Privacy Perceptions in Robot-Assisted Well-Being Coaching: Examining the Roles of Information Transparency, User Control, and Proactivity

Social robots are increasingly recognized as valuable supporters in the field of well-being coaching. They can function as independent coaches or provide support alongside human coaches, and healthcare professionals. In coaching interactions, these robots often handle sensitive information shared by users, making privacy a relevant issue. Despite this, little is known about the factors that shape users' privacy perceptions. This research aims to examine three key factors systematically: (1) the transparency about information usage, (2) the level of specific user control over how the robot uses their information, and (3) the robot's behavioral approach - whether it acts proactively or only responds on demand. Our results from an online study (N = 200) show that even when users grant the robot general access to personal data, they additionally expect the ability to explicitly control how that information is interpreted and shared during sessions. Experimental conditions that provided such control received significantly higher ratings for perceived privacy appropriateness and trust. Compared to user control, the effects of transparency and proactivity on privacy appropriateness perception were low, and we found no significant impact. The results suggest that merely informing users or proactive sharing is insufficient without accompanying user control. These insights underscore the need for further research on mechanisms that allow users to manage robots' information processing and sharing, especially when social robots take on more proactive roles alongside humans.

SRWToolkit: An Open Source Wizard of Oz Toolkit to Create Social Robotic Avatars

We present SRWToolkit, an open-source Wizard of Oz toolkit designed to facilitate the rapid prototyping of social robotic avatars powered by local large language models (LLMs). Our web-based toolkit enables multimodal interaction through text input, button-activated speech, and wake-word command. The toolkit offers real-time configuration of avatar appearance, behavior, language, and voice via an intuitive control panel. In contrast to prior works that rely on cloud-based LLM services, SRWToolkit emphasizes modularity and ensures on-device functionality through local LLM inference. In our small-scale user study ($n=11$), participants created and interacted with diverse robotic roles (hospital receptionist, mathematics teacher, and driving assistant), which demonstrated positive outcomes in the toolkit's usability, trust, and user experience. The toolkit enables rapid and efficient development of robot characters customized to researchers' needs, supporting scalable research in human-robot interaction.