Editor’s note: We welcome a guest blog post from Senka Stankovic, Head of User Services at the Jean-Léon Allie Library, Saint-Paul University.
As an early-career librarian halfway through a year-long parental leave contract at Saint Paul University, I’ve noticed that digital tools are fundamental to how we ideate and deliver educational workshops. Born-digital, recently a student, and new to the workforce, I’ve come to believe that many digital tools that are meant to improve learning instead strip away the friction that the acquisition of knowledge requires.
In this post, I explore three common classroom technologies: the slide deck, the laptop, and the gadgets[1]. I present how these so-called “tools” often get in our way, and I offer opportunities for reintroducing friction to the classroom in the classroom in the form of minor inconveniences. This friction can lead to higher engagement from, and connection with, our learners.
I believe librarians are in a unique position to pioneer a more hopeful use of technology in the classroom and beyond.
The slide deck
The slide deck is ubiquitous in academia and in white-collar work. In the classroom, slide decks turn teaching into a presentation where we strive for perfection. This is in part because slide decks, meant for presentations, allow educators to over-prepare their courses. If you’re presenting something you know well, then your slide deck is there to make sure you don’t veer off course. But off course is where you get to show students your own fallibility, which helps build meaningful connections through vulnerability. It also gives your students a framework for managing the friction that things like research inevitably create. In other words, your slide deck prevents you from modeling educational resilience by giving you something to hide behind.
Adding friction to the slide deck includes taking the time to ask whether a slide deck is the right method of sharing the information at hand. I find that very often, it isn’t. At my university, I’m responsible for delivering one-on-one Academic Integrity Workshops to students who have committed plagiarism. I was unsatisfied with the presentation-style workshop I had inherited, as I felt myself lecturing at students who were either on the verge of tears or completely oblivious as to their wrongdoings. In neither scenario were they primed for learning. I adopted a new approach that includes a self-assessment on their behalf and a print workbook where they can take notes. I still use a monitor where needed, such as demonstrating tools they can use for research instead of ChatGPT. My new approach has so far been fruitful. I’ve not had any tears since adopting it.
When you remove the slide deck from the equation, you make room for more creative methods. I saw this in April, when I attended a session at the British Columbia Library Association Conference titled “Throwing Out the PowerPoint: An Experiential Learning Approach.” Adair Harper, Chloe Riley, and Julia Lane replaced their library orientation slide deck with a zine and an accompanying exercise to help students learn to navigate library resources. Their new approach seems more communal in nature, and I’m curious to see what can happen when we start questioning why things are done the way they are.
The laptop
The laptop and the expectation that students all have one are detrimental to everyone’s learning. Besides the obvious financial inequity factor, laptops are also hugely distracting. As a student, I was frequently sidelined by the person in front of me watching Euphoria or tweaking their resume. I’m sure that my compulsive opening and scrolling of Pinterest every time I open a browser also distracted many folks sitting behind me.
Recently, I read about Dylan Kane, a middle-school math teacher and early adopter of Chromebooks in classrooms who has decided to jump ship. In removing laptops from his classroom for a month, Kane learned that “screens […] made it possible for a student to look busy for an entire class period” without him realizing they were stuck on a problem. In other words, learning is hard, scrolling on Pinterest is easy. Think of your own life. How often do you come head-to-head with something you find difficult and pull out your phone to avoid it? If we can’t muster the self-control needed to not use screens as pacifiers, how can we expect students not to do so when faced with the friction of learning?
Expecting all students to be fluent in laptops is also a barrier to those with less technological experience. Catherine Carroll, a mature student at Saint Paul’s University, describes her return to academia as a huge learning curve by way of technology. “It gets in the way of learning the actual material,” she says.
Adding friction to the laptop might look like encouraging writing by hand and taking the time to show students how to search the stacks. While these skills may not be frequently applied, I believe it would be beneficial. Born-digital students would be given an alternative, more tactile, framework for engaging with information. This may increase their attention to how information is created, accessed, and shared in the digital realm by giving them a point of comparison. On the other hand, mature students would be given permission to engage with knowledge in a way that works better for them.
The gadgets
Learning gadgets that advertise increased engagement reduce tactility and thereby advocate for the disembodiment of learning. Many of these tools are nothing more than a digitization of existing classroom activities. Miro is literally a virtual blackboard. With Quizlet, you can create digital flashcards, rendering the educational process of handwriting them obsolete. Kahoot replaces good, old-fashioned pen-to-paper trivia. They take away moments where students might otherwise be forced to write or move around in their learning environments.
