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Subject bibliography on Generative AI literacy in secondary school libraries

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Subject bibliography on Generative AI literacy in secondary school libraries

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This bibliography presents ten key resources to develop a training resource for secondary-school librarians teaching students Generative AI (GenAI) literacy.

It includes peer-reviewed articles, practitioner literature, policy guidance and practical teaching materials. These resources demonstrate the current state of research, practice, and approaches to GenAI in schools, uncovering gaps in GenAI literacy for secondary schools specifically, and the emerging role of school librarians in supporting critical and ethical use of GenAI.

Most of the current practice-based research on GenAI in school libraries comes from Australia (including several studies from the same university) and North America, confirming the limited scope of the current research on the topic.

Each entry includes an annotation evaluating its relevance, authority and contribution to the theme.

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Foote, C. (2024) ‘AI and School Libraries.’, Computers in Libraries, 44(5), pp. 32–34. Available at: https://research-ebsco-com.ezproxy.rgu.ac.uk/c/hin62q/viewer/pdf/cla3o3bydf?route=details (Accessed: 15 November 2025)

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Harrison, A. (2025) ‘Inquiry, AI, and Pedagogy in the Library Classroom’, Virginia Libraries, 69(1), pp. 1-8. Available at: DOI: 10.21061/valib.v69i1.691

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Garrison, K. and Oddone, K. (2025) ‘“It Is a Skill Everybody Needs to Learn”: Australian Teacher Librarians Steering Secondary Schools through Shifting Information Landscapes’, Journal of Information Literacy, 19(1), pp. 48–68. Available at: doi: https://doi.org/10.11645/19.1.657

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“It is a skill everybody needs to learn”: Australian teacher librarians steering secondary schools through shifting information landscapes | Journal of Information Literacy

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There is limited research available that investigates current information literacy (IL) needs of Australian secondary school students and teachers from the perspective of teacher librarians. This phenomenological research responds to this problem by exploring the perspectives of Australian secondary teacher librarians regarding the changing information landscape and its effects on supporting IL for students and teachers— “a skill everybody needs to learn” as our participant Mrs. M. highlighted. Data included from interviews with 19 secondary teacher librarians with varying years of teaching experience in different types of schools from across the country. Findings suggest the overwhelming amount of information available means users must develop better evaluation and selection skills in addition to an understanding of the differences among information sources like search engines, social media, and AI. The teacher librarians described similar issues for students and teachers, both struggling to keep up with the rate of changes and unaware of their knowledge gaps. At the same time, the shifting and expanding information environment presents opportunities for deeper discussion and engagement around issues like information ethics and copyright as well as the development of creative and critical thinking skills. This research contributes to a clearer understanding of the needs of secondary students and teachers as information users and demonstrates the diverse approaches teacher librarians employ to foster IL amid a rapidly shifting and unprecedented information context.

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Oddone, K. (2024) ‘Becoming AI Ready: A Collaborative Journey for Teachers and School Library Staff.’, Access, 38(3), pp. 16–20. Available at: https://search.informit.org/doi/epdf/10.3316/informit.T2024122700004491413904508(Accessed: 28 November 2025)

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Seales, D. L. I. (2024) ‘ADVENTURES WITH AI in the School Library.’, Computers in Libraries, 44(7), pp. 30–34. Available at: https://research-ebsco-com.ezproxy.rgu.ac.uk/c/hin62q/viewer/pdf/tbcflurunn?route=details (Accessed: 27 November 2025)

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Wong, E.K.C. and Chiu, D.K.W. (2025) ‘AI literacy instruction program in international school libraries: A qualitative study under the lens of the Big Six Information Literacy model’, Journal of Librarianship and Information Science, 0(0). Available at: doi:10.1177/09610006251331977

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Seales, D. L. I. (2025), Leveraging AI in school libraries: from basics to best practices, ALA Editions, Chicago.

