Development and validation of prompts for generating simulation scenarios in nursing education using generative artificial intelligence.
• Strategic integration enables more effective use of generative AI in nursing education. • Prompts were developed to generate simulation scenarios using prompt engineering. • Based on the plan–do–check–act model, prompts underwent four refinement cycles. • Scenarios generated using prompts were val...
| Publicado en: | Clinical Simulation in Nursing Vol. 113 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192457102&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192457102 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18761399 85TC jtl: Clinical Simulation in Nursing issn: 18761399 maglogo: N pubinfo: dt: Apr2026 vid: 113 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 192457102 192457102 192457102 10.1016/j.ecns.2026.101922 192457102 ppct: 1 formats: tig: atl: Development and validation of prompts for generating simulation scenarios in nursing education using generative artificial intelligence. aug: au: Lee, Miji Shin, Sujin affil: College of Nursing, Ewha Womans University, Seodaemun-gu, Seoul 03760, Republic of Korea sug: subj: Artificial Intelligence, Generative Program Development Program Evaluation Engineering Nurse Educators Simulations Education, Nursing Curriculum Development Human Methodological Research Nursing Models, Theoretical Vital Signs Resuscitation, Cardiopulmonary Postoperative Care Content Validity ab: • Strategic integration enables more effective use of generative AI in nursing education. • Prompts were developed to generate simulation scenarios using prompt engineering. • Based on the plan–do–check–act model, prompts underwent four refinement cycles. • Scenarios generated using prompts were validated as appropriate through expert evaluation. • The prompts allow nurse educators to generate realistic and robust scenario drafts. Generative artificial intelligence's (GenAI) implementation in the development of simulation scenarios for nursing education is undergoing, highlighting its potential as a transformative tool. The next step is to explore how GenAI can be harnessed more effectively. This study aimed to develop and validate structured prompts that enable nurse educators to generate realistic and pedagogically robust simulation scenarios using GenAI. This methodological study employed the plan–do–check–act (PDCA) model to guide prompts' iterative refinement. Four iterative cycles were conducted during prompt development, each corresponding to an application of the PDCA model. Informed by three preliminary studies, the initial prompts were refined through three rounds of prompting, one round of expert consultation, and two rounds of content validity evaluation, resulting in four rounds of prompt refinement. In the fourth cycle, simulation scenarios were developed for the following three core nursing cases: unstable vital signs, cardiopulmonary resuscitation, and postoperative nursing care. Expert evaluation of the scenarios' educational applicability and clinical representativeness demonstrated that most items exhibited a content validity score of 0.80 or higher. Two exceptions were noted: the item regarding the inclusion of essential patient-case elements scored 0.60 across all cases, while the item concerning the reflection of real clinical situations in Case 2 scored 0.60. The k* values were 0.67 for educational applicability and 0.94 for clinical representativeness. Consequently, 48 prompts were developed. The findings indicate that structured prompts enhance simulated scenarios' educational applicability and clinical representativeness. Further research is required to evaluate these scenarios' effectiveness in educational settings. The validated prompts allow nurse educators to efficiently generate realistic and pedagogically robust simulation-scenario drafts. This study presents a comprehensive approach to developing complete simulation-scenario components using GenAI. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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