Additionally, all these tools are privately owned by for-profit companies. This doesn’t sit well with me, as using them requires students to give out personal information in the form of email addresses or browsing data.
The friction of distributing papers to participate in an activity or having students physically get up to add ideas to a blackboard reintroduces a dynamism to learning that can pull us all out of the digital funk created by many learning technologies.
On the advantages of digital educational tools
The tools I mention have advantages. Laptops and slide decks both offer accessibility features, and removal of these technologies may lead to access friction – not the kind we’re going for – for learners with disabilities. The gadgets might encourage engagement, provide immediate feedback to learners, and provide educators with additional insights into their students’ knowledge.
The complete abandonment of digital technologies isn’t attractive to most of us, nor would it promote equitable access to information. Nonetheless, I believe that treating digital tools as a gold standard in education can be detrimental and limits our ability as educators to think creatively about the knowledge we share. By reflecting on ways to add helpful friction, while maintaining a balance for those that require assistive technology or prefer digital learning tools, we can create more positive learning environments.
A way forward
Adding friction to slide decks, laptops, and gadgets is a strong starting point to rebuild meaningful classrooms that center knowledge acquisition. It’s also a sign of hope that the world I want to live in is possible, one where learning is communal, present, and embodied. Librarians are in a unique position to advocate for the friction that learning requires, even when that means experiencing the friction of learning themselves.
In an interview, Kurt Vonnegut describes purchasing envelopes one at a time so that he has more opportunities to exist in the world. His wife believes it to be an inconvenience. He concludes, “And, of course, the computers will do us out of that. And, what the computer people don’t realize, or they don’t care, is we’re dancing animals.” For me, the image of librarianship that elicits joy is one with room to dance. Or fumble, or add friction, or whatever you so choose to call it.
[1] I use gadgets to loosely refer to novelty items aimed at digitally increasing student engagement. Some examples include Miro, a virtual blackboard; Quizlet, for digital flashcards; Kahoot, for quizzes and trivia; and Mentimeter, for polling. These attempts to reintroduce tactility in the classroom are simulacral.
Editor’s note: We welcome a guest blog post fromMaxwell Gray, Digital Scholarship Librarian at Marquette University.
My personal, professional relationship with generative AI (genAI) is complicated.
Like many academic librarians, I identify as a deeply anti-AI librarian, who believes AI, especially genAI, designed and built by technocratic oligarchs outside any democratic process represents a real crisis for workers, the environment and human cognition.
But I don’t believe AI refusal represents a productive alternative to the uncritical adoption of AI in higher education. Rhetorically, I don’t think AI refusal will persuade many audiences in academic libraries and higher education to approach AI critically or ethically. I actually worry AI refusal may accidentally cause some audiences to misunderstand the choice as being literally between either simplistic refusal or uncritical adoption.
As a digital scholarship librarian in Jesuit higher education, my response to genAI has been a pedagogy of engagement with the real, lived experiences of students, faculty and staff vis-à-vis genAI. I take this language of engagement and real, lived experiences from the tradition of Ignatian pedagogy where this language represents a “serious, down-to-earth engagement with the real” in the form of the “concrete, lived experience in all its diversity and particularity.”
This kind of pedagogy may take the form of direct experience and contact with the world through the senses and emotions. How do different genAI tools respond to the same prompts? How does the same genAI tool respond to the same prompts for different users? How do the different “styles” or “voices” of different genAI tools, or of the same genAI tool in response to different prompts, make us feel in our bodies, hearts and minds?
Or this kind of pedagogy may take the form of making connections between different varieties of knowledge, or between knowledge and action. How do different genAI use policies in different workplaces reflect different data privacy frameworks in different social contexts? How do problems of informants, confidentiality and reciprocity in anthropology resonate with problems of authorship, data privacy and intellectual property in relation to genAI? (These are real examples from my experience teaching professional graduate students and anthropology major capstone students this semester.)
