Giving citizens a voice in urban development processes is crucial for enabling socially sustainable cities and communities. However, citizens’ opportunities to express ideas are often limited to communication channels that offer poor incentives for participation. In this paper, we conducted an in-the-wild technology probe study (N=16) using a generative AI (GenAI) tool to allow citizens to visualise and submit urban development ideas by taking pictures and manipulating them with GenAI. The results highlight the potential of GenAI to empower, engage, and inspire citizens‘ creativity. We then conducted additional expert interviews (N=6) with city representatives and community associates. They voiced GenAI’s value in early-stage citizen participation but raised concerns about excluding senior citizens. Building on these insights, we present the design and evaluation (N=10) of UrbAI, a co-creative system tailored to urban development participation and conclude with lessons learned to inform how GenAI could be embedded in future citizen participation processes.

Language barriers pose significant challenges in our increasingly globalized world, hindering effective communication and access to information. Existing translation tools often disrupt the current activity flow and fail to provide seamless user experiences. In this paper, we contribute the design, implementation, and evaluation of MRTranslate, an assistive Mixed Reality (MR) prototype that enables seamless translations of real-world text. We instructed 12 participants to translate items on a food menu using MRTranslate, which we compared to state-of-the-art translation apps, including Google Translate and Google Lens. Findings from our user study reveal that when utilising a fully functional implementation of MRTranslate, participants achieve success in up to 91.67% of their translations whilst also enjoying the visual translation of the unfamiliar text. Although the current translation apps were well perceived, participants particularly appreciated the convenience of not having to grab a smartphone and manually input the text for translation when using MRTranslate. We believe that MRTranslate, along with the empirical insights we have gained, presents a valuable step towards a future where MR transforms language translation and holds the potential to assist individuals in various day-to-day experiences.

Research on social robots in care has often focused on either the care recipients or the technology itself, neglecting the care workers who, in and through their collaborative and coordinative practices, will need to work with the robots. To better understand these interactions with a social robot (Pepper), we undertook a 3 month long-term study within a care home to gain empirical insights into the way the robot was used. We observed how care workers learned to use the device, applied it to their daily work life, and encountered obstacles. Our findings show that the care workers used the robot regularly (1:07 hours/day) mostly in one-to-one interactions with residents. While the robot had a limited effect on reducing the workload of care workers, it had other positive effects, demonstrating the potential to enhance the quality of care.