Cori Wilhelm: Alright. Thanks everyone for joining us. This is the 1st spotlight on suny libraries of the new academic semester. So we're thrilled to have you today we're joined by Michael Casey and Jessica Hollister, both from the University at Buffalo to talk about an organizational response to AI in academic libraries. And this is definitely the big topic right now. Jessica is the dental liaison librarian at the University of Buffalo.
Cori Wilhelm: Her research interests include evidence-based practice, pedagogy and health, sciences, librarianship.
Cori Wilhelm: and academic librarians, experiences with appointment, promotion, and the tenure process. Jessica, I may have feature you in a in a future meeting about that topic. And Michael Casey is the humanities, liaison librarian at the University of Buffalo. Their research interests include digital humanities. Marcel Proust and Chinese ancient literature and philosophy and Zen Buddhism.
Cori Wilhelm: So thank you so much, Michael and Jessica, for joining us. I'll turn it over in just a second, just to let everyone know the meeting is being recording recorded, and we'll share that recording out afterward. And if you have questions, either hold them to the end or pop them in the chat, and we'll address them at the end.
Cori Wilhelm: So Michael and Jessica take it away.
Michael Kicey: Okay.
Michael Kicey: thanks, Corey. Thanks also for inviting us to do this talk. I'd also like to thank my co-presenter, Jessica, who you'll hear from shortly. Okay. So the 1st thing that I want to share with you. And this is by way of acknowledging other members of the the work of other members of of our group, but also to make a point about how groups like this can be formed.
Michael Kicey: What you see on your screen is is just a list of the people participating in the group that I chair, which is the AI advisory group at Ub in the Ub libraries. The point of putting up this screen again is both to acknowledge the work of my colleagues, but also to show that it is really vital when you are forming a group within any unit, but especially within libraries.
Michael Kicey: to work around issues brought up by artificial intelligence, that you have wide participation from people in many units. This is not AI is not an issue that can be safely sort of shunted onto one particular unit or one particular group of people. There may be a temptation to think it's something that's only going to affect tech services or something that's only going to affect people who support research.
Michael Kicey: It is a technology because of the way it works, and because of the number of tasks tasks that it can perform, and the kind of content that it can produce
Michael Kicey: it will, it is already having, but it will have an impact on virtually all library services and resources across the board, which is the reason why, when you form a group like this, as I've been saying, you do want wide participation. Now, that doesn't necessarily mean that every single unit has to be represented in a group like this. But it means that, aside from just the broad distinction of, say, technical services and public services, you need people who will be thinking about the application of AI in their work in very different ways.
Michael Kicey: and not just thinking about it, but actually undertaking it, experimenting with it, and who are also keeping up with different strands of professional discourse within. Say, you know different compartments of librarianship where the conversation may be totally different about how this particular technology is being applied, or what its risks or its its advantages are. So diversity, as usual, is is not just a a luxury. It is necessity when you're talking about issues around AI,
Michael Kicey: so to get into sort of the meat of what I want to present.
Michael Kicey: I wanna in particular. And I know this is, you know, as exciting as watching Paintry. But I want to look at the, at the charging documents for our advisory group to give an idea of the tasks that we were given by administration. What they felt was important for us to look at and to actually start undertaking. And then I'm gonna be looking briefly as well at the the charge that we composed for a policy group which is sort of the the spin off a spin off group that is sort of now in the process of being formed.
Michael Kicey: Jessica will be telling you in detail about our AI Literacy training group, which was the 1st one we sort of spun off, and which is already sort of very much underway and doing its own work. Aside from that, I will talk about issues that come up in the discussion of the advisory group things that faculty have come to us with, and also our our activities and our accomplishments in terms of communicating with people in the libraries.
Michael Kicey: Around issues related to AI. So our group formed, I think, around February or March, and I was made the chair. We have really 3 major responsibilities to. And since we're an advisory group, we are advisory. We're advisors to the Vice Provost for university libraries at Ub. Via the winerel
Michael Kicey: the our tasks that we were given
Michael Kicey: is to provide expert guidance. 1st of all, which means that we are expected to develop and provide expertise. We are, we are expected to learn, at least to a basic or intermediate level, how generative AI actually works, what the implications of the way that it works
Michael Kicey: have for library work of all kinds, right? And to to develop a certain level of knowledge. That that will allow us to do this work responsibly and intelligently, and the reason for for developing that expertise is, is, as I said, to to advise the Vp ul, and also to be to be basically sort of you know, liaisoned across the libraries for this this cause and for this, for this particular issue.
Michael Kicey: the second thing, and Jessica's going to have a lot more to say about this is to to work around issues of education and support. So what we need to do
Michael Kicey: is to support and coordinate training, and that means training for people within libraries. It means eventually, when we talk about how AI is becoming an issue in library instruction. It means supporting training of people outside the libraries which may include students. It may include graduate students. It may include faculty, but giving people basic competencies, understanding for how AI works again, what its risks and what it's what its issues are.
Michael Kicey: but really sort of educating people about about the way this technology works
Michael Kicey: to start educational initiatives, whether those are formal in terms of programmatic changes in the way that we do instruction or are less formal, and I'll talk about one of the less formal ones. Later. I I did a prompt engineering workshop last month for librarians.
Michael Kicey: and also to discuss to to lead discussions that that enhance understanding and mitigate concerns that people have around AI within the libraries.
Michael Kicey: and 3rd of all, to to make recommendations so. And and this is a sort of very broad based responsibility to basically propose changes in our policies and changes in our practices to adapt what we do to what is going on in AI. So this is a pretty broad purview, and, as you can see, you know, the reason why we have such a wide, ranging membership is precisely to to address sort of the the wide, ranging responsibilities that of necessity we we have to have
Michael Kicey: in terms of specific tasks that we were given. We we basically we were looking critically at these tasks. Some of these we've decided to take on ourselves. Sometimes we, some, some of them, we've decided, are so complex and far reaching that we need to form separate subgroups or task forces to to really sort of sink their teeth into these, into these, these parts of the mission.
Michael Kicey: Jessica is leading one of those in the Literacy training group. But, as I just said, we, we are working on forming a policy group which is probably going to be just as crucial as literacy training to give a policy infrastructure to what we're doing.
