Jeffrey C. F. Ho

Research on VR often isolates constructs on separate scales, obscuring how users themselves link presence, agency, realism, fictionality, temporal structure, and embodiment during play. We address this gap with a qualitative, participant-level mapping of user-articulated associations across nine constructs defined a priori from theory and standardized for analysis: presence (PI; feeling “there”), visual realism, physical realism, fictionality, agency, temporal structure, narrative comprehension, attentional guidance, and embodiment. Fifty-seven design-trained participants played two commercial VR games (A Fisherman's Tale; Moss on Quest 2/3) and answered open-ended prompts. Using codebook thematic analysis, we binarized construct mentions per participant and computed normalized co-occurrences (Jaccard) with bootstrap 95% CIs and permutation baselines; metrics are descriptive, valenceagnostic, and non-causal. Findings: Presence co-occurred most with layered attentional guidance and embodiment, exceeding its association with visual fidelity. A “design-side” cluster linked visual and physical realism with temporal pacing and guidance; visual realism clustered more with fictionality and narrative discourse than with presence. These patterns refine theory toward coherence, guidance, and embodiment-centered pathways to sustaining place illusion and engagement, consistent with claims that internal consistency and sensorimotor contingencies outweigh photorealism. Methodologically, the participant-level, normalized map makes qualitative associations inspectable and comparable via effect sizes with uncertainty, complementing solo-construct scales and supporting reuse on other construct sets. Practically, the results recommend prioritizing consistent world rules and physically plausible interactions, scaffolding agency with multimodal diegetic cues, and providing robust, synchronous self-representation.

When teachers introduce a new type of technology, they often choose a beginner-friendly software option. The aim of the current study was to investigate whether and how design students' perception towards a beginner-friendly software influences their perception, attitude, and intention to learn other more advanced software. Based on the Technology Acceptance Model, we proposed and tested correlations between students' perception towards beginner-friendly software and their perceptions of other software in the same category. To introduce computational tools for conducting big-data research, we developed a beginner-friendly software called Onion, which allows design students to use computational tools such as data retrieval from social media and artificial-intelligence-based object recognition through a graphical user interface. We conducted two introductory workshops, and captured students' perception towards Onion and their perception and intention to learn more advanced computational tools with a questionnaire. The results indicated that learners' perceived usefulness of the beginner-friendly software has strong and positive correlations with their perceived usefulness and learning intention of other software alternatives. This suggests that introducing new technologies with beginner-friendly software can influence learners' perception towards and intention to learn other similar but less beginner-friendly software packages. Theoretical and practical implications are discussed.

This study aims to measure the impact of bedroom privacy on residents’ social networks in a long-term care (LTC) facility for older adults. Little is known about how the architectural design of bedrooms affects residents’ social networks in compact LTC facilities. Five design factors affecting privacy were examined: bedroom occupancy, visual privacy, visibility, bedroom adjacency, and transitional space. We present a spatio-social network analysis approach to analyse the social network structures of 48 residents. Results show that residents with the highest bedroom privacy had comparatively smaller yet stronger groups of network partners in their own bedrooms. Further, residents who lived along short corridors interacted frequently with non-roommates in one another’s bedrooms. In contrast, residents who had the least privacy had relatively diverse network partners, however, with weak social ties. Clustering analyses also identified five distinct social clusters among residents of different bedrooms, ranging from diverse to restricted. Multiple regressions showed that these architectural factors are significantly associated with residents’ network structures. The findings have methodological implications for the study of physical environment and social networks which are useful for LTC service providers. We argue that our findings could inform current policies to develop LTC facilities aimed at improving residents’ well-being.

This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s daily lives (e.g., voice-controlled VAs are designed to blend in based on their natural qualities). To further understand users’ ideas of their ideal VA designs, participants were invited to generate designs of personal VAs. However, end users may have unrealistic expectations of future technologies. Therefore, design fiction was adopted as a method of guiding the participants’ image of the future and carefully managing their realistic, as well as unrealistic, expectations of future technologies. The result suggests the need for a human–AI relationship based on controls with various dimensions (e.g., vocalness degree and autonomy level) instead of specific features. The design insights are discussed in detail. Additionally, the co-design process offers insights into how users can participate in AI/ML designs.