NCompass Live: Pretty Sweet Tech: Libraries Building AI Literacy

Good morning and welcome to this week's edition of Encompass Live. I am your host Christa Porter here at the Nebraska Library Commission. Encompass Live is the commission's weekly webinar series where we cover a variety of topics that may be of interest to libraries. We broadcast the show live every Wednesday morning at 10:00 a.m. Central time. But if you're unable to join us on Wednesdays, that's fine. We do record the show as we are doing today and it will be available for you to watch later at your convenience. Both the live show and the recordings are free and open to anyone to watch. So please do share with your friends, family, neighbors, colleagues, anyone you think might be interested in any of the topics we have on encompass live. Uh for those of you not from Nebraska, the Nebraska Library Commission is the state agency for libraries. So we are similar to your uh whatever state library. So um so we provide uh training and consulting and reference and grants and databases to all types of libraries in the state. So you will find shows on encompass live for all types of libraries uh public academic K12 corrections museums historical societies. Uh really our only criteria is that it's something to do with libraries. We have uh guest speakers that come on encompass live sometimes for us and do presentations on cool things they were doing on in in their libraries across the state and across the country. Um but we also have Nebraska Library Commission staff that come on and that's what we have today. Uh today joining us is um Amanda Sweet. Good morning Amanda. >> Good morning. and she is our technology innovation librarian here at the library commission. And um it is the last Wednesday of the month. July is almost over. Oh my gosh. Um but that means it is pretty sweet tech day. Yay. Um the last Wednesday of the month, Amanda always comes on the show to talk about something techreated. Uh we do have other shows throughout the month that are that may have tech topics as well, but you can almost depend on every um last Wednesday of the month that it will be Amanda and she is going to talk to us about [clears throat] um libraries um helping to build AI literacy and introducing AI with purpose and practicality which is very important. This is something we uh I feel don't need to be jumping into with no plan or knowledge about what it is, what it can do, what it can't do, what it shouldn't do, all these things, >> and the um effects that it has on um everything in the world. Um but Amanda's going to help guide us through some of that and she's done other sessions about AI as well. So there's lots of different ways to look at this and um this is what we're going to talk about today. So I will hand it over to you Amanda to tell us all about it. >> Cool. So today we are going to go over I'm going to introduce some AI literacy frameworks that I've come across. Some of them that I've liked and some of them have that have been could have been laid out a little bit differently. But I'll go over kind of comparing and contrasting some of those different frameworks and how to translate those frameworks into actual program services and things that you can actually do in your day-to-day. And I'll go over how some of these AI literacy frameworks are geared more toward um like schools and sort of like a controlled academic setting and how those frameworks kind of start to break apart when you go into a public library setting or an adult education setting or a setting where you can't just ask people to follow through a checklist of curriculum that they have to go through. And yeah, [laughter] >> everybody has to look at it differently. Definitely. There's not a one one size fits all. Yeah. >> And that's why some of the frameworks can actually that's why I say some of the frameworks could be laid out a little bit differently because you can tell that they were written by educators and they're written well, >> but they're written for the setting of education. And there's they are frameworks that try to say that they work for everything. But you know when are you ever going to get an adult that'll come in systematically to a library to attend every single event and every single session and cover every single topic imaginable under the sun. It's not going to happen. >> We wish. >> Yeah. Right. So, I I'll go over some of those frameworks and then I'll touch on what the AI Navigator framework actually looks like. And this is something that it's I'm sure that if you find you can probably find AI Navigator somewhere, but it's a model that's still shaping out. And I'm helping shape out the version of what it looks like here in Nebraska and who what other other state wants to use it. And I'll also talk about how that framework can translate into the three main categories that libraries actually tend to see. And most libraries just kind of bucket AI literacy into everyday life. How is it going to help you get a job? How is it going to how do parents have to pay attention to it? What are kids facing in their dayto-day? And how is it shaping the future of work? How is it going to be changing our careers? That's our everyday life. And I'll go over how to turn those into um tangible programs and just build off of what you already do and incorporate AI into the thing. And then I'll go into how the li how libraries can start helping people explore the way that AI is actually shaping careers and the way that it's creating new jobs, changing other jobs, and how you can actually use AI as a tool for innovation and how libraries can start to kind of tackle that in ways that they already do in things like maker spaces and innovation spaces. innovation hubs and stuff like that. I will say that this slide deck that I put together here is actually from a three-hour workshop that I did at one point. So, we're not going to get to all of this right now, but I'll get to what I can of it. And then I'll go over some of the popular resources that um will be helpful and you'll have links to these slides so you can flip through it if you want to. >> Right. Yes. And I will mention, yes, you we'll have access um these slides will be made available when the recording goes up as well. So, um don't worry about trying to scribble down all of the information on here. You'll be able to access it later. Um and even the things that we, as you said, Amanda, don't have time to get into today. You'll still have all of those um resources and info. >> Yeah, I guarantee I won't get to it all right now, but you'll have the link to everything >> and that's okay. >> All right. So in here, this is more the foundation of what I found AI literacy to be. And it's because when people go into the library, they don't actually know what they know. They don't know what to ask for. They don't know if they don't have a solid understanding of what AI is. Even if you were to put out a survey to your community asking if people want to know about AI and