In the original publication [...]

FlexPlan: High-flexibility interactive floorplan design based on ArchiGraph

This study evaluated the effectiveness of an educational board game compared with online lectures in enhancing nursing assistants' (NAs) empathy and learning motivation. NAs play a vital role in patient-centered care; however, training programs often prioritise technical skills over the development of empathy. Game-based learning has shown potential for fostering empathy, but its application to NAs remains underexplored. A quasi-experimental design was adopted to compare the two instructional strategies in a real-world setting. This study received approval from the University-Affiliated Institutional Review Board. Written informed consent was obtained from all participants, and measures were taken to ensure confidentiality and anonymity. RESEARCH METHODS, INSTRUMENTS AND INTERVENTIONS; OUTCOME MEASURES: Eighty-three NAs were recruited and assigned to a board game group (n = 35) or an online lecture group (n = 48). Empathy, learning motivation and satisfaction were assessed at baseline, immediately after the intervention, and at the four-week follow-up using validated scales. The board game group showed significantly greater improvements in empathy at both post-test and follow-up compared with the online lecture group. Both groups reported increased motivation, with intrinsic motivation notably higher in the board game group. Learning satisfaction was also significantly greater among board game participants. Convenience sampling, reliance on self-reported measures, the single-session intervention and the short follow-up period may limit generalizability and long-term interpretation. Educational board games provide an effective and engaging strategy to enhance empathy, intrinsic motivation and learning satisfaction among NAs. Their interactive and immersive design fosters emotional awareness, perspective-taking and compassionate care. Nursing administrators are encouraged to integrate board games into empathy training programs. Future research should involve more diverse samples and longer follow-up periods to examine sustained effects. This study adhered to the TREND checklist for nonrandomized intervention studies.

Research on somatic interactive exhibition design of lacquer artwork based on multi-source data analysis

Visual perception and user experience Optimization: Exploring interaction design through static-triggered smooth pursuit systems

The integration of sustainable development elements into interaction design for ocean conservation education in natural history museum: A review

We present a novel framework for human-robot interaction designers to analyze and explore expectations of their robot designs. It consists of a model of how people form expectations of robots, and a taxonomy for classifying them. A known challenge in human-robot interactions is expectation discrepancy, in which the expectations people form when interacting with a social robot are not aligned with its actual capabilities. This can disappoint users and hinder interaction. Research has proposed ways to mitigate expectation discrepancy, but designers lack a systematic approach to analyzing and describing expectations. We developed a rigorous theoretical framework by drawing from theories and models from psychology and sociology on expectations between people, and by conducting a field review of expectations in human-robot interactions. We further propose methods for designers to leverage the framework in systematic analysis of how and why people form expectations of a given robot and what those expectations may be. This can empower designers with greater control over people’s expectations, enabling them to combat expectation discrepancy.

In the context of deepening globalization, world music serves as a vital medium for cross-cultural understanding and an important lens through which to examine how formal education fosters cultural diversity. This study analyzes three nationally sanctioned Chinese Music Appreciation textbook series through a mixed-method framework combining quantitative mapping of UNESCO cultural zones with qualitative evaluation across four representational dimensions. Findings indicate that all series incorporate intentional strategies—such as cross-cultural comparisons, contextual narratives, and participatory activities—that promote awareness of cultural diversity and encourage engagement with non-local traditions. The Hunan Literature and Art Publishing House version offers the broadest geographic coverage and richest interactive design; the People’s Music Publishing House version presents structured, in-depth exposure to Western art music alongside selected global traditions; and the Huacheng Publishing House version highlights marginalized musical cultures. However, challenges remain, including a tendency toward instrumental music dominance, limited dance coverage, and uneven geographic balance. Overall, these textbooks contribute meaningfully to the cultivation of global perspectives while requiring further diversification and contextual enrichment to realize their intercultural potential fully.