Michael Kicey: Other things that we we ourselves are taking on, and are also considering forming groups around, monitoring what is going on in terms of how AI applications are developed and how they're being used. And this means staying informed. This is actually much more challenging than it sounds. The. And this, this touches on a larger issue about how you know what the relationship between the tech industry, at least within the AI domain
Michael Kicey: and the and and librarianship, what what the nature of that relationship really is.
Michael Kicey: We have to remember when we're working with AI and libraries that we are downstream from an industry which has a lot more money, a lot more resources, a lot more lobbying power, and works at orders of magnitude
Michael Kicey: faster than our area than our profession does, or even can.
Michael Kicey: That's an unhappy realization, and it leads a lot of people to a lot of knee-jerk pessimism. It doesn't mean that we have to be completely passive or pessimistic, but we do have to recognize that in many ways the ethos of the tech industry which is to move fast and break things is the exact diametric opposite of the ethos of librarianship, which is to move slowly and to get as many stakeholders involved as possible, to have sometimes lengthy and complex conversations, and to preserve things instead of, you know, breaking them or just trying them right?
Michael Kicey: So one of the things that we have to reckon with in terms of staying informed to come back to the bullet point. Here is the fact that this industry that the industry around AI moves extremely quickly. The developments are fast, and they are often profound. And we are. We often feel when we're trying to stay up with news. And AI that we're trying to drink out of a fire hose because there is so much going on, it happens so quickly, and there is very little chance to really process it intelligently.
Michael Kicey: Each of us has to adopt methods, to figure out how to to absorb all that information and to think about it critically, and to apply it to library work. But it is a particular challenge. And we've we've tried to meet that challenge by
Michael Kicey: one of the activities I'll talk about in a minute, which is collecting and editing a monthly news digest for people within libraries. But it really is a challenge. And it's it's it's not a challenge that we have. You know. We've not figured out the silver bullet for this yet, and we are sort of struggling to individually and collectively to keep up with with developments. But it is something that that has to be faced.
Michael Kicey: The second thing that sort of goes along with the 1st of of staying informed is also to communicate what we find out about how AI is developing and being used by disseminating information on AI's potential impact on pedagogy, research teaching collections everything that the library does, and that also falls within the purview of the new news digest that we're creating. And the way that we're trying to communicate about AI throughout the libraries.
Michael Kicey: 3rd point here, building connections which is going, which is already important, but will be increasingly important, is basically using the power of liaisons and also existing outreach connections between libraries and the community in the university, and potentially even beyond the university, to find out what people are using, what they are doing with AI, the effect that it's having on their work. And that's not just librarians, but also people in the disciplines at our university
Michael Kicey: to meaningfully respond to the way that AI is being applied in research and teaching. Those connections are enormously important.
Michael Kicey: And also to conduct policy review. And of course, as I've said already, we have created a separate. We're in the process of creating a separate group to address precisely that issue because it is really really important, especially with the way the generative AI works to be very prudent, and to be very qualified and cautious in the way. Not just that we use these tools ourselves.
Michael Kicey: but in the way that we present them to to students, to faculty researchers as tools for them to use because of the specific risks and limitations associated with generative AI content. It's really important that we develop policies. We actually develop regimens that we can apply to AI tools as they come on the scene and are used by researchers in order to evaluate and and potentially limit their use, or at least recommend recommend their use, be limited by people who want to use them for serious research purposes, but more on that in a minute.
Michael Kicey: Also, part of our charge is to to constantly consider the ethics of AI and the ethical dimensions of AI use. I don't even have time to go into them in all detail, but they involve environmental costs. They involve labor costs.
Michael Kicey: They at every point involve human relations, social, political, economic relations. And every time we use AI, we make an ethical choice whether that's a good ethical choice or a bad one. We make an ethical choice about the resources that we consume, the human relations that we produce or reproduce in that use, and in the way that we use the information or or the the content that is generated by AI
Michael Kicey: as well across the board. I mean, all of you. If you've been watching the news. I mean, there are issues about the way that generative AI content is used in our culture already and is part of our responsibility in this group to reflect ethically on that, on those uses and those developments.
Michael Kicey: The next 2 exploring the impact of AI, both on the way that we teach students how to use library resources, and also on the way that we support research. It is quite clear in virtually every discipline that a big machine that can produce textual or visual content on cue has huge implications for what students do, and also for what researchers do.
Michael Kicey: especially given the fact that the quality of that content is not always of the highest, and it cannot be relied upon without careful human evaluation.
Michael Kicey: Last of all is the issue of the way that AI is being currently applied within the libraries, how we ourselves are individually using AI in our daily work, whether that's to compose emails or cover letters, or what have you? But also the way that we use it to to assign to to address all the other tests that I've been? I've been talking about in terms of research and and instruction.
Michael Kicey: So, secondly, very quickly, just to talk about the way the policy group is going. Just been the last couple of weeks we've gotten volunteers in this group that's being convened. But among the most important tasks, as you can see here, we want to 1st off conduct an environmental scan to see if if and how other institutions are addressing AI developments in their own fields, and whether and what policies they have developed to address them.
Michael Kicey: I, you know, given the fact that I have heard at least that within suny I may be wrong, but that within Suny. Our advisory group is sort of a 1 off deal like we're we're the only group that is pursuing this. So far I don't know how nationwide. It stacks up in terms of other groups. I'm sure there must be some but I I think that environmental scan will probably conclude with some some good things to use. But a lot of this is gonna have to be footwork that we do ourselves.
Michael Kicey: As I mentioned before, another major task of this policy group is to establish criteria for evaluating emergent AI tools or utilities in response to direct faculty requests, that is to say, if faculty come to us, and they say, Hey, I find out about this tool. Some of my colleagues have been using it here or elsewhere. They recommended for various functions. Can you endorse its reliability? Is this a legitimate tool for me to use in a serious research
Michael Kicey: project? It behooves us as librarians working around AI to develop basically a very strict vetting regimen that we can use for these tools to respond to faculty in a meaningful way. Aside from just generalities about generalities and caveats about the use of generative AI. That will be a major task for the policy group to take on is basically to establish a process that we use
Michael Kicey: to look at AI tools critically and to qualify them in the eyes of people who are interested in using them.
