Industrial Augmented Reality (iAR) has demonstrated its advantages to communicate technical information in the fields of maintenance, assembly, and training. However, literature is scattered among different visual assets (i.e., AR visual user interface elements associated with a real scene). In this work, we present a systematic literature review of visual assets used in these industrial fields. We searched five databases, initially finding 1757 papers. Then, we selected 122 iAR papers from 1997 to 2019 and extracted 348 visual assets. We propose a classification for visual assets according to (i) what is displayed, (ii) how it conveys information (frame of reference, color coding, animation), and, (iii) why it is used. Our review shows that product models, text and auxiliary models are, in order, the most common, with each most often used to support operating, checking and locating tasks respectively. Other visual assets are scarcely used. Product and auxiliary models are commonly rendered world-fixed, color coding is not used as often as expected, while animations are limited to product and auxiliary model. This survey provides a snapshot of over 20 years of literature in iAR, useful to understand established practices to orientate in iAR interface design and to present future research directions.

In this work, we propose a Mixed Reality (MR) application to support laboratory lectures in STEM distance education. It was designed following a methodology extendable to diverse STEM laboratory lectures. We formulated this methodology considering the main issues found in the literature that limit MR’s use in education. Thus, the main design features of the resulting MR application are students’ and teachers’ involvement, use of not distracting graphics, integration of traditional didactic material, and easy scalability to new learning activities. In this work, we present how we applied the design methodology and used the framework for the case study of an engineering course to support students in understanding drawings of complex machines without being physically in the laboratory. We finally evaluated the usability and cognitive load of the implemented MR application through two user studies, involving, respectively, 48 and 36 students. The results reveal that the usability of our application is “excellent” (mean SUS score 84.7), and it is not influenced by familiarity with Mixed Reality and distance education tools. Furthermore, the cognitive load is medium (mean NASA TLX score below 29) for all four learning tasks that students can accomplish through the MR application.

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.