As social robots enter public spaces, there remains a gap in understanding how people imagine and evaluate their roles as social actors. This study explores the social dynamics of human-robot interaction (HRI) using the Method of Empathy-Based Stories (MEBS). Participants imagined encounters with a robot at a youth center, producing 158 stories that reveal culturally situated reasoning grounded in everyday social expectations. Positive interactions were marked by the robot's ability to "pass as social", where adherence to interactional norms enabled smooth exchanges despite technological limitations. In contrast, negative stories exposed failures such as unresponsiveness, rudeness, or lack of social competence, leading to distrust or disappointment. These findings underscore the situated nature of HRI, suggesting that successful interaction depends less on a robot's "real internal states" and more on its capacity to align with normative expectations of context and practice.

Humans excel at understanding social cues in communication, but robots struggle. Social cues are crucial for humans to interpret the intentions of their communication partners. Research indicates that we typically interpret the actions of anthropomorphic robots analogously to their human counterparts, paving a clear path to the design of appropriate social cues. For non-anthropomorphic robots, however, it is an open question how humans interpret social cues with different output modalities and in different contexts. Our study investigates whether social cues signaled by typical non-anthropomorphic modalities such as lights, sounds, and gestures are consistently interpreted across people and contexts. We, therefore, conducted a contextual investigation in a hospital, derived scenarios from co-design workshop, and tested 103 cues collected from the literature in a large online survey (N = 1545). Our results demonstrate that most human interpretations vary by context, highlighting the need to design dynamic and adaptive social cues for interactive robotic systems.

Social robots have been utilized in education, healthcare, customer service, and domestic environments. However, there is limited research on their role in promoting sustainable living at home through playful interactions. This study extends the theme of family-robot interaction (FRI) within human-robot interaction (HRI) by integrating social robots and game-based interactions to enhance sustainability at home. We conducted a one-month qualitative study with 32 participants (parents and children) from eight families, using a family-centered design approach and participatory co-design method. Each family hosted a robot at home for one month. Through co-design sessions, they first shared their expectations for how social robots could support eco-friendly living, then ideated robot-based games to promote sustainability. Our findings showed that social robots were perceived as more engaging and appealing to children than adults. Children saw social robots as companions and were eager to learn pro-environmental practices through the robots’ interactive and playful features. The study’s eco-friendly focus, along with the co-design tasks around the robot, inspired some children to participate in sustainable living practices at home and encouraged their parents to do the same. The ideated robot-based games incorporated competition, collaboration, and reward elements, aiming to enhance interest and eco-friendly living and awareness. Overall, adults were more reserved about social robots, expressing concerns about the robots’ ability to maintain long-term engagement once the novelty effect fades. This study suggests that social robots have the potential to raise intergenerational environmental awareness at home, with children acting as the primary interest group who transfer their eco-friendly knowledge to their parents.

This study examines how an embodied AI receptionist may support future remote healthcare services by assessing user experiences with the Furhat social robot in a simulated wellness service reception. While AI-driven chat interfaces are common in digital healthcare, there is limited empirical work on physically embodied AI in early health encounters. In a controlled 25-minute user study (N = 12), participants completed reception tasks such as check-in, symptom pre-screening, and referral to remote or on-site services, followed by ratings of usability, trust, and comfort with information sharing and using an embodied AI receptionist. Participants understood the idea of AI, but their confidence was reduced by the robot's mechanical behavior, slow responses, and inconsistent gestures. Even so, a physical robot was viewed as offering a more social form of interaction than a purely digital interface. The results suggest that smooth interaction flow and steady social cues are central for broader uptake of AI-based services.

Generative AI (genAI) is rapidly reshaping domains such as education, sustainability, art, and health. While its democratization promises broader access and innovation, it also risks reinforcing existing inequalities and introducing new ethical concerns. This workshop explores the responsible democratization of genAI, its use, development, governance, and distribution of benefits, through the lens of HCI. We invite HCI researchers, designers, and practitioners to collaboratively reflect on how genAI is being democratized across domains and how this process might evolve. Using the four perspectives proposed by Seger et al. (💬 use, 🛠 development, 🏛 governance, ➕ benefits), we aim to identify emerging trends, tensions, and opportunities for shaping equitable genAI futures. Grounded in HCI, this workshop aims to foster interdisciplinary dialogue and contribute to inclusive and ethical genAI democratization by mapping current practices in different domains, exploring possible futures, and identifying trends and connections across the domains.

