From AI literacy to AI fluency. Why librarians are becoming the expert guides for research.

Librarians do not need to become AI engineers or data scientists, but developing a level of AI literacy enables them to lead and guide researchers, evaluate AI tools critically, and promote responsible and ethical use of AI in scholarship.

AI literacy is the ‘natural’ evolution of information literacy

From Information Literacy to AI Literacy

Today, researchers are no longer searching only databases. They are asking AI assistants to synthesize literature, explain complex concepts, generate search strategies, and draft sections of manuscripts. This shift changes the questions researchers bring to the library.

Questions have changed from which database should I use to can I trust this AI-generated summary? Or which AI tool should I use for this task and is it acceptable to use AI in my dissertation? And of course they need support on how to cite AI use or whether it’s safe to upload unpublished research into an AI platform

The Librarian’s Role Is Expanding

Historically, librarians have been trusted experts in navigating the rapidly changing information landscape. AI has introduced a new layer of complexity.

AI is reinforcing their role as trusted advisors. Their expertise in evaluating information, understanding scholarly communication, protecting research integrity, and teaching critical thinking is becoming even more valuable. The modern librarian is increasingly expected to also understand AI-powered research tools, their capabilities, their limitations, and their ethical implications.

What Does AI Literacy Mean for Librarians?

AI literacy extends well beyond learning to use a chatbot. It encompasses a set of competencies that enable librarians to support researchers effectively.

1. Understanding AI

Librarians should have a foundational understanding of concepts such as generative AI, large language models, retrieval-augmented generation (RAG), hallucinations, bias, and model limitations.

They do not need to build AI systems, but they should understand enough to explain how these technologies work and where they may fail.

2. Supporting AI-Enhanced Research

Researchers are integrating AI throughout the research lifecycle—from identifying research questions to drafting publications.

Libraries are increasingly supporting AI-assisted:

• literature discovery
• evidence synthesis
• systematic reviews
• coding and data analysis
• academic writing
• research workflows

This also means becoming familiar with AI-enabled scholarly discovery platforms. Alongside traditional discovery systems such as Primo, Summon and Alma, it’s a solution like Zendy and its AI Discovery based on intent that demonstrates how AI can enhance scholarly content discovery while integrating with existing library infrastructures. Being based on library subscriptions, makes it trustworthy and less risky to use by a student or researcher.

3. Evaluating AI Critically

Perhaps the librarian’s greatest strength is being able to evaluate the wildest of AI tools.

Questions librarians are well equipped to answer include:

• Are the references real?
• Can the citations be verified?
• Does the AI provide transparent sources?
• Is there evidence of bias?
• Are important perspectives missing?
• Can the output be reproduced?

These are extensions of their long-established information evaluation skills.

4. Promoting Responsible AI

Libraries have always championed ethical information use.

With AI, this responsibility expands to include:

• data privacy
• copyright
• intellectual property
• academic integrity
• transparency
• responsible disclosure of AI use
• institutional AI policies

As universities develop AI governance frameworks, libraries are increasingly becoming important contributors to institutional policy discussions.

5. Teaching AI Literacy

Perhaps the fastest-growing responsibility is education.

Many academic libraries now offer workshops on:

• AI for literature searching
• Prompt engineering for researchers
• Evaluating AI-generated content
• AI in systematic reviews
• AI and academic integrity
• Responsible AI use in scholarly publishing

In this role, librarians become AI educators, helping researchers use these technologies thoughtfully rather than replacing existing services. AI is expanding them.

AI Literacy Is Becoming a Core Professional Competency

There is a common misconception that librarians must become AI experts. In reality, they need to become AI-literate professionals.

The distinction matters.

Just as librarians became experts in online databases without becoming database developers, they can become trusted AI advisors without building AI models.

Their value lies in helping researchers ask better questions, select appropriate tools, evaluate outputs critically, and use AI responsibly.

Beyond AI Literacy: Towards AI Fluency

AI literacy is about understanding AI and using it responsibly. AI fluency is about integrating AI confidently into professional practice while maintaining critical thinking, ethical judgment, and scholarly integrity.

For librarians, this means moving beyond learning individual AI tools. It means developing the confidence to advise researchers, design AI-powered services, contribute to institutional AI strategies, and shape the future of research support

Every major technological shift has reshaped the role of libraries from printed catalogues to online databases, from digital repositories to open access. Artificial intelligence is the next or current chapter. We don’t have to wonder whether libraries should engage with AI, they all are in some shape or form. 

The real opportunity is for librarians to become the trusted guides who help researchers navigate an AI-powered scholarly landscape with confidence, curiosity, and integrity. In an age where information can be generated instantly, the librarian’s greatest contribution is helping them understand, question, evaluate, and use it wisely

That is the essence of AI fluency, and perhaps the defining role of the academic librarian in the years ahead.