Michael Kicey: And last of all, obviously, to establish policies that govern the use of AI by us within the organization, wherever those are not not at all, or not adequately addressed by policies that are already in place from the university level.
Michael Kicey: So that already, even though it seems kind of minimal, is actually that that will give the policy group, I think, more than enough work to to take on.
Michael Kicey: So, last of all, I want to talk about some of our substantive activities, aside from just sort of the structure and the outlook and the the charge of the group. To talk about a few things. That we have actually achieved since we were convened around March or so. One of the things that I've already mentioned is that we have. We've been coordinating, and we're we're sort of. It's still a little nebulous, and it's still kind of working, working out its kinks.
Michael Kicey: But we are trying to figure out a way to communicate with the population within our organization within our library
Michael Kicey: regularly, with relevant and meaningful material about developments in AI each month, because, as I said before.
Michael Kicey: drinking out of the fire hose of AI news and developments is challenging for anyone. It is challenging for people outside this group as well as within the group. And it's really important to offer people at least some sort of entrance into that discourse, into those developments as they are happening because of the speed of of the development of this technology. So what we've settled on is for each member of the group to contribute
Michael Kicey: a couple of links and and sort of a brief summary or sort of sort of an overview of what those links have to offer, and those are generally to articles in peer reviewed journals that we have access to through the library so that people can look at them. It's very important that we, you know, offer things that are not going to be paywalled
Michael Kicey: and basically just to sort of, you know, invite people into the conversation. The other advantage, because we have such a wide, ranging population within the group is that each group member can offer links to articles that they find with, you know, interesting within their own subdomain of librarianship, which will inevitably be of interest to others as well. So, for instance, in the last one we had, we had stuff within digital scholarship. We had stuff within medical health sciences, librarianship.
Michael Kicey: just a whole range of things to sort of bring people into the conversation and make them aware of how the technology is being developed and applied.
Michael Kicey: We have also fielded inquiries from faculty about, in particular, about AI, tools that are used for summarizing or aggregating articles.
Michael Kicey: which is to say that you can. There are tools tools that exist of varying quality that you can throw a Pdf article into, and it will generate a summary again of widely varying quality, depending on the tool and depending on how it works, and also that an aggregation tool that you can use to basically dump an entire archive of Pdfs that are interconnected in terms of
Michael Kicey: how they relate in a scholarly conversation. And the tool will basically aggregate those and kind of summarize what they all have to say in a more approachable document than say, you know, 15 or 20 Pdfs. Again, they are widely varying in quality at the point that faculty have started to approach us about these.
Michael Kicey: We've had to be honest about the fact that we we are still, you know, working towards the formation of a group that will form a regiment, as I described in connection with the policy group. We've had to be honest about that. But we have tried to express, you know, again, in case the faculty didn't already know some of the risks and limitations associated with any kind of generative AI tool.
Michael Kicey: We haven't had. Time and again. We haven't had yet had expertise to actually be able to take a tool that faculty bring to us and to evaluate it according to those criteria. But that is, that is a direction that we will be headed in in the future, because we only anticipate that inquiries about AI and about tools of this sort are going to grow in number.
Michael Kicey: We've also increasingly, and perhaps a little more importantly and persistently. We've had an increasing number of inquiries from faculty who are not only asking liaisons and other librarians, and related units to offer library instruction, as they always have, but we have an increasing number of those faculty members who are also asking librarians to talk about the use of AI as an information source
Michael Kicey: potentially in student papers or student projects. What its risks and limitations are. You know how AI actually works? Because at this point it may be only the librarian in the classroom on that one day that actually explains to students how it works and why it works the way it does.
Michael Kicey: We've seen an increasing number of of inquiries of this kind, which means that at least institutionally, there's increasing urgency that we adopt. In addition to those policies I was talking about to evaluate AI tools. We also adopt some sort of uniform set of practices about what we want to communicate to students about how AI works, how they should use it, how they should not use it, what expectations they can have with AI content, and so on and so forth.
Michael Kicey: It is becoming increasingly urgent from my point of view, and from the point of view of many people within the advisory group, and beyond that AI needs to be a regular part of library instruction.
Michael Kicey: This is also against the background of the fact that Ub is starting to wake up and revise a lot of its required curriculum around developments in AI that that basically there's going to be required AI literacy components in the curriculum that is now taking shape. It's still unclear and a little nebulous how that's going to sort out. But I at least, and the members of the group have have been trying to make sure that libraries are at the center of that conversation and the center of the changes that are going to take place.
Michael Kicey: A couple of last things we hosted a day long discussion within the libraries of a document called the Cni AI futures scenarios. This is linked in these slides, which I assume will be shared later on. That happened on July 23rd Cni is the coalition for networked information. They convened a panel over the last year year and a half or so that was doing scenario planning
Michael Kicey: for potential societal level futures and ways that AI might pan out. So basically, they took different sets of related present trends, and they extrapolated future scenarios where we might end up societally or globally in terms of the application and development of AI,
Michael Kicey: and talked about how libraries can respond to any of those scenarios. They had 4 scenarios that they finally settled on.
Michael Kicey: We had participants read the document beforehand. Come in like I said, it was a day long affair. We had discussion, questions. Each table of of people convened around a specific scenario and around specific discussion questions. And we had a very productive conversation, if only because it led a lot of people who showed up to actually think about and address AI for the 1st time, and how it was going to affect their daily work, and how it was going to affect the structure and the functioning of our institution.
Michael Kicey: Another thing that I personally did in August, because I anticipated, you know, the inquiries from faculty that we've been getting about libraries, librarians addressing AI in the context of instruction
Michael Kicey: was that I offered a somewhat higher level workshop on prompt engineering, which is basically the practice of interacting with an AI so as to get the most reliable and productive results. There are specific strategies and techniques that people should use in in writing prompts, which is an input to an AI system that are much more effective than than simply sort of falling back on our own habits in terms of
Michael Kicey: natural language in English, in the way that that we have. You know, human conversations. There are specific strategies and techniques to learn when you're interacting with an AI. And I offered a workshop, mostly internal to the libraries.
