Key Takeaways
- AI literacy explained 57.3 % of the variance in neonatal nurses’ readiness to use AI in clinical decision‑making, the strongest single predictor observed in the study.
- The standardized path coefficient linking literacy to readiness was 0.757 (p < 0.001), indicating that a one‑standard‑deviation rise in AI literacy yields roughly a three‑quarter‑standard‑deviation increase in readiness.
- Only 6.5 % of the 200 surveyed neonatal intensive‑care nurses had received any formal AI training, and just 13 % reported using AI‑based tools in practice.
- Higher AI literacy was associated with greater self‑efficacy and willingness to integrate AI, suggesting that education could boost confidence and motivation, not just knowledge.
- The authors urge hospitals to implement structured AI literacy programmes now, before algorithmic systems outpace the frontline workforce that must operate them.
Background and Significance
Neonatal intensive care units (NICUs) are among the most unforgiving environments in modern medicine, where “every decision, from ventilator settings to fluid management, can determine whether a fragile newborn survives the night.” It is precisely in this high‑stakes arena that artificial intelligence is beginning to reshape clinical workflows. The study from Türkiye offers one of the clearest quantitative portraits yet of how bedside nurses feel about that transformation, highlighting AI literacy as a pivotal lever for readiness.
Study Design and Participants
Conducted between August 2025 and January 2026 by researchers in the Department of Pediatric Nursing at Bandırma Onyedi Eylül University, the investigation was a cross‑sectional, descriptive‑correlational study. The team employed structural equation modeling (SEM) to test a network of hypothesized relationships among multiple variables simultaneously. Before analysis, they screened for multivariate outliers and excluded five participants whose response patterns deviated substantially, leaving a final analytical cohort of 200 neonatal intensive‑care nurses.
Demographics and AI Exposure
The sample reflected the typical nursing workforce in Türkiye’s NICUs: 96 % were female, with a mean age of 29.65 years (SD = 5.90). Most held a bachelor’s degree (62.5 %), and 66.5 % reported between one and five years of professional experience. Strikingly, only 6.5 % of participants had ever received any formal training in artificial intelligence, and just 13 % reported using AI‑based technologies in their clinical practice. As the authors note, “the overwhelming majority of nurses caring for the country’s most vulnerable patients are navigating the arrival of AI with essentially no structured preparation.”
Measurement Instruments
Data collection relied on three tools. A sociodemographic information form captured background characteristics. The Artificial Intelligence Literacy Scale (AILS) measured knowledge and competence across AI concepts and applications. The Medical Artificial Intelligence Readiness Scale (MAIRS) assessed readiness across three subdomains: self‑efficacy in using AI technologies, willingness to adopt them, and awareness of their benefits and limitations in medical settings. Permission was obtained from the scale authors, and the study received ethical approval from the Non‑Interventional Clinical Research Ethics Committee of Bandırma Onyedi Eylul University in June 2025, adhering to the Declaration of Helsinki.
Statistical Approach and Validation
The researchers evaluated the measurement models using established psychometric criteria—average variance extracted, composite reliability, and the heterotrait–monotrait ratio (HTMT)—to confirm that the scales measured their intended constructs consistently and that literacy and readiness remained empirically distinct. SEM combined factor analysis with path analysis, allowing the team to estimate how strongly the latent construct of AI literacy predicts AI readiness while accounting for measurement error. The study followed the STROBE guidelines for reporting observational research, strengthening transparency and completeness.
Main Findings
The SEM supported the primary hypothesis: AI literacy significantly and positively predicted AI readiness, with a standardized path coefficient of 0.757 (p < 0.001). In practical terms, “a coefficient of this magnitude indicates that roughly three‑quarters of a standard deviation increase in readiness accompanies each standard deviation increase in literacy, an exceptionally strong relationship in behavioral research.” Moreover, the literacy construct alone accounted for 57.3 % of the variance in readiness scores—a proportion rarely observed with a single predictor in social‑science research. Nurses with higher AI literacy scores reported not only greater abstract enthusiasm but also greater self‑efficacy and greater willingness regarding the integration of AI technologies into clinical decision‑making processes.
Implications for Neonatal Care
AI tools are already entering the neonatal domain—from predictive analytics for sepsis and deterioration risk to decision‑support systems for respiratory management. These technologies promise faster, more accurate decision‑making in units where minutes matter, but their benefits depend entirely on the humans operating them. As the article states, “a nurse who understands what an algorithm can and cannot do is better positioned to question its outputs, recognize when they conflict with bedside observation, and integrate them judiciously into care.” The findings suggest that literacy is not a luxury but the gateway variable: improving AI literacy may directly enhance NICU nurses’ readiness to weave AI into the decisions that determine newborn survival. The near‑total absence of prior training transforms the study from a descriptive snapshot into a warning: if 93.5 % of neonatal nurses have received no AI education, and literacy drives readiness this strongly, the bottleneck is institutional neglect rather than clinician skepticism.
Limitations
The cross‑sectional design captures a single moment in time, so while the model shows that literacy and readiness move together, it cannot prove that boosting literacy causes readiness to rise; longitudinal and interventional studies would be needed to establish causality. The sample of 200 nurses, though adequate for SEM, was drawn from a single national context and consists predominantly of young, female, bachelor’s‑educated nurses, which may limit generalizability. Self‑reported scales depend on participants’ honest and accurate self‑assessment. The authors received no specific grant from public, commercial, or not‑for‑profit sectors and declare no competing interests.
Conclusion and Call to Action
Even with those caveats, the study lands at a pivotal moment. Hospitals worldwide are racing to deploy machine‑learning systems, yet the frontline clinicians who must live with those systems are rarely asked what they know or how prepared they feel. This research supplies the numbers: literacy is the strongest lever available, and it is currently almost untouched. For the nurses watching over newborns in intensive care, the message from the data is clear. “The path to an AI‑ready neonatal workforce runs directly through education, and the time to build that path is now, before the algorithms outpace the people expected to use them.” Structured AI literacy programmes could raise readiness across the workforce, preparing nurses for technologies that are arriving whether or not hospitals plan for them. Investing in that education today may be the decisive factor that allows AI to fulfil its promise of safer, more effective neonatal care tomorrow.