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Vol. 46 No. 4 — Jul/Aug 2026

FEATURE

In my role as the health sciences liaison at Hofstra University, I approach AI use from a neutral and practical perspective. Rather than advocating for or against its use, I aim to help support AI literacy and transparency across the academic community. AI literacy means helping students and faculty members to understand how AI tools work, recognize AI’s limitations, and critically engage with AI-generated material. Part of this critical engagement is exploring how AI can support learning while maintaining ethical integrity and professional responsibility. My philosophy of librarianship has always centered on the idea that librarians serve their communities as mentors and guides. Librarians help students and faculty members navigate new information landscapes. AI is simply the latest technology development. 

Health science education is founded upon rigor, reflection, professionalism, and evidence-based reasoning. Students learn not only how to gather relevant information, but also how to evaluate it and apply it responsibly in ways that may affect patient care. Because of this, questions about AI use are also questions about professional judgment. What is appropriate use? How does an AI user maintain their academic integrity? Librarians can help address these questions without prescribing strict rules. Instead, we can provide tools and guidance to help students and librarians think more clearly about how AI fits into their work and how they can ethically use AI tools. 

Flexibility as a Core Librarian Skill

The emergence of gen AI into the mainstream is not the first time that librarians had to show flexibility and reinvent traditional ideas of librarianship. The arrival of the internet and tools such as Google and Wikipedia presented new challenges and opportunities, and they changed the way that information was accessed, evaluated, and shared. Large amounts of information were available to students and faculty members in ways they had never been before. This created a trade-off: People were suddenly able to quickly find information, but that information was not always reliable or trustworthy. 

Librarians responded by adaption and innovation. We guided students through an evolving digital landscape by explaining how it worked and teaching critical evaluation skills. We transferred old ways of assessing information onto new ways of accessing it. We also continued to reinforce the ideas of responsible information use. After all, flexibility has always been a core part of librarianship. Many librarians will recognize this in the Searching as Strategic Exploration standard from ACRL’s Framework for Information Literacy for Higher Education, which describes searching as an evolving, nonlinear process.

AI in Action and Reflection

Often, students and faculty members view AI as a productivity booster. The common conception of gen AI use involves digesting content faster or producing content quickly. Educators find this problematic and worry that AI use limits critical thinking and full skill development. However, the real issue is when students passively accept gen AI outputs instead of critically engaging with the material. This can become particularly problematic for health science students who need to learn how to interpret evidence and apply that to patient care. AI is just one tool in the toolbox, and the focus should be on teaching them how to use it thoughtfully. Educators need to concern themselves not just with worrying about whether their students are using AI, but whether students are learning how to critically engage with AI as an information tool. 

As librarians, one of the best ways we can serve students is by helping them learn to better evaluate digital information by thinking about the tools they are using and reflecting on their own process. In AI literacy sessions, we can encourage such questions as, “Why am I using this tool?” “Can I trust what it gave me?” and “What am I learning?” Activities that support this kind of reflection might include quick written or verbal responses, checklists for verifying AI-generated information, or comparing AI outputs with sources found in library databases.

Discovering Labrague’s Framework

While reviewing AI literacy literature, I came across Leodoro J. Labrague’s “A Five-Tier Framework for Guiding Responsible AI Use in Nursing Students’ Coursework: A Faculty Guide” in the January 2026 issue of Teaching and Learning in Nursing. What stood out immediately was how useful the framework is. Labrague offers a practical way for nursing faculty members to think about student AI use. Instead of approaching AI as a tool that should be outright banned or embraced, the framework places AI use along a spectrum. Different assignments can use different levels of AI involvement. Instructors ought to clearly state expectations accordingly.

Labrague’s framework outlines five levels of AI engagement: restricted, independent, guided, collaborative, and supervised. Each one represents a different level of interaction with AI tools and a different level of instructor management. For example, some assignments may expect fully independent student work, such as exams or reflective writing. Others may allow AI use for brainstorming or clarification. The framework encourages instructors to match expectations of AI use with the learning objectives of a specific assignment. It also provides students with guidance on how to use AI transparently and ethically, according to the instructor’s direction. The strength of the framework is its flexibility. It gives instructors a way to match AI expectations with an assignment’s learning objectives while emphasizing accountability and critical thinking. 