Ultimately, this kind of pedagogy should take the form of reflecting on experience and knowledge to decide upon the best, most meaningful course of action in the world. This kind of reflection, often called discernment in the tradition of Ignatius and the Society of Jesus, produces a kind of interiority oriented toward personal transformation and social justice. Why do I choose to use genAI tools in the ways I do? How may I use, or not use, genAI tools differently in response to other desires and callings? (These are abstract examples from my experience leading professional development sessions for faculty and staff over the past two years in collaboration with colleagues at Marquette’s Center for Teaching and Learning.)
To be clear, I’m not saying anti-AI librarians who believe in AI refusal don’t often already practice similar pedagogies of engagement with the real world. I’m not saying genAI is inevitable or there is no alternative. Instead, I’m trying to share language and perspective I think are more productive for stopping the uncritical adoption of AI in academic libraries and higher education.
What may it mean to be anti-AI librarians who don’t believe in AI refusal? What may it mean to be anti-AI librarians who believe in serious engagement, with the realities of AI and the realities of our colleagues and patrons? Jerome Nadal, one of Ignatius’s early companions, observed about the Jesuits (as opposed to monastic orders like the Benedictines) that “the whole world is our home.” What may it mean to be anti-AI librarians for whom the whole world, including AI, is our home in this moment when AI, especially genAI, often represents real injustices and indignities?
In a key text for Jesuit higher education, Dean Brackley, S.J, envisions Jesuit colleges and universities as being called to a mission of proyección social. Brackley writes, “Social projection includes all those means by which the university communicates, or projects, knowledge beyond the campus to help shape the consciousness of the wider society.” When I reread Brackley in this moment, I hear him saying that being at home in the world must not mean becoming comfortable with the realities of injustice, but instead must mean promoting justice in the world.
In my pedagogy in and around the library, I have tried to share knowledge I have learned from information studies, media studies and digital humanities with colleagues and patrons from across the university to help shape and raise consciousness on campus of genAI and its injustices and indignities. Over time I have learned doing this work requires seriously engaging with the real interests of students, faculty and staff, and the most effective ways of connecting with them that address their real fears, attachments and desires around genAI.
To other anti-AI librarians in academic libraries, I propose, “let us say yes to who or what turns up” in our classrooms or workshops and for our students and colleagues, wherever they are on their personal AI literacy journeys. In this way, we may open educational contexts where real engagement and discernment can take place, whether in a one-shot for students or over a series of professional development sessions for faculty and staff.
The former Superior General of the Society of Jesus Adolfo Nicolás writes, “Depth of thought and imagination in the Ignatian tradition involves a profound engagement with the real, a refusal to let go until one goes beneath the surface.” What may it mean to be anti-AI librarians who “refuse to let go” of AI until we go beneath the surface of reality toward more critical futures of AI in academic libraries and higher education? What companions may we find to join us in this work? What converts may we inspire?
Editor’s note: This guest blog post is by Natasha Finnegan, Cataloging and Metadata Librarian, Nicole Kulp, Serials and Electronic Resources Librarian, Sara Wheatley, Acquisitions Specialist, and Emily Zerrenner, Research and Instructional Services Librarian, all at Salisbury University. Emily is a regular ACRLog contributor, but because this post heavily features perspectives beyond her own, we have chosen to use the ACRLog Guest account.
This is a discussion between four librarians about Large Language Model- and/or AI-generated books. It was spurred by the influx of “AI-slop” purchased unknowingly by staff.
How can you tell if a book is AI-generated? What does that even mean?
Sara: When receiving books, alarm bells start going off when a book has a specific “look” (usually the print-on-demand look & lack of author name). Next, I flip through the book, I flip through every book, regardless of AI suspicion; for books that I suspect to be AI-generated, I look to see if there is any level of references, which they tend to lack. Then I investigate the actual text and look for repetitive writing, bulleted lists, misspellings, etc. And of course, there are always the obvious AI-generated images and charts. When a book meets these criteria, I seek a second opinion, usually from our cataloger. Admittedly, sometimes I fail my perception check when it comes to AI books and they come back to me for review after some else suspects their AI status.
Natasha: From a cataloging perspective, we have already purchased the book by the time it gets in my hands. The first tip-off is usually that there is no record of the book on OCLC or very small holdings. That forces me to slow down and thoroughly examine the material in hand, rather than just checking the usual metadata to be sure I have the correct edition in hand.
Most of the time, I do copy-cataloging; when I do original cataloging it is because it is a unique item or it is self-published. The real trick to discerning if it is AI-generated versus not-the-most-polished human-created work.
