# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 6 Conclusions and Further Research  
This paper addresses the general research need for adapted training regarding emerging technologies. It also addresses the primary research gap on the impact of the quality of immersive assistance system training scenarios on performance, acceptance factors, and technostress. Based on the confirmation of all three hypotheses, the results of this study show that QoT has a significant influence on performance, acceptance, workload, and the physiological parameters, the HR and SCL. The selected research design has high statistical validity with high effect sizes and resulting statistical power (mean = 0.92). As a secondary result, the performance index offers a new metric for evaluating the effectiveness and comparability of training scenarios. In addition, the DOE has a high degree of reproducibility; it thus may be used for further research in this area. This article also presents an approach on how an optimised training scenario for immersive systems can be evaluated. Relevant findings can be derived from this, including the need to accommodate the different system levels with different training methods. Indirectly, a causal hierarchy has been discovered: the operator must first master the hardware level (including the correct positioning of the HMD on their head) before focussing on the downstream basic functionalities, such as the interaction paradigm and the operating system. Consecutively, the learner should only learn dedicated functions of immersive applications after the operating system level. Generalised design guidelines based on the findings of the OT are provided in Appendix A. Another important finding is that it is not the personalised training (refer to DT) that yields the best results but the mixture of methods for the respective system level. Further research demands can be derived from the findings of this DOE and the results. * Verification of the results obtained and the general DOE regarding performance, acceptance, workload, and technostress by third parties;
* The mixture of methods per system level proves to be advantageous and could be used for further research. The constraints and areas of validity should be further specified;
* The OT should be investigated in more detail and the scenario further optimised;
* The dependencies of the appropriate training scenario regarding the specific application, the assistance system, and the specialised task must be investigated further;
* It is necessary to investigate in detail what weightings a faulty assistance system and a faulty training scenario have on the dependent variables. One research question could be whether the performance or loss of acceptance of the immersive system can be compensated for by adapted training;
* In addition, dependencies of the OT on hardware should be investigated. The results of the operational measurement task are presented as an added value in this article in Appendix B. These are intended to serve both as proof of the current technological maturity of the assistance system and as a motivation for the industrial sector to deploy comparable immersive assistance systems. The research association has shown that the trade-off between measurement deviation and measurement effort (e.g., time required) is indirectly correlated with the accessibility (reachability) of the measurement object. In other words, the more difficult it is for the operator to reach real reference points, the more advantageous the immersive measuring application is compared to conventional measuring methods. For this reason, the research question and the study design were examined in an industry-related context because, in the short to medium term, the industry is facing the challenge of applying practicable training scenarios. Summarising the findings, it can be concluded that QoT has a significant impact on performance, acceptance, workload, and technostress. A faulty training scenario can have a significant negative impact on these metrics.