contextual inquiry

Traditional workplace comfort surveys often fail to capture individualized, context-sensitive experiences. To address this, we propose a physical toolkit enabling employees to annotate spatiotemporal comfort/discomfort experiences *in situ* using tangible, markable artifacts. This approach introduces the novel paradigm of “contextualized input visualization,” supporting on-site, non-digital, low-cognitive-load recording while allowing domain experts to offline decode multi-user annotations and synthesize collective patterns. Grounded in physical interaction design, field-based human factors research, and participatory iterative development, the toolkit was deployed with 14 participants across two open-plan offices. Results confirm that remote experts can accurately interpret the physical annotations. From this work, we distill four generalizable design principles for multi-user physical input and visualization systems.

This study investigates the misalignment between data workers’ implicit cognitive models of complex hierarchical data (e.g., nested tables) and the explicit data models encoded in analysis code, and how such misalignment negatively impacts analytical efficiency and accuracy. Method: Through semi-structured interviews, cognitive sketching, and reflexive thematic coding with 10 collaborative data practitioners, we systematically identify divergent, coexisting cognitive models within teams. Contribution/Results: We introduce the novel concept of “parallel risk”—a form of collaborative breakdown arising from persistent cognitive misalignment between data model designers and end users. All participants exhibited internal representations inconsistent with the true data structure, leading to systematic reasoning errors. Based on these findings, we derive human-centered design principles and intervention strategies for analytical tools that promote cognitive alignment. This work establishes a theoretical foundation and practical framework for improving usability in data engineering and visualization systems.

Narrative-driven data exploration faces core challenges—including contextual discontinuity across views, difficulty in tracing analytical reasoning paths, and insufficient externalization of intermediate interpretations. Method: We conducted a qualitative empirical study with 48 participants, combining in-depth interviews and task-based observations, to code and thematically analyze multi-stage dynamic analytical behaviors. Contribution/Results: The study systematically identifies three critical impediments and derives three design principles for supporting narrative evolution in visual analytics: (1) enforcing cross-view contextual consistency, (2) explicitly tracking reasoning trajectories, and (3) structurally externalizing intermediate interpretations. These principles are operationalized into concrete interaction mechanisms and practical guidelines. The work advances visual analytics systems from static chart presentation toward next-generation tools that actively support dynamic, iterative narrative construction.

Scientific software development suffers from poorly specified requirements and inadequate management, severely compromising software quality and experimental reproducibility. To address this gap, this study formally establishes scientific software as a novel application domain for requirements engineering (RE). Through eight in-depth interviews with 12 researchers, we conduct an exploratory qualitative study employing thematic coding analysis. Our findings identify three core challenges: (1) highly ambiguous and evolving requirements, (2) latent or unidentified stakeholders, and (3) absence of systematic requirement validation mechanisms. Based on these insights, we propose a domain-specific RE vision and a challenge framework tailored to scientific software contexts. This work lays the theoretical foundation and methodological guidance for lightweight, agile, and traceable RE practices in scientific software development—thereby filling a critical void in systematic RE research for this domain.

Decision support in visualization research lacks systematic characterization of decision-context frameworks, and existing task models fail to guide design for real-world decision scenarios. Method: We propose a structured decision-problem analysis framework that, for the first time, decomposes decision problems into three core attributes—data, user, and task context—and explicitly articulates their constraints and implications for visual encoding and interaction design. Grounded in task-modeling principles and visualization design theory, we develop an operational feature-description system for decision problems and validate it through multi-case analysis. Contribution/Results: The framework addresses the critical gap in traditional task models—neglect of decision context—and provides the first systematic theoretical tool for decision-oriented visualization research. It reveals limitations in current design practices and identifies concrete pathways to enhance decision-support efficacy in authentic settings.

This study addresses the challenge of visualization design under conditions of ambiguity—such as unclear objectives, indeterminate data, and evolving requirements—where existing frameworks inadequately explain how designers proceed without a clear direction. Through a three-phase qualitative investigation analyzing practice excerpts from 11 expert designers, the research uncovers how practitioners navigate uncertainty by engaging in provisional, localized actions. These actions intertwine contextual awareness, professional judgment, and explicit reasoning to render ill-defined problems operational and dynamically steer design decisions. The study identifies core strategies through which designers use such localized moves to reveal patterns, clarify pathways, and reorient their design trajectories. These findings offer empirical grounding and theoretical insight into the inherently dynamic and non-predetermined nature of real-world visualization design practice.

This study addresses the frequent inefficiencies in human-AI collaboration caused by incomplete contextual information, which often leads to excessive iteration and suboptimal output quality. To mitigate this, the authors propose a structured context construction framework that integrates a five-role context package—comprising authority, exemplars, constraints, evaluation criteria, and metadata—within a four-stage workflow encompassing review, design, construction, and audit. Notably, this work pioneers the incorporation of information theory and reliability engineering principles into context quality assessment, yielding a reusable and auditable collaboration framework. Empirical results from 200 interaction trials demonstrate that the approach reduces the average number of iterations from 3.8 to 2.0, increases first-pass success rates from 32% to 55%, and achieves a final task success rate of 91.5%.

Existing benchmarks for knowledge work evaluation largely adhere to traditional NLP task paradigms, failing to capture systems’ capabilities in real-world knowledge-intensive settings. This work proposes a three-step framework—explicitly defining work activities, establishing realistic test environments, and focusing evaluation on deliverable outputs—and derives 18 core knowledge work activities from the O*NET database. Innovatively integrating role responsibilities, local tool usage, and downstream usability into benchmark design, the approach establishes a coherent “work activity–test setup–scoring artifact” alignment. Validation through three case studies (GDPval, OfficeQA Pro, and APEX-SWE) exposes critical misalignments in current benchmarks between tasks, environments, and actual work objectives, offering a new paradigm for evaluating knowledge work systems in practical, application-oriented contexts.

研究通过开发虚拟环境GraphXplore解决设计师在多模型间导航和信息检索时遇到的认知负担问题，与传统屏幕设置相比，GraphXplore在可用性和认知负荷上表现出优势。

This study addresses how spatial biologists can guide and validate complex tissue data analysis tasks executed by AI agents. Building upon the Claude Science agent, the authors employ contextual inquiry, formative pilots, and observational experiments to propose four key design directions: execution control, familiar views, source information transparency, and cross-environment accessible verification. The work reveals the epistemic mechanisms through which scientists rely on visual evidence to evaluate AI-generated results. Furthermore, it constructs a comprehensive empirical model of the analytical workflow encompassing both interactive control and verification. Ultimately, this research contributes a systematic design framework for human-AI collaborative scientific discovery, offering actionable insights into integrating intelligent agents within rigorous biological research practices.