ABSTRACT Parents of premature babies face extreme psychological challenges when a child is born too early — a situation for which no parents can be prepared. Parental engagement solutions, such as logging the baby’s progress, can alleviate this stress by fostering a comforting feeling of involvement in the care and a clearer understanding of the overall situation. This paper presents a multimodal journaling prototype designed specifically for parents of premature babies in the Neonatal Intensive Care Unit (NICU). The general goal was to test its feasibility and applicability as an engagement tool, while the specific goal was to evaluate user interaction methods from both technical performance and usability perspectives. The implementation, built around a multimodal integration module, demonstrated high technical performance, yielding greater than95% accuracy. However, usability evaluation revealed a significant finding: observed interaction patterns showed a substantially larger proportion of speech-only interaction compared to the intended multimodal combination of speech and gesture. To fully realise the benefits of a multimodal approach for parental engagement, and more broadly for patient engagement in digital health, further research is imperative. This work must focus on refining the interaction design to better align with the lived parental experience and seamlessly support natural multimodal usage.

The employee leave application information system is a digital solution designed to simplify the process of submitting, approving, and managing leave within an institution. This internship project resulted in the development of a system called SiCuti, which was built using the Laravel framework with a MySQL database. The system provides two main roles: employees and administrators/supervisors. Employees can apply for leave according to their entitlements, such as annual leave with a maximum of 12 working days, maternity leave for 3 months, pilgrimage leave (Hajj) which can be taken once during their service period, as well as special leave for important reasons such as marriage or the death of an immediate family member, typically lasting 1–3 days. When applying, employees may also upload supporting documents and monitor the status of their applications through the leave status menu.Administrators or supervisors have broader authority, including reviewing the list of leave requests, approving or rejecting applications, and managing employee leave quotas to ensure compliance with regulations. In addition, administrators can track the complete leave history of employees through an integrated dashboard. This feature supports faster, more transparent, and accountable decision-making processes.The results of local testing indicate that SiCuti has the potential to increase efficiency in administrative leave processes that were previously carried out manually, making them faster, more transparent, and more structured. The interactive dashboard design also facilitates monitoring from both employees and administrators. With the implementation of SiCuti, it is expected that the leave management process can reduce the risk of human error, ensure data accuracy, and support transparency and accountability in human resource management once the system is fully deployed within the institution.

This study aimed to investigate the influence of an interactive design installation on patients’ anxiety in waiting rooms. Waiting rooms are often stressful environments, which can lead to serious phobias. Therefore, it is essential to make these spaces more pleasant. It is also important to help patients reduce their anxiety and stress levels while in these waiting rooms. Interactive design installations are a behavioral distraction technique used to influence patients’ thinking and refocus their attention away from anxiety. This experimental study was conducted using speculative design theory to determine how interactive installations can help reduce anxiety among patients in waiting rooms. Could it be a powerful tool that could be used in the future to improve healthcare quality, potentially replacing direct medical treatments, such as medications for anxiety, with behavioral techniques? Keywords: interactive design installation, patient anxiety, waiting rooms, Behavioral distraction techniques, future healthcare innovations

Robots that can physically interact with humans in a safe, comfortable, and intuitive manner can help in a variety of settings. However, perceptions of the users greatly affect the acceptability of such robots. Ability of the system to understand user's perception of the physical interaction as well as adapting robot's behaviors based on user perception and interaction context can facilitate acceptability of these robots. In this paper we propose a perception-based interaction adaptation framework. One main component of this framework is a multi-modal perception model which is grounded on the existing literature and is intended to provide a quantitative estimation of the human state- defined as the perceptions of the physical interaction- by using human, robot, and context information. This model is intended to be comprehensive in many physical Human-Robot Interaction (pHRI) scenarios. The estimated human state is fed to a context-aware behavior adaptation framework which recommends robot behaviors to improve human state using a learned behavior cost model and an optimization formulation. We show the potential and feasibility of such a human state estimation model by evaluating a reduced model, with data collected through a user study. Additionally, through some feature analysis, we aimed to shed light on future interaction designs for pHRI.

