Beyond Prompting: Why AI Literacy Is Not AI Expertise in Nursing

Chris Hickman
3 days ago
13 min read

Introduction

As a technologist who works closely with nurse educators, I’ve noticed a recurring misconception: the ability to use an AI tool (for example, writing clever prompts for ChatGPT) is often mistaken for true AI expertise. In today’s healthcare and academic settings, many nurses are growing AI-literate. They know how to get an AI to produce an answer or draft a document. But being fluent in prompting a chatbot is just a starting point. It’s akin to a nurse knowing how to quickly look up drug dosing guidelines; useful, yes, but not the same as understanding the pharmacology behind those medications. In nursing, a field where decisions have real consequences for patient care, we must clarify the distinction between using AI tools and truly understanding AI systems. This article explores why prompt skills are only the beginning of AI competence in healthcare, what real AI expertise looks like for nurses, and why being precise with our language about AI competence is critical for nursing education’s future.

AI Literacy vs. AI Expertise: Understanding the Difference

AI literacy generally refers to a foundational set of knowledge and abilities that allow a person to interact with, use, and critically evaluate AI as a non-expert user. One prominent definition describes AI literacy as “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool” in various settings. In other words, AI literacy is about what nurses need to know to responsibly understand and use AI at a basic level. It includes familiarity with AI concepts, awareness of common applications (like clinical decision support or chatbots), and an ability to interpret an AI’s output with a critical eye.

AI expertise, on the other hand, goes beyond the basics. It implies a deeper competency which includes what nurses need to do to apply and guide AI effectively and ethically in their roles. The National League for Nursing (NLN) has emphasized that we must differentiate between foundational AI knowledge and the more advanced skills required to actually implement and govern AI in practice. In summary, being “AI-literate” might mean a nurse knows about AI and can use an AI-driven tool, while being an “AI expert” (or AI-competent, in workforce terms) means the nurse can explain how an AI system works, understand its limitations, ensure its ethical use, and integrate it into workflows safely. The National Academy of Medicine has likewise called for developing an AI-competent healthcare workforce; not just people who can use AI, but professionals who can lead its effective integration into healthcare. The World Health Organization echoes that sentiment, warning that AI is advancing so rapidly that we urgently need knowledgeable health professionals who grasp documentation, data quality, validation, and other under-the-hood aspects of AI tools. In short, knowing how to drive an AI tool is different from knowing how the engine works and how to steer it responsibly.

Prompting: A New Basic Skill, Not the Finish Line

There’s no doubt that “prompting” (the skill of crafting inputs to get useful outputs from AI) has become a hot topic. In nursing education circles, workshops on AI prompt engineering are popping up, and faculty are sharing tips on how to coax better answers from ChatGPT. This skill is quickly becoming part of digital literacy for nurses, much like learning to navigate an electronic health record or use a clinical database. In fact, some educators have likened prompt fluency to a new literacy for the future. Being adept at prompting is valuable: it can help nurses save time on documentation, generate lesson plans or patient education materials, and explore clinical scenarios in simulations. It’s a bit like being proficient at using a medical search engine or a drug reference app – a useful competency that enhances one’s efficiency.

However, prompting isn’t enough. Just as looking up a dose in a handbook doesn’t make someone a pharmacology expert, getting slick outputs from an AI doesn’t make someone an AI expert. AI literacy is certainly not just prompt engineering. If we celebrate prompt skills as the pinnacle of AI capability, we risk fostering a false sense of security. A nurse might become confident in getting an AI to draft a care plan, but without deeper understanding, how will they know if the AI’s suggestion is biased or clinically unsafe? In my experience working with faculty, I’ve seen this play out. For example, in a curriculum workshop one educator proudly demonstrated how she used an AI tool to generate quiz questions. When I asked how she verifies the accuracy and relevance of those AI-generated questions, she paused. It hadn’t been part of her process. Prompting helped her create content quickly, but evaluating and understanding that content requires more nuanced skill. Prompting, therefore, is a starting point. It opens the door to AI’s possibilities, but it’s only the first step on the path to true AI competence in healthcare.

