The number of HCI papers on care has grown rapidly in recent years. Despite growing interest in care both as an application domain for technology and as an ethical stance in research and design, our integrated understanding of the concept is limited. It remains unclear how various application areas of care relate to one another, to what extent their underlying assumptions align or contradict, and how they collectively shape HCI discourse on care. To address this, we present a meta-narrative review of 317 SIGCHI papers on care. We first outline the landscape of care in HCI. We then present six paradigmatic framings of care, and a conceptual map that positions these framings in relation to each other, their representative care–tech relations, and the temporal development of the field. We conclude by discussing the implications from the review, as well as gaps in the field and future directions.

Text-to-image (T2I) generative AI tools like Midjourney are growing in capability and popularity, promising a wide range of applications.However, concerns are rising over the biases in how they represent social concepts like care and the lack of guidance for designers and users to address these in practice.This paper first presents an analysis of 140 "photos of care" generated by Midjourney, and then explores how prompting might influence the results.The findings reveal that AI-generated images reproduce stereotypical and reductive representations of care by default, neglecting the broad spectrums of care practices in everyday life.Furthermore, we find that while prompt engineering might mitigate certain biases, it requires specialised skills, knowledge, and an ongoing reflexive approach to generate meaningful outputs.We conclude by proposing a reflexive prompting framework, and discussing the implications for future T2I evaluation and its responsible use and design.

Service robots are entering all kinds of business areas, and the outbreak of COVID-19 speeds up their application. Many studies have shown that robots with matching gender-occupational roles receive larger acceptance. However, this can also enlarge the gender bias in society. In this paper, we identified gender norms embedded in service robots by iteratively coding 67 humanoid robot images collected from the Chinese e-commerce platform Alibaba. We then generated four-step guidance for designers to identify and challenge the gender norms in the robot design. Our research provides both the fundamental grounding and practical guidance for designing catering robots that challenge gender norms and promote social equality.

Service robots are envisioned to be adaptive to their working environment based on situational knowledge. Recent research focused on designing visual representation of knowledge graphs for expert users. However, how to generate an understandable interface for non-expert users remains to be explored. In this paper, we use knowledge graphs (KGs) as a common ground for knowledge exchange and develop a pattern library for designing KG interfaces for non-expert users. After identifying the types of robotic situational knowledge from the literature, we present a formative study in which participants used cards to communicate the knowledge for given scenarios. We iteratively coded the results and identified patterns for representing various types of situational knowledge. To derive design recommendations for applying the patterns, we prototyped a lab service robot and conducted Wizard-of-Oz testing. The patterns and recommendations could provide useful guidance in designing knowledge-exchange interfaces for robots.