The Library Creates Conditions for Knowing

We’ve been there before, or we’ve known students who have been there: the student that arrives on campus, looks at the library, and isn’t sure where to begin or whether they belong. The building itself is imposing. The volume of materials is vast to sort through. I remember being so intimidated by the reference librarians that I went to the reference department to use chat instead of talking to a person face to face. (Thank goodness the NCSU Libraries were early chat adopters!)

When a student finally makes their way to the library, it’s more than a service transaction. We all know that a sense of belonging helps people feel comfortable using the library. Belonging as a student, a scholar, a researcher enables a person to produce, evaluate, and build on knowledge. The library doesn’t merely deliver information, but creates the conditions under which a person can become a knower.

Harding’s standpoint theory holds that where you stand shapes what you can know. Exclusion from epistemic communities isn’t just unfair, it’s a structural constraint on inquiry itself. A new student’s moment of recognition, feeling seen, that they belong here, is the moment that student gains a foothold in the epistemic community of the university. That’s not merely affective but rather it’s a structural shift. This is why Shera and Egan’s social epistemology has been so central to my thinking. It’s the social in social epistemology, and the architecture of belonging is part of the architecture of knowledge.

Epistemic belonging is not evenly distributed, and the uneven distribution is the structural consequence of what standpoint theory describes. First-generation students, students from under-resourced schools, students navigating institutional culture for the first time are precisely the people for whom the conditions of knowing are least present before they arrive. Libraries are one of the few institutions structurally positioned to address this, not by delivering more information, but by extending the conditions of inquiry to people who haven’t yet had access to them. This is what “epistemic infrastructure” means at the human scale.

The people doing this work are often public services colleagues. A lot of our profession’s training in this area is focused on customer service and user experience. And this is certainly part of the tool kit and expertise that access and public services staff provide! But I also think that on a more foundational level these colleagues are doing epistemic labor: creating, moment by moment and person by person, the conditions under which a broader range of people can become knowers. That labor takes the form of precision, presence, partnership. My library recently read Allison Pugh’s The Last Human Job which has led to a number of thoughtful discussions about the importance of Connective Labor and the work that we do, especially in the context of AI. (More on that in the next post.)

AI can deliver information. But AI cannot see the student at the door, extend belonging, or create the felt sense of “I am someone who is allowed to ask questions here.” The epistemic function that most urgently needs doing right now is not synthesis or retrieval. It is the creation of knowers, and that requires a human who is paying attention.

When I used the new chat feature to connect with that reference librarian in college, she ended up being so friendly and helpful that I revealed that I was actually in the same room and could come over to talk in more detail. She didn’t shame me for that; she welcomed me. She made me feel like my question was interesting and welcomed me into a community of people interested in learning. And I returned again and again. All aspects of library work contribute to the learning and research mission of our institutions, but none of that will matter if people don’t feel that they are members of our epistemic communities.

This is a post in an ongoing project exploring libraries, knowledge, and the epistemic stakes of artificial intelligence. I’m drawing on social epistemology, feminist theory, and two decades of practice in academic libraries.

What Libraries Actually Do

One of the biggest challenges for libraries telling their story is that the library means something different depending on who’s in the room. For a student, it’s a place to study and a staff full of people who want them to succeed. For a faculty member, it might be the invisible infrastructure that delivers electronic articles or the subject liaison who visits their class each year. For a graduate student, it’s often something more: a research partner, a data collaborator, a guide through the methodological expectations of a discipline. And that’s before accounting for the larger information environment that those users are already swimming in, where disciplines publish at high rates; trade publications and newsletters and Substacks produce sense-making content daily; and social media, messaging apps, and community channels add still more. The information environment is a flood around us, and the library is one stream of that flow.

Because of this, and for my entire career, I’ve started from the question of what the typical user actually does when looking for information. That question should shape how we structure the materials we license and steward, how we design services that meet patron behavior rather than assume it, and how we think about instruction. It was the right question when search engines were starting to work well, when Wikipedia launched, and it remains the right question now.

Which is why I keep returning to Margaret Egan and Jesse Shera. They weren’t describing what libraries do. They were describing what knowledge requires, and they built that argument from inside library science. That distinction matters enormously right now, because it means the epistemic infrastructure libraries provide isn’t only a feature of libraries as institutions. It’s a feature of how communities actually come to know things.

