The study, conducted by Gozde Kugcumen of Istanbul Medipol University and Ilkay Gungor Satilmis of Istanbul University-Cerrahpasa, set out to answer a deceptively simple question: does adding a hands-on simulation session to standard online theoretical instruction actually improve what obstetric nurses and midwives know about preeclampsia care, and how confident do they feel applying that knowledge? The researchers ran the trial between February 2019 and August 2020 at a university hospital in Istanbul, registering it prospectively as a clinical trial to signal methodological transparency from the outset. In a field where educational interventions are often evaluated with before-and-after studies lacking any comparison group, the randomized design alone marks this work as a meaningful contribution.

The technical architecture of the trial reflects careful attention to the realities of clinical staffing. A total of 76 nurses and midwives were enrolled and randomized in equal halves of 38 participants per group, but the final complete-case analysis included 61 participants with full baseline and four-week outcome data—30 in the intervention group and 31 in the control group. Eligibility criteria were deliberately strict: every participant held at least a bachelor’s degree and had accumulated a minimum of six months of experience in obstetrics and delivery services. This ensured that the study measured the incremental value of simulation on top of an already educated and clinically active workforce, rather than compensating for gaps in basic training.

Crucially, both arms of the trial received the same foundation: an online theoretical training module covering the follow-up and care of pregnant women with preeclampsia. One week after completing this shared theoretical component, the intervention group took part in a simulation session built around a standardized patient—an actor trained to portray a consistent clinical scenario, allowing learners to practice history-taking, assessment, communication, and decision-making under realistic conditions. This design choice is methodologically important. Because both groups received identical theoretical content, any difference in outcomes at the four-week mark could be attributed to the simulation experience itself rather than to extra contact time or additional reading material.

The measurement battery was equally comprehensive. Knowledge was assessed with the Preeclampsia Information Assessment Form, a validated instrument probing disease-specific understanding, and with an Objective Structured Clinical Assessment Form designed to capture applied clinical performance. Self-efficacy—the psychological construct describing a person’s belief in their own capacity to execute the behaviors required for a task—was measured with the Self-Efficacy Scale in the Follow-up and Care of Pregnant Women with Preeclampsia, abbreviated as SESPWP. The learners’ experience of the education itself was evaluated with the Educational Practices Questionnaire and the Student Satisfaction and Self-Confidence in Learning Scale, two instruments widely used in simulation research to gauge whether the training environment supported engagement and confidence.

When the four-week posttest data were analyzed, the knowledge results favored simulation. Scores on the Preeclampsia Information Assessment Form were significantly higher in the intervention group than in the control group, with a p-value below 0.05, indicating that the difference was unlikely to be a statistical fluke. In practical terms, nurses and midwives who had rehearsed preeclampsia care with a standardized patient retained measurably more disease-specific knowledge a month later than peers who had received only the online lectures. For educators, this finding supports the long-standing hypothesis that active, experiential learning consolidates cognitive material more durably than passive delivery alone.

The self-efficacy story, however, is more complicated—and the authors were careful not to overstate it. Within the intervention group, SESPWP scores rose significantly from pretest to posttest, with a p-value of 0.015, suggesting that participants genuinely felt more capable of managing preeclampsia care after the simulation. Yet when the researchers adjusted for baseline differences between the groups, the between-group effect on self-efficacy was not statistically significant, and the difference in pre-to-post change between the two arms also failed to reach significance. In plain language: simulation participants gained confidence, but the control group’s confidence also shifted over the study period, and the simulation-specific increment could not be demonstrated with statistical confidence.

The authors themselves acknowledge this subtlety in their conclusions. Adding simulation-based training to shared online theoretical education was associated with higher baseline-adjusted knowledge at four weeks, they report, although the between-group difference in pre-post change was not statistically significant in sensitivity analysis, and no simulation-specific between-group effect on self-efficacy was demonstrated. This kind of transparent reporting is increasingly valued in clinical education research, where enthusiasm for simulation has sometimes outpaced the evidence. The trial’s sensitivity analyses—re-examining the primary findings under alternative statistical assumptions—reveal how fragile some between-group contrasts can be in a sample of 61 complete cases, a reminder that even well-designed trials have limits imposed by their size.