For now, here are my biggest clues that something is AI-generated:
Organization of the material
a lot of summaries
feels like an internet page
bulleted lists (ah, irony)
Source of the information – few, if any references
AI slop imagery
Once I find enough evidence, I take it to our Acquisitions Librarian and the selector of the material, and we research the author. Key questions to answer: how often they are publishing whole books, any reviews, publisher credentials, and their other books’ ratings.
What I don’t do: I rarely read the whole book, no one has time for that! I doubt anyone has actually reviewed the material inside except perhaps to remove the AI watermarks. Hopefully, my inspection is as far as anyone has to go. I also do not run it through any AI checkers, most of them are faulty.
Emily: As a selector and as the title alludes to, I am one of the librarians who bought AI-slop. After Sara and Natasha’s investigation, the book in question came to me. I found the book on a GOBI spotlight list for student wellness in community colleges. It was listed as published under Mango Press, which didn’t immediately alert me since AI-generated slop is often self-published. I thought it would be a good addition to our student health collection. In Google Books, the publisher is listed as “Da Vinci’s Designs.” As of December 2025, there were no reviews for it on Amazon, Google Books, or Goodreads. It is in 11 libraries, according to Worldcat.
Nicole: For ebooks, the tells are hard to find. As there is no print preview for most titles, we cannot see errors or lack of references. So, we must rely on the very basic metadata our vendor provides. Some red flags may include lack of author information or that no other libraries have the material. Truthfully, we are not given enough information to tell at the purchase level. Once we click on the purchase button, there are no refunds. Something that would greatly improve our process would be a preview of all titles so that we could check for references and AI generated tables and images. Refunds would also be helpful!
Why are AI-generated books an issue? Why should we care?
Nicole: AI-generated books could be, and very likely are, full of incorrect information. One example that comes to mind is a very inaccurate diagram model of the human body. A student worker pointed this out to us last semester. It was in a textbook, albeit not one assigned in her classes. How are students supposed to learn how to help themselves and others medically if we can’t trust textbooks to contain accurate anatomy diagrams? Inaccurate information can and will cause people to harm themselves and others. Learning requires a set of facts to learn from. When you cheapen the materials with inaccurate information, how will anyone learn anything?
Sara: AI-generated books are an issue for a multitude of reasons. Chief among them is that we cannot trust the information to be accurate (how can we when it is scraping all of the, often highly inaccurate, internet). Secondly, we should not undermine real authors and researchers who put so much time and effort into their craft. It’s ridiculous that a few pushes of some keys and several seconds can create something akin to what would take a human years to do.
Natasha: Librarians have the goal of providing resources that fulfill their patrons’ information needs. We have selectors that cull through the enormous offerings of the modern world and hopefully choose things that are helpful and interesting to our users. When I am selecting art books, I want a book that has good pictures and truthful information about the artist or movement.
How can I, as a selector, trust the information inside is accurate? Because it has been created by someone who is hopefully passionate about the topic, passionate enough to correct mistakes and try to avoid errors. There are no texts that are 100% error-free, but human creators can learn.
But AI cannot do that. It cannot be passionate about anything, there is no desire to make the material as accurate as possible. There is no thinking. The quality of generative AI is based on training materials, and it puts whatever appears to fit in the output. Once the AI-generated material is over 70% correct, I think most people will consider that to be good enough — but 70% is not good enough if it’s for vital topics like medical, law, or science.
The fundamental question of whether we can trust the information is dependent on whether that formula happened to be correct. We should care, because AI doesn’t care.
Emily: To me, Generative AI is a solution to a problem we didn’t have. Librarians are in the business of trust, and how can we ask our patrons to trust us if the materials we’re buying are potentially inaccurate slop? I’m also really disappointed that GOBI would spotlight a poorly written, citationless AI-generated book on a community college list. Librarians don’t have time to check every single thing they’re buying for their collections – if it’s a community college with maybe one librarian holding down the fort, they certainly don’t. We need to hold vendors accountable for what they offer for purchase, and vendors need to hold publishers accountable. If one of those entities doesn’t, our academic standards are lowered.
What do you do if you discover that you’ve bought an AI-generated book, despite your best efforts?