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Awards, publishing, and conferences: ALA membership advocates to ensure access to information for all

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Poremba, M. (2025) 'The Proof is in the Process: Incorporating Generative AI into Student Research Projects', Canadian School Libraries Journal, 9(2). Available at: https://journal.canadianschoollibraries.ca/the-proof-is-in-the-process-incorporating-generative-ai-into-student-research-projects/?utm_source=chatgpt.com (Accessed: 21 November 2025)

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By Melissa Poremba
School Librarians are the Information Experts

For decades, all school librarians have been saying the same thing: our 'brand' is more than books - it is information, regardless of its format. This foundation perfectly positions us to guide students through the intricacies of the modern information landscape, which now includes generative artificial intelligence (GenAI). Data from the Digital Education Council Global AI Student Survey (Digital Education Council, 2024) found that 69% of students report using GenAI for information searching, yet 58% admit they don't feel confident in their GenAI knowledge and skills. This gap is our opportunity! School librarians are not just positioned to teach information literacy - it is our mandate ( Armstrong et al., 2023; Asselin et al., 2003). Our expertise in guiding students through the complexities of information evaluation, source analysis, and ethical use is more critical than ever in a GenAI-enhanced world.

GenAI Literacy: The New Kid on the Block

It is important to note that generative AI is fundamentally different from the legacy or traditional AI that we have been exposed to for a number of years. A useful analogy is that legacy AI, such as GPS, recommendation systems or industrial robots, is like playing in an orchestra where one relies on sheet music and the conductor, so the outcome is predictable and rule-bound; however, generative AI, such as ChatGPT or Midjourney, is more like jazz improvisation: dynamic, creative, and sometimes unpredictable ( Autor, 2024). Therefore, while there are already documents available for teaching AI literacy (for example see: British Columbia Ministry of Education and Childcare, 2024; School Library Systems of New York, 2024), GenAI literacy requires specific skills. According to the Modern Language Association of America (2024) the following are some of the skills that one must possess to be GenAI literate:

a basic understanding of how GenAI technologies work

can distinguish between AI and GenAI

credit GenAI contributions in your work through appropriate citation or attribution

discuss your process transparently with your instructors and peers

use flexible strategies to adapt to new and evolving GenAI tools

evaluate the relevance, usefulness, and accuracy of GenAI outputs

monitor your own learning as you use GenAI tools

understand the potential harms of GenAI, both those inherent to the technology and those that arise from misuse.

The good news is that most school libraries already have scope and sequence documents which address research skills within information literacy skill instruction, so now they can incorporate these new GenAI literacies into the existing frameworks. However, a unique challenge is that most GenAI tools have age restrictions that must be respected. For students over 13 years of age, parental permission is typically required. Onboarding everyone simultaneously - teaching similar concepts to both younger and older students - adds another layer of complexity, although eventually, AI literacy will be embedded more naturally within the existing curriculum. Creating and implementing this new GenAI literacy curriculum is also challenged by each school's unique IT ecosystem, policies, and varying levels of staff comfort. Many staff and students are hesitant about GenAI, due to legitimate concerns about bias, hallucinations (when the AI makes up information), deepfakes, academic integrity, misinformation, the use of personal information in training data, copyright, and intellectual property rights. Research from Carroll and Borycz (2024) suggests that librarian-led information literacy training helps students develop a more nuanced understanding of AI's limitations and best uses in inquiry-based work. The Canadian Association of Research Libraries' updated Digital Literacy Framework echoes this, noting that the "transformational impact of AI on the information environment adds new complexities, underscoring the need for students to be taught digital literacy" and that "libraries are uniquely positioned to teach digital literacy to our students" (2024). Thus, the skills and knowledge of school librarians are essential to better equip students with the ability to understand how GenAI tools create information, their potential problems, and how to critically evaluate their output.

The Right Tool for the Task

A critical component of AI literacy involves assessing the tools themselves as existing ones are constantly updating and more are constantly emerging. There are options specifically designed for education, such as FlintK12, SchoolAI, and MagicSchool, which offer features tailored to teachers and students, including data privacy, educational guardrails, and administrator oversight. These platforms provide purpose-built educational tools and research activities that allow teachers to monitor and assess student interaction.

