7 AI Myths Librarians Hear Most (and the Facts Behind Them)

With all this AI activities within the research process, comes a growing list of assumptions.

Some are based on real concerns. Others come from early experiences with general-purpose AI tools (e.g, ChatGPT). And some simply haven’t kept up with how AI is being developed for academic research.

For librarians, separating the myths from the facts matters. It makes it easier to evaluate AI tools, explain their benefits and limitations to researchers, and build services that support responsible use.

Here are some of the most common AI myths librarians hear today.

Myth 1: AI will replace librarians

This is probably the biggest concern.

The reality is that AI can assist with tasks, but librarians bring something AI cannot provide on its own: professional judgment and an understanding of the research environment.

Librarians help researchers assess sources, understand information quality, navigate collections, develop research strategies, and use resources responsibly. They also understand the needs of their institutions and communities.

AI can help with repetitive or time-consuming parts of research support. It can surface relevant information, summarize material, or help users explore a topic. The librarian’s role remains important in helping users understand what they have found and how to use it appropriately.

Myth 2: AI always gives the right answer

AI-generated answers can sound confident even when they are incorrect.

This is especially important in academic research, where accuracy, provenance, and context matter.

Generative AI systems can produce inaccurate information, misinterpret sources, or generate references that do not exist. That means an answer that sounds convincing still needs to be checked.

For libraries, this reinforces the importance of source-aware AI tools. Systems designed around verified scholarly content, clear citations, and transparent retrieval can give researchers a stronger starting point.

AI literacy also has a role to play. Researchers need to know that an AI-generated response is something to evaluate, not automatically accept.

Myth 3: AI and academic search are the same thing

Traditional academic search relies heavily on keywords, filters, metadata, and structured queries. These remain valuable, but researchers increasingly expect to interact with information in more natural ways.

AI can help users describe what they are looking for in everyday language and explore connections between concepts.

That does not mean traditional search is disappearing.

Instead, AI can add another layer to discovery. A researcher might begin with a natural-language question, identify relevant concepts, then move into detailed records, full texts, citations, and subject-specific resources.

For libraries, this creates an opportunity to bring AI-assisted discovery into existing research workflows without abandoning the systems researchers already rely on.

Myth 4: AI needs access to the entire open web to be useful

More information does not automatically mean better research.

For academic use, the quality and relevance of sources matter as much as the quantity of information available.

An AI tool working with a defined collection of scholarly content can provide a more focused research experience. It can help researchers discover relevant material without mixing academic sources with unreliable, outdated, or irrelevant web content.

This is one reason libraries should look closely at where an AI system gets its information, how sources are selected, and whether citations are available.

The key question is not simply, “How much information can this AI access?”

It is, “What information can it access, and can researchers trust and verify it?”

Myth 5: AI makes information literacy less important

The opposite may be true.

As AI becomes better at producing fluent answers, information literacy becomes even more important.

Researchers need to understand how AI-generated information was produced, where its sources come from, what its limitations are, and when additional verification is necessary.

Librarians are already well positioned to support this.

Information literacy programs can evolve to include AI literacy: evaluating AI outputs, checking citations, understanding bias, protecting intellectual property, and knowing when AI is appropriate for a research task.

The librarian’s role can therefore extend from helping people find information to helping them critically work with AI-assisted information.

Myth 6: Every library needs an AI tool right now

AI adoption should start with a library’s needs.

Before introducing an AI solution, libraries can ask practical questions:

• What problem are we trying to solve?
• Who will use the tool?
• What sources does it rely on?
• How does it handle citations?
• What data does it collect?
• Can librarians control or configure it?
• How does it fit into existing discovery services?
• How will we measure whether it is actually helping researchers?

There is no universal AI strategy for every institution.

A research-intensive university, a small academic library, and a national research organization may have very different priorities. The right approach depends on the users, collections, infrastructure, and resources available.

Last AI Myth: AI is only useful for researchers

Researchers are an obvious audience for AI-assisted tools, but they are not the only ones who can benefit.

Library teams can use AI to support discovery services, analyze information, develop research guides, assist with reference workflows, and explore patterns across collections.

AI can also help institutions think about how users interact with their information resources.

The important point is to identify useful applications instead of adopting AI simply because it is available.

From AI anxiety to AI fluency

What matters is having a clear framework for evaluation.

Look at the sources. Ask how the system retrieves information. Check how citations work. Understand data and privacy practices. Consider how the tool fits into existing library systems. Most importantly, test whether it solves a real problem for researchers.

Librarians already have many of the skills needed for this work: critical evaluation, information expertise, research support, and an understanding of how people interact with scholarly information.

AI adds a new set of tools to that work.

The libraries that approach it thoughtfully can help researchers benefit from AI without losing sight of the principles that make academic information useful in the first place: access, accuracy, transparency, and trust