Emily: Well, what I DID do was despair about it. Tell all my AI-skeptic friends. Felt like the cow in the meme we’ve chosen as the cover photo for this post. Then had Sara return the book and vowed to try to do even more digging on the books I choose to spend our taxpayer money on.
Nicole: Since ebooks are non-returnable, I would likely suppress the record from appearing in search results after reviewing it to be sure it is AI and that the information is untrustworthy. As far as damage mitigation, it is too much to go back through all the titles that we own electronically, but I would suppress as individual titles are brought to my attention. For databases, I would consider severing the relationship for lack of control over UI and/or if I encountered too much inaccurate AI content. I am also concerned that OA books are potentially more susceptible to AI publishing mills. For this, I would review the material before activating or deactivating as necessary.
Natasha: Review the book: does it serve the purpose that led me to purchase it in the first place? If no, then I will attempt to return it. If they don’t allow a return, eat the cost and throw it in the recycling. Maybe complain to everyone who will listen and get in an argument with my pro-AI partner, depends on the mood.
Sara: Attempt to return it to the vendor. If they refuse to return it, add to the AI Jail for use educating students about AI tells.
Picture of our AI Jail where we store AI books that are remaining in our possession. The sign on the side says “AI Jail: These books are not to be trusted. Proceed with caution!”
What can we do about our big vendors pushing AI-slop on us?
Emily: I mean, part of what we can do is what this post is doing. It’s unacceptable that GOBI spotlights AI-slop, or that Libby uses GenAI to run their book suggestion feature. We as their customers should tell them so. Keep the pressure on. Shame is a powerful tool (just saw that San Francisco Comic Con revoked their allowance of AI-generated art after backlash), and so is our spending power.
Sara: I have had to push back at two of our vendors (GOBI & Midwest) with varying degrees of success… (once GOBI took the book back, no questions asked, a second time GOBI took the book back but reminded me that it was technically against their return policy, and Midwest took a book back, but let me know that it was a one-time curtesy). GOBI stated in the fall that they had implemented an AI-generated tag. We struggled to find this tag and found it buried in options when doing an advanced search.
Natasha: I don’t necessarily think that big vendors are pushing AI-slop. It’s easiest for them to just sell whatever fits their particular algorithm. No humans involved means better bottom line. They certainly don’t want to add to their costs — they don’t want to add paying a human to do the boring work of checking an AI’s work (or reverse-centaur as Cory Doctorow discusses in his blog). So how to push back? Demand actual tags that work, request that vendors verify the publisher, have them provide the author’s rate of publications. That’s the least they can do, and probably the only thing worth their time. They likely don’t check the work visually when offering it to purchasers.
Nicole: We can push for greater transparency around AI incorporation and for more control over our user experience. What it will boil down to is the power of the purse. One library cancelling a database or refusing to buy from a repeat offender won’t do much, but if enough of us say no then it will squeeze out any profits they make from pushing AI. Also, we need to stop putting it on a pedestal. The higher the pedestal, the more incentive for vendors to push it.
What happens when we can’t tell anymore or there isn’t a push for transparency?
Emily: Have a little scream about it. There are feelings wrapped up in this, and that’s important to acknowledge them in order to continue pushing for what we want and what our patrons deserve. I’m also going to go back to the book Imagination: A Manifesto by Ruha Benjamin. As I said in my previous book round up on ACRLog, it dares us to imagine a world without these techno-utopianists pretending they are going to save us.
Sara: Stare off into the void and wonder how we got here… But really, keep calling out the authors that utilize AI, refuse to purchase and send back AI books to the distributors. And when the distributors get mad about returns, remind them that if there was some level of transparency, we wouldn’t have purchased the book, thus necessitating a return (or loss for them on items that have already been physically processed). At this point we have to hit them where it hurts, their wallets. That’s the only thing they’re going to listen to. If they stop making money on AI-slop, maybe they’ll stop producing it.
Nicole: We are going to have to fact-check academic materials at such a level that it will be pretty much impossible to simply purchase a book. We will either need to spend insane time to overcome the “ease” that AI has pushed and/or only purchase books direct from trusted authors and publishers, which also goes against everything I stand for in terms of getting information disseminated.
Natasha: We are already there, but for now we just want to catch the slop. That’s all that librarians have ever tried to do, keep the good stuff and toss the bad. Don’t waste your time, don’t become paranoid. If we can’t tell anymore, then it isn’t necessarily slop.
