Prior research has highlighted users’ preferences for embodiment when interacting with virtual agents in augmented reality headsets. However, open questions remain regarding users’ preferences towards agent placement and gaze direction. In our study, we asked 48 adults to wear the Microsoft HoloLens 2 and find objects in a hidden object game with the help of embodied agents. We examined four distinct agent configurations for both male and female agents: a human-size agent standing beside participants, a human-size agent sitting beside participants, a small desk agent facing the screen, and a small desk agent facing the participant. Overall, participants preferred male over female virtual agents when receiving assistance, and no consistent preference emerged regarding the agents’ position or gaze direction. From our results, we build upon existing guidelines for designing better virtual agents for AR with headsets.

Although companion virtual agents (CVAs) are increasingly adopted to support well-being, interaction patterns between adults and CVAs remain underexplored. This study examines how users engage with CVAs over time, exploring conversation patterns, interaction frequency, attitudes, and emotional responses over a seven-day period. Twenty-four adults engaged with a GPT-4-powered embodied CVA daily, discussing topics from personal interests to emotional reflections. Quantitative measures, including loneliness and affect scales, revealed no significant reduction in loneliness but noted decreases in positive affect and nervousness. Qualitative analysis highlighted evolving conversational dynamics, with participants shifting from exploratory questions to more reflective and personal discussions. Participants appreciated the agent’s ability to engage in fluid and meaningful conversations. However, participants also noted shortcomings, including limited recall and occasional conversational unnaturalness. These findings inform the design of CVAs, emphasizing the need for adaptive conversational strategies, enhanced emotional responsiveness, and improved memory systems to foster meaningful connections.

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.