what they want to know, they might just say they'll check the box that says yes because they're curious about it. But they'll also check that same box for a 3D printer or a laser cutter, too. And we all know that if people check the box for they might go to a session or two out of curiosity, but once they kind of get a feel for what it actually is, what the technology is, whether it's AI or something else, when they satiate that curiosity, the next thing on their checklist is how is this relevant to my life? >> Why do I actually need to learn this technology? What's it going to do? So that's why if you were to go to a laser cutter class, you might get someone who comes in and does the introductory activity to build out garden stakes, but if they can't think of anything else to do with it, they're not going to do it. >> So AI is like this overarching amalgam thing that it's said so much that it barely means anything. So libraries can actually start to reframe it and say what are the thing what are the problems that people face in their dayto-day life what are the everyday challenges what are the things that people care enough about to take action to solve and now these are the things that we need to start shaping AI around and for the most part that's going to be what are we running into for people want to know which jobs are going to be there in five or 10 years. >> You have kids that are actually going through school right now. My nephew just turned 11. So, he's going to be starting to think about his career in like seven years when he actually hits high school and he's starting to figure out what's next in life. But the careers that will exist for him, a lot of them we don't know what they are yet. And when people are actually going through and people are getting laid off from their jobs right now, they're getting they're changing their careers. They're trying to figure out what's next. And the library can actually work with community partners to be able to say, let's get a feel for what these careers actually look like. And these are the programs that actually bring people through the door. But these are the programs that libraries also can't do on their own. you usually have to partner with a or with another organization to be able to do it. And like here it would be like the AIM Institute or it might be some departments a department of labor or it might be um an economic development group or an innovation group or an entrepreneurial ecosystem group. And then the next one is going to be how is this actually impacting us in our day-to-day. Right now people are freaking out about data centers. But then we're also looking at how our data is actually being used, what's going on. But I also hear back from libraries that when they host a workshop or something like that about how data is being used in cyber security, people don't show up. And some of that is like marketing and phrasing because if people hear the if people see the word ethics they kind of shy away from it. And if people see the word cyber security they kind of shy away from it. But when you start saying how to stay safe online, or if instead of hosting a workshop, you were to put out a video through the library blog that's just like a 10-minute video that talks about how AI is reshaping the way that you're you have to pay attention to things on the computer, then that's different. Um, I've subscribed to a blog online called Malware Bites. and Malware Bites is they put out um email newsletters that talk about the latest biggest threats that are coming through online right now. And the recent one was actually how Meta is automatically toggling on the settings so that any images that are stored on a public Instagram profile can now be used to train and generate AI images. So, you can now type in a person's name that you know and use it to generate an AI image and it's going to look pretty dang good because it's using the training set from their Instagram profile. >> Is that the one I saw? I saw something about that about something in there and there there was a lot of um posts going around about how to um turn off that you had >> in a weird weird place too. >> Yeah. That to go to turn that off and I did that. I did that on mine. It's like, "Nope, you're not using mine." But then I saw and I never went back and looked at them. But then I saw some follow-up things um headlines and I am a bad um I'm bad at sometimes when I'm looking at stuff online. I just read the headlines, as they say, and saying that they actually backed off on doing that because of the backlash and everybody turning it off that it's actually like if you go now and look to try and turn off that option, it's not there anymore because they said, "Oh, well, you all didn't like that, so we're just going to get rid of we're gonna turn we're not gonna do it." That that's the lead in to the policy and advocacy section. >> Okay. >> Which See, I I had a system. I had a system. [laughter] >> Sorry, I didn't want to put you off track, but I was like, wait, I haven't You're good. >> Sometimes, you know, you do your sessions and some of it I'm like really really like learning learning and sometimes wait, I've heard of that one. >> Yeah. Yeah. It's and I use that one because it like it's the one that everyone's been freaking out about >> and it's also it's when we actually get the word out there systematically enough and in simple enough terms >> it yeah and we make a difference. we make them like >> if it's something we all really don't want. >> Okay, I won't say they will change it, but often times >> yeah, >> we do have we can make them change things to something we'd prefer or not have them do. And yeah, >> it's and when people start actually Yeah. >> And when people can find that setting and enough people find the setting, that's actually how policy change happens. Mhm. >> And part of the AI literacy frameworks is that we're trying to educate the public enough to be able to know their rights and know how their data works and know how the system works and know how AI works to be able to know which tools they should use, how to use them, and what they should use them for. And then they need to know how AI is already being incorporated into their everyday life. and then how they should be incorporating AI in ways that they don't know exists yet. And so the other side of that is we're also trying to educate the businesses and local entrepreneurial groups for how to actually build human- centered systems. So a lot of these AI systems are going to be Yeah, I actually used AI to build this because AI is actually really good at this sort of thing. But so this chart up here, this is a tool called Plexity AI if you haven't used it before. So, I will show I just built a little chart that will say that lists out the most popular AI literacy frameworks categorized by target audience. And then it'll spit out this and I specified which columns to include in the spreadsheet. And when you specify which columns to in include in the