While ChatGPT has become almost synonymous with GenAI, school librarians can direct students to many other publicly available tools. To address concerns about hallucinations, students can improve the reliability of responses by using "grounded" tools such as Perplexity, Google Gemini, and Microsoft Co-Pilot. These tools can incorporate web search results into their responses alongside their training data. Some also offer 'deep research' options where students can capitalize on the opportunity to observe as the tool identifies and performs each step in its 'thinking.' "You can see that the AI is actually working as a researcher, exploring findings, digging deeper into things that 'interest' it, and solving problems" ( Mollick, 2025).

There is an entire of class of GenAI-powered research tools such as Scite, Consensus, Research Rabbit, and STORM from Stanford designed specifically to assist with research. While most have age requirements that high school students won't meet until graduation, school librarians can demonstrate them to introduce students to what will be available in post-secondary education. Teachers may be enticed to use these tools for their own research during AQ courses, PD, or other continuing education programs; NotebookLM is particularly useful for these applications. One tool designed specifically for research, Elicit, can be used by students over 13 with parental permission. Engaging with and analyzing its feature-rich interface can show students some of the more advanced techniques in gathering, vetting, and organizing information. (For a comparison of traditional library databases with AI-powered academic search tools, see Texas A&M University Libraries, 2025).

As stated, students must learn to evaluate each tool's strengths, weaknesses, and suitability for different tasks ( Hervieux & Wheatley, 2020). Indeed, sometimes the best course of action is a combination of tools. Academic libraries have been proactive in curating this ever-evolving landscape, regularly updating tool reviews and support materials - resources that school librarians can adapt for their own communities. (For excellent examples, see: Georgetown University Library, 2025; McGill Libraries, 2024; University of Alberta Library, 2025; University of Waterloo Library, 2025. For instructional videos for students, see: University of Calgary Libraries and Cultural Resources, 2024. For a more academic treatment, see Archambault & Rincón, 2024.)

Academic Integrity: It's About the Task, Not the Tool

The conversation around GenAI and academic integrity is nuanced. Most teachers are comfortable with students using tools for spelling and grammar correction, but many of these now offer features that can substantially rewrite or even generate entire assignments. However, the real issue often isn't the tool itself, but the function it performs.

A helpful analogy for students is comparing the use of GenAI tools to using tools like park assist, a calculator, or an escalator. There is nothing inherently wrong with using them, unless the student is being assessed on their driving, numeracy skills, or fitness level in climbing stairs. A crucial part of AI literacy is knowing when to use AI and, just as importantly, when not to use it. This distinction requires teachers to be very intentional and clear about the skills they are teaching and assessing. The AI Assessment Scale (Perkins et al., 2024) is a tool that can help students understand that using GenAI is not necessarily an 'all or nothing' decision in schoolwork. Ultimately, whether students can use GenAI depends on the teacher's judgment regarding the skill being taught and assessed. Further, any use of GenAI must be acknowledged, and school librarians and teachers will need to provide templates and guidelines for how to do this.

Assessment: It's About the Journey

Despite efforts by teachers to delineate the acceptable extent of GenAI assistance, students can succumb to the temptation to let GenAI do the work, especially for writing tasks like research reports. This issue makes it even more important for teachers to collaborate with school librarians to design research assignments that emphasize the process of research rather than the traditional final written product.

This focus on process represents a newer paradigm for both teachers and students and necessitates increased focus on the steps and stages involved, particularly in documentation. Teachers need to demonstrate techniques for collecting, analyzing, and interacting with research materials at different points in the process. This documentation becomes critical if a teacher suspects unauthorized AI generation when a final report is assigned. It is also necessary to teach students to be transparent in their use of GenAI. This goes beyond simple citations; while much attention has been given to citing AI-generated information, students should first consider whether they should be citing it at all unless they have verified and interacted with the original source.

As mentioned, instead of evaluating only a final report, teachers can consider assessment strategies that capture the process. These could include:

interviews with students, querying them about their research

oral presentations, demonstrations, or poster sessions where students present findings and defend their position

research portfolio compilation

creation of a product (website, video, infographic, fact sheet, podcast) that demonstrates their process and findings

peer-assessment

resource analysis (verify every source referenced by the GenAI tool to ensure existence and quality)

concept mapping

reflection papers or journals commenting on the research process, challenges, and learning.