In modern multimodal interaction design, integrating information from diverse modalities—such as speech, vision, and text—presents a significant challenge. These modalities differ in structure, timing, and data volume, often leading to mismatches, low computational efficiency, and suboptimal user experiences during the integration process. This study aims to enhance both the efficiency and accuracy of multimodal information fusion. To achieve this, publicly available datasets—Carnegie Mellon University Multimodal Opinion Sentiment Intensity (CMU-MOSI) and Interactive Emotional Dyadic Motion Capture (IEMOCAP)—are employed to collect speech, visual, and textual data relevant to multimodal interaction scenarios. The data undergo preprocessing steps including noise reduction, feature extraction (e.g., Mel Frequency Cepstral Coefficients and keypoint detection), and temporal alignment. An improved Kuhn-Munkres algorithm is then proposed, extending the traditional bipartite graph matching model to support weighted multimodal matching. The algorithm dynamically adjusts weight coefficients based on the importance scores of each modality, while also incorporating a cross-modal correlation matrix as a constraint to improve the robustness of the matching process. The enhanced algorithm’s performance is validated through information matching efficiency tests and user interaction satisfaction surveys. Experimental results show that it improves multimodal information matching accuracy by 28.2% over the baseline method. Integration efficiency increases by 18.7%, and computational complexity is significantly reduced, with average computation time decreased by 15.4%. User satisfaction also improves, with a 19.5% increase in experience ratings. Ablation studies further confirm the critical contribution of both the dynamic weighting mechanism and the correlation matrix constraint to the overall performance. This study introduces a novel optimization strategy for multimodal information integration, offering substantial theoretical value and broad applicability in intelligent interaction design and human-computer collaboration. These advancements contribute meaningfully to the development of next-generation multimodal interaction systems.

Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling individual users. First, a video-based observational study of expert and novice museum docents identified service strategies that enable socially adaptive communication. Building on these insights, three controlled laboratory studies investigated how specific cues from robots influence user perception. A video-based controlled study examined how recognition accuracy shapes users’ social impressions of the robot’s intelligence. Additional studies based on the Wizard-of-Oz (WoZ) method tested whether explanatory content aligned with participants’ background knowledge and whether explicit preference inquiry and memory-based continuity strengthened perceptions of personalization. Results showed that recognition accuracy improved social impressions, whereas knowledge alignment, explicit preference inquiry, and memory-based continuity cues increased perceived personalization. These findings demonstrate that micro-level personalization cues, interpretable within a short-term encounter, can support user-centered interaction design for social robots in public environments.

Although instructional coaching has been increasingly adopted as a tool for faculty professional development in Chinese higher education, it often encounters resistance due to signals of authority, emotionally detached delivery, lack of sustained follow-up, and non-specific or generic feedback. This qualitative study was carried out to explore how faculty at a public university in Gansu Province perceive the instructional coaching. To achieve that, the data collected through semi-structured interviews with 15 instructors from various disciplines and career stages were analyzed using thematic analysis, under a theoretical standpoint combining institutional theory with social constructivism. The findings revealed that faculty often perceived instructional coaching as a supervisory mechanism rather than developmental support, primarily due to ambiguous protocols, hierarchical feedback dynamics, limited disciplinary alignment, and emotionally distant communication. These conditions—rooted in institutional structure and interactional design—fostered anxiety, guarded engagement, and performative compliance, underscoring that faculty responses are shaped less by the idea of coaching itself and more by how it is enacted in context. Overall, the study suggests that faculty engagement with instructional coaching is shaped less by the model itself and more by how it is enacted—particularly in relation to trust, clarity, and relevance within a hierarchical context.

This paper systematically reviews the theoretical foundations, technological evolution, and current research landscape of embodied intelligence (Embodied AI), with particular attention to its interdisciplinary value and applications in the field of design. It first revisits key theories such as embodied cognition, in-telligence without representation, and morphological computation, illustrat-ing how embodied intelligence promotes a paradigm shift in design logic through the coupling of “body–environment” interactions. It then outlines the de-velopment trajectory of technologies ranging from robotic hardware and mul-timodal fusion to large multimodal and world models, revealing their ap-plica-tions across product, architectural, fashion, and service design. Further-more, the study analyzes theoretical extensions and technical applications of embodied intelligence in user experience, interaction design, and sustainable design — including motion capture, multimodal interaction, and digital twins. Finally, it summarizes the technical, ethical, and interdisciplinary challenges currently faced and proposes future research directions: focusing on affective and light-weight intelligent agents, promoting paradigm innovation in “intelli-gent co-design,” and constructing a responsible interdisciplinary ecosystem.