spreadsheet that it generates, it'll also indicate um more information and descriptions about what you want the AI tool to actually do and how to format everything. So if you are a library that's building resource databases and resource panels, this is actually a faster way to do it. But then you also have to go through and make sure that it's linking to the actual correct thing. So when I and you also have to make sure that it's linking to um the actual place that you want it to go. So I'll give the example of this Department of Labor AI literacy framework. If you go to the one that it sent, it actually goes to this page here, which is not the one that I actually wanted. This is the one that organizations have been using to understand the AI literacy framework and guidance. So, this is a helpful page, but it's also not intuitive. What I want is people to go right over here there. So, this is the AI literacy framework that I've been pointing people to. >> But if you were to send people over here, they're not going to magically know that this is what I want them to look at. [clears throat] >> And one, this is a lot of tech, like plain text that's just in government in typical like government document style. You'll find it on all government websites. Very plain, very not >> go here. Not very like interesting [laughter] >> or attentiongrabbing. No. >> Yep. There is nothing on here that says look here. So I update it to just say go here. And then these are actually the things that this is the one of my favorite AI literacy frameworks. It's actually geared toward uh the future of work, but I think it actually applies to pretty much everybody because people just want to know what in the world AI actually is. And then when you say explore AI use cases, you're actually asking the question of how is this relevant to my life? How is this relevant to my job? How is this relevant to what I do at home? when I go at the end of the day and I go start streaming on Netflix or streaming music on Amazon Music, how does this use my data to build out a profile and do I want it to do that? And then you start saying, now that I know how AI is actually being used in my everyday life, which tools do I actually want to use to be able to solve the problems that I face in my everyday? and which tools can I just filter out of my inbox because I they don't do anything for me. And then once I start using these tools, how can I make sure that the information that it spits out is right? So once you find a tool like perplexity, how can you make sure that perplexity is the best tool out of clawed out of all these different options to be able to solve the problem at hand? And how can you start building your AI toolbox to be able to do what you want to do? And then how can you make sure that AI is being used so that it's actually designed and developed for humans? And how can you make sure that it's um your data is being used effectively? How can you make sure all this different stuff? So that's when I started putting this together. So this is actually what I'm updating and reformatting right now with the help of some AI groups across the state and right now this is like the initial baseline draft which is just saying what is this stuff what I'm updating right now with the help of a few different groups is figuring out which different subtopics and categories because when you actually think about AI, you can think about AI is basically just a tool that helps the computer replicate human senses. So when you think about generative AI, it's actually replicating the ability of a human to be able to like condense information, comprehend information, and translate it into a written document. And it's actually pulling from millions of different examples of the human written word online. And you can either tell AI to search the open web and use the examples all across everywhere or now you can just tell it to use specific sources. And if you use like a tool, if you use a tool like Perplexity and you don't specify which sources to go through, it'll just pull from everywhere under the sun. But what a lot of people don't know is that tools like the pretty much every LLM that's out there, large language model, the training model and training data actually cuts off from about a year previously. So, anything that you put into these model into these prompts, if you put if you if you're asking for a tutorial for Microsoft Word and Microsoft Word has updated itself within that last year, there's a solid chance that you're actually going to get the wrong like outdated information. You're going to get a tutorial from like a year ago. And if you Google it and you get that little AI generated snippet across the top, it's really good at summarizing some stuff like why you're like what is the life cycle of a frog because that information isn't going to change. But if you're asking to generate a tutorial about how to update the like a setting on Etsy and Etsy changed where that setting was in the last year, that summary is probably going to be wrong. And so then they started >> that's why I always think with whenever we're talking about using AI or just I mean this is something before it was called AI we had just Google it you know just search it online and it a lot of that was the same thing. Um, I mean, if you're still just going to your Google or search your search engine of choice and typing something in, >> it's still the same thing you always did before >> everyone started using the words AI. Um, generative AI and all that with artwork and and and literature, that's different. But, um, we always taught critical thinking along with that. When you are looking up something on the internet, you need to make sure that it's accurate and good and, um, where what is the source that it came from? We've been teaching that since the internet started. [laughter] Um, and we need to continue doing that with anyone who tries to use AI and say, "Hey, look, AI told me this or um, >> why are you just suddenly now believing it? It's not any different than what we've been doing for the since the internet in came about." So, >> and so, and that's the next problem is that as librarians, we we've always been teaching information literacy. >> Yeah. >> And those same frameworks that are like that are tried and true for evaluating this information, those are the same. >> But what's under the hood is different. So, most of the libraries that I talk to and most of the workshops, they live over here. These are the large language models that are like what Claude spits out. These are the ones that um Grole spit out. These are the ones that all these different tools will spit out. But now there's another layer to it that is a the retrieval augmented generation layer. So when I talked about how large language models their training data is a lot of times about a year out of date. So after libraries figured that out, they just said, "Okay, we're just going to tell people that stuff is out of date." But if you just say that, we're