Michael Kicey: But I actually have had a couple of requests from faculty that I've taught, for within the last few weeks since the beginning of the semester, who are also interested in sharing information about how AI works which I introduced in that workshop, but also in how to interact with an AI productively, which is which is what the rest of that workshop is concerned with. So I've actually started to share the link to this recording a little more broadly than just among librarians.
Michael Kicey: So that sums up really what the Advisory group has been doing so far, and now I'm going to hand it off to Jessica.
Jessica Hollister: Thank you so much, Mike and Mike. Thank you for being our navigator through the presentation today. So again, everyone, my name is Jessica. I'm the dental liaison librarian here at Ub. I work with Mike.
Jessica Hollister: As Mike was saying, Mike is in the umbrella group for the Ub. Libraries on a on AI, the AI Advisory group, and I am on a subgroup or a subcommittee underneath the governance of the AI Ag. So my subcommittee is the Literacy Training Subcommittee, and, as you can see, as Mike
Jessica Hollister: kindly mentioned before, this is recognition of the people across the libraries who I work with on this team recognition of the hours and time that they're putting into this. So I'd like to thank them whether or not they're here. I appreciate all of their work. This subcommittee was formed in the spring, like Mike said earlier
Jessica Hollister: And over the course of this presentation my portion of the presentation. I'm going to talk a little bit specifically about our charge. And then the project we've been working on, as it relates to that charge. And hopefully, this particular project. If you are thinking of forming this type of group at your own institution or your institution is forming the group, and you're thinking of being a part of it. Hopefully, this can inform how you go about.
Jessica Hollister: Maybe this would work for your institution. Maybe it wouldn't, but I would just like to share it with you, and hopefully, something very will come of it. So next slide, please, Mike.
Jessica Hollister: looking closely at the charge for today. So importantly, we're here to this subcommittee is here to assess current AI use and competence in the UV libraries. And I'm going to be focusing a lot on this part of the charge today, because that is the project that we've been working on moving down from there like Mike was talking about
Jessica Hollister: from there. We're need to determine a path for providing both intra and extramural training programs on AI literacy. So these programs would be both internal to Ub libraries, faculty and staff, but also offering them out to the larger university community. And how can it be helpful there through those training programs.
Jessica Hollister: articulating principles that support both ethical and responsible use of AI in the libraries. Mike mentioned a couple
Jessica Hollister: the ethical the ethical part of AI can go on for hours and hours here, but, as Mike mentioned before.
Jessica Hollister: sustainability workforce. These are the types of things we think about when we think about ethics. And AI. So, considering those in the training that we design and implement and then finally working towards organizational compliance with ub administration, particularly the larger Ub. AI task force. So our libraries group is the AI Advisory group, Aiag, above that, a larger university group kind of at the institutional level
Jessica Hollister: is called the Ub AI task force, and a lot of the directives that we're getting are going to be coming out of the Ub. AI task force. So what Mike and I are doing in our respective committees is
Jessica Hollister: hopefully trying to align
Jessica Hollister: our committee work with the goals and the mission of this larger Ubai task force. So
Jessica Hollister: keeping that in mind as we move forward next slide, please.
Jessica Hollister: And so, as I mentioned before, I'm going to be focusing a lot on our very 1st charge, which is assessing current AI competency and use in the ub libraries. And naturally, what comes out of wanting to assess those things is a survey.
Jessica Hollister: and so breaking down each part of the survey process. Starting with the pre survey here, we started out by establishing our timeline, so, as a group we formed in the spring, and we decided we would take the spring to create the survey design the survey, take the summer to implement the survey, deliver the survey.
Jessica Hollister: and now, where we are right now in the fall, is reviewing the survey, drawing our conclusions.
Jessica Hollister: making our recommendations, and creating a port report out of that, to report up to our higher ups.
Jessica Hollister: So we've established our timeline, established the scope
Jessica Hollister: understandably. The scope is rather small. It is just ub libraries. We put this survey out to our fellow ub libraries, faculty and staff, notably student assistance. Were not included in this so kind of full part time, ub libraries, faculty and staff
Jessica Hollister: the length of this survey. I think all of us on this call can agree that we sometimes feel, survey fatigue. So, with understanding
Jessica Hollister: of the time commitments of our colleagues. Here we tried to put together a survey that would be
Jessica Hollister: would be achievable for our colleagues to complete, but also long enough that we would obtain enough information from our colleagues to be able to really produce valuable trainings that would be useful to our colleagues. Coming out of this dissemination. We'll go into this a little bit further. In a second this survey was created using qualtrics, and it was disseminated using our ub libraries, listserv
Jessica Hollister: and obviously our desired outcomes. We're not going to establish a baseline knowledgeability of AI using competency in the ub libraries, but also to kind of get a general
Jessica Hollister: idea of the feelings our colleagues, we and our colleagues have about AI, so that we can address any future trainings we have with the sensitivity we need to approach people at and reach people at their level and at their AI comfort level.
Jessica Hollister: So, moving down from there
Jessica Hollister: it became very apparent to us that we needed to self educate on AI literacy so that we could best implement the most informed survey possible. So Mike touched on this on his part of the presentation, too, about the self learning that these committee members have to do in order to produce material that's helpful to our fellow colleagues. So in our particular subcommittee.
Jessica Hollister: we started what we would call like a literature review or research review at the top of each of our bi-weekly meetings, where people could present on an AI scholarly article, or an AI resource or an AI tool that they learned about or found useful.
Jessica Hollister: and they would be able to kind of present on that particular, on that particular resource in a smaller, safe environment. And I think maybe a lot of us on this call could agree
Jessica Hollister: that the amount of information about AI out there can sometimes feel very overwhelming, and it's hard to know where to start. So, being able to recommend these resources, or these scholarly articles to your colleagues, is kind of a really approachable way to
Jessica Hollister: dig into the literature and really start to inform yourself, and it also gives your colleagues an opportunity to kind of teach on what they've learned, and it's fostering
Jessica Hollister: fostering a good sense in the committee. So we found that to be very useful.
