| Sumario: | • 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.
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