actually giving the wrong information. because now we also have this new model where a lot of the customized tools that are out there, they actually have um you can now gather a set of curated information and load it into your own custom model to be able to fill that gap. So now the coding models and the developer tools and the any tech tools they actually gather the no the most recent documentation from that missing year and load it into the system. So now if we tell people that that information is year out of date and they're using a system that is actually a rag tool that fills in and solves that problem, we just gave them the wrong information. But the problem is that not all reggg tools are actually labeled to say that some of them will actually specify that they use the most upto-date information and some tools have the ability to pull from the open web like perplexity like if I had gone through and I had told perplexity to um focus on com like focus on resources from common sense media from department of labor and given it a list of five different websites, it will actually go through and manually check those websites from current day information. So this to these tools actually continue to evolve. And so this is when the library is actually trying to give most recent information. We need to have a baseline literacy about what these terms are looking like and what these tools can do so that we give people the right literacy that they actually need going forward. And these tools are also these terminologies are also going to change and evolve in another 1 year, 2 year, 3 years. So, we need to know enough to ask the right questions and to be able to refer people to the right support organizations to know what to look for because after RG, we're actually looking at Agentic and Agentic is where um if you remember when the Amazon AWS server went down, it was because of Agentic gone wrong. Agentic is what happens when you have different AI tools talking to each other and they are trying to string together multiple different tasks in a row. And a good agentic system will actually have a human in the loop that's going to be checking the output of these different systems and making sure that all these different AI tools are communicating smoothly and the output and the in the effects of these systems are actually going the right way. what happened with um the Amazon AWS when all those servers went down and there was just chaos. It was because the engineers that were over at Amazon, they were using AI to be able to update their coding and they were also using AI to write some of their code. So when you start automating that process and you're not paying attention, um that AI tool wound up saying, "Oh, I can optimize this if I just delete this entire section of code." And it did and everything broke. >> Trust Don't trust the AI. Use your own brain in addition to it. You cannot just use it blindly. >> Yep. And so and now more libraries are actually starting to use this too because if you look at some of the different ways that you can use AI, if you're starting to use AI to analyze and generate um feedback analysis reports, if you were to use a survey tool that gathers feedback and information from your patrons in the library and then you feed that into an AI tool to generate it into a spreadsheet output. And then you tell it to find the most common keywords and the most common topics and um issues that were detected across all of the feedback forums and then you tell that to spit it into um an aggregated report with charts. You can start to automate that using an AI tool that syncs to another AI tool that syncs to another AI tool. So, one little AI tool is where most people live. But now, when you start stringing them together and having one of them communicate the output to the next one, that communicates the output to the next one that then generates a final report with charts. That's when people start getting lazy. It's because the end result might look really good, but you may not. You might be overloaded and you might not actually have the time to go back and look at the source information and you're trying to hit a deadline and you just need to get it sent over. Otherwise, you're going to get yelled at because it's the end of the week and you have to you're trying to get to a vacation. And this is the pressure point that actually makes people crack and just put stuff out there. So it's not information literacy skills all the time. Sometimes it's the pressure of being human >> and the ease of actually using these AI tools and because that error that is generated by AI might not be caught for months because you might have a deadline to actually finish this report. But who reading that report is going to go back through to that source information and actually check it? M now that's on you. Yeah. >> Yeah. Yeah. So now you're making decisions based off of this report and this feedback, but is it true? So there are good ways and bad ways to start using this. But what the library is actually trying to do both for their patrons and for themselves is to say how does this fit in your everyday life? Can you use it and can you use it well? And then what is the intended or unintended consequence of using it in this way? And this is how policies, guides and frameworks and regulations actually get built because a lot of times we start doing this stuff and we start experimenting with this stuff and then stuff happens and then we have to go back and say, "Yeah, we didn't expect that to happen. We really don't want that to happen again. So let's write a policy to make sure that it doesn't and let's start updating regulation and start updating all this different stuff. So that's why we started having these AI policies that evolved over time, but now we have AI policies and regulations and frameworks at there's some that are at the federal level. There are some that are at the state level. There are some that are at the local and city level. There are some that are on the organizational level and then there are some that are at the vendor level to make sure that the policies at the federal and state level are enforced. And then there are regulatory frameworks that are used by the vendors and the organizations to make sure that all these policies are actually followed, updated and used. And you have tools like Google that will have to go through not just the US frameworks, but they'll also have to be going through basically global frameworks. And the European Union has a different framework and a different um level of stringency for data sharing than anywhere else. So it's actually helpful to be able to go through and track out what the policy cycle is and the different so libraries don't actually need to know all the everything. They just need to know the keywords and the topics that they should be paying attention to which are here. How is this actually impacting all of us as a whole? How is it reshaping society? First it was computers that reshaped