To use another analogy: Prompting AI is like using a GPS in your car. It gets you where you want to go, most of the time. But AI expertise is like knowing how to drive in any condition and understanding the mechanics of your car. You can follow the GPS without knowing how the engine works, but if the GPS gives a wrong turn or fails, the truly expert driver still knows how to reach the destination safely. In nursing, if an AI tool gives a questionable recommendation, the AI-literate nurse might not catch the problem. However, the AI-competent nurse will recognize “something doesn’t look right here” because they understand how the AI arrived at that output and what its limitations are.

What Real AI Competence Looks Like in Nursing Education

If prompting alone doesn’t equal competence, what does real AI expertise entail for nurses and nurse educators? In my work developing AI training for faculty and clinicians, I define true AI competence as a blend of data savvy, contextual understanding, ethical reasoning, and integration skills. Here are some key components of AI expertise in nursing:

Data Literacy & Quality Awareness: An AI-competent nurse understands that AI systems are only as good as the data feeding them. Nurses generate volumes of health data in practice, and knowing how that data is collected, cleaned, and used is crucial. For instance, a nurse educator with AI expertise will teach students not just how to use an AI clinical decision tool, but also why the tool might mislabel a patient risk if the input data are skewed or incomplete. Competence means recognizing potential data biases or errors. For example, knowing if a predictive algorithm was trained mostly on data from adult males, it might underperform in obstetric care. This goes beyond using the tool; it’s understanding the data under the hood.
Understanding AI Models & Limitations: Real expertise means having a working knowledge of how AI models operate and what their limitations are. Nurses don’t need to become software engineers, but they should grasp basic concepts of AI reasoning. An educator might explain to students that a diagnostic AI is essentially pattern-matching based on probability, which means it can sometimes hallucinate (produce confident-sounding but incorrect output) or miss nuances that a human would catch. A recent nursing vision statement put it well: nurses should be confident in explaining the workings and implications of AI technologies, and using them responsibly in any environment. In practice, this might look like a nurse leader who can articulate why an AI tool flagged a certain patient as high-risk and can validate whether that makes clinical sense. It also means knowing when to trust the AI versus when to be skeptical. For example, an AI might suggest a treatment plan that is efficacious on average; the AI-competent clinician will recall that the patient in question has a unique comorbidity not accounted for in the model, and thus will adjust accordingly.
Bias & Ethics Awareness: Every nurse is taught to uphold ethics and advocate for patients. AI expertise extends this ethical vigilance into the digital realm. A truly competent nurse in AI will be vigilant about algorithmic bias and equity. The American Nurses Association’s position statement on AI ethics urges nurses to ensure AI use is transparent, fair, and does not exacerbate health disparities. For nurse educators, this means teaching students to ask critical questions: “Could this AI tool be biased against certain populations? Who was represented in its training data? Are its recommendations fair and evidence-based for every patient?” AI expertise involves knowing strategies to mitigate bias perhaps by tuning algorithms, advocating for more diverse data, or simply double-checking AI-driven decisions against ethical standards. It also encompasses privacy and governance: understanding data privacy laws and ensuring patient information is protected when using AI, and following guidance like the World Health Organization’s call for documentation, transparency, and validation in all AI for health. In short, an AI expert in nursing doesn’t blindly trust AI outputs; they question and verify, keeping patient well-being at the center.
Governance & Policy Competence: With AI tools proliferating, there’s a growing need for nurses who can shape policies and guidelines around AI use. AI expertise includes knowledge of governance frameworks – for example, awareness of the National Academy of Medicine’s AI Code of Conduct for healthcare or your institution’s guidelines for AI-assisted clinical practice. Nurses with this competency will participate in committees deciding how AI gets implemented in their hospital’s workflow or how it’s taught in the curriculum. They can interpret regulatory requirements and contribute to developing best practices for AI in patient care. As one recent nursing education review noted, without nurses stepping up to help govern AI, the profession risks ceding influence over how these technologies are designed and deployed. An AI-competent nursing faculty member might lead efforts to create an “AI in clinical practice” module that covers governance like when to override an AI recommendation, how to report an AI-related error, or how to continuously monitor an AI tool’s performance for safety.
Workflow Integration Skills: Finally, true AI competence means knowing how to integrate AI into real-world nursing workflows and education in a seamless way. It’s not enough to have a cool AI app; a nurse expert will understand how that app fits into the daily routine without disrupting care. For educators, this might involve redesigning simulation scenarios to include AI tools in a realistic way (e.g. having students use an AI-driven vital signs monitor during a sim lab, then debriefing how it affected their decisions). It might also involve teaching future nurses when to use AI and when not to. For example, an AI symptom checker might be great for triage, but a competent nurse knows it’s a supplement, not a replacement, for a thorough patient assessment. When I work with faculty on curriculum design, I often focus on this practical side of AI: how do we train nurses to incorporate AI into their process, rather than treat it as a fancy black box? The goal is for new nurses to graduate not just knowing that “AI exists,” but having the experience of working alongside AI in their training – critically appraising its suggestions, cross-checking its outputs, and using it to augment their own clinical reasoning. In other words, AI becomes a teammate, and the nurse is the team leader who understands both the tech and the patient context.