Jessica Hollister: along those lines. Not only do we have to self educate ourselves, but we had to research about what other academic libraries are doing in terms of literacy training or AI literacy at their universities. We honed in on a particular survey out of the University of New Mexico. This survey was designed and implemented by Professor Leo S. Low, last name LO.
Jessica Hollister: Many of you on the call may be very familiar with this person. This is a professor and librarian out of University of New Mexico, who's very prominent currently in the AI literacy and academic libraries conversation, particularly in prompt engineering. So we honed in on this particular survey. The scope of the survey was much larger. It was
Jessica Hollister: delivered to academic libraries across the us. Obviously our scope is much smaller, just internal to the ub libraries, but it was really useful to see what topics were being discussed here, and it really helped us to go and design our own survey.
Jessica Hollister: So from there we were able to start drafting our questions, and very quickly we realized that we wanted to
Jessica Hollister: include both multiple choice and open-ended questions which we'll dig into a little bit further in the following slides.
Jessica Hollister: and as we were digging into the questions we realized that was a great opportunity for us to turn, determine how to put together the survey layout and how we wanted the survey, to behave, and when I say behave, I mean these are the times to ask yourselves, do you want all the questions to be required? Do you want respondents to be able to go back and change answers to their questions. Do you want to
Jessica Hollister: collect information from incomplete surveys? So it was an opportunity in drafting these questions, to also understand how we wanted people to interact with the survey itself, and what information we would draw from that
Jessica Hollister: and in drafting this questions and in figuring out the layout and format and the behavior of the survey. It was
Jessica Hollister: It all came together to help us finalize our questions.
Jessica Hollister: And next slide, please.
Jessica Hollister: So, looking closely at our survey questions, I wanted to give you an idea of the topics that we wanted to touch upon in our particular survey, and I'm indicating in each bullet point here whether or not it was multiple choice or an open, ended question to see what information we were trying to garner from this. So essentially, we were asking our colleagues to, you know, identify or characterize their own familiarity with AI, and we had a multiple choice question
Jessica Hollister: for that. Similarly, being able to identify or characterize your attitudes towards both AI in the libraries, in academic libraries, and just in society in general multiple choice. Being able, asking our colleagues to identify, you know, their own efforts to stay informed on AI multiple choice. The next one concerns. This was a a really important part of our survey to us. We wanted
Jessica Hollister: this to be an open, ended question, where people in their own words could
Jessica Hollister: could indicate to us
Jessica Hollister: what if any concerns they were feeling, and we felt that this would be a really telling question, and would really inform any trainings that we designed moving forward.
Jessica Hollister: moving on from there. For our librarians or information professionals here at Ub. Who facilitate or teach
Jessica Hollister: in the libraries. We're curious to know if if
Jessica Hollister: they are integrating AI into their teaching, either AI tools or are they teaching on AI concepts? So we had both multiple choice and open ended questions regarding those also, just in your day to day work.
Jessica Hollister: do you interact with AI? Do you use AI? How how do you do that? Another open-ended question.
Jessica Hollister: and the only demographic information we took from this particular survey, if I can call it demographic information, was the library unit in which people worked in. And so this again was administered through Paul tricks. It was an anonymous survey, so the information was de-identified. We did put at the end of our survey.
Jessica Hollister: a. An indicator asking if people wanted to identify themselves and wanted? If we had follow up questions, could we reach out to them, and a few people did take us up on that. I thank them for that. So again, an anonymous survey. But the information we did take was from what area of the libraries were people answering from?
Jessica Hollister: And importantly, another part of this survey to distinguish itself was, we wanted some kind of knowledge, assessing question. And in this particular question we wanted these to be open, ended responses where people could in their own words, describe what they know
Jessica Hollister: about AI or AI tools. And so we ask people to in their own words, please describe. You know, how does a large language model or an Llm. Generate a textual response. And how does AI generate an image output? And so we can not only gauge people's
Jessica Hollister: People's knowledge and competency on AI, but we could also gauge their confidence levels. And so in the confidence levels, that's another great indicator to us when we are designing our trainings moving forward.
Jessica Hollister: So next slide, please.
Jessica Hollister: So a bit of a shorter slide. Here we were in our pre survey process. Now we're in actually administering and delivering the survey. As I said before, this was built and administered, using qualtrics, and it was disseminated over the Ub libraries Listserv. We gave our participants 2 weeks to answer this particular survey.
Jessica Hollister: and we offer one reminder for people to
Jessica Hollister: one reminder for people to complete the survey. And so next slide, please.
Jessica Hollister: Thank you, Mike. And so moving into our post survey process. This is where our subcommittee is right now in our assessment process. So
Jessica Hollister: immediately, in the after hours of the survey coming back. We're all reviewing the results. We're taking a look at how people answered. We're take. We're taking a look at
Jessica Hollister: what people tend to feel on certain topics. I will note that we had about a 37% response rate. We have about 130 employees here at the up libraries. So I thought it was a pretty significant response rate.
Jessica Hollister: Importantly, with this survey, because we had open ended responses. One of the key points in our review here was to code the open end responses. So when I say that, I mean that we went to the open ended questions, and we read all of the responses. And then we created categories based off of the information our participants put in those responses. And from those categories we went through and read the responses again, and then we would check in which category
Jessica Hollister: those particular of that particular response lies. We had a few of our colleagues do this. We had them do it individually, and then we came together afterwards to see how our categorizations compared, so we could resolve any conflicts.
Jessica Hollister: And after we kind of categorized all of our open, ended answers. We created a summary page on this summary page. It's essentially
Jessica Hollister: A quick look to quantify the qualify the qualitative information we had from the open ended responses. So we looked at our, you know, our
Jessica Hollister: categorizations, and we
Jessica Hollister: from highest response to lowest response, we were able to see. You know what categories we're getting, a lot of responses and what we're getting lower responses so we could see which.
