progressive disclosure

This study addresses the absence of a systematic approach for selecting appropriate human-AI interaction interfaces based on user needs and task complexity. It proposes a three-dimensional classification framework grounded in workflow complexity, AI autonomy, and AI reasoning capability, developed and validated through co-design workshops and longitudinal qualitative user studies. The work establishes, for the first time, a mapping between task complexity and interface modalities, offering actionable guidance for designing context-aware, scalable AI interfaces that support seamless transitions between modalities and enable users to exercise progressive control. The framework also balances operational impact and safety risks in high-autonomy scenarios. By providing product teams with a shared conceptual language, this research enhances the ability to achieve an effective balance between human oversight and AI autonomy in collaborative settings.

HCI research suffers from numerous context-dependent, non-replicable empirical findings. To address this, we propose *Interaction Cycle Diffraction*—the first method to formalize and compare user interaction behavior across experimental conditions using *interactional properties* (e.g., feedback latency, action reversibility, or mode-switching cost) as fundamental analytical units, rather than interface morphology. This framework systematically enables identification, extraction, and validation of reproducible interactional properties across diverse prototypes, technologies, tasks, and user populations. Through iterative user studies and prototype refinement, we demonstrate its utility in continuously optimizing design workflows and accumulating reusable empirical knowledge. Our work establishes the first reproducibility framework for interactional properties in ubiquitous UIs, offering a novel paradigm for building a theoretical taxonomy and empirical foundation for an interaction science. (138 words)

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.

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.

Existing AI agent architectures are typically described along a single dimension—either execution topology or cognitive function—making it difficult to characterize their design trade-offs and failure modes. This work proposes the first two-dimensional classification framework that orthogonally integrates cognitive functions (seven types, e.g., perception, memory, reasoning) with execution topologies (six types, e.g., chain, parallel, routing), yielding a 7×6 matrix that systematically defines 28 design patterns, including 15 newly named ones. Through cross-domain validation in finance, legal reasoning, network operations, and medical triage, the study distills five empirical guidelines for pattern selection and establishes a principled, framework- and model-agnostic terminology. This significantly enhances the describability and reusability of agent architectures.

This work addresses the scarcity of user customization in everyday productivity tools and the lack of flexible, natural language–based mechanisms for adapting system behavior. It proposes embedding generative AI–driven conversational customization into email systems, enabling users to iteratively reshape their inbox structure, interface, and workflows through natural language, thereby transforming static interfaces into malleable data layers. Through a user-centered design probe, the study reveals that users prefer adaptive modifications grounded in existing patterns over creating configurations from scratch. While such customization significantly enhances tool flexibility, it also introduces risks of misconfiguration, necessitating continuous oversight and iterative refinement mechanisms. The findings illuminate practical pathways and design implications for integrating conversational customization into routine productivity applications.

This work proposes a new paradigm for software design tailored to AI agents as primary users, addressing the limitations of traditional human-centric approaches. It formally defines the concept of an “agent interface” for the first time, centering on callable capabilities and emphasizing machine interpretability, composability, and invocation reliability. Guided by the interaction requirements of large language model–based agents, the study introduces a capability-oriented software architecture and corresponding interface specifications. By establishing a conceptual foundation and design framework for AI-native systems, this research advances software engineering beyond monolithic applications toward dynamic, composable ecosystems of interoperable capabilities.