The research team, led by Xuebing Jing of Zibo Central Hospital and Yanyan Men of Qilu Medical University’s College of Nursing, used a statistical technique called latent profile analysis to sort students into subgroups based on their scores on an artificial intelligence literacy scale. Rather than treating AI literacy as a single continuous dimension on which everyone differs only by degree, latent profile analysis asks whether the population contains qualitatively distinct clusters, people whose patterns of knowledge, skills, and attitudes resemble one another more than they resemble the rest of the sample. This person-centered approach has become increasingly popular in educational research because it recognizes that competence is multidimensional and that different combinations of strengths and weaknesses may require different remedies.

The analysis converged on a three-profile solution that fit the data better than any alternative. The smallest group, accounting for 24.57 percent of participants, showed low AI literacy across the measured dimensions. A moderate-literacy group made up 37.22 percent of the sample, while the largest cluster, 38.21 percent of students, demonstrated high AI literacy. That so many students cluster at the high end might seem reassuring, but the researchers emphasize the heterogeneity: nearly one in four future nurses enters clinical training with a weak grasp of AI concepts, even as AI tools are being embedded in electronic health records, triage systems, and patient monitoring platforms worldwide. A curriculum pitched at the average student will inevitably leave the low-literacy quarter behind while boring the high-literacy group.

What determines which profile a student lands in? The researchers ran multivariate logistic regression to identify factors significantly associated with membership in each group, and six predictors emerged. Internship experience mattered, suggesting that exposure to real clinical environments, where AI tools increasingly appear in daily workflows, builds practical AI understanding that classroom instruction alone does not. Formal AI-related training was another significant predictor, an intuitive but important confirmation that structured coursework moves the needle. The frequency of daily AI use in students’ personal lives also predicted higher literacy, hinting that familiarity with chatbots, recommendation systems, and smart devices outside school translates into institutional confidence. Participation in innovation and entrepreneurship activities, such as research projects or design competitions, similarly distinguished the higher-literacy profiles.

Perhaps the most intriguing predictors, however, were psychological rather than experiential. Critical thinking, measured with the California Critical Thinking Disposition Inventory, and ethical sensitivity, assessed with the Ethical Sensitivity Questionnaire for Nursing Students, both significantly predicted profile membership. This finding reframes AI literacy as more than a technical skill set. Students who habitually question assumptions, weigh evidence, and reason through moral dilemmas are the same students who engage more deeply with AI, perhaps because they recognize both its power and its limits. For educators, the implication is that ethics and critical thinking courses are not adjuncts to AI training but integral components of it, shaping the cognitive dispositions that allow technical knowledge to take root.

The quantitative survey only tells half the story. To understand what students actually want from AI education, the team conducted 15 semi-structured interviews and analyzed the transcripts using conventional content analysis, a qualitative method in which codes and themes emerge from the data rather than being imposed in advance. The interviews revealed that educational needs spanned two broad domains: the format of teaching and the content of learning. On the format side, students expressed a clear preference for interactive, case-based learning over passive lectures. They wanted to work through realistic clinical scenarios in which AI tools play a role, discussing in small groups where an algorithm’s suggestion should be followed, when it should be questioned, and how to explain decisions to patients and families.

The students also identified specific technologies they believed should be woven into the curriculum. Virtual simulation, which allows learners to practice clinical decision-making in safe, repeatable digital environments, was viewed as a natural fit for AI education. Generative AI tools, the same large language model systems that have transformed public discourse since their emergence, were seen as useful teaching instruments in their own right, capable of supporting practice and exploration, provided their limitations are made explicit. This appetite for hands-on engagement with the very technologies reshaping healthcare suggests that students are not afraid of AI; they want structured, supervised opportunities to master it before they encounter it unsupervised on a hospital ward.

On the content side, students’ requests were concrete and pragmatic. They wanted foundational instruction in how AI actually works, the basic principles underlying machine learning and algorithmic prediction, so that they could understand what these systems can and cannot do rather than treating them as opaque oracles. They wanted clinical application skills: how AI is used in nursing practice today, from early-warning scores to documentation support, and how to operate and interpret such tools competently. And they wanted sustained coverage of legal and ethical guidelines, including data privacy, accountability, and the boundaries of algorithmic decision-making in patient care. Notably, students in the high-literacy profile emphasized one further need that the researchers found particularly telling: support for professional identity formation, the process of understanding what it means to be a nurse in an era when machines participate in diagnosis and monitoring.

The study’s conclusions point toward a differentiated approach to AI education in nursing. Because literacy is heterogeneous and shaped by multiple, identifiable factors, the authors argue that educational strategies should be tailored to specific profiles. Students in the low-literacy group may need foundational, confidence-building instruction with abundant scaffolding, while high-literacy students may be better served by advanced applications, innovation opportunities, and reflective work on professional identity. Across all groups, the integration of practical clinical experience, deliberate critical thinking and ethics training, and diverse teaching formats, from case-based discussion to virtual simulation, appears essential. As AI continues its advance into every corner of healthcare, this research offers a rare, evidence-based map of where the nursing workforce stands, and a practical blueprint for bringing every student, not just the technologically fluent majority, up to the demands of intelligent medicine.

Subject of Research: AI literacy profiles and educational needs among undergraduate nursing students

Article Title: Profiles of AI literacy and educational needs of nursing students: a mixed‑method study

Article References: Jing, X., Men, Y., Wang, Q., Wang, J., Li, R., & Zhou, X. (2026). Profiles of AI literacy and educational needs of nursing students: a mixed‑method study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10381-w

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10381-w

Keywords: artificial intelligence, AI literacy, nursing education, nursing students, latent profile analysis, mixed methods, educational needs, critical thinking, ethical sensitivity, virtual simulation, case-based learning, healthcare