Jessica Hollister: What our colleagues are identifying is their priorities when it comes to AI. What they're identifying is their concerns when it comes to AI. So this summary pages are really
Jessica Hollister: crucial tool, especially when it comes to creating a report, which is what we're doing right now. So now we're breaking into individuals and teams to compile a report, and that report's going to be submitted to our libraries administration
Jessica Hollister: And after that report is submitted there we are looking to present our results and our conclusions and our general recommendations to our staff in some kind of an all staff meeting. And then ultimately the goal we're here is to use these findings to inform future trainings, and to be sensitive to the answers that our colleagues have provided to us, and also to
Jessica Hollister: meet them at the level that they're at.
Jessica Hollister: and next slide, please, link.
Jessica Hollister: So we talked a little bit about the questions or topics we presented. I'd like to talk for a moment about the major takeaways that we saw out of this particular survey.
Jessica Hollister: So for the
Jessica Hollister: the majority of our respondents with these particular questions on a scale from unfamiliar to very familiar. We saw that
Jessica Hollister: the majority of respondents were identifying as somewhat familiar with AI concepts. Similarly, on a scale from pessimistic to optimistic. We're seeing that generally our colleagues are somewhat pessimistic of AI and society. A similar question, but kind of a different majority answer here
Jessica Hollister: from pessimistic to optimistic. We're seeing that our colleagues were somewhat optimistic about AI and libraries. I thought that was kind of an interesting finding compared to the previous one, and on a scale from avoiding AI news to being very well informed or well read on AI News. Most of our colleagues identified as occasionally reading about AI news.
Jessica Hollister: I'd like to take this moment to acknowledge that we had a lot of our discussions in our subcommittee about whether or not to include a neutral option in the multiple choice answers. This was a lot of back and forth. What we ultimately decided was to not include a neutral option.
Jessica Hollister: and to kind of push our colleagues to see if they would
Jessica Hollister: put their answer as
Jessica Hollister: unfamiliar or familiar, or, to use pessimistic versus optimistic. And thankfully. Our colleagues were kind enough to kind of interrogate their feelings and answer that in a particular way. So I will note that if you're going to create this a type of survey like this, that that might be a question that you run into with your particular group, too. We decided not to use a neutral option for our multiple choice questions, but that may be a different one.
Jessica Hollister: You may choose to do that differently at your own institution.
Jessica Hollister: Moving down the bullet points here. So when we categorized our open end responses when it comes to concerns the top concerns we've received from our colleagues were one reliability and accuracy, 2 ethical use and 3 control and autonomy. I think not a huge surprise there.
Jessica Hollister: moving down
Jessica Hollister: the top day to day, use people using or interacting with A To with AI on their day to day use. A lot of people indicated that their interaction was with vendors and the AI tools that the vendors have, so that I thought that was kind of an interesting take away from there
Jessica Hollister: and then finally moving into instruction, are colleagues who facilitate or actually teach
Jessica Hollister: for those who do
Jessica Hollister: mention or use AI tools in their teaching, they indicated that what they do is, do an introduction to AI or cover AI Basics and the basics of evaluating sources so integrating AI literacy into already existing information literacy.
Jessica Hollister: So from this particular slide, here is Mike. Was there anything that you would like to add to this particular slide?
Jessica Hollister: Great? Okay. Next slide, please.
Jessica Hollister: And we already slightly touched on this here. But now in our report stage as we're compiling our conclusions and compiling our results. This is what we're going to be using to develop trainings that are in line with the needs of our colleagues in the Ub. Libraries. Our colleagues are back fellow faculty and staff here.
Jessica Hollister: and as Mike alluded to before, we're receiving a lot more inquiries from our university faculty whether it's teaching on AI, or whether it's
Jessica Hollister: providing recommendations on AI tools or resources. So being able to inform our trainings around those particular inquiries, and, as I mentioned before, there is a larger ub AI task force that
Jessica Hollister: will require us to answer to them, and so making sure that we're compliant with that administrative goal there. As
Jessica Hollister: Mike indicated before, one of the really successful workshops that they were able to implement was a prompt engineering workshop, and that is an excellent template for us to build off to either do inter or outside of the library's trainings. And so that is one of our goals moving off of the report stage here. So that's actually the end of my portion of the presentation. So unless Mike has anything to add.
Jessica Hollister: That's what I've got.
Cori Wilhelm: Thank you so much, Michael and Jessica. And this provides us some time for questions. So we did get one in the chat about whether there is buy in or work going on with the center for teaching and learning, or you know, whatever that is referred to on your campuses.
Michael Kicey: Yeah. So we've, we've been kind of in this slow process of trying to reach out to various units that we think are gonna have buy in for for talking and and working around issues of AI across. Ub.
Michael Kicey: the center for teaching and learning. I'm not, which is what it's called the Ub. We're not getting contact with them. It's actually a really great idea that you you've mentioned. We've mostly been talking to these, these centers that are kind of like popping up on Ub's campus because we are.
Michael Kicey: We got a whole bunch of funding from Kathy Hochul and the State government to start Empire AI, which is going to be major center for AI research.
Michael Kicey: So there have been these centers and institutes that are sort of popping up over the last like 6 months to a year, like mushrooms, one of which is actually even in our library building. That's that are going to be devoted to AI as as well as existing centers. For, say, data, analysis, and artificial intelligence that that pre existed Empire. AI, we've been talking to those we've been talking to the Uvai task force that Jessica mentioned. I think it probably behoove us now that you brought it up for for me, or
Michael Kicey: group in general to approach the center for teaching and learning as well. I think a lot of their energy has been devoted to talking to
Michael Kicey: upper levels of Ub administration to get policies in place for how
Michael Kicey: well researchers, but but for the most part undergraduate students to get a policy in place for how they should be talking to undergrads about the use of AI in their coursework. I think that's been. That's been the priority so far for the University and its task force efforts. I think, as as we get a little longer in the tooth, and as we, as as you know, this group and and other responses to AI in the institution.
Michael Kicey: Get get sort of on a on a firmer footing. I think it's going to make sense to to for us to be talking to the center for teaching, learning about about other, more more advanced issues. But yeah, I mean, that's that's a very good point, and they need to be at the table.
Cori Wilhelm: Thanks.
Cori Wilhelm: Any other questions from our audience. You can raise your hand and unmute, or you can put them in the chat, and we can direct them that way.