By covering these areas from data literacy to ethical practice, nursing programs can produce professionals who are not only AI-literate but truly AI-competent. This well-rounded competence is what will enable nurses to harness AI in ways that genuinely improve patient care and education, rather than introducing new risks. It’s also what separates a casual AI user from a nurse who can champion AI-driven innovations in their organization.

Precision in Language: Literacy vs. Expertise

Why fuss over terms like “AI literacy” vs “AI expertise” or “competence”? Because words shape mindset. In education and certification, what we say someone is competent in drives what we actually teach and measure. Nurse educators must be precise with language around AI competence. If we loosely say “Our students are AI-proficient because they learned to use ChatGPT,” we may overestimate their preparedness and mislead stakeholders about what has been accomplished. It’s similar to conflating a nursing student’s ability to perform a check-off skill in lab with them being ready for independent practice – one is a basic demonstration, the other is true competency.

Leading organizations are calling for clarity here. The NLN, for instance, urges nursing programs to establish standards that differentiate between foundational AI knowledge and advanced application skills – “This distinction is crucial in preparing nurses across all roles to safely and ethically engage with AI”. In other words, we need to explicitly say, “After this workshop you’ll have AI literacy (you can use the tool), but it takes further training to claim AI expertise (you can lead with the tool).” Being precise in terminology helps set the right expectations. Faculty development programs can then be tiered appropriately – perhaps an introductory course for AI literacy (covering the AI-ABCs basics) and an advanced practicum for AI competency (covering hands-on data projects, bias mitigation strategies, etc.).

Moreover, precision in language can protect us from complacency. If an educator believes they have “mastered AI” just by learning prompt engineering, they might not seek deeper learning opportunities. By contrast, recognizing that prompt skills = literacy (basic proficiency), and that there’s an entire ladder of competence beyond that, encourages continuous learning. I always tell colleagues in nursing education: we wouldn’t label someone “informatics expert” just because they can use Microsoft Excel; likewise, let’s not label someone “AI expert” just because they can generate a decent response from a chatbot. It’s not about gatekeeping – it’s about ensuring honesty in our self-assessment and curricula. Nursing students and practicing nurses alike deserve to know where they stand and what growth areas remain in their AI journey.

Bridging the Gap: A Technologist’s Perspective

Coming from the tech world into nursing education, I’ve found it helpful to serve as a “translator” of sorts. Nurse educators and clinicians are deeply expert in patient care and pedagogy; I bring expertise in data, analytics, and AI. Together, we’ve learned a lot about where the gaps are. One striking observation is how quickly nurse faculty can pick up AI usage – in simulation labs, they eagerly adopt AI-driven patient simulators or use generative AI to create case studies. But they sometimes hesitate when discussions shift to AI mechanics or data issues. For instance, during a faculty development session, we introduced an AI tool that analyzes student reflective essays to provide feedback. The nursing faculty were excited by the automation, but we spent much of the session clarifying how the AI decides what “good reflection” looks like, and what bias might be inherent (Was the model trained on English-only text? Does it favor a certain writing style?). This reinforced for me that interdisciplinary collaboration is key – those of us with AI and data expertise need to work hand-in-hand with nursing experts to fill these knowledge gaps. It’s exactly why I’m involved in curriculum design: to ensure that as nursing programs integrate AI, they also build in content about data ethics, model biases, and limitations.