What constitutes 'research' varies depending on the students' age and stage, as well as the academic discipline. Shifting to assessment of the process or journey aligns with the principle that in K-12 education, uses of GenAI should always begin with human inquiry and conclude with human reflection, insight, and empowerment ( Washington Office of Superintendent of Public Instruction, 2024). By having students engage with GenAI output, we can empower them to discover its limitations for themselves rather than being lectured to about the dangers. Some strategies to consider are:

assign students a research topic where GenAI output might reasonably be expected to be biased, have them experiment with prompting to obtain results, and then critically analyze the output by referencing other sources.

assign students to research a topic using traditional methods and sources, or after class lessons, and then research the same topic using GenAI and critically evaluate the differences.

provide a rubric the teacher would use for student research projects and have students prompt and grade AI-generated output using the rubric to evaluate its performance.

require students to compare resource suggestions or output results from different GenAI tools, as well as with subscription databases and print resources.

have students construct citations for resources themselves, then compare them to citations created by GenAI, and to those made by citation management tools such as MyBib or Zotero.

students have been taught not to use full questions when searching traditional interfaces such as a library catalogue, subscription database, or search engine, but now they can use natural language when querying GenAI tools thus they could experiment with the output obtained by interchanging tools and techniques.

While devising such new approaches represents a paradigm shift for school librarians, an ironic twist is that GenAI tools, particularly those designed for education, can be prompted for suggestions on how to use GenAI in the research skill being covered, including being asked to devise an assessment tool or rubric.

The Research/Inquiry Process

While there are various models for research/inquiry, they all share basic steps ( Canadian School Libraries, 2022). The interactive nature of research connects well with the fact that students often need to experiment with prompts to get the best response. Prompt engineering is an important new skill students need to learn. Many GenAI tools now feature the ability to suggest related or refined prompts, further aiding this iterative process.

GenAI is particularly effective at helping students brainstorm research questions or project ideas. By providing 039text or a topic, students can prompt GenAI tools to suggest various questions, approaches, or methods which can accelerate the initial stages of research and inspire novel directions. For example, asking GenAI for research questions on a topic can quickly generate a list for refinement. While GenAI can hallucinate, it also often provides creative suggestions that students might not conceive on their own.

GenAI-powered tools can help students find open-access academic articles, summarize key points, and suggest other relevant sources such as books, videos, and websites. Tools such as Elicit, Consensus, and Connected Papers are specifically designed to search scholarly databases and recommend or synthesize articles. GenAI can also aid in developing a key skill for students, learning to read scholarly peer-reviewed articles by providing clarifications, analogies, definitions, quizzes, or summaries. These tools can save time and help students explore a wider range of research more efficiently than manual searching alone.

For many teachers and students, their key concern is that GenAI tools can hallucinate and invent sources. However, verifying sources is a skill school librarians have always taught; this is simply a new application of established principles like SIFT or CRAAP. CTRL-F by CIVIX offers excellent, ready-made lessons for teaching this skill.

It is vital for students to understand that articles in subscription databases are not accessible to GenAI tools, even research-focused ones. Thus, it is still critical that school librarians continue to teach traditional database searching methods. It is, however, still possible to include GenAI in suggesting keyword strings and Boolean operators for the search. Most database vendors prohibit downloading articles for uploading to GenAI. However, these vendors are developing their own interactive GenAI tools supported by Retrieval Augmented Generation (RAG), with features like natural language searching and article querying. JSTOR, for instance, has such a tool; EBSCO had a beta program in 2024 with the promise of AI Reference Assistance and Literature Review in 2025.

GenAI tools may even open up new opportunities for students working on research projects. Tools like Otter.ai can facilitate personal interview research by helping with content collection and analysis. Knowledge translation, an important skill, can be aided by GenAI tools that rewrite content for different audiences or reading levels. If a final research report is required, a GenAI tool can act as a peer assessor, providing feedback on the writing. With the efficiency gained from GenAI-assisted retrieval, secondary students might even undertake more literature reviews than previously possible.