Cori Wilhelm: And I guess, while we're waiting for additional questions, have you had any
Cori Wilhelm: push back, or you know negativity backlash at all about proceeding with this. I know Jessica's survey, you know, kind of talked about optimism, pessimism.
Cori Wilhelm: but this is kind of you know. It can be a touchy subject. Any
Cori Wilhelm: resistance from anyone on campus.
Michael Kicey: I'll let Jessica talk about certain responses to the survey which you might want to address. But I think so there is, I mean, and this this kind of like zooms out to talk about the profession in general. We are. We're after the pandemic. A lot of us are struggling with burnout, with a feeling of like what our investment in our job is, what our relationship to the actual physical space of our job is
Michael Kicey: where higher Ed is right now, which is fraught with crisis in many, many ways.
Michael Kicey: So I think that for AI to emerge at this particular moment with the disruptive force that it's had. A lot of people have not maybe not a lot of people. But some people have responded with
Michael Kicey: maybe a little more hostility and resistance than they would under more specific circumstances. Let's say, with the result that I think that a lot of people are not looking forward to having their jobs and their routines upended yet again by a major disruptive technology. However, we are librarians. We are devoted to learning, and I think the last 25 years at least have shown us all that we need to be learning constantly, not just new technologies, but also new practices and formulating new policies.
Michael Kicey: and as a result, I think some of that resistance can be fruitful because we can. We can sort of re-pitch it into an opportunity for a learning experience. But yeah, I you know I myself have struggled with with my own resistance to to AI and to its disruptive force and its effect.
Michael Kicey: And I'm speaking, as you know, chair of an advisory group. So it is. And Jessica touched on this before. It is worth addressing the fact that there are feelings in the room when these things come up right, that people are intimidated by AI. They don't feel like experts. They don't feel like they have the knowledge necessary to address this meaningfully.
Michael Kicey: and that that makes them have an emotional response very often that needs to be addressed. And I think that that it makes sense, and that, you know we can talk about those emotions in a rational adult way, and I certainly am not a cheerleader for AI. I am acutely aware of its risks and its limitations, and I try to communicate those on every opportunity that I have, and we also try to tell the people that we work with, especially, you know, under the auspices that they like
Michael Kicey: presentation. We did. We're not here as like some sort of 5th column to shove AI down people's throats. That is not our purpose.
Michael Kicey: and I think allowing people to express a certain amount of resistance and a certain amount of sort of emotional discomfort with stuff around AI is a doorway to doing that, and very often those emotions have a basis in reality, and they are the basis that they come from a place of moral concern, of not just sort of self-interested or professional concern, but also concern about what this technology might do to our planet and to our society
Michael Kicey: and to people, you know, flat out. And I think a lot of that is justified and should be part of the conversation. But, Jessica, you can. You can talk about your own. Your point of view.
Jessica Hollister: Thank you. That was very well, said Mike. I'd like to say that going off of what Mike was saying here. The title of today's presentation was today's Goliath, and if that in any way made it sound like Mike and I were the Davids
Jessica Hollister: coming to save everyone. That is not the case, you know. If that if that was the case, run in the opposite direction right now, cause I would, I characterize myself as still learning every single day. And that's why I'm on. This subcommittee is to learn and figure out how it fits into my position and how
Jessica Hollister: I can be ahead of the curve when it comes to using it in my position. What Mike alluded to before was in the survey. We did receive what we affectionately call a couple of spicier responses, and I think that I you know I work with really tremendous colleagues across university, and I think, going off of what Mike said is that the
Jessica Hollister: the negativity of those particular responses, I think, are reflecting a very true and
Jessica Hollister: based and realized emotion. And perhaps a fear when it comes to AI, and I think that that
Jessica Hollister: whether or not it was misdirected. I think that is really telling to us as a committee, and it is really telling, and how we go about creating our trainings so that we can better meet our colleagues at their levels and at their comfort level. So
Jessica Hollister: The remark remarks whether they be negative. That's still information for us, and that's still something that we can use to move forward in a way that it's productive for everyone. That's what I'll say on that.
Cori Wilhelm: Those were both excellent. Excellent responses. Thank you. Susan, go ahead.
Cori Wilhelm: Susan. Susan, we can't hear you.
Cori Wilhelm: Nope, we still can't hear you. Sorry.
Susan Davis (UB): Can you hear me now?
Jessica Hollister: Oh yes, yes.
Susan Davis (UB): I know I have these old, this older headset, and sometimes the
Susan Davis (UB): doodad doesn't like
Susan Davis (UB): it gets a little loose. So anyway, I had a I had a question first, st but I will. I will put that last because this is really interesting. And the the session that that Mike and the other Co. Task Force committee members put together was really interesting because we based it on those you know, scenarios from Cni, and at least one of them was like doom and gloom all over the place. And we're all like, yeah, we don't want to be here for that. But
Susan Davis (UB): the aspect of sort of accepting that there's like emotion and resistance is real. That is sort of a new approach, in my view, to change, because usually it's like you're gonna accept this change or or else. So I'm glad that we have colleagues who will be a little more
Susan Davis (UB): maybe patient empathetic because it
Susan Davis (UB): this stage in my career and life.
Susan Davis (UB): This is just one more darn thing you know, to have to learn. And I watch people say, Well, yeah. And I just checked in with, you know, Chat Gpt. And I got this answer, and I'm like, you know, I just don't want to do that, but that's me and you know I will do. I will do the best I can. So I'm I'm gonna be looking forward to more guidance from
Susan Davis (UB): people at Ub, who are more facile with these things. But I just wondered Mike and Michael and Jessica, if
Susan Davis (UB): you've had any feedback from some of the groups that you've reached out to and and if there's any sort of surprise that the libraries are trying to push this agenda, or, you know, take take leadership. So I love
Susan Davis (UB): the idea that the library
Susan Davis (UB): is taking the lead, and I know we discussed that in the in the session, because if we don't. Somebody else will, as was described about like the, you know, high, the tech industry, motion picture. And just you know, all of those groups. They have a lot more financial skin in the game, and and they'll just blow bias unless we do something. So thanks.
