Virtual assistants (VAs) are increasingly positioned not just as tools, but as potential social companions—capable of offering either emotional or informational support. Yet, how these forms of support should adapt to varying task difficulties and embodiment styles remains underexplored. We conducted two user studies with cognitive and physical tasks to investigate how support type (emotional vs. informational) shapes user perceptions across variations in task difficulty (easy vs. hard) and embodiment (non-embodied vs. embodied). In Study 1, emotional support positively influenced users’ impressions of VA in easy tasks, while informational support was more effective in difficult tasks. In Study 2, participants also preferred emotional support for easy tasks, but differences between support types were less pronounced for difficult tasks. Notably, embodiment exerted no significant influence in either study. These findings underscore the role of context in shaping effective support strategies, offering design insights for VAs as social companions.

People often attempt to mask their true emotions through deliberate facial expressions, but such efforts are not always successful. In contrast, emotional concealment could be more easily achieved in Virtual Reality (VR) when appropriate functionalities are available. This study introduces three techniques in VR that enable users to manually adjust emotional facial expression while still reflecting real-time facial tracking results. The Ekman (Ek) technique allows users to select six discrete emotions via button interaction, while the Scrollable-Ekman (SEk) technique extends this by allowing users to scale the intensity of the selected emotion. The Arousal-Valence (AV) technique offers nuanced control within a two-dimensional arousal-valence space. We evaluated these techniques against a baseline condition that synchronizes users' natural facial expressions, focusing on the expression of happiness, sadness, and anger. In most measurements, the Ek and SEk showed better results compared to the baseline and the AV techniques. Notably, the SEk technique was particularly effective in enhancing hedonic quality. During the free-flowing conversation, the most critical factor was the timely and well-synchronized coordination between speech and controlled facial expressions. Participant satisfaction also varied by usage style: those who tried to use the techniques continuously and naturally to mimic real-life communication reported lower satisfaction, while those who used them occasionally for playful or exaggerated expressions tended to report higher satisfaction.

Managing negative emotions through emotion regulation (ER) is key to mental well-being. While virtual reality (VR) shows promise for supporting ER, prior work has primarily focused on selfregulation. This paper introduces a virtual agent that helps users manage emotions through conversation-based ER strategies. We compared three conditions: no agent, an agent with non-supportive responses, and an agent with ER-supportive responses. Results showed that the ER-supportive agent significantly improved users' emotional states and overall experience. Building on this, we conducted a second experiment to examine how the agent's appearance (realistic vs. cartoon) and voice tone (emotional vs. neutral) affect ER. Results indicated that an emotional voice tone improved users' ability to regulate emotions. Although a realistic appearance did not directly improve ER, it increased users' trust and sense of social presence. This paper contributes to VR and human-agent interaction by demonstrating the potential of virtual agents to support ER and offering design implications for future ER-supportive agents.

Co-speech gestures are crucial for enriching both human-human and human-agent communications. Yet, the specific impacts of gesture size—especially when being generated by advanced data-driven techniques—remain underexplored. This study investigates how varying gesture sizes affect human-agent interactions across two distinct persuasive contexts (informational and emotional), with a focus on social outcomes such as persuasion and empathy. We conducted two controlled experiments, each involving 36 participants, comparing three gesture conditions: Minimal gesture, Small gesture, and Large gesture conditions. Experiment 1, set in an informational sales context, showed that small and large gestures significantly enhanced persuasive effectiveness, social presence, and communication quality compared to the minimal gesture condition, although no meaningful differences emerged between small and large gestures. In contrast, Experiment 2, situated in an emotionally charged context, revealed that larger gestures progressively amplified both persuasive impact and perceived empathy. These findings highlight that gesture size matters in emotionally intensive communications and the substantial social benefits of deep-learning techniques for gesture generation.

In this paper, we introduce a novel Virtual Reality (VR) navigation method using gaze ray and hand, named RayHand navigation. It supports controlling navigation speed and direction by quickly indicating the initial direction using gaze and then using dexterous hand movement for controlling the speed and direction based on the relative position between the gaze ray and user's hand. We conducted a user study comparing our approach to the head-hand and torso-leaning-based navigation methods, and also evaluated their learning effect. The results showed that the RayHand and head-hand navigations were less physically demanding than the torso-leaning navigation, and the RayHand supported rich navigation experience with high hedonic quality and solved the issue of the user unintentionally stepping out from the designated interaction area. In addition, our approach showed a significant improvement over time with a learning effect.