Adapting the Framework Beyond Nursing

That said, the framework was developed specifically for nursing faculty members. As a health sciences liaison working with multiple programs, I began to consider how well this approach would translate across disciplines and how it might be adapted to include a librarian role. Many of the same questions about AI use—such as what is appropriate, what counts as original work, and how to verify AI outputs—arise across the health sciences, not just in nursing. Labrague’s framework did not need to be replaced. Instead, I wanted to expand the core idea of aligning AI use with learning objectives to include other health-related programs, such as public health, physician assistant studies, and mental health counseling. I saw an opportunity to incorporate the role of librarians more clearly, particularly in supporting evaluation, verification, and information literacy. The challenge was making the language and examples broad enough to fit those contexts. 

In adapting the framework, I made a few intentional changes. I moved away from discipline-specific examples and toward language that reflects shared practices across the health sciences. This included working with scholarly sources, interpreting evidence, and applying professional judgment. These changes allow the framework to be used in a wider range of classrooms. This shift also created space to more clearly integrate the role of librarians to work alongside faculty members and students. In many ways, this is how librarians fit into the AI literacy conversation. We already teach about evaluating information, using databases, and thinking critically about sources, and AI does not change that. Instead, it just becomes another source of information and another student tool. In addition, I placed more emphasis on verification and transparency using reliable sources such as PubMed and CINAHL. 

I also simplified the categories to make them more readily applicable to everyday teaching and learning contexts. The result is a framework that keeps Labrague’s original strengths of flexibility and focusing on ethical use to make it more applicable across disciplines. More importantly, it shifts the conversation slightly. Instead of focusing only on what students are allowed to do with AI, it also helps us think about how they can use it responsibly and thoughtfully. 

Putting the Framework Into Practice

This framework was so compelling to me because it is easily applied to everyday library work. Rather than being a theoretical model, it provides a practical way to guide conversations about AI use across multiple contexts. In my own work as a health sciences liaison, I have started to incorporate this framework in several ways. When working with faculty members, it offers a shared language for discussing the expectations about AI use in assignments. Instead of centering the conversation around whether AI should be allowed or prohibited, we can focus on how it can be used in alignment with learning objectives. This makes those conversations less reactive and more productive.

I’ve also begun using the framework in instruction settings. I am currently developing an introductory, one-credit library class and plan to use the framework to introduce AI to students as an information tool rather than as a shortcut. It helps frame discussions around when AI supports learning and when AI interferes with it. In one-shot information literacy sessions, I can use the framework to introduce the idea of various levels of AI use to help students understand expectations and reflect on their own practices.

The framework is also useful in one-on-one interactions with students. When students ask if they can use AI for a specific assignment, the framework guides that conversation. I can help them think through how they are using AI, what their instructor expects, and how to verify and use information responsibly. Beyond instruction, I see this framework as a valuable addition to online resources. It can be easily incorporated into libguides, and I am specifically working on an AI in Healthcare libguide that this would naturally supplement. Having this framework reinforces the role of the library as a place not just for finding information, but for understanding how to use emerging tools thoughtfully. 

Where Librarians Fit In

AI is not a passing trend. Whether educators like it or not, it has quickly become a part of how students learn, write, and engage with information. For librarians, this shift raises important questions and provides new opportunities. The work of academic librarians—teaching students to evaluate sources, use databases, and critically think about information—is particularly relevant to increasing student AI use. These skills are even more important now. AI tools may be able to generate information quickly, but they do not replace the distinctly human ability to evaluate that information through critical engagement. Frameworks like the one I adapted help provide clear guardrails and expectations. They focus on responsible and thoughtful engagement. 

As AI continues to evolve, so will the ways it appears in academic work. Flexibility is essential. Rather than trying to keep up with every new tool, librarians can focus on guiding how those tools are used. By helping students ask better questions, verify what they find, and reflect on their own learning, librarians support academic integrity. For health science librarians, this also helps to direct the development of professional judgment when using AI tools. Helping students with these tools is where librarians make the greatest impact.

Resources 

“Searching as Strategic Exploration,”  ala.org/acrl/standards/ilframework#exploration.

“A Five-Tier Framework for Guiding Responsible AI Use in Nursing Students’ Coursework: A Faculty Guide,”  doi.org/10.1016/j.teln.2025.09.012.