AI and Open Access: What Librarians Should Know

Open access has changed research in remarkable ways. Millions of scholarly articles that were once available only through expensive subscriptions can now be read by anyone with an internet connection. This has made research more equitable, more collaborative, and more visible than ever before.

Much of the discussion around AI in libraries has focused on efficiency. Can it speed up literature reviews? Can it summarize articles? Can it help researchers search more effectively?

Conversations about AI and open access overlap because both change who finds research and how they find it.

Those are useful questions, but perhaps not the most important ones.

The bigger question is this: how can AI help people understand research without distancing them from it?

This is the central challenge of AI and open access, making discovery easier while keeping researchers connected to the evidence.

Open Access solved one problem. AI is trying to solve another

The open access movement addressed one of the biggest barriers to scholarship: availability. If people could not read research, they could not build on it.

That work is far from over, but it has changed the landscape. Today, many researchers are no longer limited by whether they can access information. They’re limited by how much information they have to sift through.

Every year, millions of new research papers are published. Even within a narrow discipline, it can be difficult to keep up. Researchers jump between databases, refine searches repeatedly, skim dozens of abstracts, and still worry they may have overlooked something important.

The challenge is no longer simply opening the door to knowledge.

It’s helping people find their way through it.

AI Is Changing How Researchers Begin Their Search

For decades, academic discovery has revolved around keywords. Researchers learned to adapt their questions to suit the search engine.

AI turns that around.

Researchers can ask a question much as they would ask a colleague. AI can recognize concepts, identify related ideas, and surface papers that may never have appeared in a traditional keyword search. This is a core example of AI and open access working together to finde relevant work

For many researchers, that’s a genuinely better experience.

But it also changes the relationship between the researcher and the literature.

Increasingly, the first thing a researcher may see is an AI-generated summary, not an abstract or a journal article.

That’s not necessarily a problem. In many cases, it’s incredibly helpful.

The important thing is that the summary becomes the beginning of the research journey, not the end of it.

Speed Means Very Little Without Trust

Trust has always been One of the great strengths of academic libraries

Researchers know where information comes from. They know who published it, when it was published, how it was reviewed, and how to trace every claim back to its source.

AI shouldn’t weaken that relationship.

It should make it stronger.

Good AI doesn’t hide the evidence behind an answer. It points researchers toward it, shows where ideas come from, acknowledges uncertainty, and makes it easy to move from a summary to the original publication.

Because research is about understanding why that answer deserves confidence. For AI and open access to fulfill their promise, transparency and clear attribution are essential

The Librarian’s Role isn’t Getting Smaller

Every new technology seems to raise the same question: Will libraries become less important?

History has consistently answered that question with a “no.”

Search engines didn’t replace librarians. Digital collections didn’t replace librarians. Open access didn’t replace librarians.

AI won’t either.

If anything, it makes their expertise even more valuable.

Researchers still need someone to help them evaluate evidence, understand bias, navigate competing viewpoints, and develop good research habits. Those responsibilities don’t disappear because an AI can summarize a paper in thirty seconds.

In fact, they become even more important.

Teaching someone how to ask better questions, evaluate AI-generated responses, and verify sources may become one of the defining information literacy skills of the coming decade. Librarians will increasingly mediate AI and open access tools to preserve research quality and context.

AI and Open Access Should Move in the Same Direction

Sometimes, there is a tendency to treat AI and open access as separate conversations.

They aren’t.

Open access ensures knowledge can be shared. AI helps people navigate that knowledge.

One provides the content, and the other improves discovery.

Neither works particularly well without the other.

The opportunity for libraries is to bring those two ideas together while protecting the values that have always underpinned scholarly communication: trust, transparency, attribution, evidence, and critical thinking.

Those principles matter just as much in an AI-assisted workflow as they do in a traditional one.

Perhaps even more.

Looking Ahead

Today’s researchers expect search to feel more natural, discovery to be faster, and information to be easier to understand. AI can help meet those expectations, but only if it remains centered in trusted scholarly content and encourages researchers to engage with the evidence.

That is something libraries have been doing all along.

They now have a new set of tools to help them do it. The future will depend on how effectively AI and open access are integrated into scholarly practice.