Immersive Learning in Industrial Robotics: User Evaluation and Workload Assessment of the X-MAIN Platform

This work details the design, technical deployment, and empirical validation of X-MAIN (eXtended Reality for Maintenance and Inspection Training), an Extended Reality (XR) environment designed for preventive maintenance and safety protocols in industrial robotics. By replacing physical robotic cells with a safe and highly repeatable virtual workshop, X-MAIN enables learners to perform hands-on tool manipulation and fault diagnostics without relying on physical hardware availability or real-time supervision. The framework is structured into a Simulation Zone for operational checks and a Maintenance Zone for hands-on tasks, governed by a generic, rule-matrix pair-based interaction model that formalizes user actions as tool–target pairs across Virtual Reality (VR) and desktop deployments. To evaluate its efficacy, a counterbalanced cross-over pilot study was conducted with a cohort of $N=23$ technical vocational students in immersive VR mode. Quantitative results demonstrate statistically significant technical knowledge acquisition. System usability assessments yielded a highly favorable Net Promoter Score of $8.6/10$, while subjective workload mapping via an adapted NASA-TLX index confirmed low frustration levels and optimal cognitive engagement. Automated event-driven telemetry further substantiates system efficacy through low procedural error rates and high diagnostic accuracy (75.8%).