"...librarians already have a significant set of competencies that are essential in the successful, responsible, and ethical development of AI..." — Harper & Groth, 2025
Artificial intelligence (AI) literacy for librarians(and other library and information professionals) is a relatively new concept, but was championed initially and promoted by American librarian Leo Lo and UK librarian Andrew Cox. Recently, Montesi et al, 2025 published an AI literacy framework. See also, Shirin, 2024.
The document expands on Lo's (2025) broad definition of AI literacy,⁵ tailoring it into a comprehensive, library-specific set of competencies applicable to academic library workers. It is meant to serve as a guiding framework for the creation of training programs and as a foundation for communities of librarians to develop their own AI competency frameworks. Given the diversity of roles and job duties among academic library workers, it is not possible to create a set of competencies that apply uniformly to everyone. Therefore, individuals, institutions, and others who use this framework are encouraged to adapt it to specific job functions, responsibilities, or organizational contexts.
Students and faculty are increasingly beginning their research by asking AI systems for explanations rather than by searching library resources. Chatbots and AI enhanced search tools now deliver fluent answers before users ever see a list of sources. This commentary argues that this “answer first” environment changes the starting point of academic inquiry and calls for a corresponding shift in academic librarianship. Librarians need an answer first mindset that recognizes AI responses as texts that require interpretation. I propose two related constructs to support that stance, a brief “answer typography” that helps librarians notice what kind of work AI answers are doing, and the CARE approach (Classify, Assess, Review, Enhance), which articulates a critical way of engaging those answers with users. Together, these ideas position librarians as leaders in helping their communities read, question, and build upon AI generated answers in ways that keep human judgment and scholarly evidence at the center of inquiry.
Definition
AI literacy involves understanding, using, and critically evaluating artificial intelligence technologies to enhance library services and educate users. Given the proliferation of AI tools, librarians are advised to work together using various communication techniques to sort through what AI literacy means to them. The idea is to apply traditional information literacy to AI, including technical knowledge, practical skills, critical thinking, and awareness of social impact. Any discussion of AI literacy for librarians begins with ethical, legal, institutional and strategic concerns.
The framing of “AI” literacy as a necessary set of skills is viewed as problematic by some librarians. For some, AI literacy is less an examination of what constitutes human intelligence and an understanding of information as performed by skilled human beings than a set of justifications for wholesale adoption of these tools. For its part, AI is also quite simply a computer science discipline that includes machine learning and deep learning, which utilizes neural networks to create large language models (LLMs). LLMS draw from huge sets of data, some of it copyrighted from the web, and calculate the words and text most likely to come next in a response, resulting in naturally human sounding narratives. In sum, many AI tools function superficially as human language prediction machines.
"...Librarians play a crucial role in developing AI literacy among researchers. ... they can ensure that AI research tools enhance, rather than compromise, academic research integrity, maximizing benefits while mitigating risks." — Archambault et al, 2024.
Librarians have had exposure to artificial intelligence (AI) for years using a range of functions in libraries such as: predictive text, autocorrection, grammar checking, personalized recommendations, search ranking, captioning, automated transcription and translation, to name a few. Apple's AI assistant, Siri is an example of conversational AI — understanding and responding to voice commands. GenAI tools provide instant feedback to students, and answer questions in real-time 24/7, which may be helpful for students without access to tutors or teachers outside class hours.
In libraries, AI is asked to summarize information, suggest resources, and generate ideas for projects. AI is used for coding, writing drafts, or brainstorming as creative problem-solving using AI is required in modern workforces. AI can assist students with disabilities by generating audio descriptions, simplifying texts, or translating content, making learning more inclusive.
For health sciences librarians, the rise of AI-powered search tools, coupled with their work in support of knowledge synthesis (KS), means that there will be some demand for librarians who understand these news tools, and their underlying technologies, in advising evidence synthesists.
The IFLA Internet Manifesto, published in 2024, aims to bring together all the different aspects of library engagement in how the internet does - and should - work. Naturally, AI is becoming an ever larger element of that work. "The strategy most aligned to existing library practices and librarian identities, particularly in university, school and public libraries, is to take a lead role in promoting AI literacy. There is a widespread understanding that the public, as citizens and workers need to understand the new technologies.
Further "AI literacy is likely to include the ability to identify when AI is being used; to appreciate the differences between narrow and general AI; to understand what types of problems AI is good at solving[sic]; to understand how machine learning models are trained. It would also include awareness of ethical issues such as bias, privacy, explainability and social impact." - IFLA AI SIG.
What is the context for AI literacy for librarians?
Technical Knowledge
While librarians will not need to be experts, at least in the short term, it might be helpful to start by defining key concepts in this domain.
Librarians can pressure Silicon Valley AI companies to work with us through our national and international library associations to have a say in shaping tools in the AI era.
Practical Skills
Hands-on experience and testing of AI tools (e.g., chatbots, image generators, or metadata assistants) enables librarians to critique their application in research support, cataloguing or digital collections.
Experimentation is thought to foster confidence in using AI effectively. It takes up a lot of time and is inefficient in my experience.
Critical Thinking
Librarians must apply information literacy skills to assess AI-generated content for biases, inaccuracies, or ethical concerns, such as non-neutral outputs or data privacy issues. Tools like the LibrAIry’s ROBOT Test help to evaluate AI applications critically.
Social and ethical literacies
Understanding AI’s broader implications within our work — cultural, economic, and environmental — will be crucial in the future.
Librarians will want to address a range of issues in any teaching or assessment of AI by drawing on examples of algorithmic biases, privacy erosion for our users, and inequitable access to AI tools. It's unclear how to ensure inclusive and ethical uses of AI. Librarians will want to question its use at every step.
Library tools and services: AI may (likely not) automate tasks such as metadata creation, indexing and answering some queries, freeing librarians for strategic roles in specialized research or community engagement.
AI literacy: Librarians may want to (or not) lead workshops or create resources (e.g., LibGuides) to teach patrons how to use AI responsibly, identify deepfakes, and evaluate AI-generated information.
Accessibility and equity: AI tools may (or not) improve accessibility (e.g., text-to-speech for visually impaired patrons) but require librarians to address digital divides and biases in AI systems.
Challenges and Opportunities; surveys show librarians may not have AI training, with 24% of public librarians feeling prepared to teach AI. Comprehensive professional development is needed.
Ethical problems: Librarians will aim to guide users through AI’s ethical minefield, combating misinformation or recognizing biased outputs, aligning with their roles. This includes issues related to information authority and intellectual property rights.
Strategic & institutional contexts: Libraries may want to (or may not want to) lead AI literacy initiatives within their libraries, by drawing on their expertise in information literacy for critical engagement with AI. In Canada, one interesting program has been the Toronto Public Library’s “The A(i), B, Cs of Artificial Intelligence.”
Presentation
Leo Lo, Dean and Professor for the College of University Libraries and Learning Sciences at the University of New Mexico, discusses his approach to create AI literacy initiatives at his institution with an eye toward interdisciplinary collaboration, pedagogy, and ethics.
The CLEAR path: A framework for enhancing information literacy through prompt engineering
The CLEAR Framework for Prompt Engineering is designed to optimize interactions with language models such as ChatGPT by OpenAI. The framework encompasses five core principles—Concise, Logical, Explicit, Adaptive, and Reflective—that facilitate more effective AI-generated content evaluation and creation.
Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.
