Extended Reality (XR) encompasses Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), blending the physical and digital worlds to create immersive experiences. Healthcare simulation has evolved significantly with XR technologies, yet a critical gap exists between technical skill development and emotional resilience training. This overview of literature synthesizes current implementation on biometric-driven adaptation in healthcare simulation, highlighting opportunities for innovation through the integration of physiological monitoring with adaptive learning environments. The healthcare simulation field overlooks key clinical competencies, with emotional resilience explaining more than 40% of clinical performance and around 30% of patient outcomes (Hamstra et al., 2014), yet training remains focused on technical skills. XR tools emphasize anatomical realism over emotional regulation (Li, 2024), reinforcing the "fidelity fallacy", the false belief that visual realism ensures clinical authenticity (Carey & Rossler, 2023). Fixed XR scenarios fail to reflect real-world complexity, where clinicians face significant unexpected variations per event (Armstrong et al., 2024), and cannot adapt to users’ emotional states, widening the authenticity gap (Birt et al., 2024). Physiological monitoring enables adaptive learning, with strong links between biometrics and emotion: heart rate variability reflects cognitive load, pupillary response signals stress, and vocal cues reveal anxiety (LeBlanc et al., 2024). Yet, integration is limited to three fourth of simulation centers collect data, but only less than 5% use it in real time (Lam et al., 2021). This gap persists despite simulations with biofeedback improving stress management, decision accuracy, and learning transfer over standard methods (Farsi et al., 2021). Review was conducted on literatures covering only the implementation of biometric-driven simulation in various domains. Biometric-driven adaptation has proven feasible in adjacent fields like military training (Yockey, 2023), aviation (Bernabei & Costantino, 2024), and competitive sports (Gorski et al., 2021), which have successfully integrated physiological monitoring with environmental adaptation. However, healthcare education adoption faces barriers including equipment costs, technical integration challenges, standardization issues, and privacy concerns (Li et al., 2023). Biometric-driven adaptation in healthcare setting is limited. This gap is particularly consequential in Australasian healthcare contexts, where cultural variation introduces complexity. Research identifies significant cultural differences in stress manifestation and decision-making patterns among healthcare students from diverse backgrounds (Kelly et al., 2018), yet these dimensions remain unaddressed despite increasingly multicultural workforces and patients. Multi-modal XR technologies offer promising opportunities for comprehensive simulation environments. Combined approaches utilizing projection environments, augmented reality, and haptic feedback achieve greater psychological fidelity than single-modality implementations (Cochrane et al., 2020; Akhtar et al., 2024a; Akhtar et al., 2024b). The MESH360 framework provides a foundation for integrated approaches but lacks biometric adaptation. Emerging research on personalized cognitive load management shows dynamic adjustment of scenario complexity based on real-time indicators improves learning outcomes by 23% compared to standardized approaches (Pears et al., 2024), allowing learners to progress at optimal challenge levels without triggering performance-impairing stress (Couarraze et al., 2023). The literature reveals a significant opportunity for innovation through integrating biometric monitoring with adaptive XR environments. We hope to address the documented gaps by creating a bidirectional relationship between learner and environment, establishing a continuous feedback loop that better prepares healthcare professionals for the complex interplay of technical and emotional challenges in clinical settings. The presentation will provide an overview of the approach and how it might be implemented.
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