Why are you interested in this topic?
Natasha: I’m a creative, I write and make art and enjoy others’ efforts to make the world their own, to share their voice and emotions. It takes work to really speak in your own voice: learning from the things you like, practicing, failing, experiencing, and experimenting. I understand the urge to get a lift from technology, but when it makes choices for the creative, both the artist and the enjoyer lose the uniqueness of that effort and voice. In information science, being able to track and verify the information stream is critical to others building on our society’s discoveries. My job as an academic librarian requires me to support information literacy and thus, here I am.
Nicole: I care very deeply about making accurate and trustworthy information accessible to everyone. Now that AI has become entrenched in academics, I am genuinely frightened about the future of learning when people are too lazy to look for accurate information, or even worse, are incapable of wading through the slop to determine what is factual. By training students to rely on AI, we are missing critical steps in information literacy that will have rippling effects. AI is not a calculator or a mechanical pencil as some have compared it. It will upend all we know about thinking and learning by diminishing critical thought and generating confidence in inaccurate information. *Gestures around broadly.
Sara: Being anti-AI is the hill I will die on. There are so many reasons why AI use is bad (I won’t deny that there are good applications of AI, but there is much more bad about it and that bad far outweighs the good). Not only is it destroying the environment and our ability to think critically (or even at all), it is also taking opportunity away from individuals. AI is trained on information that a human spent years creating, then takes seconds to spit something out that is often incorrect. Half the point of doing anything is the journey to get there; any amount of effort or struggle put into anything makes the outcome that much greater. Also, I’m tired of questioning everything I see every day, my anxiety cannot take this level of paranoia.
Emily: I consider myself very AI-savvy; I’ve been AI-critical in a variety of spaces like LOEX Fall Focus and the Generative AI in Libraries conference. A lot of emotions came up when I realized that I had, indeed, bought AI-slop: defensiveness, guilt, anger, and despair, to name a few. I’m supposed to be an expert in this – how did I get tricked into buying a book that said a whole lot of nothing, lacked citations, and had horrible illustrations? I know that sounds incredibly dramatic, but as someone who has confidently said that GenAI is ruining the information landscape and making my job actively harder, it was a blow. I want to share this experience, though, so other librarians can see it’s getting easier and easier to fall into these traps.
The illustration from chapter 2, “Eating for Energy and Wellness,” in College Student Health Guide by Jules Carson. It features poorly illustrated food, such as fish, cheese, fruits, and vegetables. The carrot in particular looks rather dubious.
Any final parting words?
Natasha: Most humans try to do the right thing, most people aren’t aware of the issues involved with AI, and a lot of people are struggling to make ends meet so it’s not surprising they turn to scammy methodologies of money-making. AI-slop creates mistrust of formerly reliable information-providing institutions. We can’t lose that trust, we need to verify the information coming into our libraries as best we can. Authors, publishers, and vendors need to step up their game.
Sara: I just hope that the end of the AI wasteland is coming soon; that enough people realize how harmful it is, and we can all collectively do better.
Nicole: While I am a huge fan of all the ways technology has improved our lives, I do think we are at a point where the dangers of AI are overpowering benefits. Rather than advancing technology to help increase accessibility, it seems to be a money grab and a profit maximization scheme to the detriment of us all.
Emily: Have you bought AI slop? Do you have other thoughts about the topic we didn’t hit on? Feel free to comment below.
References:
Benjamin, R. (2025). Imagination: A manifesto. W. W. Norton & Company.
Editor’s note: We welcome a guest blog post from Jay Singley, Document Delivery and Circulation Desk Manager at North Carolina School of Science and Mathematics.
In March 2025, Ex Libris unveiled their AI-powered Research Assistant tool for institutions using Summon. Within a week, Summon users reported error messages with specific search terms and topics. The first error reports shared in a listserv for Summon users contained “Tulsa race riot” and “Tulsa race massacre.” Test searches by reference librarians and systems librarians containing these search terms generated no results.
I work in an academic library in a public state university system serving high school students and have since turned on Research Assistant in our Summon Preview Environment (a beta testing environment hidden from users). I have begun testing terms and topics systematically using a methodology akin to Matthew Reidsma’s auditing of algorithms, which seeks to expose the implicit biases of opaque technological systems.