With GenAI becoming even more ubiquitous, even a simple Google search results in students being supplied with an 'AI Overview.' Adobe Acrobat and Google Scholar have also added extensions to allow GenAI-powered interactive chats and article outlines. At whatever point students encounter GenAI output when searching for information, it is essential that teachers remind them to engage with the output by analyzing, revising, questioning, investigating, and comparing it.

Considerations

The main advantage of using generative AI in research is its potential to improve efficiency and productivity and automate so-called tedious tasks like summarizing, organizing materials, finding sources, and data analysis ( Khalifa & Albadawy, 2024). However, school librarians must pause and ask: are 'efficiency' and 'productivity' our primary goals in K-12 education? Do we still need to ensure students can complete research tasks in the traditional, sometimes 'hard' way? These questions must be addressed when developing GenAI scope and sequence guidelines. It seems reasonable to ensure students are well-versed in all forms of information access, new and old, so school librarians and teachers need to balance their instruction even though some may not be entirely comfortable yet.

Perhaps the major issue is how will students acquire the foundational skills needed to critically assess GenAI output? As suggested earlier, it might be helpful to consider introducing these tools after students already know the answer or are capable of judging the output. Let them use GenAI as a source after they've researched using lessons, textbooks, or traditional sources. Assign a topic known to produce biased AI output. Give students the rubric for a research report and have them apply it to one generated by AI.

Another approach is to have students make GenAI the focus of their research. Students can investigate the resulting environmental issues, underlying business models, ethical considerations, privacy issues, labour exploitation potential, job elimination or restructuring, climate change mis/disinformation spread, use in war/conflict, spread of misogyny, impact on scholarly research and publication, or the increase in the number of young people turning to GenAI tools for companionship/advice. (For excellent suggestions, see Furze, 2023.) Letting students explore GenAI themselves allows them to reach their own conclusions about its costs versus benefits and discover its limitations firsthand, rather than simply being told about them.

Conclusion

GenAI serves as a valuable tool for inspiration and exploration, but human judgment and verification remain essential in academic research. School librarians are uniquely positioned to guide students through this evolving landscape, teaching them to navigate information ethically and effectively. Rather than viewing generative AI as a threat, school librarians can embrace it as an opportunity to reaffirm their role as information experts, equipping students with the critical literacy skills needed for the 21st century. As education adapts to the rise of GenAI, school librarians can empower students and teachers to approach the complex information landscape with confidence, curiosity, and integrity. While the tools may change, the core mission endures: fostering literate, critical, and creative thinkers ready for the future, with school librarians leading the way in preparing every student to thrive in the age of GenAI.

Acknowledgements

The author wrote this paper based on the research she conducted for her presentation at the OLA Super Conference 2025. It was edited with suggestions from three colleagues (J.J., S.H., and R.B.) and the GenAI tool, NotebookLM.

References

Archambault, S. G., & Rincón, J. (2024). An evaluation of cutting-edge AI research tools using the REACT Framework. Computers in Libraries, 44(8), 4-11. https://www.infotoday.com/cilmag/oct24/Archambault-Rincon-An-Evaluation-of-Cutting-Edge-AI-Research-Tools-Using-the-REACT-Framework.shtml#Tables

Armstrong, L., Brooks Kirkland, A., Jeffery, J., Koechlin, C., Mulcaster, M., & Sykes, J. (2023). Foundations for school library learning commons in Canada: A framework for success. Canadian School Libraries. https://www.canadianschoollibraries.ca/wp-content/uploads/2023/11/CSL_Foundations-Frameworks_FINAL_Nov2023.pdf

Asselin, M., Branch, J. L., & Oberg, D. (Eds.) (2003). Achieving information literacy: Standards for school library programs in Canada. Canadian Association for School Libraries. https://accessola.com/wp-content/uploads/2020/08/2003-AchievingInfoLiteracy.pdf

Autor, D. (2024, February). Applying AI to rebuild middle class jobs. National Bureau of Economic Research [Working paper]. https://www.nber.org/system/files/working_papers/w32140/w32140.pdf

