Virtuality and Reality in Designing Future Services that Utilize Past Episodes

Abstract

The success of AI services is predicated on the availability of relevant data and their acceptance by users. However, AI service design often faces a dual challenge: the opacity of the past, where critical historical data remains inaccessible, and the opacity of the future, where the value and risks of new technologies are difficult to envision before adoption. These challenges are particularly evident in services that aim to utilize personal histories and life experiences that have not been digitized, as well as in emerging AI applications whose practical benefits and limitations are not yet fully understood by prospective users. To address these issues, we propose an analytical framework derived from Virtual Reality (VR) design—spatiality, interactivity, and self-projection—and apply it to the design and evaluation of a narrative-based care ecosystem.We exemplify this approach through two grounded case studies within the care sector. First, we conducted a narrative collection experiment involving an elderly individual with dementia. The participant walked through familiar locations while engaging in conversations with a care worker. The results demonstrated that aligning environmental contexts with empathetic interaction can elicit latent personal histories that are otherwise difficult to access. Familiar surroundings acted as memory triggers, while confirmation-based communication and positive feedback encouraged spontaneous narrative disclosure. Analysis using the proposed framework revealed that effective narrative collection depends on the construction of a cognitive space where past and present coexist, supportive interaction that connects fragmented memories, and opportunities for individuals to project their authentic selves through storytelling. These findings suggest practical approaches for overcoming the opacity of the past and improving the collectability of personal data required for AI services.Second, we evaluated a Proof of Concept (PoC) for an AI-based care advice tool with experienced care staff. The tool referenced narrative profiles of care recipients and generated individualized communication advice. While participants appreciated the tool’s ability to organize information, provide objective perspectives, and support personalized care, the evaluation also highlighted a critical dissonance between designer-focused technical accuracy and the operational reality of frontline care. Staff emphasized concerns related to mobility, workflow integration, timing of interaction, privacy, emotional labor, and the need for multiple alternative suggestions rather than a single optimal answer. Analysis through the VR framework revealed differences between how designers and practitioners envisioned the service environment, expected interactions, and the role of AI in care decision-making.These cases provide universal insights applicable to broader AI service design. To overcome the opacity of the past, designers must create interfaces that ensure psychological safety, establish trust regarding data use, and employ contextual cues that facilitate spontaneous data disclosure. To mitigate the opacity of the future, AI should be positioned not as a replacement for human expertise but as a co-creation partner that offers objective insights while preserving the user’s decision-making sovereignty. Furthermore, successful AI adoption requires attention to physical work environments, temporal continuity, and the contextual realities of everyday practice. By shifting from feature-centric design to an approach that respects the continuity of user context, personal narratives, and professional expertise, designers can successfully address both forms of opacity and foster sustainable AI integration across diverse industries.

Keywords: Artificial Intelligence, Past Data, Future Service, Virtual Reality, Opacity, Service Design

DOI: 10.54941/ahfe1008098