society. Internet reshaped society. Now AI is reshaping society. >> Y >> and it's not a different process. It's just a different tool. >> And it's all these different all these same things that are being impacted but in different I need to make this not all black and white. I just said that about government websites and what did I do? [laughter] It's one line of code to fix this. It is >> you can do it. I have faith. Yeah. [laughter] >> So, this is what libraries actually need to pay attention to. And if all the library does is send people over to a linked article to be able to read more information about it, that's a win. And if you send them over to an information, like an information resource to understand how AI regulation actually works, that's a win. If you send them over to the different levels and examples and different ways that people are writing and adapting and adjusting this stuff and going over to the whole database of how AI is actually being regulated because there are groups that actually track regulatory updates across the country. The library doesn't have to do this. just find the source that does. And just sending people over to this stuff is what people need to know. and then finding the people that will need to actually be contacted to be able to help change this or help advocate for it and learn the process for how advocacy works and learn the process for how people can take control over the data and take control over how and how a tool is being used and which tools being used and how to actually drive change instead of just reacting to change. This is what libraries need. This is AI literacy, but it's not all in the library. It's not something that the library can reasonably be expected to know all the everything and do all day every day. And I really need color on this page. [laughter] So, these links are here if you want to be able to understand from curated resources what all these different topics are about and without actually having to read an onslaught of all the different sources. And then the next thing that you'll actually want to do is you can flip through the activities to be able to introduce people to different AI concepts. So this is probably the thing that libraries will use the most and it's categorized by age grouping right now. And what I've been doing is lately is actually updating the metadata behind this so that it's easier to be able to search by um specific keyword. So right now these are by age range but now I'm adding in the the basically adding in this the ability to add more search filters so that you can search by AI ethics and then you can go to any tools that have AI ethics or any tools that talk about computer vision and it'll map out the keywords and the tags. So instead of actually doing that manually, I use AI tools to be able to generate what those keywords are and then synthesize it into um its own kind of lexicon and then turn it into a database. And it's easier to use the AI tools to be able to do that cuz it would take for freaking ever to go through all of it manually. And then I use another like scraping tool to be able to check the links. But the caveat to that is that I have to remember to run the scraping tool to check the links. So, but I could use an AI agent to automate running the scraping tool to check the links and get it sent over to my email that says fix these links dum dum, which is exactly the spread like the subject line that I would use. And then it would remind me to do it. So, this is where it's getting easier to actually be able to use and navigate this stuff. And then the other one was I have some partner vendors that do the educational technology stuff. So they requested that there's a separate educational technology database that has curated recommended tools that will introduce specific concepts so that when libraries are actually searching for a tool that will introduce computer vision or introduce image classification or introduce some uh neuroscience or neurochnology, it'll be easier to find the stuff. So, and all of that uses AI and automation tools to generate it, maintain it, and fix it cuz otherwise um one human and that's not going to happen without AI. So, this is how AI is actually going to make things possible that would not have been possible before. So, long story short, flip through here. There's cool stuff here. [snorts] And let me jump back up here. [clears throat] So, the other thing that you'll want to look at is when you start actually getting people into the library to start asking about this stuff, people are naturally going to get curious about it. And you as a librarian are not necessarily going to be able to answer all the everything about how all this stuff works, where to go and all this, but you can send people over to either an online resource or a human organization that can answer those questions. So that is where the navigator model actually comes in. Mhm. >> So the trend tracking side of this is that if people come into the library and they're super curious about how machine learning works and they want to ask 8 jillion questions about how this works and then you look at them with like deer in the headlights eyes because you don't know, you can send them over to this machine learning mastery blog or you can send them over to AI magazine or these podcasts and then they can start using this to track those trends. and using this to be able to figure out what's going on. And the library connecting people over to resources like this is AI literacy. And the library being able to connect people over to like an education and training resource that's either local, state, or national is AI literacy. So, we go back over to what is an AI navigator? How are we helping people navigate the landscape of AI? And then you go into this is what an AI navigator actually is. AI is actually a giant massive topic. [snorts] So each library can't be expected to cover AI fully and wholeheartedly. Most libraries actually choose a focus area. They say, "We're going to as a library, we have three these three staff members who are super curious about how AI works. They're in it to win it. And this one is a children's librarian. She's going to focus on kids and teens. And this adult librarian, she's going to work with the children's librarian. She's going to work with parents and older adults. And now we also have a partner organization that's doing workforce development. So they are going to do the career navigation and career resources. And so these are our three focus areas. Now we're going to start figuring out what people actually need to know most in these areas. And a lot of this is going to be talking to um industry experts. And the reason that I started that tech ready is because most libraries don't actually have time to do this step. They don't have the time to do the step of actually figuring out what people need and talking and having a jillion conversations with industry experts and educators and entrepreneurs and all this different