The study took a deliberately unusual methodological approach: scenario-based foresight, a technique borrowed from strategic planning and futures studies. Rather than simply surveying attitudes toward AI, the researchers had fourteen students enrolled in an AI elective course construct individual written narratives imagining nursing a quarter century from now. Each student worked with a structured scenario development form that incorporated SWOT prompts, prompting them to consider strengths, weaknesses, opportunities, and threats associated with AI integration. This framework pushed participants beyond vague optimism or pessimism and forced them to articulate specific mechanisms by which technology might transform their profession.

Once the narratives were complete, the researchers applied thematic analysis, a qualitative method that involves systematically coding text to identify recurring patterns of meaning. The themes and subthemes that emerged were then classified under the four predefined SWOT dimensions, creating a structured map of how future nurses perceive the trajectory of their own profession. Ethical approval for the research was granted by the Istanbul University-Cerrahpasa Social and Human Sciences Research Ethics Committee, and written informed consent was obtained from all participants after the educational activity was complete.

What the analysis revealed was a strikingly dualistic picture. On one side of the ledger, students identified serious risks to professional identity. They worried about algorithmic dominance, the scenario in which clinical decision-making gradually cedes authority to predictive systems whose outputs nurses feel obligated to follow. They also flagged ambiguity in professional accountability, a technically thorny problem: if an AI recommendation contributes to a patient harm, who bears responsibility, the nurse who acted on it, the clinician who deployed it, or the developers who built it?

The third major threat students identified was subtler but arguably the most consequential: the potential weakening of relational care. Nursing has always been defined by the therapeutic relationship between nurse and patient, the continuous presence, the physical assessment, the emotional labor. Students envisioned futures in which screens, dashboards, and automated monitoring mediate so much of the patient encounter that the human core of the profession erodes. In their scenarios, the danger was not that AI would replace nurses outright, but that it would hollow out the elements of the work that make nursing distinct.

Yet the students did not simply write cautionary tales. Across the narratives, a second trajectory emerged in which AI becomes a vehicle for strengthened professional agency rather than diminished autonomy. Students imagined expanded roles for nurses as ethical stewards, professionals who oversee the moral dimensions of algorithmic care and advocate for patients when automated systems conflict with human values. They envisioned nurses as digital advocates, translating between data systems and patient needs, and as decision-support partners who work with AI tools rather than beneath them.

This reframing is significant because it inverts a common narrative about automation. Much public discourse treats AI as an external force that happens to workers, but the students’ scenarios position nurses as active agents who can shape how these technologies are deployed. In their visions, autonomy is not something AI takes away; it is something that must be deliberately redesigned and defended as care becomes increasingly data-driven. The researchers note that autonomy and human-centred care emerged as central values across all the scenarios, functioning as the evaluative lens through which students judged every potential application of AI.

The pedagogical implications are where the study becomes most actionable. The authors argue that scenario-based AI education can support critical reflection on professional identity in ways that conventional lectures or technical training cannot. When students are required to project themselves decades into the future and construct coherent narratives about their profession, they must grapple with questions that ordinary coursework rarely raises: What parts of nursing are irreplaceable? Where should machines decide and where should humans? What does accountability mean when care is co-produced by algorithms? These are precisely the questions that will define nursing practice in AI-saturated clinical environments.

The futures methodology also carries technical appeal for educators and researchers. SWOT-based scenario development provides a replicable scaffold that converts open-ended imagination into analyzable data, allowing qualitative researchers to capture how professional values interact with technological change. The thematic analysis of fourteen student narratives, reported in accordance with the Standards for Reporting Qualitative Research, demonstrates that even a small cohort can generate a rich conceptual map of anticipated challenges and opportunities. The authors caution, however, that the findings derive from a single cohort of volunteers at one institution and represent perceptions rather than predictions, meaning the scenarios should be read as informed speculation grounded in professional values, not forecasts of what will actually happen by 2050.

Still, the core message lands with urgency. AI in healthcare is advancing faster than nursing curricula are adapting, and the study suggests that preparing future nurses requires more than technological literacy. It requires education that integrates foresight activities with ethical and professional discussion, so that students enter the workforce having already rehearsed the identity questions that AI will force upon them. As the authors conclude, educational approaches that combine scenario-building with reflection on autonomy and human-centred care may better equip nurses to navigate algorithmic medicine without surrendering the relational essence of their profession. In an era when health systems are rapidly purchasing predictive tools, teaching nurses to imagine the future may be one of the most practical investments educators can make.

Subject of Research: Scenario-based AI education and professional identity formation in undergraduate nursing students

Article Title: Scenario-based artificial intelligence education and professional identity in nursing students: a qualitative futures study

Article References: Scenario-based artificial intelligence education and professional identity in nursing students: a qualitative futures study. (n.d.). https://doi.org/10.1186/s12909-026-10618-8

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10618-8

Keywords: artificial intelligence, nursing education, nursing students, professional identity, scenario-based learning, foresight methodology, SWOT analysis, thematic analysis, autonomy, nursing ethics, human-centred care, qualitative research