Information-First Design for Usable and Acceptable Social Robots: Lessons from Dentistry

Social robots are increasingly proposed as affective support tools
in healthcare, yet adoption can be limited by poor contextual fit,
unclear clinical value, and unresolved concerns around privacy,
trust, and security. Dentistry provides a uniquely demanding tested
for human–robot interaction: patients are conscious, physically
constrained, often anxious, exposed to aversive sensory stimuli,
and required to make real-time consent decisions under stress. The
dentist has to manage a stressful environment with high speed and
precise instruments and cannot afford to be distracted.
Drawing on ecological video analysis of real dental encounters
and our recent work on social robots, privacy, and cybersecurity
in dentistry, we argue that designing useful and acceptable dental social robots requires a shift from generic affect detection to
context-sensitive, phase-aware, and privacy-minimal information
collection. This paper reframes social robot design as a question
of what information is necessary, when, and under which ethical
constraints in high-intensity environments. We outline a preliminary information management framework for dental robotics and
show how insights from dentistry generalise to other consumer
and healthcare settings involving stress, constrained interaction,
and high trust requirements. We argue that dentistry serves as a
productive testbed for rethinking information collection in social
robots more broadly, and we invite discussion on how these insights
might inform future empirical and design work across healthcare
and consumer robotics.