# Towards the Evaluation of Augmented Reality in the Metaverse: Information Presentation Modes
###### Abstract  
In the future, many activities will be carried out in the Metaverse: hybrid offices and video-based education are just some examples. The way research is carried out could change, too. In this context, this work investigates the possibility of simulating Augmented Reality (AR) user studies on information presentation in a virtual environment. Organizing an industrial setup is complex; thus, most studies are executed in laboratories. However, lab experiments present limitations, e.g., the number and variety of participants and the availability of facilities. User studies may also be carried out by exploiting simulated AR, as an initial step for the Metaverse, where people are connected regardless of their location. This alternative could be used to carry out experiments on AR information presentation to solve common issues, such as the lack of physical equipment to perform component location tasks and the long time required to collect a large sample of users. Indeed, researchers could propose CAD models with information that simulates the same visual realism achieved with true AR. Moreover, multiple tests could be conducted in parallel by not relying on a limited amount of physical equipment per user. In this work, we developed and evaluated a desktop-simulated testing environment (DSTE) to conduct AR information presentation experiments remotely. We applied it in a pilot user study, revealing that the proposed DSTE was effective for the related research goals. Furthermore, 40 participants reported a positive user experience. The evaluation confirms that using a DSTE is promising for collecting and analyzing data from a wide range of people. AR simulation; user interaction; information presentation; user study; industrial metaverse +
Footnote †: journal:  
laboratories. For example, it is difficult to recruit industrial operators or involve people from different geographical locations. Thus, the number and variety of participants in the sample are actually limited, and, therefore, the effects of social factors such as language, culture, and education are hard to demonstrate. Furthermore, in some cases, laboratories are not accessible due to ongoing renovation, a lack of laboratory staff, and safety reasons, as experienced during the SARS-CoV-2 epidemiological emergency. A solution to increase the number of people involved in user studies is to simulate real-world content through a virtual environment. The virtualization of the testing environment is coherent with the emerging concept of the Metaverse, a non-face-to-face service that breaks the boundaries between real-world space and virtual space [7; 9]. It is generally intended as an immersive, three-dimensional, virtual, and multi-user environment that allows people to interact with each other regardless of their location using computational tools such as simulations [8]. In this way, experiments may be carried out from a remote location (e.g., the user's office or house) rather than in a real testing environment. This technique, which is called simulated AR [11], indirect AR [12], or immersive virtual AR [13], was effectively used as a design tool before, both to simplify the test execution and to increase the number of people involved in the user study. However, only in recent years has it become more and more widespread as a precursor to the Metaverse, especially after the SARS-CoV-2 pandemic. In addition, previous works [13; 14; 15; 16] used complex Virtual Reality (VR) devices that are not commonly owned by users and are hard to distribute. Thus, the main novel aspect of our research is the development of a testing tool for desktop devices that exploits simulated AR. The aim is to allow users to carry out experiments on information presentation in AR regardless of their location and use their personal computer/laptop with a mouse and keyboard. In particular, this tool could overcome common issues, such as the lack of physical equipment to perform component location tasks and the long time required to collect a large sample of users.