Through initiatives in simulation, analytics projects, and coursework, I’ve seen the benefit of moving faculty and students from using AI to questioning AI. One approach that’s worked is simulation debriefings focused on the AI’s performance. For example, in a simulation scenario we ran, a virtual patient monitor (powered by AI predictions) indicated a patient was stable when in fact, by clinical signs, they were deteriorating. Students initially trusted the monitor. In the debrief, we dissected why the AI might have missed the decline through discussing how the algorithm was weighting vital signs and how it lacked certain contextual inputs that an experienced nurse would note. Faculty commented that this exercise opened their eyes to the importance of understanding the AI’s “thinking.” These are the kinds of experiences that turn a nurse from a passive user of AI into an active, critical user. This is an emerging expert who can both utilize and scrutinize AI in practice.

From my perspective, every nurse educator doesn’t need to become a data scientist, but they do need enough fluency to critically appraise AI in their field. By bringing together tech experts and nursing experts, we can create learning experiences that make this possible. It’s gratifying to see nursing faculty who initially say “I’m not techy” transform into leaders who champion AI-driven improvements in their programs – because they took the time to go beyond the surface and really understand these tools. That, to me, is the reward of bridging this gap: a future where nurses are not just consumers of AI, but co-creators and shapers of how AI is used in healthcare.

Conclusion: What’s at Stake if We Don’t Differentiate

As we look to the future of nursing in an AI-enabled world, getting the distinction between literacy and expertise right is not just semantics. It has real consequences for healthcare. If we allow “AI literacy” to be mistaken for “AI expertise,” we risk preparing a workforce that is only superficially ready for the challenges ahead. Nurses who have only been taught prompt-level skills may struggle when facing the nuanced realities of AI in clinical settings. Without formal training that goes deeper, clinicians may either misuse AI tools or resist them altogether, leading to workflow inefficiencies and potential harm to patients. For example, an overconfident nurse might rely on an AI’s advice without recognizing a dangerous bias in its recommendation or, conversely, a nurse unsure why an AI suggestion is made might ignore a useful alert. In both cases, patient care suffers.

Beyond immediate risks, the long-term stakes for the nursing profession are high. If nursing as a discipline does not cultivate true AI experts and leaders, it could become marginalized in the era of digital health. A recent review warned that without integrating AI competency into nursing education, the profession could cede influence over the design, implementation, and governance of emerging healthcare technologies. In plain terms, that means others (technologists, administrators, or private companies) will make the decisions about how AI is used at the bedside and in education possibly without a nuanced understanding of nursing values and patient needs. Nurses have fought hard to earn their place as equal partners in healthcare decision-making; we don’t want to lose ground because we failed to step up to the plate with AI. Ensuring our colleagues move beyond prompting into true AI expertise is how we safeguard the nursing voice in the future of healthcare innovation.

The good news is that nurses, by our nature, are always learning. The same profession that adapted to electronic health records and genomics is fully capable of mastering AI as long as we commit to teaching it properly. We need to champion faculty development, curriculum reform, and continuing education that treat AI as a multifaceted competency, not a one-and-done skill. This includes advocating for national standards (as AACN and others are beginning to do) and sharing best practices across institutions. When we get it right, nurses will not only use AI tools but also improve them, ensuring these technologies augment our care in line with nursing’s core values of compassion, ethics, and patient-centeredness.

In the end, the difference between AI literacy and AI expertise in nursing comes down to leadership. Literacy lets nurses participate in an AI-driven system; expertise empowers them to shape it. For the sake of our patients and our profession, we must aim for the latter. By looking beyond prompting and investing in comprehensive AI competence, nurse educators and clinicians can ensure that nursing remains at the forefront of safe, innovative, and equitable healthcare, not just today, but for the years to come.

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