The current best practice for hands-free selection using Virtual and Augmented Reality (VR/AR) head-mounted displays is to use head-gaze for aiming and dwell-time or clicking for triggering the selection. There is an observable trend for new VR and AR devices to come with integrated eye-tracking units to improve rendering, to provide means for attention analysis or for social interactions. Eyegaze has been successfully used for human-computer interaction in other domains, primarily on desktop computers. In VR/AR systems, aiming via eye-gaze could be significantly faster and less exhausting than via head-gaze. To evaluate benefits of eye-gaze-based interaction methods in VR and AR, we compared aiming via head-gaze and aiming via eyegaze. We show that eye-gaze outperforms head-gaze in terms of speed, task load, required head movement and user preference. We furthermore show that the advantages of eye-gaze further increase with larger FOV sizes.

Augmented Reality (AR) gains increased attention as a means to provide assistance for different human activities. Hereby the suitability of AR does not only depend on the respective task, but also to a high degree on the respective device. In a standardized assembly task, we tested AR-based in-situ assistance against conventional pictorial instructions using a smartphone, Microsoft HoloLens and Epson Moverio BT-200 smart glasses as well as paper-based instructions. Participants solved the task fastest using the paper instructions, but made less errors with AR assistance on the Microsoft HoloLens smart glasses than with any other system. Methodically we propose operational definitions of time segments and other optimizations for standardized benchmarking of AR assembly instructions.

Augmented reality (AR) is a promising tool for many situations in which assistance is needed, as it allows for instructions and feedback to be contextualized. While research and development in this area have been primarily driven by industry, AR could also have a huge impact on those who need assistance the most: cognitively impaired people of all ages. In recent years some primary research on applying AR for action assistance and learning in the context of this target group has been conducted. However, the research field is sparsely covered and contributions are hard to categorize. An overview of the current state of research is missing. We contribute to filling this gap by providing a systematic literature review covering 52 publications. We describe the often rather technical publications on an abstract level and quantitatively assess their usage purpose, the targeted age group and the type of AR device used. Additionally, we provide insights on the current challenges and chances of AR learning and action assistance for people with cognitive impairments. We discuss trends in the research field, including potential future work for researchers to focus on. CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI).

Traditionally, laboratory practice aims to establish schemas learned by students in theoretical courses through concrete experiences. However, access to laboratories might not always be available to students. Therefore, it is advantageous to diversify the tools that students could use to train practical skills. This technology report describes the design, development, and first testing of a mobile augmented reality application that enables a hands-on learning experience of a titration experiment. Additionally, it presents the extension of the TrainAR framework for chemical education through the implementation of specific domain features, i.e., logbook, graph, and practical oriented hints. To test the application, 15 participants were recruited from five different high schools and two universities in Belgium. The findings reflect that the MAR Lab app was well-received by the users. In addition, they valued the design elements (e.g., logbook and multiplechoice questions), and the system has "good" usability (SUS score 72.8, SD = 14.0). Nevertheless, the usability and learners' experience can be improved by tackling technical problems, providing more explicit instructions for subtasks, and modifying certain features. Therefore, future development will concentrate on improving upon these shortcomings, adding additional levels to target a larger audience, and evaluating the improvements' effects with more participants.

The potential of Augmented Reality (AR) for educational and training purposes is well known. While large-scale deployments of head-mounted AR headsets remain challenging due to technical limitations and cost factors, advances in mobile devices and tracking solutions introduce handheld AR devices as a powerful, broadly available alternative, yet with some restrictions. One of the current limitations of AR training applications on handheld AR devices is that most offer rather static experiences, only providing descriptive knowledge with little interactivity. Holistic concepts for the coverage of procedural knowledge are largely missing. The contribution of this paper is twofold. We propose a scalabe interaction concept for handheld AR devices with an accompanied didactic framework for procedural training tasks called TrainAR. Then, we implement TrainAR for a training scenario in academics for the context of midwifery and explain the educational theories behind our framework and how to apply it for procedural training tasks. We evaluate and subsequently improve the concept based on three formative usability studies (n = 24), where explicitness, redundant feedback mechanisms and onboarding were identified as major success factors. Finally, we conclude by discussing derived implications for improvements and ongoing and future work.