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Shared-gaze visualizations (SGV) allow collocated collaborators to understand each other's attention and intentions while working jointly in an augmented reality setting. However, prior work has overlooked user control and privacy over how gaze information can be shared between collaborators. In this work, we examine two methods for visualizing shared-gaze between collaborators: gaze-hover and gaze-trigger. We compare the methods with existing solutions through a paired-user evaluation study in which participants participate in a virtual assembly task. Finally, we contribute an understanding of user perceptions, preferences, and design implications of shared-gaze visualizations in augmented reality. 

Motivational agents are virtual agents that seek to motivate users by providing feedback and guidance. Prior work has shown how certain factors of an agent, such as the type of feedback given or the agent’s appearance, can influence user motivation when completing tasks. However, it is not known how nonverbal mirroring affects an agent’s ability to motivate users. Specifically, would an agent that mirrors be more motivating than an agent that does not? Would an agent trained on real human behaviors be better? We conducted a within-subjects study asking 30 participants to play a “find-the-hidden-object” game while interacting with a motivational agent that would provide hints and feedback on the user’s performance. We created three agents: a Control agent that did not respond to the user’s movements, a simple Mimic agent that mirrored the user’s movements on a delay, and a Complex agent that used a machine-learned behavior model. We asked participants to complete a questionnaire asking them to rate their levels of motivation and perceptions of the agent and its feedback. Our results showed that the Mimic agent was more motivating than the Control agent and more helpful than the Complex agent. We also found that when participants became aware of the mimicking behavior, it can feel weird or creepy; therefore, it is important to consider the detection of mimicry when designing virtual agents. 

Augmented reality (AR) headsets are being utilized in different task-based domains (e.g., healthcare, education) for both adults and children. However, prior work has mainly examined the applicability of AR headsets instead of how to design the visual information being displayed. It is essential to study how visual information should be presented in AR headsets to maximize task performance for both adults and children. Therefore, we conducted two studies (adults vs. children) analyzing distinct design combinations of critical and secondary textual information during a procedural assembly task. We found that while the design of information did not affect adults' task performance, the location of information had a direct effect on children's task performance. Our work contributes new understanding on how to design textual information in AR headsets to aid in adults’ and children's task performance. In addition, we identify specific differences on how to design textual information between adults and children. 

Children are being presented with augmented reality (AR) in different contexts, such as education and gaming. However, little is known about how children conceptualize AR, especially AR headsets. Prior work has shown that children's interaction behaviors and expectations of technological devices can be quite different from adults’. It is important to understand children's mental models of AR headsets to design more effective experiences for them. To elicit children's perceptions, we conducted four participatory design sessions with ten children on designing content for imaginary AR headsets. We found that children expect AR systems to be highly intelligent and to recognize and virtually transform surroundings to create immersive environments. Also, children are in favor of using these devices for difficult tasks but prefer to work on their own for easy tasks. Our work contributes new understanding on how children comprehend AR headsets and provides recommendations for designing future headsets for children. 

Situational awareness is the perception and understanding of the surrounding environment. Maintaining situational awareness is vital for performance and error prevention in safety critical domains. Prior work has examined applying augmented reality (AR) to the context of improving situational awareness, but has mainly focused on the applicability of using AR rather than on information design. Hence, there is a need to investigate how to design the presentation of information, especially in AR headsets, to increase users’ situational awareness. We conducted a Systematic Literature Review to research how information is currently presented in AR, especially in systems that are being utilized for situational awareness. Comparing current presentations of information to existing design recommendations aided in identifying future areas of design. In addition, this survey further discusses opportunities and challenges in applying AR to increasing users’ situational awareness. 

Virtual agents are systems that add a social dimension to computing, often featuring not only natural language input but also an embodiment or avatar. This allows them to take on a more social role and leverage the use of nonverbal communication (NVC). In humans, NVC is used for many purposes, including communicating intent, directing attention, and conveying emotion. As a result, researchers have developed agents that emulate these behaviors. However, challenges pervade the design and development of NVC in agents. Some articles reveal inconsistencies in the benefits of agent NVC; others show signs of difficulties in the process of analyzing and implementing behaviors. Thus, it is unclear what the specific outcomes and effects of incorporating NVC in agents and what outstanding challenges underlie development. This survey seeks to review the uses, outcomes, and development of NVC in virtual agents to identify challenges and themes to improve and motivate the design of future virtual agents. 

Augmented Reality (AR) headsets are being employed in industrial settings (e.g., the oil industry); however, there has been little work on how information should be presented in these headsets, especially in the context of situational awareness. We present a study examining three different presentation styles (Display, Environment, Mixed Environment) for textual secondary information in AR headsets. We found that the Display and Environment presentation styles assisted in perception and comprehension. Our work contributes a first step to understanding how to design visual information in AR headsets to support situational awareness. 

Fitts’ law has accurately modeled both children’s and adults’ pointing movements, but it is not as precise for modeling movement to small targets. To address this issue, prior work presented FFitts’ law, which is more exact than Fitts’ law for modeling adults’ finger input on touchscreens. Since children’s touch interactions are more variable than adults, it is unclear if FFitts’ law should be applied to children. We conducted a 2D target acquisition task with 54 children (ages 5-10) to examine if FFitts’ law can accurately model children’s touchscreen movement time. We found that Fitts’ law using nominal target widths is more accurate, with a R2 value of 0.93, than FFitts’ law for modeling children’s finger input on touchscreens. Our work contributes new understanding of how to accurately predict children’s finger touch performance on touchscreens. 

Mode switching allows applications to support a wide range of operations (e.g. selection, manipulation, and navigation) using a limited input space. While the performance of different mode switching techniques has been extensively examined for pen- and touch-based interfaces, investigating mode switching in augmented reality (AR) is still relatively new. Prior work found that using non-preferred hand is an efficient mode switching technique in AR. However, it is unclear how the technique performs when increasing the number of modes, which is more indicative of real-world applications. Therefore, we examined the scalability of non-preferred hand mode switching in AR with two, four, six, and eight modes. We found that as the number of modes increase, performance plateaus after the four-mode condition. We also found that counting gestures have varying effects on mode switching performance in AR. Our findings suggest that modeling mode switching performance in AR is more complex than simply counting the number of available modes. Our work lays a foundation for understanding the costs associated with scaling interaction techniques in AR.