In addition to “Tulsa race riot” and “Tulsa race massacre,” my audit of “controversial” search terms conducted in March-April 2025 turned up error results for the following:
Genocide in Palestine
Gaza war
Rwandan genocide
Armenian genocide
Genocides across the world
History of genocides
lynching
lynching in the united states
lynchings in the united states
january 6
covid
covid data
COVID-19
Ex Libris responded to initial reports about “Tulsa race riot” and “Tulsa race massacre” error results with the following:
“This is due to safeguard policies enforced by our AI service provider to support ethical and responsible AI use. It’s not something the Primo or Summon applications control.
If a query does not return a result, please try different phrasing. For example, ‘tulsa black wall street’ will return results that are directly related to the race riots.”
Despite requests for more information in the same listserv, Ex Libris has not provided a comprehensive response to librarians’ concerns about who the third-party provider is, what the ethical and responsible AI use policies are, whether local control can be made available, what known search terms and topics are obstructed, and more. As one librarian aptly noted, academic users want and need access to information that may be blocked by AI use policies meant for the general public.
An essential note is that Summon’s Research Assistant is not limited to an institution’s catalog. Instead, the AI tool searches the entire Central Discovery Index of Ex Libris’ available records regardless of whether the material is actually available to the user or not. (As of June 2025, Ex Libris has provided an option to limit search results to a user’s institutional catalog.) When the AI generates an error message (or perhaps more aptly refuses to run a search or share results), it is not necessarily because the material is unavailable. Rather, the third party “safeguard” policies obstruct the search to begin with.
In running test searches, I also uncovered troubling instances of suppressed results. I recently saw the Oscar-winning documentary No Other Land, co-directed by Palestinian and Israeli filmmakers. I decided to ask Research Assistant about this film. Searches for most of the directors generated the same no results error messages. A search for one of the co-directors generated partial and inaccurate results. Research Assistant made several guesses as to what the film No Other Land was about and why it was important. Could this be an example of biased third-party policies preventing searches about this Palestinian film and filmmakers? Is it also possible that Research Assistant is accurately reflecting the lack of materials related to No Other Land available in Ex Libris’ entire Central Discovery Index and widespread censorship of this film (and more broadly Palestinian people, culture, and experiences) in the United States? Are there truly no academic sources talking about this film and these filmmakers, one of whom was recently attacked outside his own home by Israeli settlers? More research is needed to clarify why “controversial” searches return error messages, inaccurate results, or partial results. The current state of Research Assistant partial results and error notices for “controversial” topics warrants more inquiry.
I know with near certainty that if a student user at my workplace received an error message when searching for “Tulsa race massacre,” they would switch their topic and probably not tell me or their instructor about the error message. To my users, “we couldn’t generate an answer for your question” translates to “your topic is not worthy of pursuing—change it.”
Uncritically adopting AI tools in discovery systems will perpetuate, if not exacerbate, existing biases and suppression of minoritized people. Try this safer topic. Try this approved topic. Try this unobstructed topic.
Libraries, museums, and information sciences are a frontline in resisting the federal administration’s targeting of minoritized people as well as their whitewashing, disinforming, censoring, and defunding tactics. In the age of AI, our access to information is not spared from these attacks. We must uncover answers to the questions Safiya Noble, Joy Buolamwini, and others elicit: Who and what is safeguarded through AI tools in discovery searches? Whose safety and comfort are prioritized? At whose expense?
This week I was on a panel at the Generative AI in Libraries (GAIL) virtual conference. Along with my fellow panelists Andrea Baer and Emily Zerrenner, I joined moderator Sarah Appedu to discuss the cognitive dissonance that we recognize between the widespread exhortations to adopt GenAI tools in libraries and the harms that we see in the usage of these tools. The panel was well attended and well received, with the most frequent comment we received in the chat was that attendees hadn’t heard about the concept of AI refusal before.
AI refusal can refer to a spectrum of approaches to AI, whether that’s refusing to use AI tools entirely, refusing the use of AI as much as possible, refusing to prioritize the use of AI, refusing to accept either boosterism or doomer narratives, refusing to accept the idea that AI is inevitable, or some other refusal. It can also refer to more cheeky ways of refusing AI, such as using scare quotes around “artificial intelligence” to indicate disbelief that these tools are actually displaying intelligence (hat tip to librarian Dave Ghamandi!).
