Editorial

AI Literacy and Evidence Based Practice in Libraries

Ann Medaille

Editor-in-Chief

Professor, Director of Research & Instructional Services

University of Nevada, Reno Libraries

Reno, Nevada, United States of America

2025 Medaille. This is an Open Access article distributed under the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 4.0 International (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one.

DOI: 10.18438/eblip30797

An evidence based approach to library practice involves the ability to identify problem areas in the library, review and evaluate relevant evidence, design and implement rigorous research approaches, and apply strategies for improvement. Increasingly, AI is being incorporated into various facets of library and information work, including search tools and discovery layers, reference assistance, metadata generation, digital preservation, predictive analytics in collection development, information literacy instruction, accessibility services, and image recognition. In order to competently implement evidence based practice (EBP) in libraries, it is becoming imperative for library professionals to have some understanding of AI technologies and their impact on society.

Evidence based practice is a way of making decisions based on the integration of research evidence, professional expertise, and user values and experiences (Sackett et al., 1996). In the field of library and information science, this means that practitioners seek out the best available evidence to answer their questions, whether that evidence comes from prior literature, original research studies, or local evidence sources such as statistics, assessments, and observations (Koufogiannakis & Brettle, 2016). To adopt an evidence based approach to practice, library professionals have to be well versed in the process and skilled in a number of different areas, such as analyzing problems, synthesizing literature, designing program evaluations and research studies, collecting and analyzing data, critically appraising research, generating solutions, and making decisions (Koufogiannakis & Brettle, 2016).

In addition to possessing these skills, library professionals will also need to be AI literate to be effective evidence based practitioners in the current age. AI literacy is “the ability to understand, use, and think critically about AI technologies and their impact on society, ethics, and everyday life” (Lo, 2025, p. 120). It includes having a basic understanding of AI technologies, including concepts such as machine learning, training data, and algorithms; the ability to responsibly use AI tools to complete tasks; the ability to critically evaluate AI tools and their outputs; an understanding of some of the social and ethical implications of AI usage including issues related to bias, privacy, and inclusivity; and a willingness to engage with continued exploration of AI technologies and to discuss emerging issues (Hervieux & Wheatley, 2024; Lo, 2025).

These AI literacy skills are now essential for EBP in libraries. Because AI tools are becoming increasingly integrated into library and information resource and service delivery, it is necessary to fully understand these tools to be able to assess problems and design solutions. AI tools will help library professionals to better understand user needs, deliver content, and design future services. In addition, AI will be used to facilitate the process of EBP by helping library professionals to review prior literature, generate questions for inquiry, design studies, conduct data analysis, visualize results, and explore implications. Thus, library professionals must be knowledgeable about the tools they use, and their strengths and limitations, asking critical questions such as: What data is the tool trained on? How does a tool generate results? How does the tool respect the principles of privacy and intellectual property? Are the results reliable and useful? Is the tool transparent about all facets of its operation? Library professionals must approach AI tool usage with a critical stance before relying on and integrating their results in EBP. In addition, AI tools can be used to communicate about the results of EBP, and library and professionals need to make choices about responsible use of AI tools for any number of communication tasks, such as summarizing prior literature, generating figures, and improving writing.

The decision to use AI tools for EBP in libraries should always be made with regard to respecting the importance of human judgment and decision making, which includes the deep professional expertise that library professionals bring to their roles, as well as an understanding of local needs and situations. AI tools can be helpers but not authors; their output can suggest possibilities for action but should not go unquestioned. Thus, the role of the human practitioner should be prioritized throughout the entire process of conducting and communicating about EBP.

In conclusion, AI literacy skills should be incorporated into library preparation and training for EBP. These skills can be cultivated through activities such as critiquing the strengths and limitations of different types of AI tools, experimenting with ways to implement them into EBP, and exploring different workplace situations and social issues that involve the use of AI in decision making. AI tools have enormous potential to augment EBP practice in libraries, if used responsibly and critically.

References

Hervieux, S., & Wheatley, A. (2024). Building an AI literacy framework: Perspectives from instruction librarians and current information literacy tools. Choice. https://www.choice360.org/research/white-paper-building-an-ai-literacy-framework-perspectives-from-instruction-librarians-and-current-information-literacy-tools

Koufogiannakis, D., & Brettle, A. (2016). Being evidence based in library and information practice. Facet Publishing.

Lo, L. S. (2025). AI literacy: A guide for academic libraries. College & Research Libraries News, 86(3), 120–122. https://doi.org/10.5860/crln.86.3.120

Sackett, D. L., Rosenberg, W. M. C., Muir Gray, J. A., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: What it is and what it isn’t. The BMJ, 312(7023), 71–72. https://doi.org/10.1136/bmj.312.7023.71