Nursing Students Learn to Trust, Question, and Master AI Tools in Four Stages

Generative artificial intelligence has swept into classrooms faster than almost any educational technology before it, and nursing schools are no exception. Students preparing for one of the most consequential professions in healthcare are now routinely turning to AI-generated content to draft assignments, explain clinical concepts, and rehearse case-based reasoning. Yet most research on this shift has captured only snapshots: a single course, a single semester, a single survey. A new longitudinal qualitative study published in BMC Medical Education takes a different approach, following twenty-two vocational nursing students over time to map how their experience with AI-generated content, or AIGC, actually develops from first contact to something resembling professional fluency.

The research team, led by Yanyan Zhang and Chunxiu Xiao of Fujian Health College with colleagues from Fujian Medical University, framed their work within the constructivist paradigm, the view that learners actively build knowledge from experience rather than passively receive it. Participants were recruited through purposeful maximum variation sampling, a technique designed to capture as wide a range of backgrounds, skill levels, and attitudes toward AI as possible. Rather than relying on questionnaires alone, the researchers triangulated three data streams: semi-structured interviews, reflective diaries kept by the students themselves, and their actual coursework. This combination allowed the team to compare what students said about their AI use with what they demonstrably did with it.

Methodologically, the study is notable for its integrated analytical framework. The researchers anchored their analysis in Kolb’s experiential learning theory, a four-stage cycle in which concrete experience feeds reflective observation, which in turn supports abstract conceptualization and active experimentation. They layered onto this the Information Seeking and Communication Model, or ISCM, a framework from information science that describes how people search for, evaluate, and use information in task contexts. By combining the two, the team could track not only how students learned from their encounters with AI but also how their underlying information behaviors, from simple retrieval to verification and synthesis, changed along the way. The study was approved by the Ethics Committee of Fujian Health College and conducted in accordance with the Declaration of Helsinki, with written informed consent from all participants.

The analysis revealed four distinct developmental stages. The first, described as exploration and uncertainty, captures the disorientation of initial contact. Students in this phase approached AIGC tools with a mixture of curiosity and anxiety, unsure what the tools could do, what they were permitted to do, and whether the output could be trusted. Information behavior at this stage was dominated by retrieval: students typed questions, copied answers, and moved on, with little systematic checking of accuracy. The researchers characterize this as a period of trial without much error correction, because students often lacked the domain knowledge to recognize when the AI was wrong.

The second stage, reflection and boundary recognition after trial and error, marks a turning point. As students accumulated concrete experiences, including moments when generated content proved inaccurate, generic, or misaligned with clinical reality, they began to reflect on the limits of the technology. This is where Kolb’s cycle becomes visible in the data: reflective observation of failed or flawed outputs led students to form abstract concepts about where AI could help and where it could not. Boundary recognition, in the researchers’ terms, meant students developed a working map of the technology’s capabilities and its hazards, particularly around hallucinated facts and the superficial plausibility of AI-written clinical text.

The third stage, strategy construction and systematic application, represents the most technically interesting transformation. Students stopped treating the AI as an answer machine and started treating it as a tool to be orchestrated. They reported developing prompting strategies, cross-checking generated content against textbooks and clinical guidelines, and integrating AI output into their own writing and reasoning rather than substituting it. In ISCM terms, their information behavior shifted from primarily retrieval-focused use toward generative, verificatory, and integrative patterns: they used AI to produce drafts, then verified those drafts against authoritative sources, and finally integrated the surviving material into their own work. This progression mirrors what information literacy researchers have long described as the difference between searching and thinking.

The fourth stage, professional repositioning in human-AI collaboration, is perhaps the most consequential for nursing education. Students began to articulate a changed sense of their own professional role: not replaced by AI, but repositioned relative to it, with responsibility for final judgment resting firmly on the human practitioner. The authors report that students perceived a deepening of critical judgment and professional role awareness as their experience matured, although the study is careful to note that these patterns varied considerably across participants. Not every student reached the later stages, and the trajectory was not uniform; individual differences in prior digital literacy, confidence, and reflective habits shaped how far and how fast each student progressed.

That variability is central to the study’s conclusion. The authors argue that the potential relevance of AIGC experience to nursing decision-making-related development depends on students’ evaluation, monitoring, and responsibility. In other words, the benefit is not automatic. A student who copies AI output uncritically may bypass exactly the cognitive work, weighing evidence, checking sources, taking responsibility for a recommendation, that clinical decision-making requires. A student who verifies, monitors, and owns the final answer may strengthen those same capacities. The technology is the same in both cases; the developmental outcome diverges because of how the learner engages with it.

The practical implication the researchers draw is that nursing education should deliberately combine experiential learning with critical information literacy cultivation. On the experiential side, Kolb’s framework suggests structured cycles in which students use AI, reflect on what worked and what failed, form principles, and test them again. On the literacy side, the ISCM framework suggests explicit instruction in seeking, evaluating, and communicating information: teaching students not just how to prompt a model but how to verify its output against clinical guidelines, recognize the limits of generated content, and document their reasoning. The study’s findings, drawn from interviews, diaries, and coursework rather than controlled outcome measures, are suggestive rather than definitive, and the authors frame their conclusions in correspondingly cautious terms, describing perceived deepening of judgment and potential relevance rather than proven causation.

Still, the study offers one of the clearest temporal pictures to date of how future nurses actually come to terms with generative AI. It suggests that the first weeks of AI use, dominated by uncertainty and uncritical retrieval, are not a failure state but a predictable early stage of a longer developmental arc, one that educators can shorten and enrich through structured reflection and explicit verification training. As generative AI becomes embedded in clinical documentation, patient education, and decision support, the difference between a nurse who learned to interrogate AI and one who learned only to use it may matter far beyond the classroom. This research indicates that the interrogation skills can be taught, and that the earlier they are built into the curriculum, the more likely students are to emerge as responsible, critically engaged human-AI collaborators rather than passive consumers of machine-generated answers.

Subject of Research: Longitudinal qualitative study of nursing students' developmental experience with generative artificial intelligence in education

Article Title: Early developmental trajectory of nursing students’ AIGC application experience: a short-term longitudinal qualitative study

Article References: Zhang, Y., Cai, L., Liu, F., & Xiao, C. (2026). Early developmental trajectory of nursing students’ AIGC application experience: a short-term longitudinal qualitative study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10562-7

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10562-7

Keywords: generative AI, nursing education, AIGC, experiential learning, information literacy, qualitative research, longitudinal study, clinical decision-making, human-AI collaboration, Kolb experiential learning theory, ISCM, vocational education