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PaperXR: Immersive virtual reality for industrial printing machinery training

Year: 2026
Type: article
Abstract: The integration of Extended Reality (XR) into industrial training has shown significant potential in enhancing learning experiences. However, its application in the paper printing industry remains largely unexplored, with existing research either lacking empirical validation or focusing on simplified, modular tasks. This study presents PaperXR, an immersive Virtual Reality (VR) application designed for training operators of industrial paper printing machinery. Developed under the XR2Learn project, PaperXR represents a focused, applied contribution to an under-studied industrial niche. While immersive VR simulations and structured instructional design frameworks are well-established in other manufacturing sectors, their rigorous application to the paper printing industry remains scarce. Rather than introducing fundamentally new XR interaction techniques, PaperXR applies known immersive training principles to provide a high-fidelity simulation of a complex Man Roland 700 offset printing machine, focusing on integrated quality control processes that mirror real-world industrial workflows. Learning experience is systematically designed using the XR2Learn-ID model, which integrates the established ASSURE and TPACK frameworks to ensure pedagogical soundness and optimize immersive learning. A between-groups study was conducted with vocational students and an industrial partner to evaluate PaperXR's effectiveness against traditional training methods. Results indicated that participants using PaperXR demonstrated significantly improved task performance in a real-world setting, with enhanced engagement, knowledge retention, and practical skill application. The findings confirm the value of a pedagogically structured, high-fidelity immersive VR approach for effectively training operators in the specific and complex context of the paper printing industry.
Language: English
Open Access status: gold
Estimated APC paid: $1,850
OpenAlex ID: W7155172569
FWCI:
Cites: 29
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