Explanations play a central role in shaping users’ trust and acceptance of autonomous vehicles (AVs). While existing AV explanation research has emphasized cognitive elements such as content, timing, and presentation fidelity, it offers limited guidance on how explanations might incorporate affective elements or adjust to varying driving contexts. To address this gap, we introduce a stance-strategy-tone framework for designing affective explanations, supported by scenario-specific guidelines and illustrative example utterances. Through interviews with seven domain experts and six co-design workshops involving 27 prospective AV users, we identified the components that influence how affective explanations are constructed and mapped them onto key driving scenarios. Our findings reveal design opportunities such as tailoring emotional framing to situational demands, combining empathy with informational clarity, and calibrating tone to balance warmth with directive precision. The study provides practical guidance for creating emotionally responsive explanation systems for AVs.

Shared living—where unrelated adults share domestic infrastructure—relies heavily on the interpretation of material traces: physical cues such as food arrangements, cleaning states, and object placements that residents produce and encounter asynchronously, largely without direct exchange. When the interpretive frame under which a cue is produced diverges from the frame under which it is decoded, coordination friction results. Through semi-structured interviews (N=13), we identify three interpretive frames—evidential, normative, and communicative—that residents apply to the same domestic cues, and document folk tuning: improvised strategies by which residents adjust three parameters—attribution, granularity, and temporality—to govern the distribution of ambiguity in shared space. A generative design workshop (N=20) then demonstrates how data physicalization can expand these parameters from constrained physical ranges into designable continua, yielding design orientations. We propose tunable ambiguity as a design concept that reframes ambiguity from a static artifact property to an inhabitant-controlled social process, and contribute actionable principles for interactive systems that support unspoken domestic coordination without collapsing into surveillance.

Wearable sensing systems increasingly depend on textiles that are both materially wearable and electronically functional. Their design requires collaboration between textile designers, who reason through stitches, yarn behavior, and machine constraints, and interaction designers, who reason through electrodes, signal paths, and insulation. However, these forms of expertise do not easily translate across disciplinary boundaries. This poster presents CapSenseBand, a knitted capacitive-sensing wristband developed through a research-through-design process organized around Analysis, Synthesis, and Detailing. We document an artifact chain spanning material swatches, a rapid wearable prototype, Paper Models as shared negotiation surfaces, a double-layer knitted structure, and an insulated Swept Frequency Capacitive Sensing breakout board. We show how Paper Models functioned as boundary objects, helping collaborators externalize intent, negotiate spatial and technical constraints, and preserve disciplinary expertise while converging on a shared design. We contribute a reusable swatch-to-sleeve pattern for material-centered HCI: keep discipline-specific probes open early, then converge through artifacts that make material, spatial, and electronic decisions legible before fabrication locks them in.

Senior architects are pivotal in shaping architectural projects, yet integrating Generative AI (GenAI) into their workflows presents notable challenges. A formative study (N=11) identified key pain points in their early concept design process. To address these, we developed EarlyArchi, a GenAI-driven system supporting automated concept generation and evaluation. In a within-subject study (N=13), participants used EarlyArchi for early-stage design tasks. Results showed enhanced perceived creativity, improved design competency, and more efficient ideation. However, concerns emerged regarding controllability and domain-specific accuracy, highlighting the need for features that preserve professional autonomy and trust. Further analysis revealed three GenAI involvement modes—fully AI-driven, GenAI-led, and human-led—emphasizing the importance of adaptive role allocation in balancing creative exploration with expert leadership. These findings offer insights into supporting senior architects through GenAI while identifying key considerations for designing future human–AI co-creation systems.

Older adults often hesitate to use canes due to a mismatch between their specific needs. This study proposed a user-centric design framework, designed a personalized smart cane, and evaluated its usability through a user-centered design cycle. Initially, we recruited 142 older adults to explore their attitudes and requirements. A design framework for smart canes was then proposed, encompassing seven key elements: safety, user-friendliness, multifunctionality, ergonomic fit, education, modularity, and affordability. Other 25 older adults were further recruited to complete an after-scenario questionnaire and a system usability scale in the usability testing. The results indicated a high level of satisfaction (6.09 ± 0.93 scores) and good usability (80.40 ± 11.13 scores). The user-centered design cycle employed in this study proved effective in achieving a functional design, and the proposed framework provides valuable guidance for future mobility aid designs aimed at enhancing adoption and adherence among older adults.

In architecture design, while Generative AI can effortlessly create initial prototypes, architects struggle to update designs to stakeholders’ evolving requirements. Through a formative study (N = 12), we identified specific obstacles that architecture designers face when meeting the dynamic design demands of various project stakeholders. We therefore developed ArchiConnect, a proof-of-concept interactive system that helps architects communicate with multiple stakeholders and update final design deliverables. ArchiConnect supports creativity and engagement by visualizing evolving demands, conflicts, and concept extractions from diverse stakeholders. We evaluated our system in a week-long user study (N = 8) with a simulated project. Participants found ArchiConnect effective for improving multi-stakeholder communication and management, describing it as intuitive and useful. Our findings offer design considerations for future AI tools to better handle dynamic stakeholder needs, including how to address sustainability requirements in line with development goals.

Data transparency is critical for fostering user trust in smart home ecosystems. However, prevailing approaches often present transparency information on a per-device basis, creating fragmented experiences that complicate interaction and obscure a holistic understanding of privacy practices. To tackle this fragmentation, we developedPriLens, a unified platform that uses Augmented Reality (AR) to integrate digital privacy information directly with the physical environment. Through a comparative study of AR, Graphical User Interfaces (GUI), and Virtual Reality (VR), we demonstrate that AR uniquely supports the formation of a coherent mental model of data flows by bridging the physical and digital worlds. Our findings reveal that users synthesize observations of physical entities with overlaid digital data to assess credibility-a process poorly supported by the segmented nature of GUI and VR. Furthermore, cross-age analysis indicates that older adults prefer contextually grounded physical controls over abstract digital dashboards, reporting higher trust in AR. Based on these results, we contribute three design guidelines for effective transparency-enhancing technologies.

