Exploring operator responses to augmented reality training: insights from the SELFEX platform case study - Escallada, Oscar; Lasa, Ganix; Mazmela, Maitane; La Carrubba, Dario; Bosani, Enrica; Dacal-Nieto, Angel; García, Marcos Villar (Frontiers in Computer Science, 2025)
Traditional industrial training methods often fail to capture the tacit expertise of experienced personnel, limiting instructional quality for new staff. This study examines the SELFEX platform, an augmented reality (AR)–based training system enabling junior operators to learn autonomously by replicating recorded performances of senior operators. Using a mixed-methods design, the research combined a technical analysis of AR’s functionality, benefits, and constraints with an empirical evaluation in an industrial setting. Seventeen participants completed training tasks using either conventional screens or AR headsets, with subjective measures including satisfaction, perceived usefulness, ease of use, and flow state, alongside objective performance metrics. Results showed that AR training was particularly beneficial for novices, enhancing engagement, understanding, and perceived ease of learning, though no statistically significant performance differences with screen-based training were found. Correlation analyses revealed strong links between flow, satisfaction, and ease of learning, highlighting the importance of intuitive, well-integrated design. Challenges in integrating AR into professional workflows—such as technical stability and user adoption—were also identified. These findings position AR as a promising tool for accessible and immersive industrial training, capable of supporting both initial skill acquisition and potential future upskilling. Further longitudinal studies are recommended to evaluate long-term impacts on performance, retention, and cost-effectiveness, and to refine system usability for diverse user profiles.