Michael Kicey: Yeah, I mean, I think so you know it was. It was so for those of us who aren't from Ub, we? We have. We have a a license to Microsoft Office 3, 65, and as a result of that, because all of a sudden Microsoft decided to build in Microsoft Copilot, which is a chat Gpt, based, generative AI model, decided to build in copilot to its office. 3, 65 system. All of a sudden you'd be had a license access to a generative. AI
Michael Kicey: there was a rollout. You know people have different opinions on how this rollout went. But we suddenly discovered that that basically our our approved software system had AI at the heart of it. The result of this, at a meeting a general meeting of I think it was libraries faculty, but it might have been libraries, staff in general.
Michael Kicey: libraries, faculty with the President and the Provost. I raised the question with them, and this was very early days. This was last year I raised the question with them of, Okay, we have this generative AI product. Presumably policies are in the work, in the works to advise students on how to use it, how not to use it. And alongside. You know the ethical considerations that at that point in the AI discourse the conversation were were sort of at the forefront for people in libraries.
Michael Kicey: There's also the question of effective use of AI. I think that that ethical, like the ethical questions, are enormously important. And they have to be addressed and they they lead to a lot of sort of, you know, a lot of emotional responses. When people recognize, you know the the actual stakes, environmentally, socially, economically
Michael Kicey: to the use of AI, and they have to be addressed. But also, if AI is going to continue to be a technology that people use, and it, you know, if it becomes a sustainable technology as well. There are also issues which I've mentioned before about actually using it effectively. Like, if you're going to burn through 10 bottles of water
Michael Kicey: for a single prompt to an AI model, which is literally how much water it takes to cool a data center to process one question.
Michael Kicey: the it behooves you both from an ethical viewpoint and also from an effective use of resources standpoint, to interact with the AI in a way that is well informed about the kinds of interactions that are going to produce content that you can use right to instead of ha! Instead of having a conversation that takes 50 questions and answers across a few days. You can have one that maybe takes 5. Right? So it's it's super important. And this is what I tried to get across to the President and Provost that we also train people how to use AI right instead of just throwing it at them
Michael Kicey: and saying, Look, it's a 0 friction. Natural language interface. Anybody can can use this right? It's it's a wonderful tool to say no. Actually, it's it's apparent. 0 friction interface actually hides a lot of friction in the back end.
Michael Kicey: And there are ways of interacting with it that are more productive than other ways of interacting with it. Right at that point I basically got stonewalled by them. Since then, as I already mentioned, there have been responses in the forms in the form of reformulating our curriculum, which is now underway to basically build AI literacy into the way that departments and their majors run their programs and the expectations they have of students.
Michael Kicey: But it's really important alongside ethical use of AI that we also adopt a language of effective use of AI, because it's not going to go away. We have to address the ethical issues. But in order to do that, in order to use less resources and tax less people's labor. We also have to learn how to use AI effectively. And that is a set of skills just like learning how to search in a traditional search interface is a set of skills that librarians have to teach
Michael Kicey: to students to faculty and so on. So that, I think is a really important point to get across is that that we have to be sensible about that.
Cori Wilhelm: Again. That was that was an excellent response.
Cori Wilhelm: Michael, you've obviously given this a lot of thought. And I really appreciate bringing up the yeah, the environmental impact of
Cori Wilhelm: what using these tools is is really doing.
Cori Wilhelm: So we have just a couple of minutes left. If there are any last minute questions?
Cori Wilhelm: The audience can raise their hand or throw them in the chat.
Cori Wilhelm: and maybe just as a closing.
Cori Wilhelm: where? Where do you. Where do you think we're going to be with this? Oh, Maureen may have a question.
Maureen Clements (SUNY): Yeah, well, I actually just wanna say, this has been such an incredible session so much to think about. And I know that Olis and Sls want to help. And you know, sort of coalesce thinking about these questions and all of these things that you raised so we'd love to reach out and see ways that we can maybe, partner, or assist with getting the word out.
Michael Kicey: Thank you, Maureen likewise.
Cori Wilhelm: Yeah.
Michael Kicey: I wanted to mention just as a last note before I go. You know. I'm sure there are people in this call who have sort of all varying levels of expertise about AI, and that is totally fine. One of the things that I I neglected to mention was that if if you're thinking about putting a group like this together, it's very important to stress
Michael Kicey: that it's not a requirement that you be an expert about AI, it actually leads to more productive discussions both in the group and outside the group within the libraries. If you have people that are sort of at all different skill levels and all levels of confidence or knowledge.
Michael Kicey: I myself, I'm not a computer scientist. I I'm a humanist. Okay, I have a Phd in comparative literature. For pity's sake. Okay, I am not. I'm not a tech head right? But there is. There is something worthwhile about educating yourself up to a certain point at least as much as you need to communicate to others. When you do instruction about how AI works
Michael Kicey: and not necessarily to be Sam Altman. Okay, what the the one suggestion I want to make is that there is a really great course in the Khan Academy called AI for education.
Michael Kicey: It's where I learned how generative AI actually works. It's based on very brief readings, but mostly short videos like 5 to 10 to 15 min. They have a series of videos that explain how a generative AI actually creates a textual response to a prompt
Michael Kicey: that are really approachable. They're they're painless to watch. And they are very eye opening in terms of of actually getting a basic understanding of the operation of AI without a whole lot of math or a whole lot of computer science that is not necessary to watch these videos. And they're also oriented towards people who work in education. Admittedly, I think it's it's geared towards secondary education. But they're enormously useful for for people who work in higher Ed, too. So just Google Khan Academy, AI for education.
Michael Kicey: The course will come up. It's completely free. It takes like a couple hours to get through. I watched it actually on a train ride, if that gives you any idea. And but it really gets you up to speed to to where I think any any sort of responsible librarian should be in terms of understanding how the technology works.
Cori Wilhelm: Thank you. That's great. And
Cori Wilhelm: Michael and Jessica, you will share your slides. I heard you mentioned earlier. Yes, so we'll share the slides and the recording to this. Thank you so much for a great session, thanks to our audience for coming. You both did a wonderful job. Presenting this, we really appreciate it.
Michael Kicey: Thank you.
Jessica Hollister: Thanks, Corey, thanks for having us.