Quality of life and well-being are hardly ever an issue when life itself is at stake. The advantages of high-quality walkable streets and public spaces are underestimated when larger problems need to be addressed first and seemingly more serious solutions need to be applied. Hence, a quantitative approach to evacuation route planning and design prevails over a qualitative one or at least a hybrid one. The scope of the ongoing study partially presented in this paper is to find methods for addressing the complicated present and the disastrous future at the same time. The one applied in the case study reported here—Susaki City in Kōchi Prefecture, Japan, which is preparing for the next Nankai earthquake and tsunami, expected sometime soon—was a cycle of active research and international workshops organized in cooperation with the local community and administration. The aim was to understand the challenges that concern the design of dual spaces that are suitable for both everyday life and emergency situations and are connected by walkable spaces. As a result, the paper offers insight into the limits of punctual treatments as well as the relativity of objective and subjective dimensions of urban walkability in the context of risk. Despite the complexity of the issue, a walkable built environment was revealed to be a countermeasure rather than a fad.

Background: The selection of an end-of-life meal for a person nearing death is often framed as a deeply personal and meaningful act. Distinct from the well-documented practice of last-meal requests before execution, food and meal choices during the terminal phase of illness are shaped by medical constraints, family dynamics, cultural expectations, and ethical judgements. Palliative-care nursing literature increasingly recognises food and mealtimes as sources of both meaning and distress for patients and families. As generative artificial intelligence (AI) systems are increasingly introduced into healthcare contexts, they may also generate recommendations in ethically sensitive areas such as end-of-life care. Such recommendations are not neutral; they may reflect normative assumptions about safety, comfort, and what constitutes a “good death.” Research aim: This study examines how generative AI constructs and justifies end-of-life meal suggestions, and how these outputs reflect, reinforce, or silence particular ethical and cultural imaginaries of dying. Research design: A qualitative, interpretive research design grounded in science and technology studies (STS) and critical AI-ethics perspectives on non-human actors as normative agents (see Theoretical framework, Section 4.1) was adopted, treating AI-generated outputs as cultural texts rather than clinical guidance. The analysis focused on both proposed meals and the justificatory language accompanying them. Participants and research context: No human participants were involved. The dataset consisted of 75 AI-generated meal proposals produced by a single large language model. Fifteen systematically varied prompts were generated five times each, manipulating age, cultural background, religious orientation, family presence, swallowing difficulty, and preference deviation. Ethical considerations: The authors’ institutional research ethics committee confirmed that the study was exempt from full ethical review because it involved no human participants, patient data, or identifiable personal information (determination no. 001, dated 4 August 2026). The relevant institutional office also confirmed that no separate approval was required for the use and analysis of LLM-generated outputs. The analysis was conducted in accordance with the applicable terms of use of the LLM provider. Findings: Safety-oriented language dominated across outputs (93.3%), alongside emotional comfort and nostalgia. By contrast, explicit conflict, ritual specificity, and strong desire prioritisation were consistently absent. Even when preference deviation was prompted, AI outputs tended to neutralise tension and redirect choices towards harmonised, risk-averse narratives. Conclusions: Across the outputs analysed, generative AI reproduced a narroaw moral script of dying that privileged safety and emotional tranquillity while marginalising conflict, ritual, and embodied desire. We do not claim that clinicians currently rely on generic AI tools to determine patients’ final meals; rather, the findings speak to how such tools may function as an informal reference point when preferences cannot be directly confirmed, or as an unexamined “first draft” in busy clinical settings, and to the moral script such tools default to when consulted in this way. Nursing ethics must therefore interrogate not only what AI recommends, but the normative visions of care and death that such recommendations sustain, and must keep the nurse’s own moral agency—rather than AI output—at the centre of end-of-life decision-making.