Midwifery students' and teachers' perceptions and needs regarding Extended Reality (XR) in educational training and its potential role in anxiety-related learning contexts.

Abstract

Introduction: Some midwifery students report anxiety or insufficient preparedness for clinical practice by the end of their initial education. Integrating Extended Reality (XR) into midwifery training may transform traditional teaching by offering immersive, interactive, and safe learning environments. While XR is becoming more common in healthcare education, a better understanding of how midwifery students and teachers perceive its utility, remains underexplored. This study aims to: 1) assess students' and teachers' expectations, needs, and experiences with the use of XR in training; and 2) explore perceptions of XR's potential to support training-related anxiety.

Methods: A cross-sectional survey was conducted among 86 students and 4 teachers from the Midwifery Department at Grenoble Alpes University, France. Participants rated the appropriateness of five XR training scenarios on a 10-point Likert scale, and later re-evaluated them considering XR's potential for anxiety management. One open-ended question provided qualitative insights into expectations and perceived benefits.

Results: Participants viewed XR as an appropriate tool for midwifery education, emphasizing immersive, hands-on scenarios, such as gesture simulation and clinical case management. Emergency and crisis management emerged as the main use case (68%). When anxiety-related learning contexts were introduced as an additional consideration, participants' evaluations remained consistent, suggesting that XR was perceived as relevant for high-pressure clinical training scenarios.

Conclusions: Participants' perceptions highlight the educational relevance of XR for simulation-based midwifery training, particularly in high-pressure clinical scenarios. These exploratory findings provide a basis for future studies involving direct XR exposure and validated outcome measures, and guided the development of an AR-based shoulder dystocia training module.