In Autonomous Public Transport (APT), particularly with shuttle buses, passengers travel in smaller, more intimate vehicles—and in the future, such vehicles may operate without an authoritative driver or host. This setup may lead to potential safety concerns, as passengers are left alone together. Additionally, this future absence of a driver or host means that there is no one to address questions or uncertainties that may arise. One proposed solution is introducing a robot onboard the bus, serving a similar role to a human host. To explore this solution, an experiment was conducted in Barkarby, Stockholm, Sweden. Passengers, generally unfamiliar with APT or social robots, experienced two short rides on a bus equipped with either an embodied Furhat robot as the host or a disembodied voice agent in the ceiling. Data were collected from passenger-agent interactions, post-questionnaires, and semi-structured focus group interviews. Results indicate a division in passenger preferences, with some favoring the robot and others the voice assistant. Passengers asked more questions to the robot, suggesting a clearer affordance for interaction. While the questionnaires did not show significant differences, passenger behaviors indicated that they anthropomorphized the robot more. The interviews revealed that passengers felt more secure with a human operator and doubted the robot’s authority during incidents with aggressive passengers or accidents. Our findings show that social robots can help make autonomous buses feel more welcoming and interactive. Future APT systems have many design issues that need to be resolved before riders can find them safe and appropriate to use, and social robots can play a role in resolving such issues—both the ones we see today, and potentially ones that will appear in the future.

A lack of self-awareness of communicative behaviours can lead to disadvantages in important interactions. Video recordings as a tool for self-observation have been widely adopted to initiate behaviour change and reflection. Seeing oneself in a recording can lead to negative affect. Forcing an external perspective can lead to cognitive dissonance. Avatars and virtual agents have the advantage that they can copy a human's behaviour while potentially avoiding this dissonance. To explore the design space of mimicking agents, we set up a user study where a video baseline is compared to agent-mediated conditions ranging from idle non-verbal behaviour to complete mimicry of the voice and face. We show that participants gain increased self-awareness from seeing themselves mediated through the virtual agent. We further discuss qualitative observations for the future design of systems that aid in self-reflection, and particularly note that partial mimicry seems to be less appreciated than full mimicry.

When a human makes a decision, an observer may want to understand the reasons and motivations behind the decision. This understanding is important when IVAs are involved in contextual decision-making or coaching practices. To address this challenge, we propose that an agent’s understanding of its user should include knowledge of the user’s underlying values. Humans prioritise different values – sometimes contradictory – in a manner that depends on the context. We present a method where the agent and user build the required context-sensitive value model together. We use Schwartz’s value theory, which places individuals’ values into ten categories. In a between-subject experiment, with three sessions on different days, we elicit user values by presenting them with moral dilemmas in different contexts on the first day, refine the model by asking users to argue about contradictions on the second day, and let them reflect on the model that they have built together with the system on the third day. We find that users exposed to a value-aware condition are more likely to agree with the robot’s representations of their values post-reflection than those in a baseline. Participants also prioritise different values depending on the context, agreeing with previous findings.