Participatory robot design projects with older adults often use multiple sessions to encourage design feedback and active participation from users. Prior projects have, however, not analyzed the learning outcomes for older adults across co-design sessions and how they support constructive design feedback and meaningful participation. To bridge this gap, we examined the learning outcomes within a "longitudinal panel." This panel comprised seven co-design sessions with 11 older adults of varying cognitive abilities over six months, aimed at designing a robot to guide a photograph-based conversational activity. Using Nelson and Stolterman's framework of the hierarchy of design-learning, we demonstrate how older adult panelists achieved multiple design-learning outcomes- capacity, confidence, capability, competence, courage, and connection- which allowed them to provide actionable design suggestions. We provide guidelines for conducting longitudinal panels that can enhance user design-learning and participation in robot design.

Past research with participants in the United States showed that, in competitive group tasks, they have more positive attitudes and behaviors toward robots on their team over humans in another team. Here we present a study in which two Japanese students and two robots, placed in a randomly assigned group, compete with another student-and-robot team in a digital game. We explored participants’ moral behavior towards the robots, measured through their assignment of loud noise blasts to human and robot participants, and their perceptions of and attitudes towards the robots. We then compared this data to that which was collected within the United States. Results indicated that participants in Japan favored their ingroup humans and robots over outgroup agents and differentiated ingroup members more than outgroup members, as within the US. Japanese participants also anthropomorphized robots more than US participants and treated them more positively than US participants.

The sources of a person’s ikigai—their sense of meaning and purpose in life—often change as they age. Reflecting on past and new sources of ikigai may help people renew their sense of meaning as their life circumstances shift. Building on insights from an initial Wizard-of-Oz robot prototype [1], we describe the design of an autonomous robot that uses a semi-structured conversation format to help older adults reflect on what gives their life meaning and purpose. The robot uses both pre-determined (scripted) and Large Language Model (LLM) generated questions to personalize conversations with older adults around themes of social interaction, planning, accomplishments, goal setting, and the recent past. We evaluated the autonomous robot with 19 older adult participants in a lab setting and at two eldercare facilities. Analysis of the older adults’ conversations with the robot and their responses to an evaluative survey allowed us to identify several design considerations for an autonomous robot that can support ikigai reflection. Interweaving simple yet detailed predetermined questions with LLM-generated follow-up questions yielded enjoyable, in-depth conversations with older adults. We also recognized the need for the robot to be able to offer relevant suggestions when participants cannot recall events and people they find meaningful. These findings aim to further refine the design of an interactive robot that can support users in their exploration of life’s purpose.