The urgent challenges of global climate change and rising energy costs underscore the critical need for energy efficiency in residential buildings. Smart meters offer real-time insights into household energy consumption, but their effectiveness depends on how well they influence user behaviour. This systematic literature review synthesizes existing research to assess the role of smart meters in shaping inhabitants’ energy use. Key findings indicate that personalized, appliance-specific feedback and messages highlighting potential losses from excessive consumption can help reduce energy use. However, technological barriers—such as poorly designed interfaces and complex pricing structures—limit long-term engagement with smart meters. While social influence and gamification encourage short-term reductions in energy consumption, they can also lead to unintended increases once incentives are removed. Financial incentives are particularly effective in promoting energy savings among low-income households. Demographic factors—such as homeownership, age, and technological literacy—significantly affect user engagement. Older populations and renters, in particular, often face challenges in benefiting from energy efficiency initiatives. This study emphasises the need for a combination of behavioural and policy interventions, advocating for user-centered feedback designs, equitable pricing models that adjust based on real-time energy demand and hybrid approaches that combine smart meter data with financial support for energy-efficient home improvements. By bridging the gap between technological capability and residents’ behavioural change, this research offers strategies to transform smart meters from passive monitoring devices into active tools that encourage more consistent energy-saving behaviour over time. The findings provide actionable insights for researchers, technology developers, and policymakers.

Blue-collar work is often highly collaborative, embodied, and situated in shared physical environments, yet most research on collaborative AI has focused on white-collar work. This position paper explores how the embodied nature of AI agents can support team collaboration and communication in co-located blue-collar workplaces. From the context of our newly started CAI-BLUE research project, we present two speculative scenarios from industrial and maintenance contexts that illustrate how embodied AI agents can support shared situational awareness and facilitate inclusive communication across experience levels. We outline open questions related to embodied AI agent design around worker inclusion, agency, transformation of blue-collar collaboration practices over time, and forms of acceptable AI embodiments. We argue that embodiment is not just an aesthetic choice but should become a socio-material design strategy of AI systems in blue-collar workplaces.

The increasing artificial intelligence (AI) introduction to manufacturing domain and various forms of human-AI collaborations are transforming everyday manufacturing work towards more digital. While human-AI collaboration in manufacturing offers significant potential benefits, like improving productivity and worker safety, designing effective and human-centred collaborations remains challenging due to the complexity of manufacturing domain and the lack of comprehensive and domain-specific design guidance. This challenge is particularly relevant in the context of Industry 5.0, which emphasises human-centricity, worker well-being, resilience, and sustainable technological development. In our study, we shift the design focus from AI systems to human-AI collaboration as the primary design challenge. Hence, in this study, we aim to identify design requirements and synthesise them for human-centred design guidance for human-AI collaboration in manufacturing. Building on a previously proposed conceptual design framework, our study extends the understanding of human-AI collaboration through additional literature and expert interviews. Our study results reveal eight design dimensions to consider: (i) pre-design considerations , (ii) collaboration task , (iii) collaboration team , (iv) skills and skill development , (v) AI agent characteristics , (vi) interaction and communication , (vii) collaboration infrastructure , and (viii) organisational responsibilities . Together these dimensions provide s socio-technical perspective to the collaboration design. The contribution of this study is a refined design framework and related design guidelines that support the design of human-centred human-AI collaboration in manufacturing, aligned with the principles of Human-Centred AI (HCAI) and Industry 5.0 and promoting human-centricity, worker well-being, resilience, and effective human-AI teamwork.

Climate change represents an existential threat that places young people at an increased risk of mental distress. However, climate anxiety, mental distress about climate change, can also coexist with positive emotions such as climate hope. This qualitative study explores the potential of social robots to act as climate communicators, based on their previously demonstrated potential as engaging mediums. Three written scenarios that utilize different climate communication strategies (empathy, information, action) were presented and evaluated with 42 groups of ninth graders (n=115, 42 group responses) through an online questionnaire. The results show that all scenarios elicited positive but also negative reactions. The reactions were the most mixed regarding the empathy scenario. Our findings suggest that a robot could attract young people’s interest and should be designed to communicate objectively about climate issues and inform about concrete ways to act. Engaging interaction with such a robot implies the support of artificial intelligence in communication, even though the factual information should be drawn from reliable and objective sources rather than be generated with AI. The sustainability implications of the concept require careful consideration.