stuff to be able to figure out what's going on. Because if you ask the average librarian to go start a navigator program and do all this leg work, maybe one will do it, but it'll be a tiny tiny program that may not be super sustainable because libraries are stretched thin. M >> so part of that is getting the resources together so that the library can skip some of that leg work and identifying the right organizations means that you also have the enough um literacy of the terminology and what's going on in the world of AI to be able to identify the right partners and know where to go and that also takes time. So identifying the actual right partners and getting synced up to the people that do what you want to do without having a million conversations that hit a dead end because you got referred to someone who maybe might do something but then they don't. Part of that tech ready is actually n is understanding identifying those potential organizations and having the warm referral so that you go to the right spot faster and there are already pre-baked um activities and bridges over to those organizations so the library doesn't have to do extra work. And then it's either choosing from some of those lesson plans and activities that are already a thing or putting together a pot of services and resources together so that each library isn't reinventing the wheel. So when we actually all work together on this stuff, it's more possible because if you just look at this stuff as a whole, uh it's not going to happen or it's going to take for freaking ever to do it. >> Mhm. >> But this the tech ready is designed to accelerate this process which just takes a hot second to do. And then the next side of this is what does it actually look like to go through a tech support um service. So, if anyone on this call has actually done a digital literacy support service before, uh you know that even if you try to do workshops and you try to do all this different stuff, the vast majority of people that come through the door just want a one-on-one tech consultation. They have a general idea of what they might want to do and they or they have a question about it. And you as a librarian will have zero idea what they want when they walk in, but you have to just start asking questions. And half the time, if you've ever done a reference interview, >> those questions are not what they actually are asking. >> Oh yeah. >> Yep. And so once you actually find out what in the world people want, then you go through and say, "Okay, can I actually help this person?" And if you can send them over to Sometimes it's going to be a parent that's going to be asking for a resource to be able to set up the right privacy security settings on their kids Roblox. And you can set them over to the Common Sense Media Guide. I have a little collection of these digital skill resources in the tech hub that make this part easier. And sometimes you're able to just send people over there and they're like, "Cool, this is exactly what I needed." And then they go about their day. And in some cases, it's not going to be so easy. Sometimes people are going to be asking, "Do you know where I can go to um get a basic machine learning certification?" And then you're going to start asking questions and you start saying, "How in-depth are you going to be getting on this? Are you going to be do you need a certification or are you just curious about it? Are you trying to turn this into a job? Are you trying to solve a problem at home?" and you start learning to ask the right questions to send them over to either maybe Corsera, you might be sending them over to a community college, to a university, but you also need a database running on the back end of this for a good navigator program that actually identifies what some of those organizations are. And that database is going to be dependent on which topic areas your library chose to focus on. Because if you try to build a database for every single topic under the sun, that's going to take a hot second. But if you try to customize one that's geared toward a specific problem or subject area, that's when you can start getting somewhere. And that's when you're just giving the to the librarian the tool that they need to get people over to the support organization that will help them do the thing. And then if you get 20 people over the course of two months that are all asking about machine learning resources, that's when you start building out a wraparound service and saying, "We're going to build a workshop to help people understand what this stuff is. We're going to do we're going to bring in a guest speaker that's going to be talking about this stuff." And if you only get two people or one person and once in a blue moon asking about it, then you just send people over to the referral and you're good. >> Mhm. >> And so this is how you start building out and then designing better services over time based on the questions that people ask. And if you track the right metrics, then you can feed it into an AI model that'll also start guiding service design in the future. And you can also share the information with partner organizations so that they can also build better services. >> And this looks like how you would do any question that comes into the library. I think any any question that comes into comes to you about from about any topic. >> Yum. So it's very familiar I think to librarians. But the whole, "Oh, this is techie, this is technology, I don't know that," I think is um scaring them away from the not scaring them away from doing it, but they're like, "I don't know. I don't know." It's the same as what you've always done. It's okay. It's just a different topic. >> You just [snorts] got to find the right place to send for to pass them on to. Um and then of course decide is this actually something that we need to be offering as a program or a service or more than just a referral. So um say yeah >> it's a reference interview. That's pretty much it >> like any other one different topic. >> You shouldn't handle it any differently. So this is the same process you you'd use for any reference question you get. >> Yeah. >> And >> do it. I have faith in you. >> Yeah. [clears throat] So this is basically kind of a little sampling of the common problems that people come into the library trying to get support with. And some of them are just looking for I mean I was actually just doing this two days ago trying to figure out which new meal plans I want to like which new things I want to cook because I was sick of the same thing over and over again. >> Yeah. And so it's not again it's not a new thing. You just have AI tools that are layered over the same problems that people were facing 2 years ago. Now you just have different tools to be able to solve it. So now you're looking at AI to be able to generate um resumes, cover letters, and all these different things. And now people are saying, "Okay, now I know AI can actually do this." Question one, which tools should I be