## 7 Discussion  
The criterion for evaluating the effectiveness of the proposed method was its capacity to produce interfaces that enable users to achieve high levels of SA, which is quantitatively measured through SAGAT. For this evaluation, a minimum SA score of 70% was set as the acceptable threshold for effectiveness within the context of a simulated scenario, as described in Section 6.4.2. The results show that the design concepts derived from the proposed interface design method enabled participants to achieve high SA scores for level 2 SA (comprehension of the current situation: 100% AR, 93% MR) and level 3 SA (projection of future status: 71% AR, 86% MR), with no statistically significant differences between AR and MR (all \(p\)-values > 0.05), demonstrating the method's effectiveness in supporting SA for both immersive environments. This effectiveness lies in the method's structured approach, which systematically provides the conditions for achieving high SA. The first condition is ensuring that only the necessary information is presented, which reduces the practitioner's cognitive load and enables rapid access to relevant information. The second condition is leveraging the immersive capabilities of AR/MR to present this information in a way that facilitates rapid access and interpretation within the user's immersive environment. These conditions increase the users' perception (level 1 SA), thereby facilitating their comprehension of the situation (level 2 SA) and enabling a more accurate projection of its future status (level 3 SA), that is, achieving a high level of SA. While the inherent capabilities of AR/MR, such as presenting information directly within the user's field of view, significantly support decision-making, it is the method's structured alignment of these capabilities with the users' cognitive needs that ultimately contributes to enhanced SA and more informed decision-making. Although AR and MR scores were similar across most SA requirements, a notable difference was observed in SA requirement 8, \"comments on the constraint removal\", where participants scored lower in MR. This could be attributed to how that information was presented in MR, as it was displayed as a paragraph occupying a large portion of the text box, potentially causing information overload and hindering quick comprehension. In contrast, while AR also presented the information as a paragraph, participants performed better, possibly because users are more familiar with reading large amounts of information on smartphones than on MR headsets. These findings suggest that the amount of information (one of the SA enablers) is particularly sensitive to the MR environment. This observation supports the findings of [PERSON] and [PERSON] [PERSON] and [PERSON] (2007), who emphasized the need to tailor information presentation in AR/MR to effectively support users' SA. Another interesting finding is that there was no statistically significant difference between the SA scores of students and practitioners, suggesting that experience was not a determining factor for achieving higher SA. This contrasts with the idea that seasoned practitioners have an advantage because they rely on experience to easily comprehend the situation by associating it with a known mental model [15]. Two factors may explain this finding: the nature of the task and the formalized information presented in the AR/MR prototypes. The task \"verify the constraints removal from the weekly work plan\" involved identifying activities with constraints not removed, reviewing the corresponding information, and deciding accordingly. Therefore, it could be said that the task was not particularly demanding, making it suitable for both experienced and inexperienced participants. In addition, the AR/MR prototypes presented only the information required for decision-making.