Wearable technologies offer significant potential for promoting active ageing, yet their design often overlooks the embodied preferences of older adults. This study employs movement-based co-design to explore how aesthetics and materials serve as affordances for age-inclusive wearables. Through workshops with older adults (n=23) and movement experts (n=13), we investigated aesthetic meaning-making and material preferences for wearables used in physical activity. Older adults prioritised discreet designs that respect personal style and health histories, valuing materials that support somatic flow and physical autonomy. Conversely, movement experts emphasised integrating everyday movements with high-performance, skin-friendly materials to ensure safety and confidence. By identifying the design tensions between these stakeholders, specifically regarding visibility, support modalities, and system authority, we propose tangible design considerations for age-inclusive wearables that aesthetically resonate with older adults’ identities and sense of agency.

Large Language Models (LLMs) are increasingly integrated into mental health and well-being technologies, yet little is known about how they are perceived by older adults or how they should be designed to meet later-life needs. Mindfulness technologies, often promoted as tools for healthy ageing, provide a useful context for exploring these questions. We conducted participatory workshops with sixteen older adults using LugnAI, a prototype LLM-based system for guided mindfulness practice. Participants reflected on their experiences with AI-guided mindfulness and contributed design preferences for future systems. Analysis revealed tensions between adaptivity and autonomy, supportive versus intrusive engagement strategies, and AI-enabled emotional support versus the preservation of human connection and self-regulation practices. Based on these findings, we provide concrete design considerations for LLM-based mindfulness technologies that are sensitive to the socioaffective realities of ageing. While situated in mindfulness, the insights extend to broader applications of LLMs in supporting older adults’ well-being.

Understanding user enjoyment is crucial in human-robot interaction (HRI), as it can impact interaction quality and influence user acceptance and long-term engagement with robots, particularly in the context of conversations with social robots. However, current assessment methods rely solely on self-reported questionnaires, failing to capture interaction dynamics. This work introduces the Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES), a novel 5-point scale to assess user enjoyment from an external perspective (e.g.by an annotator) for conversations with a robot. The scale was developed through rigorous evaluations and discussions among three annotators with relevant expertise, using open-domain conversations with a companion robot that was powered by a large language model, and was applied to each conversation exchange (i.e.a robot-participant turn pair) alongside overall interaction. It was evaluated on 25 older adults' interactions with the companion robot, corresponding to 174 minutes of data, showing moderate to good alignment between annotators. Although the scale was developed and tested in the context of older adult interactions with a robot, its basis in general and non-task-specific indicators of enjoyment supports its broader applicability. The study further offers insights into understanding the nuances and challenges of assessing user enjoyment in robot interactions, and provides guidelines on applying the scale to other domains and populations. The dataset is available online.

Storytelling is an effective evidence-based practice as an accepted intervention by therapists for the therapy of children with autism spectrum disorder (ASD). Digital storytelling, particularly using smart tangibles, offers a structured, interactive and engaging environment for children with ASD allowing for repetition, offering feedback with visual supports, and giving the child more authority over the learning experience.

Voice Agents (VAs) are touted as being able to help users in complex tasks such as cooking and interacting as a conversational partner to provide information and advice while the task is ongoing. Through conversation analysis of 7 cooking sessions with a commercial VA, we identify challenges caused by a lack of contextual awareness leading to irrelevant responses, misinterpretation of requests, and information overload. Informed by this, we evaluated 16 cooking sessions with a wizard-led context-aware VA. We observed more fluent interaction between humans and agents, including more complex requests, explicit grounding within utterances, and complex social responses. We discuss reasons for this, the potential for personalisation, and the division of labour in VA communication and proactivity. Then, we discuss the recent advances in generative models and the VAs interaction challenges. We propose limited context awareness in VAs as a step toward explainable, explorable conversational interfaces.

This paper describes the first steps of an ongoing participatory design with teachers from Switzerland to co-create Human-Robot Interaction setups for integrating children with migration history. The herein presented phase had two main goals: (i) initially mapping the current issues and the teachers’ strategy when integrating these children, and (ii) understanding teachers’ perceptions regarding social robots for this goal. Results show that teachers we interviewed are already using technology to communicate with immigrant children, not necessarily for inclusion or promote socialisation with their peers, but simply to understand them. Findings also point to a well-defined application of social robots in inclusion activities, even when never seeing or using them, which contradicts previous results in the literature and which gives potential ways to unfold the next steps of the participatory design.

Neurodivergent children with cognitive and communicative difficulties often experience a lower level of social integration in comparison to neurotypical children. Therefore it is crucial to understand social inclusion challenges and address exclusion. Since previous work shows that gamified robotic activities have a high potential to enable inclusive and collaborative environments we propose using robot-mediated games for enhancing social inclusion. In this work, we present the design of a multiplayer tangible Pacman game with three different inter-player interaction modalities: semi-dependent collaborative, dependent collaborative, and competitive. The initial usability evaluation and the observations of the experiments show the benefits of the game for creating collaborative and cooperative practices for the players and thus also potential for social interaction and social inclusion. Importantly, we observe that inter-player interaction design affects the communication between the players and their physical interaction with the game.