using? Which tools are actually the best ones to use so that I can try this? Question two, how do I know that the information that it's spit out is actually accurate and correct? And question three, how can I best use this AI generated resume or tool in an actual interview? because they're running into problems where people will generate an resume using AI or generate a cover using cover letter using AI but then they submit the AI generated resume and then they might get an interview like a month later and they have zero idea what their own resume says and because they didn't go through the process of writing it so they don't have any retention of what it head. And if you don't have a guiding like a guiding force that says you need to actually process and memorize the output of this resume and make sure that the phrasing is reflective of what you actually did instead of letting AI accidentally generate certifications and the stuff that makes you look awesome that fits the job description, but that you didn't actually do. and then you wind up in a job that you can't actually do because you're not actually qualified for it. You just sounded awesome. [laughter] So this is what it what AI literacy is in a nutshell is what are the problems people are facing every day. Which problems can AI help solve and which ones can't it help solve? Which tools are best to help solve those problems? And how can we use those tools effectively to be able to solve it? And will the use of these tools affect society, how we interact with each other, how we how our brain shapes, how our kids are growing up, how our day-to-day lives actually look. and do we want this change or do we need to do something about it? And that's basically all that AI literacy is is asking those questions >> and figuring out on a case-byase basis what that actually looks like. So, we have about a minute I can hang around to ask to answer more questions. I did say that I wasn't going to get to all this stuff. >> I didn't. And that's okay. That's why we've got the slides. Yep. >> Um so does anybody have any questions you want to ask of Amanda? Um we yeah right at 11 o'clock, but we did start after a little after 10. So we will um and handle any questions that you do have. Um anything you want to her to expand on? um any anything you've dealt with like the about AI um and people coming into your library asking about it that you want to share any of your experiences, please do type into the question section. It's the um speech bubble at the top of the question mark on that. I've got that open here, so I'm monitoring that as well. Um, >> and in the slides I also did put in library service ideas, which is one of the more specific things like the tangibles that I didn't >> get to, but you can also flip through here for brainstorming on things that you might actually want to try doing. And you can also try the lesson and activity plan in that tech readyation thing. So, this is a category and concept thing that you might want to try. And the other one is actually lesson plans, activities, gadgets, and things that you can >> get or do use. Most of it's free stuff online. So you can also flip through here for um links to the resources and you can flip through for information about hey how AI career literacy works and how all this different stuff fits together. [clears throat] [snorts] >> This workshop they wanted a thing on AI policy and regulations. So that's the last little section in here. And these are some of the ways that you can introduce uh AI literacy. Same different levers that you can pull, but it's just framed differently. >> Yeah. And somebody did say, "Yes, these slides will be available." Yes. Um so you will have them when I get the recording up tomorrow. Uh so yes, all of those things you'll be able to read through them at your leisure. And then of course reach out to Amanda to um ask about any of the things anything on her slides here uh to expand anything more. Um >> I also started adding a link to book a time on my calendar because some people had specific questions about projects they were doing or whatever you were doing. Yeah. >> So my email's on here, the link to the tech ready nation is on here and my calendar is on here. So you can either grab it now or you'll have the link to the slides later >> and I will give the link to here is the public view link. Copy and >> Oh, they always put it in a different spot on every single platform. There it is. >> Yeah. And and of course like I said at the beginning we have a new uh new interface for uh go to webinar which I do like. >> And the chat says hidden while screen sharing show my private messages. So apparently I can't do chat while I have a screen share. >> I've got it though. I've got it though. So um >> as long as you have it I call it a win. >> Yeah. And um did I Let me see if I did this correctly here. question over here. Send a message to everyone. There we go. Okay. So, I should have shared the actual link to the slides in um the chat which is next to the question section for all of you. So, you should be able to grab that Canva link um if you want to grab it right now. But also, like I said, it'll be on the recording that'll be available for you tomorrow, too. So >> there was a bunch of stuff in here. So you can also just flip through here later. >> And some of these things may become some of these more you like you said this was a longer workshop that you did. Some parts of this may become another a future um session that Amanda does for us and yeah >> just a part of it. We'll see what kind of uh interest in future um sessions. There was a request before for just how libraries can introduce AI careers and um entrepreneurial support resources >> also specific just for that. Yeah. Yeah. >> Cuz I did mostly the everyday life stuff here. >> Mhm. But there's also a whole bunch of um uh how to track which types of careers, how AI is changing careers and tools and um links over to career guides and identifying how to translate this into grab a gadget and use it to introduce this stuff and send people over to the right resources and It's a whole lot of stuff. >> Yeah. All right. All right. Well, I don't see any other Oh, wait. Uh, we got some Thank yous. Great information and resources. Thank you coming through. Thank you. [laughter] You're welcome. Well, hopefully it is. I know this AI is a huge topic now on everyone's mind and we're all trying to figure it out and um Amanda's done multiple sessions from about different aspects of it. Um, so hopefully this will be um helpful to you as well. All right, I'm going to bring up my screen here. There we go. Um, thank you. Thank you. Thank you for the information. People are saying great. Yeah. Um, so as I said, yep, I've got the link here to the slides as well that will be available. Um, [snorts] so that wraps up today. Thank you everybody for joining us. Thank you, Amanda, for being with us here. Again, um as I said, uh the recording will be available uh by the end of the day tomorrow