Trust is essential in human-machine interaction, yet generational differences in trust remain underexplored. This study examines how different worker generations perceive and interact with Automated Guided Vehicles (AGVs) in warehouses. We conducted a two-phase study: first, contextual user study with semi-structured interviews (N = 6), using illustrated decision-making scenarios. The second phase was an online study (N = 95) featuring video-based briefings and the same scenarios. Our research addressed two key questions: (1) How do different tasks contexts influence trust in AGVs’ during collaborative activities? and (2) How do perceptions of safety and reliability impact trust in HMI across generations? Findings indicate that task success and visible safety features enhance trust, while task failures reduce it. Although generational differences were not always significant, specific scenarios revealed notable variations, suggesting that context and prior experience shape trust development. Moreover, discrepancies between reported trust and actual reliance behaviours highlight the need for dynamic trust calibration strategies. These insights contribute to understanding trust in human-machine interaction, emphasizing the importance of designing AGVs that balance safety and efficiency. Our findings underscore the necessity of adaptive systems that account for varying trust levels across generations to foster effective human-machine collaboration in warehouse environments.

The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.

In the field of child-robot interaction, social robots promote motivational learning across diverse educational contexts. This study explored primary school pupils' learning experiences, perceptions, and interactions in physical education activities facilitated by three social robot instructors. We employed human-centered and persuasive design approaches to co-design robot-assisted activities with two physical education professionals. These activities were qualitatively evaluated with 22 pupils aged 12 to 13 years within a 2-hour summer camp. Despite most pupils having minimal prior robotic experiences, they reported positive learning outcomes by showing an improved understanding of physical education concepts and robot literacy. Our study highlights that physically embodied robots benefit physical education, as their multimodal features and physical presence effectively sustain pupils' motivation and engagement. We conclude with four design implications for integrating social robots as motivational instructors in children's physical education.

This paper reflects on the role of Human-Computer Interaction (HCI) research and practice both in addressing and exacerbating current environmental crises. Observing a discrepancy between the urgency of these crises and the attention they receive in HCI, we generated and analysed twelve fictional narratives that speculate about what it could mean if HCI prioritized environmental sustainability. This exercise helped us identify possible strategies towards this aim through addressing HCI practices such as conferencing, teaching and research. These strategies include building on existing meso-level initiatives in the community, reorienting reviewing processes, practising prefiguration, being mindful of diverse perspectives, and adding a touch of humour. Publishing our set of narratives in the form of a book of fairy tales alongside this paper is one step in that direction. We hope the book and the paper contribute to raising and nuancing the topic of (un)sustainability in HCI.

Social robots are designed to mimic embodied human interaction capabilities and are envisioned as social interaction partners either for individuals or groups of people. To interact with such robots, human sensemaking and active practical effort is required. In this microanalytic study, we examine video-recorded multi-party interactions with the social robot Pepper to illustrate some interactional and collaborative practices that humans engage in to achieve interactions with the robot. We examine situations where a group of university students engages in embodied practices trying to get responses from Pepper. We coined two concepts to describe these encounters: “robot speak” refers to embodied, spoken utterances directed at the robot with the aim of prompting a response. It is a specific, situated way of speaking shaped by assumptions about the robot’s interactional competence. “Framing talk” describes the participants’ collaborative commentary used to make sense of the situation, co-constructing the human-robot interaction as a meaningful social event. Additionally, in reference to our previous work, we illustrate a practical-ethical dimension related to social robots by examining how even a minor gaze shift of a robot can become immediately recognized as contextually significant for a participant, even when such are not explicitly designed as invitations to interact.

As cities explore intelligent technologies to support urban living, youth perspectives remain underrepresented in design processes despite their role as future residents. This study explores how young people (aged 13-17) envision intelligent technologies that support neighbourhood communality in the emerging Nordic Superblock urban model. Through four co-design workshops, we investigated youth perspectives on shared spaces, social interaction, and community participation, applying a multi-level framework of micro, macro-, and meta-level considerations. The findings highlight youth's expectations for intelligent technologies that organise and optimise shared space use, facilitate social interaction, and support collective decision-making, such as planning of shared spaces. The study identifies concerns regarding privacy, inclusivity, and over-reliance on AI. The study presents four initial technology concepts for advancing communality, ranging from AI-facilitated shared dining to organising events and space use. This paper contributes to ubiquitous technology design understanding by foregrounding youth perspectives to community-centric intelligent neighbourhood technologies that can support residents' wellbeing.