Abstract
In this 45-minute session, the presenters will open with an interactive poll, then share an overview of issues and possibilities related to professional refusal and resistance to Generative AI and Large Language Models in academic libraries. Drawing from their personal and professional experiences, critical writing, and official guidance on AI from mainstream library organizations, they will conclude the session by sharing a bibliography and will lead attendees in a group discussion to collectively grapple with opportunities, challenges, and spectrums of AI refusal.
Discussions of generative AI in academic libraries tend to coalesce around common themes, including promoting “AI literacy,” modeling “ethical uses of AI,” overviews of current tools in the marketplace, and explorations of how generative AI might be incorporated into library and research workflows. Underpinning these discussions (which range from neutral, to celebratory, to critical in scope) is an implicit assumption that generative AI is inevitable. Thus libraries and library workers must acknowledge, accept, and incorporate it, else they risk irrelevancy, inefficiency, or neglect for their users’ information needs and behaviors.
This narrative of inevitability sits neatly against Big Tech’s marketing of generative AI, which tends to employ rhetorics of “hype,” technosolutionism, and ubiquity as a means of manufacturing need and justifying mounting pressures to incorporate it into all areas of life (Bender & Hanna, 2025). The widespread acceptance of the inevitability narrative has left little room for library workers to explore the legitimate (and prudent) possibility of refusal, and to, in turn, connect that refusal to our espoused values as a profession, including information literacy, data privacy, environmental sustainability, and social justice.
How might the acceptance of generative AI in various aspects of library work (however cautious or ethically-minded) be inadvertently upholding marketing narratives of inevitability? How might this narrative limit our ability as library workers to both identify and reify our values and ethics as a field? And lastly, how might the belief that library workers lack the agency to resist these products, and inherent fear of being perceived as technophobic often latent to this belief, contribute (even inadvertently) to our collective disempowerment as a field?
Drawing from personal teaching, professional, and administrative experiences, emergent critical discussions from within librarianship (Slater, 2025), and writing from scholars and journalists from outside of Library and Information Studies (Hao, 2025), the presenters will discuss these aforementioned questions while thoughtfully acknowledging the legitimate pressure to incorporate generative AI into library work, particularly in the areas of teaching and public service. By exploring possibilities, challenges, and strategies for resisting the inevitability narrative of generative AI in libraries and education, against administrative and societal landscapes which normalize the use of such technologies, this presentation will posit that critical AI refusal is both possible and necessary.
References
Bender, E. M. and Hanna, A. (2025). The AI Con: How to Fight Big Tech’s Hype and Create the Future we Want. Harper, an imprint of HarperCollinsPublishers.
Generative AI is Not Inevitable w/ Emily M. Bender and Alex Hanna—Tech Won’t Save Us. (2025, May 22). Tech Won’t Save Us (Podcast). https://techwontsave.us/episode/277_generative_ai_is_not_inevitable_w_emily_m_bender_and_alex_hanna.html
Hao, K. (2025). Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI (1st ed.). Penguin Publishing Group.
Large Language Muddle | The Editors. (2025, September 10). N+1. https://www.nplusonemag.com/issue-51/the-intellectual-situation/large-language-muddle/.
Slater, K. “Kay.” (2025). Against AI: Critical Refusal in the Library. Library Trends, 73(4), 588–608. https://doi.org/10.1353/lib.2025.a968497