When Shera and Egan introduced the term “social epistemology” in 1952, they were writing at a particular moment in information science history, when the field was working to establish its intellectual legitimacy. They were pushing against a narrower, technically retrieval-based conception of the field, arguing that the social dimension wasn’t supplementary to the epistemic function but constitutive of it. “Social” in this sense doesn’t mean communal or community-oriented in a casual way. What they meant was structural: knowledge is not produced by individual minds in isolation and then deposited into libraries for safekeeping. It is produced through systems of validation, circulation, critique, and preservation, and libraries are part of the infrastructure that makes those systems work. Shera would return to and refine this framework across the following decades, and the thread runs forward through Wilson’s work on cognitive authority, through Chatman’s research on information poverty, and eventually into the ACRL Information Literacy Framework, even when the explicit vocabulary of social epistemology wasn’t used. The social is epistemic.

I’ve been thinking about this framework for over twenty years, and I keep returning to the same question: why didn’t it take over the profession? The framework was there in 1952. Information literacy has been moving toward it for decades, most recently with the ACRL Information Literacy Framework document. And yet the dominant self-description of libraries remained access-delivery-focused for most of that period. Part of this, I suspect, is a cultural problem. Whenever I talk with someone about libraries, they want to reminisce about the last one they used in any significant way, which means the conversation often starts with card catalogs, or surprise that students can eat in the library now, or wondering where all the books went. When people carry such varying, yet book-based, memories, it’s hard to talk about where libraries are going without first establishing where they actually are. And when the people you’re trying to reach are administrators facing their own funding pressures and a desire for metrics, the epistemic argument can feel harder to make than an argument based in easy-to-report circulation counts.

What does it mean to be epistemic infrastructure rather than an information warehouse? The warehouse metaphor, which was easy to count in gate entries and items circulated, treats knowledge as something that exists prior to the library and gets stored there. The infrastructure metaphor treats the library as part of what makes certain kinds of knowing possible at all, not a convenience for accessing knowledge that would exist regardless, but a condition for the scholarly practices through which knowledge gets produced, validated, and preserved. The metaphor here is road versus car. The road doesn’t move the car. But without it, the car doesn’t go anywhere useful. The library is the road. But it might be even more accurate to say it’s the whole Department of Transportation, responsible not just for the surface you drive on but for whether the road reaches your neighborhood at all.

This is the frame through which I think the current AI moment becomes legible. The proliferation of AI tools in research and information seeking isn’t asking libraries to become something new. It’s asking libraries to be more fully what Shera and Egan described seven decades ago. As AI systems become embedded in how people search, synthesize, and evaluate information, the question of what epistemic infrastructure exists to support genuine knowing becomes more urgent, not less. The road matters more when the vehicles are faster and harder to steer. Libraries that understand themselves as epistemic infrastructure, as systems that make certain kinds of knowing possible for their communities, are positioned to do that work. Libraries that understand themselves primarily as access points to content are in a harder position to articulate why they matter when access has become frictionless and ubiquitous.

For the librarians reading this: this is why the work you’re already doing is philosophically serious, not just practically useful. For the administrators and institutional leaders in the room: understanding libraries as epistemic infrastructure changes what decisions about them actually mean.

The Knowledge We’ve Always Built

There’s something genuinely strange about this moment for libraries. The tools that seem most likely to make us obsolete are also the ones that most clearly reveal what we were doing all along. More information is available than ever before, synthesized, immediate, apparently authoritative. And yet the questions that matter most are only getting harder to answer. What’s trustworthy? How do you know? Who decides? Libraries have been working on those questions for a long time. We just didn’t always have to say so out loud.

Information vs. Knowledge

When I hear excitement about AI, it’s almost always about access to information. And access to information is genuinely useful. But having spent a lot of time with the data-information-knowledge-wisdom framework¹, I’m aware that information and knowledge aren’t the same thing. Moving from one to the other requires context: understanding the nuance of what you’re seeing, where it came from, and how it fits into what you already know.

Libraries are centered around exactly that work. We pay attention to publishers, to trends in a literature, to publication types. We help students understand why a publication date matters, whether a study is quantitative or qualitative, how to evaluate whether a source actually supports the argument they’re building.

Outcomes not metrics

Libraries have a long history of understanding the need to demonstrate their value. One place we turn are the metrics and statistics we can share with stakeholders to prove the community benefits from their library. We count the number of items in our collection, the number of people through the door, the number of reference consultations we provide, and the number of classes we teach. Those numbers were the right answer to the questions we were being asked, but AI is changing the questions.