• The study emphasizes knowledge graph-powered product iteration within an under-explored NPD domain of newer and less-established novel products. • An interpretable conceptual design knowledge graph (I-CDKG) is constructed to facilitate designers in generating innovative and cost-effective heuristic product ideations. • A hybrid method combining deep-learning ERNIE-BiGRU-CRF model, BIESO labeling mode, and triple-extracting algorithm is proposed to facilitate the I-CDKG construction. • The I-CDKG boasts both inherent and acquired interpretability reinforced by a Cluster-Relation-Nest organizational strategy for the intuitive locating of design knowledge. Novel products emerge over time to survive the competitive landscape as no existing product can perpetually satisfy all evolving customer expectations. These products are often characterized by groundbreaking solutions previously unavailable on the market. However, the swift imitation of successful novel products by competitors underscores the need for sustained iteration and continuous improvement. Designers increasingly face challenges in keeping up to date with the growing volume and fragmented nature of design information from diverse sources. While knowledge graphs show promise in structuring and organizing complex design information, their effective application in the ideation process remains limited due to difficulties in automatic knowledge extraction and the lack of interpretability aligned well with designers’ cognitive processes. This study proposes an integrated method to construct an interpretable conceptual design knowledge graph (I-CDKG) that features both inherent and acquired interpretability for heuristic product ideation. First, the schema layer models product design knowledge and governs the semantic connection of design information reinforced by design cognition principles to create a reasonable organizational framework to foster intuitive knowledge exploration. Second, the data layer mainly fulfills automatic and smooth design knowledge extraction for I-CDKG construction through the deep learning ERNIE-BiGRU-CRF model combined with BIESO labeling mode and triple-extracting algorithm. Third, the application layer empowers designers to visually delve into interpretable design knowledge to locate inspiration from cluster, relation, and nest levels and enable constant I-CDKG expansion as design schemes proliferate. A case study on the smart cat litter box demonstrates the feasibility of the proposed methodology. The evaluation results confirm the I-CDKG’s advantages as a productive design tool for inspiring creative, practical, and cost-effective product ideations, thereby empowering the iterative development of competitive novel products.

The rapid increase in the number of individuals with Autism Spectrum Disorder (ASD) has drawn extensive attention from both the general public and researchers. Artificial Intelligence (AI) has been applied in the assessment, early diagnosis, and intervention of ASD to enhance the efficiency of clinicians and reduce tension in medical resources. However, the adoption of AI systems in clinical practice is relatively limited due to the challenge of complexity and diversity of ASD. Thus, involving insights into clinicians' perceptions and barriers toward the role of AI is crucial for enhancing clinicians-AI cooperation for autism. Through conducting the semi-structured interview with 18 physicians across tertiary and secondary hospitals in various regions, this study indicates the positive attitude toward collaborating with AI among physicians. Additionally, some concerns are also reported, such as the complexity of ASD, uncertainty of AI capabilities, and understandability of AI. The findings of this study highlight the significance of human-centered AI in satisfying different stakeholders' needs and discuss the potential implications of AI capabilities for adopting AI in future autism research.

Human-computer interaction has seen growing interest in emotion detection. To gain deeper insights into the physiological indicators of emotions, researchers have delved into utilizing electroencephalography (EEG) and micro-gestures (MGs). This study assesses the efficacy of EEG and MG features in emotion detection by recruiting 15 participants to gather EEG and MG data in response to diverse figure-based emotional stimuli. To incorporate these features, this article introduces Emo-MG, a multimodal interface that integrates EEG and MG features and employs a long short-term memory (LSTM) model to predict emotional states within the valence-arousal-dominance (VAD) space. This study presents an in-depth analysis of feature importance and correlation results based on EEG and MG features for feature selection in emotion detection tasks. Through accuracy and F1-score metrics, Emo-MG achieves outstanding performance in emotion detection by comparing it to baseline and deep learning models, validating the efficacy of integrating EEG and MG features

Data transparency plays a critical role in understanding IoT privacy practices and making informed decisions. To gain a comprehensive understanding of transparency in the IoT environment, a systematic literature review of 58 academic articles is conducted to investigate the progress and status of existing data transparency studies from a design perspective. Data transparency was identified as a signifier to bridge the connection between user behavior and privacy risks. The level of transparency achieved was shaped by users’ privacy perceptions, which in turn influenced their privacy behavior. GUI-based transparency design has been widely used in IoT, but it is not sufficient to provide users with accessible, understandable, and unified transparency information. A conceptual transparency design is proposed based on the extracted design opportunities and practices. This paper provides an important resource on transparency issues in the IoT environment, and will benefit the design and computer science communities.

A rising number of HCI scholars have begun to use materiality as a starting point for exploring the design's potential and restrictions. Despite the theoretical flourishing, the practical design process and instruction for beginner practitioners are still in scarcity. We leveraged the pictorial format to illustrate our crafting process of Puffy, a bio-inspired artifact that features a cilia-mimetic surface expressing anthropomorphic qualities through shape changes. Our approach consists of three key activities (i.e., analysis, synthesis, and detailing) interlaced recursively throughout the journey. Using this approach, we analyzed different input sources, synthesized peers’ critiques and self-reflection, and detailed the designed experience with iterative prototypes. Building on a reflective analysis of our approach, we concluded with a set of practical implications and design recommendations to inform other practitioners to initiate their investigations in interactive materiality.