British Columbia Ministry of Education and Childcare. (2024). Curriculum connections: Digital literacy and the use of AI. https://www2.gov.bc.ca/assets/gov/education/administration/kindergarten-to-grade-12/ai-in-education/curriculum-connections-digital-literacy-and-the-use-of-ai.pdf

Canadian Association of Research Libraries. (2024, April). Digital literacy framework. https://www.carl-abrc.ca/wp-content/uploads/2024/06/DLF_April2024_EN.pdf

Canadian School Libraries. (2022, June). Canadian resources to support inquiry learning. https://www.canadianschoollibraries.ca/canadian-resources-to-support-inquiry-learning/

Carroll, A. J., & Borycz, J. (2024). Integrating large language models and generative artificial intelligence tools into information literacy instruction. The Journal of Academic Librarianship, 50(4), 102899. https://doi.org/10.1016/j.acalib.2024.102899

Digital Education Council. (2024). Digital education council global AI student survey. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024

Furze, L. (2023, January 26). Teaching AI ethics. Leon Furze. https://leonfurze.com/2023/01/26/teaching-ai-ethics/

Georgetown University Library (2025, February 2). Artificial intelligence (generative) resources: AI research tools. https://guides.library.georgetown.edu/ai/tools

Hervieux, S. & Wheatley, A. (2020). The ROBOT test [Evaluation tool]. The LibrAIry. https://thelibrairy.wordpress.com/2020/03/11/the-robot-test

Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145

McGill Libraries. (2024, August 16). Artificial intelligence: Using AI tools for research. https://libraryguides.mcgill.ca/ai/plan

Modern Language Association of America. (2024). Student guide to AI literacy. MLA Style Center. https://style.mla.org/student-guide-to-ai-literacy/

Mollick, E. (2025, February 3). The end of search, the beginning of research: The first narrow agents are here. One Useful Thing. https://www.oneusefulthing.org/p/the-end-of-search-the-beginning-of

Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(06), Article 06. https://doi.org/10.53761/q3azde36

School Library Systems of New York. (2024, December 2). AI scope and sequence: PreK-12 concepts for teaching about AI. https://libraryready.ai/wp-content/uploads/2024/12/AI-Scope-and-Sequence.pdf

Texas A&M University Libraries. (2025, April 25). Selected AI-based literature review tools. https://tamu.libguides.com/c.php?g=1289555

University of Alberta Library. (2025, March 13). Using generative AI: Which AI tool should you use? https://guides.library.ualberta.ca/generative-ai/which-tool

University of Calgary Libraries and Cultural Resources. (2024, September 20). Generative AI tools for research. https://libguides.ucalgary.ca/genairesearchtools

University of Waterloo Library. (2025, May 12). ChatGPT and generative artificial intelligence (AI): AI tools for research. https://subjectguides.uwaterloo.ca/c.php?g=734164&p=5340288

Washington Office of Superintendent of Public Instruction. (2024, July 1). Human-centred AI: Guidance for K-12 public schools. https://ospi.k12.wa.us/sites/default/files/2024-08/comprehensive-ai-guidance.pdf#page=51.10

Melissa Poremba, BA, BMath, BEd, LIT Dipl., MLIS. Starting her career as a high school math teacher, Melissa Poremba transferred into library work after volunteering at her children's school library. She has worked in academic, public, and school libraries and is currently the Senior School Librarian at Hillfield Strathallan College, a PreK-12 independent school in Hamilton, ON. She thanks her colleagues in the Science and Social Science departments at HSC, especially J.P.B., for collaborating with her on combining GenAI literacy with traditional information literacy in student research projects. While she loves working on and talking about the topic, she sincerely hopes GenAI is the last disruptive technology she must master before retirement!

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The Proof is in the Process: Incorporating Generative AI into Student Research Projects

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By Melissa Poremba For decades, all school librarians have been saying the same thing: our 'brand' is more than books - it is information, regardless of its format. This foundation perfectly positions us to guide students through the intricacies of the modern information landscape, which now includes generative artificial intelligence (GenAI).