AI clarifies this for us. When information is available from anywhere, talking about access becomes less useful. We have to say something truer about what we actually do, and that means recovering language for work we’ve been doing all along.

Libraries were never only about access. They were about the social infrastructure of knowledge: the systems through which communities come to know things together, evaluate what’s trustworthy, and preserve the conditions for doing that work well. Margaret Egan and Jesse Shera understood this in the 1950s². The ACRL Framework for Information Literacy, with its insistence that “authority is constructed and contextual,” is evidence that the profession has been moving toward that understanding for years. AI didn’t create this argument. It just made it impossible to avoid.

This is where the library’s expertise becomes irreplaceable, and exactly the area that is at risk when people accept AI outputs without understanding what’s missing. Librarians learn context not as background information but as the substance of the work. Understanding how knowledge is produced: the research process, peer review, publication venues, the difference between a preprint and a published study, is what makes it possible to build collections worth preserving, and to help students and faculty find not just information but validated knowledge they can actually build on.

Load bearing shoulders

In trying to find a theory or framework to describe the importance of scaffolding knowledge and the expertise that librarians bring to this work, I keep being drawn to constructivism. These days I keep coming back to it as useful framing for how scholarship itself works.

Constructivism holds that knowledge builds on existing knowledge, and research articles are grounded in literature reviews, citations, and peer review. It describes knowledge as socially constructed through dialogue, which happens in research as the ACRL Framework describes “scholarship as conversation.” It requires authentic context, which is exactly what AI strips out. And it expects active engagement with ideas, not passive receipt of synthesized outputs.

You can only stand on the shoulders of giants if someone has been paying attention to which shoulders are load-bearing. And there is an entire profession doing exactly that work.

In all the AI discourse I continue to think about what it means for librarianship. I know that we will always be in the business of access to information. But I can’t help believing we’ll shift towards centering knowledge in the future, and I am thinking about what that might mean for the work. I’m curious what you see from your position in the field.

Ackoff, Russell (1989). “From Data to Wisdom.” Journal of Applied Systems Analysis. 16: 3–9. ↩︎
Egan, Margaret E. and Shera, Jesse H. (1952). “Foundations of a Theory of Bibliography.” The Library Quarterly. 22.2: 125–137. ↩︎

The Obsolescence Argument Has It Backwards

Everyone seems to agree that artificial intelligence is going to change education, research, and libraries. The disagreement is about direction. The dominant narrative, at least in some technology circles is: AI can find information, synthesize sources, and answer questions. It’s not a surprise that people hearing that argument in media and from tech commentators point out that libraries and librarians do those things and then assume that libraries are in trouble.

But to anyone who sits at the intersection of technology and libraries it’s abundantly clear that AI doesn’t make libraries obsolete, but rather it makes them more essential.

I’ve been thinking about knowledge systems for a long time. My undergraduate degrees were in philosophy and in communication, with a minor in Women’s and Gender Studies, and the questions that animated these fields were the same ones: Who knows? Under what conditions? With what authority, and on whose behalf? Those questions led me to library science, and they’ve shaped how I’ve understood this work ever since.

Two frameworks have always been particularly generative for me. The first is social epistemology. This term was developed by Jesse Shera and Margaret Egan in the mid-twentieth century, which understands libraries not as warehouses of information but as infrastructure for how communities produce and share knowledge. Libraries, in this view, are epistemic institutions. They don’t just store what we know; they shape the conditions under which knowing is possible. (Incidentally social epistemology also developed within Philosophy, with a slightly different implementation, a few decades later.)

The second is feminist epistemology, particularly Donna Haraway’s concept of situated knowledges. Haraway’s argument, made in a landmark 1988 essay, is that all knowledge is produced from somewhere: from a particular body, a particular history, a particular set of social relations. Claims to view-from-nowhere objectivity, what she calls the “god trick,” are not neutral. They are themselves a kind of power move, one that erases the conditions of knowledge production and forecloses accountability. Sandra Harding’s standpoint theory extends this: knowledge produced from the margins, from positions of accountability rather than dominance, is often more comprehensive, not less, because it cannot afford to ignore what the center takes for granted.

These frameworks were developed to critique science. But you can see why I keep coming back to them today.

