# Development of a Virtual Reality Program for Internationally Standardized Non-Face-to-Face Nursing Practicum Education: Design and Validation of a Sensor-Integrated XR System

## TL;DR

A new virtual reality system for nursing training was developed and validated, using sensors to measure and assess student performance accurately.

## Contribution

The development of a controller-free, sensor-integrated XR system for precise and standardized nursing skill assessment.

## Key findings

- XR-based automated scores showed high agreement with expert instructor ratings (ICC = 0.932).
- XR baseline scores predicted post-training performance and showed incremental validity beyond instructor scores.

## Abstract

What are the main findings?
A controller-free, sensor-integrated XR nursing practicum system enabled precise capture and quantification of fine motor and procedural performance.Automated XR-based assessment demonstrated discriminatory power comparable to instructor-based evaluation and was technically validated through accredited V&V testing.

A controller-free, sensor-integrated XR nursing practicum system enabled precise capture and quantification of fine motor and procedural performance.

Automated XR-based assessment demonstrated discriminatory power comparable to instructor-based evaluation and was technically validated through accredited V&V testing.

What are the implications of the main findings?
Precision sensing transforms XR from an immersive training tool into a reproducible, measurement-oriented assessment system for nursing skills education.The proposed framework supports data-driven standardization of non-face-to-face nursing practicum education across institutions and contexts.

Precision sensing transforms XR from an immersive training tool into a reproducible, measurement-oriented assessment system for nursing skills education.

The proposed framework supports data-driven standardization of non-face-to-face nursing practicum education across institutions and contexts.

Extended reality (XR) has increasingly been applied to nursing practicum education; however, most systems rely on controller-based interfaces that limit precise capture of continuous fine motor performance and objective assessment. This study developed and validated a sensor-integrated, controller-free XR nursing practicum system (Smart Nursing v1.0) grounded in continuous precision sensing. Based on internationally standardized intravenous injection protocols, the system integrated optical hand tracking and speech recognition to quantify hand kinematics, spatial accuracy, procedural sequencing, and verbal compliance. A three-phase validation framework was implemented. Internal technical verification confirmed stable real-time performance (≥60 FPS) and consistent action recognition. In a user-based study involving 63 undergraduate nursing students, XR-based automated scores demonstrated high agreement with expert instructor ratings (ICC = 0.932, 95% CI = 0.91–0.96, p < 0.001). XR baseline scores significantly predicted post-training performance (β = 0.632, p < 0.001) and showed significant incremental validity beyond instructor pre-training scores (ΔR2 = 0.186, p < 0.001). Independent verification confirmed high recognition accuracy (100%) and system stability. These findings indicate that precision sensing enables XR environments to function as reliable performance measurement systems, supporting standardized non-face-to-face nursing practicum education.