A data-analytics framework for optimizing user-centered virtual reality training

Abstract

Safety training in high-risk industries often lacks user-centric design, leading to ineffective learning outcomes. This study presents a novel framework to optimize Virtual Reality (VR) safety training by integrating two decision-making methods to align user needs with technical design. The research addresses the problem of inadequate training efficacy by prioritizing user requirements and mapping them to technical solutions. A four-phase methodology identifies user requirements through expert consensus, prioritizes them using the Analytic Hierarchy Process (AHP), determines technical measures, and aligns them with user needs via Quality Function Deployment (QFD). SMART-FAST-CLEAR framework and consistency check used to validate expert agreement, though empirical user testing is recommended for future work. Results highlight VR’s superiority over augmented reality and computer-based training, emphasizing enhanced learning effectiveness and immersion without relying on complex numerical metrics. This framework offers a replicable model for designing effective, user-focused VR safety training systems, contributing to improved safety practices in high-risk environments.