Effectiveness of Gamification with a Narrative Adapted to the Player’s Profile in Obstetric Nursing Competencies: A Cluster Randomized Controlled Pilot Trial Protocol

Abstract

Background/Objectives: Simulation-based education often lacks personalization, focusing on technical competence rather than individual student profiles. This protocol describes a study designed to evaluate whether adapting gamified narratives to nursing students’ personality profiles has the potential to support academic performance in obstetrics. This study aims to validate the integration of psychometric profiling and AI as a sustainable strategy for personalized clinical training. Methods: A cluster-randomized controlled longitudinal pilot trial will be conducted at the University of Atlántico Medio. The protocol has been submitted for registration at ClinicalTrials.gov (Registration Pending). Thirty-eight second-year nursing students meeting inclusion criteria (excluding repeaters or those with prior specialized training) will be assigned by natural practice to either a control group (generic gamification) or an experimental group (gamification adapted according to Player Personality and Dynamics Scale profiles using AI-generated content). The intervention comprises four clinical simulation sessions focusing on pregnancy and childbirth, which are managed via the Wix platform. The primary outcome is academic performance, measured as “Learning Gain” (post-test scores minus pre-test scores). Secondary outcomes include student satisfaction measured via the Gameful Experience Scale. Data will be analyzed using Mann–Whitney U tests to compare overall efficacy and intragroup evolution. To minimize observer bias, knowledge assessments will utilize automated, objective scoring, and participants will be blinded to the study hypothesis. Expected Outcomes: The study aims to establish the technical and pedagogical feasibility of integrating AI-adapted narratives into nursing curricula. It is anticipated that the personalized approach will show positive trends in learning gains and engagement patterns, providing a baseline for larger multicenter trials. Conclusions: This protocol presents a framework for “Precision Education” in nursing, shifting from “one-size-fits-all” simulations to student-centered adaptive training. The use of Generative AI makes such personalization sustainable and cost-effective for health science faculties.