Library Workers in Times of Hype: AI Edition
Library workers can be AI literacy leaders without using, or encouraging the use of, generative AI tools.
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Library workers can be AI literacy leaders without using, or encouraging the use of, generative AI tools.
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When AI tools collapse complex search processes into seamless responses, they can obscure uncertainty, mask gaps in understanding, and smooth over meaningful distinctions of meaning, relevance, and confidence. Users may feel informed without ever confronting the limits of their knowledge or the assumptions guiding how information is interpreted. The challenge for libraries is not just teaching people how to use AI tools, but how to think with them without surrendering judgement.
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In this paper, we explore successful AI training initiatives, resources and networks for librarians, AI integration strategies, and implications of AI for library services. Join me in discovering best practices for preparing librarians as leaders in the generative AI era and integrating AI into library services.
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Are students learning in a “dark forest”? Al in education is raising new fairness questions- use it and feel judged, avoid it and risk falling behind. We need AI literacy, fair policies, and student voices at the center.
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In the classic 1950s TV sitcom I Love Lucy, when Lucy did something outrageous her husband Ricky would exclaim “Lucy, you’ve got some explainin’ to do!” Typically, Lucy would come up with some sort of implausible response. Hilarity ensued. Well, it’s not the 1950s anymore but 70+ years later Large Language Models (LLMs) and AI chatbots (e.g., ChatGPT, Gemini) are doing outrageous things (hallucinations, fabrications, misinformation, and worse) and the explanations, if there are any, are just as implausible. And it isn’t funny.
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When teaching brand new doctoral students in library and information science (LIS), much discussion is dedicated to theories, paradigms, and the distinctions, and divides, between library science and information science. After reading Marcia Bates’ iconic article, The invisible substrate of information science (1999), a student asked: How do I explain what I do to others? How do I describe the relationship between data, information, and knowledge?
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On August 7, 2025, OpenAI officially launched GPT‑5, marking a watershed moment in the evolution of AI. But is this a giant leap forward, or a stumble in the rush to innovate?
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As a mixed Ojibwe woman from Wiikwemkoong Unceded Territory, I carry both pride and a sense of responsibility in how I represent my identity through academic and artistic spaces. I recently graduated from the Master of Information in Library Science Program at the University of Toronto’s Faculty of Information. My goal is to become a full-time librarian who integrates Indigenous knowledge into library and information systems. I am excited to share my painting with the Information Matters community as one way to contribute an Indigenous perspective in our shared field. This acrylic self-portrait, funded by an Ontario Arts Council bursary, reflects the concept of the “Red Thread of Information” (Bates,1999) and visually embeds my Anishnaabe worldview. The piece invites viewers to reflect on how identity and information phenomenon can be fused together into creative expressions.
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A library is a people-oriented institution that evolves with civilisation. To avert chaos and ensure smooth functioning, libraries rely on a set of guiding principles known as library policies. These policies are not static—they must be regularly updated to address emerging needs and challenges. Yet, in many parts of the Global South, library policies are seen as an unachievable task—a mountain too steep to climb, not something easily conquered or routinely adopted. This perception is misleading. In reality, library policies are not complex. They are usually simple rules, often expressed in a few words, that help govern the day-to-day operations of a library.
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In today’s academia, the lines between the identity of a researcher devoted to following the trail of knowledge and the identity of a performance-driven academic focused solely on producing publications are increasingly blurred. For many scholars, publishing is no longer just a contribution to science but a mandatory step for climbing the academic career ladder. This is not a matter of individual choice, it is deeply structural. Evaluation systems that reward where research is published rather than the quality of the process itself are a major driver of this shift.
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