that sort of thing.

So that's a big reason we use textbooks, is because it's a way to organize everything really easily.

But who knows in 20 years or 10 years.

Yeah.

Well, I see especially with young faculty, they really rely heavily on textbooks for what they want to do.

For that exact reason.

My more mature faculty have been doing this for 20, 20 plus years.

They're more apt to feel like I kind of know the path, the direction I want to go.

And so is the value going to be on mentoring young faculty on how to utilize these tools to curate and create a well designed course as opposed to asking our students to spend $150, $200 on a textbook to go through that process for them?

So it's going to be really interesting to see how that process plays out.

Or they can just use a textbook that doesn't cost that much like ours.

Amen.

It's going to put pressure on the textbook publishers and I think that's a good thing.

Overall, there's a huge problem with it right now though.

Once you close out that window, that session is gone forever.

So right now it cannot save sessions, which is a big drawback for instructional use.

You can just save the, save it as a webpage and that seems to work reasonably well.

But you can't go back in and say, oh, I should have asked it this question, it's just gone.

But my guess is that'll get solved eventually.

But where?

I think this is maybe a little bit of a watershed moment.

Something that higher ed has been pushing, particularly in our field, information systems, for as long as I've been around, is we need to help students become self directed, lifelong learners.

We're not so great at explaining exactly how they should do that, but we do push that, especially in a dynamic field like information systems.

This could be a big step in that direction and maybe even more importantly to democratizing learning.

So where you don't have to pay somebody, you don't even have to sit at the feet of the masters to learn things, you can learn it on your own.

And that might be bad for higher ed, but I think not if we do things correctly.

But it really is good for learning.

So I think that's why this learn about.

And I'm sure there will be other tools along these same lines.

I think that's where this is.

This is really, really important.

Yeah, absolutely.

I think, you know, you've probably experienced with your students as well, is they come up to you after class and they want to go deeper onto a topic.

They want to know where else they can go.

And I can see this absolutely.

Engaging those who are becoming curious about the topics and wanting to engage with them further beyond where the classroom activities may have taken them.

Yeah.

And this could be a great assignment.

Just here's your topic.

Create a learn about.

Or you could do the same kind of thing with perplexity.

Create a perplexity page.

That's not easy to say perplexity page about this topic.

So I'm really excited about these tools.

I know a lot of our colleagues at higher ed are justifiably feeling a little bit overwhelmed right now with all of this.

It's going to settle out over the next five or six years, I think, and we're here to help you.

So one of the best, and this is a plug, but I really mean this.

One of the best things you can do is listen to podcasts like this, read newsletters like AI Goes to College, and help us sift through some of these things where you can direct your energies, where they're going to do the most good.

All right, we've covered a lot of ground here.

Any last thoughts, Rob, before we close?

Yeah, two thoughts.

One, just a reminder, play with the tools, get to know them, get to figure out how you would use them, and settle on the ones that you like.

Another plug I'll make is if there's something you want Craig and I to talk about that we haven't touched on, please let us know and we can dig into it and we can potentially bring it in as a topic.

And maybe, if Craig's willing, maybe we'll have guest visitors who might be able to shed some light from a different perspective along the way.

Yep.

And along those lines, you can go to aigostocollege.com and there's a little contact us button.

I think it's in the lower right that you can use to contact us or you can email me@craigaigoestocollege.com One of the things that Rob and I have talked about is hosting an Ask Us Anything session.

So if that's something you'd be interested in.

Send me an email, use the contact us@aigostocollege.com and let us know if that's something.

You should see the look on Rob's face.

He's like, what you did.

We talked about this.

So I think it'd be maybe a useful thing for people.

All right, two other real quick things.

If you want either one of us to come to your campus, either virtually or live, and talk to your faculty, talk to your staff, your colleagues, your students, let us know.

Talked about how you can contact us.

We are both willing to do that.

We do that sort of thing on a regular basis and we'd love to help you out.

And then the last thing is, I have, I talked about Lex.

Lex Page is a AI enabled low distraction writing environment which I use all the time.

Now they reached out to me and they are offering a 25% discount on the annual pro plan if you use the discount code aigtc.

We don't get anything out of this.

It's not an affiliate deal.

It's just something that they wanted to do for AI Goes to College listeners.

If you haven't used it, check it out.

The free version is actually quite good.

It's like the copy editor that you wish you had on call 24 7.

It's really a fantastic tool, so I encourage you to check it out.

25% off using the discount code AIGTC.

All right, I'm tired.

Anything else, Rob?

All right, that's it for this time.

We will see you next time on AI Goes to College.