This paper presents the development of a game-based learning module for teaching risk management, modelled on the traditional board game Snakes and Ladders. The aim is to transform a simple and familiar game into an innovative educational tool that engages learners in understanding and applying the fundamental stages of risk management: identification, analysis, evaluation, treatment, and monitoring. In this adaptation, ladders symbolize effective risk responses, proactive decision-making, and opportunities that allow participants to progress, while snakes represent unmanaged risks, misjudgements, or adverse outcomes that cause setbacks. This metaphorical representation offers a clear and memorable connection between gameplay and real-world risk management practices. The module integrates scenario-based learning within the game structure, requiring players to assess risks, choose strategies, and reflect on outcomes as they advance. This experiential format emphasizes active learning by encouraging players to move beyond theoretical knowledge and directly apply decision-making skills in simulated contexts. The interactive design fosters critical thinking, problem-solving, and collaborative discussion among participants, ensuring that abstract concepts are reinforced through practice and reflection. By gamifying the learning process, this innovation addresses limitations of traditional lecture-based instruction, making complex processes more engaging, accessible, and easier to retain. The use of a familiar game structure lowers entry barriers, motivates participation, and creates a dynamic environment for sustained learning. Ultimately, this game-based module demonstrates the potential of playful pedagogy in enhancing comprehension of risk management principles and equipping learners with transferable skills relevant to both academic and professional settings.

This article advances a critical reimagining of German language acquisition for globally mobile learners navigating non-immersive, digitally mediated contexts. Positioned at the intersection of Second Language Acquisition (SLA), cognitive linguistics, and human-computer interaction, the study interrogates the structural and pedagogical limitations of current Mobile-Assisted Language Learning (MALL) platforms—particularly their failure to support integrated, skill-balanced development in linguistically demanding contexts such as DaZ/DaF. Drawing on Design-Based Research (DBR), the authors propose a multi-tiered mobile learning ecology anchored in a prototypical application that combines AI-driven adaptivity with narrative-based gamification. This hybrid architecture simulates immersive conditions through city-based tasks, interactive dialogue flows, and CEFR-aligned communicative scenarios. Empirical findings from a purposively sampled cohort of fifty globally mobile learners reveal persistent cognitive and affective challenges, including fragmented input, insufficient feedback, and metacognitive disorientation. The prototype responds by embedding linguistic scaffolding, affect-sensitive interaction design, and dynamic personalization mechanisms to bridge the gap between learner autonomy and structured progression. By repositioning non-immersed learners as central actors in the design of pedagogically rigorous, technologically augmented language learning environments, this study articulates a scalable and theoretically grounded framework for post-immersion language pedagogy. In so doing, it offers a substantive contribution to ongoing debates in SLA, educational technology, and transnational language instruction—proposing not merely an app, but a paradigm shift in how language acquisition is imagined and operationalized in the 21st century.

ABSTRACT Taking Macao Historic Centre as an example, this study adopts an exploratory sequential mixed-methods approach to construct the tourism image of Macao Historic Centre and quantify the discrepancy between its official projection image and tourist perception image in bilingual contexts. A comprehensive tourism destination image (TDI) model for Macao Historic Centre is developed and reveals a dynamic cognitive progression from human geography to psychological perspectives that collectively shape heritage tourism image formation. Key findings reveal that the English projection image emphasizes ‘Social Function’ and ‘Cultural Value’ while the Chinese projection image highlights ‘Architectural Features’ and ‘Basic Information.’ English-speaking tourists pay more attention to ‘Cultural Value’ and ‘Tourism Value,’ whereas Chinese tourists prioritize ‘Architectural Features’ and ‘Historical Value.’. The consistency between official projection and tourist perception is higher in the Chinese context, while the discrepancy is more pronounced in the English context. This study offers new perspective for understanding heritage tourism image and provides empirical evidence for optimizing bilingual promotional strategies and enhancing interactive experience design in heritage destinations.

Architectural robotics enables physical spaces and their components to act, think, and grow with their inhabitants. However, this is still a relatively new field that requires further improvements in portability, customizability, and flexibility. This study integrates spatial embedding knowledge, small-space design principles based on human scales and behaviors, and robotic kinematics to propose a prototype robot capable of efficient batch storage, habitability, and autonomous mobility. Based on the spatial distribution of its user’s dynamic skeletal points, determined using a human–computer interaction design system, this prototype robot can automatically adjust parameters to generate a customized solution aligned with the user’s behavioral habits. This study highlights how considering the inhabitant’s personality can create new possibilities for architectural robots and offers insights for future works that expand architecture into intelligent machines.