to everyone. Um if you use whatever is your search engine of choice, and type in the name of our show, Enco Compass Live, you'll find our main page uh which is right here. We have our upcoming shows and underneath at the very bottom a link to our archive shows. So today's show will be at the top of the list, most recent one at the top here um with a link to the recording on our you on the Nebraska Library Commission's YouTube channel and a link to the Canvas slides that um Amanda put together. Uh everyone who attended today's show and registered for today's show will get an email from me. Uh we also letting you know when the recording is ready. Uh we also push it and notice out into onto our mailing list we have here in Nebraska for Nebraska libraries and onto our social media. We have a Facebook page for Encompass Live. If you'd like to use Facebook, give us a like. Uh we post reminders. Here's your reminder to log in today's show. Uh presenter um announcements and then when a recording is available. Here's the one from last week. Uh we also use the library commission social media uh Instagram page, blog. I put out onto my LinkedIn. Um and we always use this try to I always try to use the hashtag encount name. So, uh, you should see things all over the place about Encompass Live and, um, please do share and spread the word about the show. As I said, um, on our archive here, I will show you. You can, um, search our show archives to see if we've done a show on a particular topic of interest to you. Um, you can search the full show archives or just the most recent 12 months if you want something very current. Um, as Amanda was mentioning earlier, uh, things become old and, um, there's old information out there on the internet. And there is an encompass live, too. Um, this is our full show archives. And I'm not going to scroll all the way down because it is a huge list here. Uh, but this is our full show archives going back to an encompass live first premiered in January 2009. 2009. Um, we're officially an adult this year, 18th year of the show. [laughter] Um, but uh we do have all of our show archives uh uh recordings out there. So do pay attention to the original broadcast date of anything you watch. Some shows will be fine to watch, still stand the test of time, still be good, useful information, but some things will become old and outdated. Resources may have changed drastically. Uh links may be broken. Uh people will work in different places than when they presented for us 10 or 15 years ago. Um but this is something libraries do. We keep things for historical purposes and as long as we have a place to host all of these shows, which is right now the uh Library Commission's YouTube channel, we will always um keep them available for you out there. Uh um [clears throat] there we go over here. Uh some people do ask about receiving um continue education credit for watching our shows and yes, you can. Uh about an hour from now, everyone will see everyone who attended the live show receive an email letting you know um there's your official um proof that yes, you've attended the show. Thank you for attending. It's got a PDF certificate if you need that or like to have that. Um if you're a Nebraska um staff person and you are in um our certification program, your CE hours hour will be automatically added. If you're outside of the state, you'll have to work with whoever are your CE granting um organization to get that. Um, for our archive shows, you we do have a form that um you would uh apply for for Nebraska libraries. Same thing. If you're not from Nebraska, talk to your CE people to find out how you would get credit for watching any of our recording shows. Recorded shows. Yeah. >> All right. So, um, Amanda will be back with us at the end of August. I believe that's your next scheduled session on August 26. Topic to be determined. So, uh, keep your eyes open on our schedule to see what we what she comes up with for next month. >> I might have the SJICU students come in and talk about the Nuclei pilot. I don't think we did. I don't think we did an encompass for that yet. >> The what pilot? >> Nuclei, the AI story generator. >> Oh, okay. Yeah, sure. >> Yeah, >> I'd love to have >> probably either either August or September, >> right? because you're in for August and September. Um October uh if you saw on our inter slides there will not be a pretty sweet tech in October. The last week of October is the internet librarian conference. >> Yeah. >> Um it's an online conference that but Amanda uh participates in that sometimes as presenter sometimes as moderator but um that's a >> I'll be giving a workshop and I think three sessions this time. >> Wow. Okay. >> With a panel so the panel is not >> you know. Yeah. Um but look at attending internet librarian. It's a free it's an online sess um conference. It's the last week of October. Uh and it um we do usually through the library commission offer discount um registration. So keep an eye open for that. Um so yeah, so keep an eye on schedule here. Um here's our August dates. I'll be getting some September ones up as well. We are taking next week off. Uh taking a little summer break. Uh so there will be no show next week. No encompass live on the on August 5th. Uh but we will be back on August 12th uh to talk about United for Libraries. This is the American Library Association section for trustees, advocates, friends, and foundations. Uh so for your friends, groups, foundations, uh library boards, this is a great resource. Lots of um um trainings and workshops and information in there. Um here in Nebraska, we do have statewide membership for it. So it is available for all Nebraska libraries, library staff, library board members to use all of their resources there. Uh and right now at this very moment is happening uh was first day was yesterday and then today and tomorrow the United for Libraries virtual 2026 online conference is going on. >> So um if you are uh a Nebraska library and you're from anywhere register for it um they have each day is specific for uh has a focus. Yesterday was trustees and boards. Today is foundations. Tomorrow's is friends. Um here in Nebraska, we have paid for statewide re um registration for anyone in Nebraska to attend. Other states do have statewide um registration as well possible. Um if not, you can just sign up yourself. Uh and then the all three days is the entire virtual online conference. All of it is recorded and the recordings are available afterwards as well if you register. So, um please do uh look into attending that. And our um next week's show about United for Libraries, uh Beth Nolinsky, who's the executive director of United for Libraries, will be joining us to talk about everything that you can access through them. So, please do register for that and for any of our future shows. Thank you everybody again for being here. Thank you, Amanda. Good to see you. We'll see you next month and um hopefully see you all on a future episode of Encompass Live. Bye-bye.