human-computer interaction

Existing visualization research predominantly focuses on *how to use* interactive features, neglecting the critical question of *how to construct* them. Method: We propose the first three-layer decoupled interaction authoring task model—intent–technique–component—derived from empirical coding and abstraction of 592 interaction units across 47 real-world applications. Contribution/Results: This model provides descriptive, evaluative, and generative capabilities, enabling the first unified formalization of interaction authoring intent, technical implementation, and component instantiation. It yields a reusable, theory-grounded classification framework that supports critical evaluation of existing visualization tools and informs the design and validation of next-generation low-code interaction authoring systems.

This paper addresses the limitations of human-centered design in HCI—specifically its neglect of interface ontology and embodied experience—by proposing an “interface-centered” paradigm. We designed and empirically evaluated Umbilink, a wearable haptic interface inspired by umbilical connectivity: it employs rhythmic vibrotactile stimulation and enveloping pressure sensing to simulate uterine conditions, inducing pre-subjective, sensory-reduced states. Grounded in Hookway’s interface philosophy, phenomenology, and embodied interaction theory, the study integrated haptic sensing, rhythmic feedback, and semi-structured interviews, with data analyzed via grounded theory. Results demonstrate that Umbilink effectively supports liminal experiences—including meditation, therapeutic relaxation, and sleep—and reveal the design significance of wearing rituals as transitional practices. Contributions include: (1) a taxonomy of embodied interfaces; (2) the ontological positioning of interfaces as cognitive mediators; and (3) an extensible exploratory prototype and methodological framework.

This work addresses the lack of explicit modeling of abstraction mechanisms in existing interactive system design, which hinders actionable design guidance. Through a systematic review of 457 publications, the study proposes the first abstraction-centered design space for interactive systems, structured around six core dimensions. Leveraging this framework, it reconceptualizes the Gulf of Execution and Evaluation model to reveal the cognitive and design mechanisms by which users and systems bridge the abstraction gap. By explicitly integrating abstraction into the theoretical foundations of human–computer interaction, this research synthesizes prior work, establishes a coherent theoretical basis, and offers systematic practical guidance for designing and evaluating abstraction mechanisms in interactive systems, thereby charting new directions for future inquiry.

Current learning interfaces suffer from a fragmentation among technical robustness, user-centered design, and grounding in educational theory, lacking effective interdisciplinary integration. This work addresses this gap by synthesizing insights from artificial intelligence, human-computer interaction, and the learning sciences to propose a cohesive set of design principles and a research agenda for next-generation learning interfaces centered on human-AI collaboration. By integrating interactive AI technologies, theoretically informed models of learning, and user-centered system design, the study identifies key challenges and articulates a scalable framework that is pedagogically effective, technically reliable, and aligned with learners’ needs. The resulting approach offers both a theoretical foundation and a practical pathway for advancing the next generation of learning technologies.

Traditional data management systems fail to simultaneously satisfy the low-latency, strong consistency, and high availability requirements of Human-Data Interaction (HDI), primarily because interaction bottlenecks are driven by user availability—not query semantics—and architectural separation between interfaces and backend systems precludes joint optimization. Method: We propose a novel “system-interaction co-design” paradigm that integrates database theory with visualization and interaction modeling, thereby unifying previously siloed layers; we design and implement an HDI infrastructure enabling sub-second response times, end-to-end consistency guarantees, and real-time interactive feedback. Contribution/Results: Evaluated across multiple generations of prototype systems, our approach significantly improves reliability and efficiency of interactive AI applications. It establishes a scalable, foundational architecture paradigm for next-generation human-AI collaborative intelligent systems.

This work addresses the lack of a unified definition of “proactivity” in human-computer interaction (HCI) and artificial intelligence (AI), which has led to inconsistent system designs and evaluation criteria. Through interdisciplinary expert workshops, the study systematically develops the first conceptual framework for proactive systems, clarifying their core characteristics and identifying critical gaps in existing approaches—particularly concerning timing, appropriateness, user control, and trust. Integrating HCI theory, AI techniques, and collaborative deliberation, the authors propose a human-centered paradigm for designing and evaluating proactive systems. The resulting framework maps key challenges and opportunities, laying the groundwork for a coherent and rigorous research and practice agenda in this emerging domain.

This study addresses the frequent disconnect between design metaphors employed by platforms and users’ actual experiences, as well as the lack of systematic evaluation methods in this domain. It proposes a novel comparative framework that juxtaposes designer-intended metaphors with user-generated ones, integrating mixed-method approaches—including metaphor extraction, historical web content analysis, and user surveys—to examine 21 official design metaphors and 554 user metaphors across three major platforms (ChatGPT, Twitter, and YouTube) since their launch. A user rating mechanism is introduced to quantitatively measure resonance levels. Findings reveal that design metaphors often misalign with user cognition, and even when form matches, they do not necessarily elicit broad resonance—offering a new pathway for evaluating user experience and optimizing metaphor-driven design.

This work addresses the limited accessibility of immersive network visualization due to complex interaction paradigms by proposing a novel voice-based interaction system powered by large language models (LLMs). For the first time, natural language is leveraged as the primary interaction modality within immersive environments for network visualization. Through a Research through Design (RtD) approach, the system integrates speech recognition, LLMs, and immersive visualization technologies to enable users to perform complex, multi-parameter analytical operations via spoken natural language, substantially reducing cognitive load. User studies demonstrate that, compared to traditional controller-based interactions, this approach significantly enhances perceived usability and facilitates more fluent articulation of analytical intent, particularly benefiting data analysis tasks in social and computer sciences.

This work addresses the lack of tools supporting non-technical performers in rapidly prototyping responsive environments during early-stage immersive theater creation. To bridge this gap, the authors propose a rehearsal-oriented, no-code visual system that directly maps sensor inputs—such as gesture, position, and voice—to lighting and sound outputs, enabling creators to configure, test, and iterate interactive spaces in real time during workshops. By abstracting sensing and actuation mechanisms into manipulable “compositional materials,” the system emphasizes visible mappings and low technical barriers, fostering integration between embodied practice and interactive technology. Evaluated across six professional workshops, eight performer-creators successfully employed the system to develop audiovisual scores, trigger-based scenes, responsive architectural prototypes, and multi-room improvisational performances, demonstrating its effectiveness and usability in early creative exploration.

This study addresses the challenges UX designers face in data visualization due to limited domain knowledge and tool expertise. It presents the first systematic comparison of three guidance approaches—static presentation, scrollytelling, and chatbot-based interaction—in authentic design tasks, proposing three core design dimensions for effective visualization guidance: narrative structure, visual content layout, and navigation flexibility. Through a controlled experiment, surveys, and in-depth interviews with 25 UX designers and students, the research evaluates both performance and user experience. Findings indicate that interactive guidance (either scrollytelling or chatbot) significantly enhances adherence to design conventions and user engagement, while also providing clearer instructions compared to static guidance. However, no significant differences emerged between the two interactive modalities, and all groups demonstrated comparable levels of visualization comprehension.