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European Commission and OECD (2025) Empowering learners for the age of AI: An AI literacy framework for primary and secondary education. Review draft (May 2025). Available at: https://ailiteracyframework.org/wp-content/uploads/2025/05/AILitFramework_ReviewDraft.pdf (Accessed: 12 November 2025)

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In her article, Foote explains how school librarians are beginning to work with generative AI, outlining the main opportunities they can support (e.g. helping students create better prompts) and describes the key risks of GenAI (e.g. bias, misinformation). Her article draws on interviews with various experienced professionals, including librarians and educators. The paper offers practical insights but mostly focuses on the US context and only provides limited empirical evidence.

Although the article is not peer-reviewed, the source is authoritative: Computers in Libraries is a long-established professional journal. Carolyn Foote is a multiple award-winning school librarian who focuses her work on libraries and technology.

The content is relevant, as it directly addresses school librarians’ responsibilities and the challenges students face when using GenAI tools. She also works as a high school librarian, making her perspective valuable to the secondary school context.

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Harrison's peer-reviewed article examines how librarians can incorporate AI into "inquiry-based learning". She uses the eight-stage Guided Inquiry Design process to show how AI tools can support questioning, research, creation etc. She states that librarians can support and guide students in using AI responsibly by helping them understand its potential and limits while still focusing on inquiry-based learning. Her analysis is conceptual, but it gives a good structure that librarians can use when planning AI activities.

Harrison is an assistant professor in Library & Information Sciences at the University of Central Missouri and a former school librarian. She also holds a teaching degree and focuses her research on school libraries, making her work an authoritative source for the topic.

The paper is relevant to the topic because it links AI literacy to an established instructional model already used in school libraries.

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Oddone, K., Garrison, K. and Gagen-Spriggs, K. (2024) ‘Navigating Generative AI: The Teacher Librarian’s Role in Cultivating Ethical and Critical Practices’, Journal of the Australian Library and Information Association, 73(1), pp. 3–26. Available at: doi:10.1080/24750158.2023.2289093

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This paper discusses how Australian secondary teacher librarians support information literacy (IL), leaning on interviews with experienced librarians. It discusses students’ evolving IL needs and how librarians adapt their support in a rapidly changing information environment.

This article is published in the Journal of Information Literacy, a peer-reviewed academic journal. Both authors are academics at Charles Sturt University, a leading institution for school-librarianship in Australia. Garrison is a Senior Lecturer and Associate Head of School for Research, and Oddone holds a lecturer role and manages the Master’s programme in Teacher Librarianship. Their research and work in/on school libraries make them a reliable source.

Although the focus is broader than AI-literacy instruction in school libraries, it addresses issues raised by AI (such as copyright and misinformation), and informs which priorities and challenges should be considered when developing AI-literacy instruction, making it a relevant resource for the topic.

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Oddone's article explains how school librarians and teachers can work together to prepare students for a future where GenAI tools are widely used. She emphasises the importance of teaching AI literacy and positions the school library as a key place where students can learn how to evaluate AI-generated information, understand engineering prompts and use AI tools responsibly. Oddone also discusses the ever-present challenges in a fast-moving environment (academic integrity, verification of AI content).

The article is peer-reviewed and published in Access, the professional journal of the Australian School Library Association. Kay Oddone's profile was discussed in resources 3 and 6, and was found to be authoritative.

Despite not having full-text access, the abstract and the author’s background and expertise make it more valuable than other previously filtered sources.

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The article also places school libraries at the centre of helping students navigate GenAI and integrate it into teaching and learning. The goal is to support GenAI literacy so that students use these tools ethically, responsibly and critically. The authors use an exploratory approach and apply the CATWOE model to consider how teacher-librarians could teach both with and about generative AI.

The article is published in Access, the professional-reviewed journal of the Australian School Library Association. Kay Oddone (see resource 3) collaborates with Garrison and Gagen-Spriggs, colleagues and researchers in education and learning technologies at Charles Sturt University. Taken together, these factors make the article a credible and contextually relevant source on AI in school libraries.

Published in early 2024, soon after GenAI tools surfaced in schools, the article offers an early, mainly conceptual discussion of teacher-librarian responsibilities in the GenAI era.

