ACRL

There are 2 posts tagged ACRL (this is page 1 of 1).

Crossing Thresholds in an Age of Answers

My first library staff role allowed me to focus on technology. I was interested in how search, “web 2.0,” and ecommerce changed people’s expectations of engaging with the library through web browsers. Then I moved into my first librarian position and focused on instruction. I taught classes, I offered instructional design support for faculty across campus, and I eventually managed our credit-bearing instruction program. Because of this I spent a lot of time with the ACRL Information Literacy Standards and then the Information Literacy Framework.

And as with any foundational introduction that shapes your mental models and worldview, I come back to these lenses that shaped me as a librarian pretty routinely. In the AI conversations I keep finding myself in, I return to these two questions: how does this tool change people’s expectations of the library? How does the ACRL Information Literacy Framework support our communities in navigating this evolving world?

The ACRL Information Literacy Standards were a set of skills that a student could demonstrate. This was extremely useful when building a program and assessing the impact of library instruction. But the standards also felt like they didn’t capture the entirety of what was happening in teaching students to navigate the information environment. In 2016 ACRL adopted the Framework for Information Literacy. This was a new way of explaining what we were teaching our students to do: a framework based on the idea of Meyer and Land’s “threshold concepts.” These are central ideas in a field that permanently change how a learner understands the subject and views the world. Meyer and Land described them as transformative, irreversible, integrative, bounded, and troublesome. The information literacy concepts are: authority is constructed and contextual, information creation as a process, information has value, research as inquiry, scholarship as conversation, and searching as strategic exploration.

In 2023 ACRL launched a Framework for Information Literacy for Higher Education Review and Revision Task Force to “review the current Framework for Information Literacy for Higher Education to determine if it still reflects current thinking, theory, and practice on information literacy. Review lenses could include equity, diversity, and inclusion, and the emergence of generative AI.” In 2026 a draft was shared for comment, in which the concepts remain (though “scholarship as conversation” moves to “knowledge systems as conversation”) and much of the explanatory text is updated. The revised draft went to the ACRL Board for consideration this summer, and a final version hasn’t been announced yet.

The details of the process matter less than what it signals: librarianship treats its own understanding of information literacy as unfinished, and it is revisiting that understanding because the information environment has changed again. That seems right to me, and is a part of the ethos of the field. In an interview, Sara Miller, one of the leads for the project, shared “[a]t this juncture, the concepts expressed in the Framework still seem to provide enough context for framing and informing an IL-based exploration of technologies like AI.”1 I’d agree and go a bit further. AI makes these concepts more urgent, because it smooths over the places where students used to struggle in their search and use of information.

“Troublesome” is the quality I keep coming back to. Meyer and Land meant that these concepts resist easy understanding, and that the resistance is part of how the learning happens. A student who wrestles with why one source carries more weight than another comes out the other side seeing information differently. Generative AI is remarkably good at removing that resistance. It answers fluently, attributes selectively or not at all, and hands over a finished synthesis where a student once had to build one. Each frame names an understanding that grows out of the friction an AI answer smooths away.

So how does a student come to understand:

  • That authority is constructed and contextual when output has no visible author?
  • That information creation as a process when production is hidden?
  • That information has value when the people whose work fed the model go unseen and uncredited?
  • Research as inquiry when they simply ask a question and receive an answer?
  • Scholarship as conversation/knowledge systems as conversation: when sources dissolve into a synthesis?
  • Searching as strategic exploration: when it’s just a matter of designing a single prompt returning a confident answer?

Of course there are cases when AI can also support threshold crossing. It’s been clear to me in my own use that having a knowledge base when working with AI can be generative and useful. But when I’m thinking about working with a student who is just forming their foundational knowledge of their discipline and is making sense of new information, the encounter should produce enough friction for the learning to stick. AI is most useful to people who have already crossed these thresholds and is riskiest for those who haven’t (a theme I find myself ruminating on a lot these days).

Libraries have spent decades teaching toward these exact understandings, and we have long known that struggle is part of the lesson. I remember debates in my graduate program about whether to teach with a live search or a carefully planned one, and they resolved quickly: students learned more from watching someone conduct a search and evolve their thinking in the process than from a smooth, canned demonstration. That lesson feels newly relevant. People increasingly expect information to arrive polished and finished, and part of our work is to help them notice what that polish disguises. The emphasis in our teaching should be shifting from finding the right sources toward thinking well about whatever information arrives, and the Framework holds up for both.

  1. Miller, Sara, Ross, Leslie, & Maye, Kay. “The Framework’s Next Chapter: An Interview with ACRL’s Framework for Information Literacy for Higher Education Review and Revision Task Force Leaders Sara Miller and Leslie Ross” College & Research Libraries News [Online], Volume 87 Number 4 (31 March 2026). https://crln.acrl.org/index.php/crlnews/article/view/27281/35079 ↩︎

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.

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

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.