Large language models perform exactly the god trick Haraway identified. They synthesize at scale without provenance. They produce authoritative-sounding outputs whose origins are opaque, whose training data encodes historical power imbalances, and whose confident tone actively discourages the epistemic humility that good inquiry requires. They are, in Harding’s terms, knowledge produced from nowhere. And this means they are making claims from a position that cannot be held accountable.

This is not primarily a technical problem. It is an epistemic one. And it is precisely the problem that libraries, at their best, are structured to address.

Libraries curate situated knowledge. They preserve provenance. They maintain the bibliographic infrastructure that allows a reader to ask: who said this, when, from what position, in conversation with whom? They select, describe, and organize materials in ways that make the conditions of knowledge production visible rather than erasing them. They employ people (librarians!) whose professional expertise is not only finding information but teaching the critical practices that allow communities to evaluate it.

None of that is replicable by a system that has been specifically designed to flatten those distinctions into fluent prose.

I’m not arguing that AI is useless, or that libraries should resist it, or that the landscape isn’t changing. It is changing, and libraries need to engage with that change thoughtfully and without too much nostalgia. What I am arguing against is the idea that AI supersedes libraries. When someone asks whether AI makes libraries obsolete, the questioner implicitly accepts a definition of libraries as information retrieval systems. That is a definition that was always reductive and is now actively misleading. Libraries are epistemic infrastructure. They are, in Shera and Egan’s terms, the social mechanisms through which communities organize their relationship to knowledge.

AI doesn’t replace that. It creates new urgency for it.

The more our information environment is shaped by systems that perform objectivity while encoding power, the more we need institutions committed to making those dynamics visible. As synthetic text becomes more fluent and authoritative, it will become more important for human thinking to maintain the skills in citation, provenance, critical evaluation, and the slow work of understanding where knowledge comes from. These are the skills that libraries cultivate.

The obsolescence argument has it exactly backwards. This is the moment libraries were built for.

This is the first post in an ongoing project exploring libraries, knowledge, and the epistemic stakes of artificial intelligence. I’m drawing on social epistemology, feminist theory, and two decades of practice in academic libraries.

Before we begin

Years ago I kept a blog (at this URL, even!) where I thought out loud about libraries, knowledge, and the profession I’d built my career around. I was good at it for a while, and then I wasn’t, and then I stopped for all the usual reasons: changing life phase, less personal time to spend on it, increasingly demanding institutional role, the way the platforms evolved from places of earnest and open discussion… I drifted so far away from blogging and this website that when a back up didn’t capture all the files I wasn’t even all that disappointed.

But lately I’ve really missed thinking in public with other colleagues interested in exploring the same ideas. And lately I’ve been thinking a lot about academic libraries, our information environment, and the ways we talk about and use artificial intelligence.

AI is reshaping how people find, evaluate, and trust information. Within libraries we have people all across the spectrum: from those who fully embrace it to those who believe it has no place near our work. One of the dominant narratives outside of the profession treats libraries as information retrieval systems and concludes that AI makes them redundant. This framing mistakes the symptom for the disease. Libraries are epistemic infrastructure. They are the mechanisms through which communities organize their relationship to knowledge. AI doesn’t replace that, but it does make that role all the more urgent.

This lens keeps coming up for me in conversations in varied spheres. Jesse Shera and Margaret Egan’s social epistemology, which understands libraries not as warehouses but as institutions that shape the conditions under which knowing is possible, is foundational to how I think about this work. So is feminist epistemology, particularly Donna Haraway’s concept of situated knowledges and Sandra Harding’s standpoint theory. These frameworks were built to interrogate science. But it turns out that they are extremely useful when interrogating AI as well.

I’m writing as a person who has spent two decades in academic libraries and who has been thinking about knowledge, power, and institutions since an undergraduate philosophy degree made those questions unavoidable. At this URL, I am not writing as an institutional voice. This is a thinking space. I’m hoping that arguments will develop, get complicated, and occasionally get revised. I expect to adapt to new information.

What follows this post is the first real argument: why the obsolescence narrative has it backwards, and what a clearer account of libraries and knowledge reveals about the epistemic stakes of this moment.

I’m still trying to understand where people talk about these things today. In some ways everything was a lot cleaner when the answer was a blog with open comments, an RSS reader, and Twitter. The messiness of our knowledge environment today (LinkedIn? Bluesky? Mastodon? SubStack? Chat threads? Everywhere?) resonates with the messiness of the information ecosystem I’m trying to write about.