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Wong and Chiu's study examines how librarians from two international schools and Grade 12 students view AI literacy. Drawing on interviews, they identify how students currently use GenAI (e.g. a primary information source) and what challenges they face, mostly around accuracy and verification. Using the Big6 information literacy model, the article identifies what elements librarians consider essential when designing AI literacy instruction (e.g. fact-checking, evaluating AI outputs).

The article is published in the academic journal Information and Learning Sciences. The second author Chiu is a lecturer at the University of Hong Kong in information science and has recognised research in AI-supported learning. Wong's institutional affiliation appears in the article although further biographical information is limited. His contribution is supported by the peer-review process and by collaboration with an established researcher, giving this article adequate credibility.

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This paper offers practical ways for school librarians to use GenAI when guiding students through research and inquiry tasks. Poremba gives concrete examples, such as using AI to explore topics, shape research questions, clarify ideas and compare AI responses with reliable sources to spot errors. Her approach treats AI as a helpful tool for planning and thinking, but always with careful checking.

The article was published in the Canadian School Libraries Journal, an established and professional journal. Melissa Poremba is an experienced secondary-school teacher librarian, providing practical credibility for understanding how GenAi tools are used in real school library settings.

Her paper is based on work she prepared for the OLA Super Conference 2025 and is supported by numerous academic references. It is more descriptive than empirical, grounded in her experience rather than formal research.

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This guideline provides practical examples that school librarians can integrate GenAI into instruction and library operations. Seales addresses key areas such as prompt engineering, ethical and critical use of GenAI, data privacy and plagiarism. She gives concrete ideas for improving instruction with AI tools and helping students develop AI literacy.

This source is produced jointly by the American Association of School Librarians (AASL) and the American Library Association (ALA) Editions, two major authoritative bodies in the field. Delandrus L.I. Seales is a teaching and learning librarian at the University of North Carolina-Wilmington, with over a decade of experience as a school librarian and teacher. She is pursuing a PhD and serves on various committees, all related to learning and technology, which gives her credibility.

Because this is a professional guideline, it relies on professional experience and is framed within the US context. Nevertheless, it remains a useful resource because it translates AI literacy concepts into concrete strategies that school librarians can adapt to their own settings.

Even without the full text, the publishers and the practical strategies make this a valuable resource.

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This draft framework from the European Commission sets out what AI literacy should look like for primary and secondary students. It presents four key areas: "Engaging with AI, Creating with AI, Managing AI and Designing AI", and provides concrete school examples (e.g. checking whether a chatbot's explanation is reliable, comparing model outputs).

This was produced by the European Commission, the Organisation for Economic Co-operation and Development (OECD), Code.org and supported by an international group of researchers and policymakers. As a joint initiative from major global organisations, it provides one of the most authoritative definitions of AI literacy in schools.

Although not library-specific, the framework includes many activities that librarians could use when teaching students GenAI literacy. As the only European framework identified in the search, it provides a more balanced global view of the topic alongside the predominantly US and Australian sources.

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Hossain, Z. (2025) ‘School librarians developing AI literacy for an AI-driven future: leveraging the AI Citizenship Framework with scope and sequence’, Library Hi Tech News, 42(2), pp. 17–21. Available at: https://doi.org/10.1108/LHTN-10-2024-0186.

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Hossain’s article proposes an AI Citizenship Framework to help librarians and educators develop students’ AI literacy from awareness to what he calls “AI citizenship”. The framework is broken into five stages: "AI awareness, familiarity, literacy, critical and ethical literacy, and citizenship". Each step comes with goals, skills, and practical ideas for librarians.

Hossain is a school librarian and an information literacy coach. The article is published in Library Hi Tech News, an Emerald professional journal. This resource has a strong practitioner authority, although it is based on the author's own experience and it is not peer-reviewed.

The framework is very relevant for the topic since it offers a structured progression and practical activities that school librarians can use when teaching GenAI literacy.

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Casal-Otero, L. et al. (2023) ‘AI literacy in K-12: a systematic literature review’, International Journal of STEM Education, 10(29), pp. 1–17. Available at: https://doi.org/10.1186/s40594-023-00418-7