What the participants thought of the experience adds another layer to the findings. Scores on the Educational Practices Questionnaire and the Student Satisfaction and Self-Confidence in Learning Scale indicated high levels of satisfaction and positive evaluations of the simulation experience across the board. High satisfaction matters for a practical reason: educational interventions only work if clinicians are willing to complete them, and simulation formats that learners find engaging are more likely to be adopted and sustained in busy hospital environments. The consistency between the objective knowledge gains and the subjective learning experience strengthens the case that the standardized-patient session created a genuinely effective educational environment, even if its effects on confidence could not be isolated statistically.

The broader implications reach well beyond a single Istanbul hospital. Preeclampsia affects a meaningful share of pregnancies worldwide and demands rapid, coordinated recognition and management, typically initiated by bedside nurses and midwives before physicians become involved. Educational bodies have increasingly recommended simulation as a core component of obstetric emergency training, yet high-quality randomized evidence in nursing and midwifery populations has been comparatively scarce. This trial, conducted as part of a doctoral thesis in gynecologic nursing and presented orally at the 3rd International and 4th National Women’s Health Nursing Congress in May 2025, contributes precisely the kind of controlled, instrument-based evidence that such recommendations require. Its message is measured but useful: simulation appears to help clinicians retain what they learn about preeclampsia care, learners value the experience highly, and claims about self-efficacy benefits should await larger trials with the statistical power to detect them. For hospitals weighing the cost of standardized-patient programs against the return in clinical preparedness, the study offers a data point worth serious attention—and a clear template for the replication studies the field now needs.

Subject of Research: Effects of simulation-based education on nurses' and midwives' knowledge and self-efficacy in preeclampsia care

Article Title: Effects of simulation-based education on nurses’ and midwives’ knowledge and self-efficacy in preeclampsia care: a randomized controlled trial

Article References: Kugcumen, G., & Satilmis, I. G. (2026). Effects of simulation-based education on nurses’ and midwives’ knowledge and self-efficacy in preeclampsia care: a randomized controlled trial. BMC Nursing. https://doi.org/10.1186/s12912-026-05428-8

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05428-8

Keywords: preeclampsia, simulation-based education, randomized controlled trial, nursing education, midwifery, self-efficacy, standardized patient, clinical training, obstetric care, BMC Nursing, Istanbul, knowledge retention

The study, led by Tingting Dong, Zijuan Yu, Xiao Zhou and Yifan Cui of the hospital’s Department of Nursing, used a qualitative exploratory design rather than a trial of educational effectiveness. The researchers recruited seventeen novice nurses who had already used the AISP system at the tertiary grade A general hospital, applying purposive sampling to ensure participants had direct, first-hand experience with the technology. All participants held a bachelor’s degree or above, and their mean age was just twenty-four years, placing them at the very beginning of their clinical careers, precisely the stage at which symptom assessment skills are still fragile and heavily dependent on structured practice.

Data collection took place in January 2026 through three semi-structured focus group interviews, each involving five or six participants. Focus groups were chosen to encourage nurses to build on one another’s reflections, surfacing shared experiences and points of disagreement that one-on-one interviews might miss. The researchers then analyzed the transcripts using Braun and Clarke’s reflexive thematic analysis, a widely used method in which two researchers coded the data independently before negotiating themes together. The study team also reported their work in accordance with the COREQ checklist, a thirty-two-item standard designed to make qualitative research transparent and reproducible, and the project received ethics approval from the hospital’s Ethics Office in December 2025.

From the analysis, three major themes and eight subthemes emerged, and together they sketch a technology that nurses found genuinely useful but visibly unfinished. The first theme captured a favorable user experience, which the researchers broke down into three facets: the system functioned as a pocket-sized inquiry coach offering convenience and accessibility; it acted as an immersive inquiry partner providing scenario authenticity and a sense of engagement; and it served as a personalized review mentor delivering real-time and structured feedback. That trio of metaphors, coach, partner and mentor, reflects how the nurses positioned the AISP relative to their own learning process, as a tool that accompanies rather than replaces human instruction.

The convenience dimension deserves particular attention because it addresses one of the oldest bottlenecks in clinical skills training. Traditional standardized patient programs depend on scheduling actors, booking simulation rooms and coordinating groups of learners, all of which constrain how often a novice nurse can rehearse. An AI-based patient, by contrast, is available on demand, allowing trainees to practice symptom inquiry whenever a gap appears in their schedule. For nurses working rotating shifts in a large hospital, that accessibility transformed practice from a scheduled event into something closer to a habit, repeated in short sessions whenever the learner felt ready.

