# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
###### Abstract  
Emerging assistance systems are designed to enable operators to perform tasks better, faster, and with a lower workload. However, in line with the productivity paradox, the full potential of automation and digitalisation is not being realised. One reason for this is insufficient training. In this study, the statistically significant differences among three different training scenarios on performance, acceptance, workload, and technostress during the execution of immersive measurement tasks are demonstrated. A between-subjects design was applied and analysed using ANOVAs involving 52 participants (with a statistical overall power of 0.92). The ANOVAs were related to three levels of the independent variable: quality training, manipulated as minimal, personal, and optimised training. The results show that the quality of training significantly influences immersive assistance systems. Hence, this article deduces tangible design guidelines for training, with consideration of the system-level hardware, operational system, and immersive application. Surprisingly, an appropriate mix of training approaches, rather than detailed, personalised training, appears to be more effective than e-learning or 'getting started' tools for immersive systems. In contrast to most studies in the related work, our article is not about learning with AR applications but about training scenarios for the use of immersive systems. training; onboarding; immersive assistance systems; augmented reality; acceptance; performance; guideline; measurement accuracy; workload; stress +
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what is currently measured. Due to fundamentally new interaction paradigms and the interplay of virtual assets within the real world, users face major obstacles in understanding the logic of immersive systems. Especially for beginners, the error rates are very high due to the participation gap. This leads to masking of the added value of the immersive features along the user journey. This dilemma poses major challenges for both developers and users. The question from our research association is whether the system design or the training design has a greater influence on user experience. According to [PERSON] and [PERSON] (2018), the reasons for this paradox are multi-layered, including exaggerated expectations, adjustment delays, and misjudgements of the added value. However, a key aspect is the mismanagement of these technologies and the lack of complementary preconditions. The operators lack the necessary skill level to be able to use the systems effectively. Corresponding to (2018), insufficient resources are invested in the necessary training. This training error, as outlined in the AR failure taxonomy (2018), contributes to adverse effects on performance (as discussed by [PERSON] et al. (2018) and [PERSON] and [PERSON] (2018)) and acceptance (indirect correlation as discussed by [PERSON] et al. (2018) and [PERSON] et al. (2018)). It also contributed to technostress (2018); (2018); (2018), which negatively impacts overall productivity. In general, immersive technologies promise significant potential for education and training in the industrial environment (2018); (2018); (2018), such as speeding up the reconfiguration of production lines, supporting shop-floor operations, or virtual training for the assembly of parts. Initial concepts have also been explored regarding how the use and handling of immersive systems can be trained in the context of training and teaching in a university environment (2018). The critical literature search revealed that there is no contribution that deals with training scenarios, evaluation methods, or guidelines specifically for learning the usage of immersive systems in an industrial context. However, there is a demand for research (see (2018); (2018); (2018); (2018); (2018)) on how immersive assistance systems can be introduced to user groups effectively and efficiently in order to avoid negative effects.