## 1 Introduction
For instance, [PERSON] et al. [19] used multi-perspective renderings, transitional interfaces, and multi-modal data (visual/audio) techniques to extend the AR's limited field of view, improving user perception and interaction in the immersive environment. [PERSON] et al. [20] proposed a framework to transform 2D graph data from the real environment into 3D layouts in AR, showing how these visual transitions can enhance interpretation and task performance. Moreover, [PERSON] et al. [21] reviewed how AR/MR improves decision-making through real-time context-aware data visualizations, providing guidelines for designing effective decision support systems. Notably, SA and IA provide complementary approaches for designing decision support systems. SA addresses the user requirements by formalizing and presenting information to enhance context awareness and decision-making. On the other hand, IA leverages AR/MR technologies to present and interact with information in ways that support analytical reasoning and decision-making [18], complementing SA from a technological perspective. Both approaches are necessary, as even the most advanced systems may fail in utility and acceptance if they do not consider the user needs [22]. By integrating SA and IA, AR/MR solutions can align information presentation and interaction with user requirements, achieving high levels of perception, comprehension, and projection of the situation, i.e., enabling effective situation awareness for better decision-making. Despite being complementary, SA and IA are usually treated separately in the literature. In this regard, there is still no formal and comprehensive method that systematically integrates both approaches. That is, there is a lack of a structured framework that (a) defines interface requirements for achieving high levels of SA; (b) translates these requirements into content, presentation, and interaction design concepts aligned with IA principles; and (c) can be replicated and evaluated across projects. Current research often relies on ad hoc design guidelines or single-case implementations, which limits their transferability. As a result, designers are left without a repeatable workflow and researchers without a reference model to test. This gap limits research and development efforts for effective AR/MR decision support systems in construction. To address this gap, this study proposes a method that integrates SA and IA frameworks to guide the design of AR/MR decision support interfaces. The method leverages AR/MR immersive capabilities to improve the practitioner's perception, comprehension, and projection of the situation, supporting more informed decision-making. Its effectiveness was evaluated through a practical application, using as criterion its capacity to produce AR/MR interfaces that enable users to achieve high levels of SA. The evaluation was conducted in the context of the decision-making of field managers during indoor construction activities of high-rise building projects. Field managers were selected due to their relevance, as they are considered responsible for the major decisions that control the on-site construction process [23; 24]. The proposed method is not intended to replace existing methods, techniques, or best practices for interface design, but to complement them from the standpoint of SA and IA. That is, the method focuses on the processes where SA can be enhanced through design, supporting current research and development efforts to create more effective decision support solutions. The following sections review the SA and IA frameworks, explain the research processes, introduce the method and its application, and discuss conclusions, limitations, and implications for future research.