Stained glass window in the Windmill public house, Westhoughton, England, representing the Luddite attack on Westhoughton Mill. Source: Wikimedia Commons
Many readings were suggested in the chat during our panel presentation. If you’d like to learn more about AI refusal, in libraries and beyond, here are some approachable, mostly non-scholarly resources I recommend. As our moderator Sarah Appedu emphasized, it’s vital to read and learn across disciplines on this topic, so we’re not siloing our discussions at this crucial moment.
Zines
Shard volume 31 by Thomas Vose (2024). Subtitled the “A.I. in Libraries” issue, Vose discusses the disconnect between library values and AI adoption. He asks readers “Who benefits? Who suffers?”
AI Is Very Bad, Actually: A Manifesto by Julie Setele (2024). Using just one sheet of paper, librarian Setele lays out their primary qualms with GenAI, including their distate of the way it’s integrated into everything and framed as “magic.”
A Librarian Against AI; or, I Think AI Should Leave by Violet Fox (2024). My 32-page zine has been popular especially with library school students. I use the ALA Code of Ethics as a starting point to discuss how using AI tools is antithetical to several of the stated values of librarianship and share potential approaches to AI refusal.
Blog posts and news articles
Saying No to AI in Education by Allie Lopez on the blog Front Porch Republic (2024). Lopez describes the impact of educators and professionals uncritically adopting technology on the students they serve and argues against the passive acceptance of AI in educational settings.
The People Refusing to Use AI by Suzanne Bearne at the BBC (2025). I despise the framing of this article, in that the AI ethics expert states it’s too late to opt out of using AI (that’s true for certain AI tools, but certainly not all). Interesting to note that it features a series of women describing their approaches, then a man saying they’re all wrong (!). But if you need more proof that this is a newsworthy topic even outside of academia, here you go.
Guide
Refusing GenAI in Writing Studies: A Quickstart Guide by Jennifer Sano-Franchini, Megan McIntyre, and Maggie Fernandes (2024). I’ve come back to this guide frequently in considering what AI refusal looks like. The authors describe “ten premises that ground refusal as a disciplinary response to GenAI technologies,” many of which are rooted in ideas also discussed in LIS scholarship.
Popular books
The AI Con: How to Fight Big Tech’s Hype and Create the Future We Wantby Emily M. Bender and Alex Hanna (2025). An enjoyable, informative read! I especially love their “strategies for popping the hype bubble” section, which include AI refusal methods as a consumer and as a worker.
Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant (2023). You’ll note that many discussions about AI refusal grapple with the history of the Luddites, especially as that term is frequently used as a pejorative to describe anyone supposedly fearful of new technologies. This book is a great introduction to the real history of the Luddites and the parallels between their story and the current day environment of the gig economy and tech industry overreach.
Resisting AI: An Anti-fascist Approach to Artificial Intelligence by Dan McQuillan (2022). If you’re looking for a more in-depth political approach to AI, McQuillan lays out the case for why GenAI is inherently anti-worker, leading to a more precarious state for those on the margins.
Podcasts
Everyone’s Writing with AI (Except Me!) by Maggie Fernandes and Megan McIntyre (2024–present). Writing studies scholars and coauthors of the quickstart guide mentioned above, Fernandes and McIntyre discuss what they’re reading and invite guests to talk about the harmful impacts of AI.
Mystery AI Hype Theater 3000 by Emily M. Bender and Alex Hanna (2022–present). I appreciate the approach from Bender (a linguist) and Hanna (a sociologist) in examining the hype that powers the impetus to adopt AI immediately. A wide range of topics and guests keep these episodes from feeling repetitive.
Better Offline by Ed Zitron (2024–present). Zitron rants about the “rot economy” of the tech industry. If you want to learn more about the economic basis of the business models behind companies investing in AI, this is a great place to start.
Discord server
Alliance for Refusing Generative AI (2025) founded by Cara Marta Messina, Stacy Wittstock, Kat Gray, Maggie Fernandes, and Megan McIntyre. A community space for discussing AI refusal “in writing studies, the humanities, and beyond.”
Keep an eye out for the writings of my copanelists, Sarah Appedu, Andrea Baer, and Emily Zerrenner, who all have such insightful things to add to the conversation—I’m grateful for their inspiration. I encourage everyone to learn more about GenAI from critical sources. Keep learning, keep sharing!