The second major theme concerned perceived learning value. Participants described the structured guidance embedded in the system as helping them organize the logic of their inquiries, an important benefit because novice clinicians often know individual questions but struggle to sequence them coherently during a patient encounter. Symptom assessment is not a checklist but a branching conversation in which each answer should shape the next question, and the AISP’s structured scaffolding appeared to give the nurses a framework for building that branching logic. Participants also valued the opportunities for communication practice that arose through immersive interaction, rehearsing not just what to ask but how to ask it in a conversational setting that felt closer to a real encounter than a written case exercise.

The third theme, however, tempers the enthusiasm. The nurses identified clear limitations and offered optimization suggestions in three areas. They judged the coverage of diverse scenarios to be insufficient, meaning the system could not expose them to the full range of patient presentations they would eventually meet on the wards. They found the flexibility of voice interaction limited, a technical constraint that matters enormously in a system whose entire purpose is spoken conversation. And they called for functional iteration of the evaluation mechanism, seeking richer and more nuanced assessment of their performance. The researchers noted that positive accounts were often qualified by concerns about speech recognition accuracy and the rigidity of the interaction, a reminder that conversational AI in medicine still stumbles over the messiness of natural human speech.

These limitations are not trivial quirks; they map directly onto the hardest open problems in spoken dialogue systems. Speech recognition degrades with accents, background noise, hesitation and the fragmented grammar of real clinical conversation, and a trainee whose carefully phrased question is misheard may lose confidence in the entire exercise. Similarly, if the simulated patient responds in formulaic ways, the interaction can feel scripted rather than alive, undermining the very authenticity that participants praised. The study’s authors argue that future development should therefore focus on improving interaction technologies, enriching training scenarios and refining feedback mechanisms, a roadmap that reads as a to-do list for the engineering teams behind medical conversational agents.

Equally notable is the intellectual honesty of the study’s framing. The authors explicitly state that their findings reflect subjective perceptions rather than demonstrated educational effectiveness. In other words, the nurses liked the system and believed it helped them, but the study was not designed to measure whether AISP training actually improves diagnostic inquiry skills, patient outcomes or assessment accuracy compared with conventional methods. That distinction matters in a field where enthusiasm for AI tools often outruns the evidence, and the researchers call for future studies to evaluate learning outcomes using objective measures rather than experience alone. It is a caution that applies well beyond nursing: perceived usefulness is a necessary signal for any educational technology, but it is not proof of learning.

Even with those caveats, the study lands at a consequential moment. Hospitals worldwide face persistent pressure to train large cohorts of novice nurses quickly and consistently, while standardized patient programs remain expensive and difficult to scale. An AI standardized patient that nurses perceive as accessible, engaging and genuinely instructive, even one still hampered by rigid dialogue and narrow scenarios, points toward a hybrid future in which human actors handle the most complex and emotionally demanding simulations while AI systems provide unlimited low-stakes rehearsal. The Beijing team’s work, co-developed with an AI technology company as part of the hospital’s standardized nurse training program and described with unusual transparency about the authors’ limited design role and absence of commercial interests, suggests that the technology has crossed a threshold of acceptability among its youngest users. What remains is the harder scientific task of proving that hours spent talking to a machine produce nurses who ask better questions of human beings, and the next generation of studies, with objective outcome measures, will determine whether the pocket-sized inquiry coach earns a permanent place in clinical education.

Subject of Research: Novice nurses' experiences of AI standardized patient training for symptom assessment

Article Title: Symptom assessment experience of novice nurses based on artificial intelligence standardized patients: a qualitative study

Article References: Dong, T., Yu, Z., Zhou, X., & Cui, Y. (2026). Symptom assessment experience of novice nurses based on artificial intelligence standardized patients: a qualitative study. BMC Nursing. https://doi.org/10.1186/s12912-026-05447-5

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05447-5

Keywords: artificial intelligence, standardized patient, nursing education, symptom assessment, novice nurses, qualitative research, thematic analysis, focus groups, conversational AI, speech recognition, clinical training, BMC Nursing