user research

Practitioners face significant challenges in effectively transforming customer feedback data into actionable software improvements. Method: This study proposes an end-to-end, data-driven improvement framework that systematically integrates feedback collection, multidimensional metric design, descriptive and inferential statistical analysis, interactive visualization dashboards (UX prototypes), and cross-departmental change-enabling mechanisms. Contribution/Results: The framework’s key innovation lies in the deep integration of statistical inference with user experience design, enabling a closed-loop feedback system for real-time insight generation and collaborative decision-making. Empirical evaluation demonstrates substantial improvements in feedback processing efficiency and response accuracy; product teams can rapidly identify high-priority enhancement opportunities using evidence-based insights. The results validate both the feasibility and practical efficacy of data-driven software evolution in industrial settings.

A persistent gap exists between empirical research and tool development in data visualization. This paper addresses this gap by examining data videos as a case study. We systematically review 46 empirical papers and 48 tool-oriented papers, and conduct interviews with 11 domain experts to construct a classification framework elucidating how empirical findings inform tool design—specifically in problem framing, technology selection, and feature implementation. Employing a mixed-methods approach—including corpus analysis, contextual citation analysis, and structured feature characterization—we identify recurring citation patterns and key factors governing the applicability of empirical insights. Our work clarifies the practical mechanisms through which empirical results translate into design decisions and proposes actionable strategies to foster deeper synergy between empirical and tool-development research. The findings provide both an evidence-based foundation and practical guidance for theory-driven design of data storytelling tools. (149 words)

HCI scale development has long suffered from nonstandardized processes, poor construct-theory alignment, and low item reuse rates. This paper introduces the first interactive support system integrating large language models (LLMs) with a structured, empirically grounded measurement knowledge base, enabling a closed-loop workflow: construct identification → theory-informed custom definition → context-aware item generation. The system retrieves theoretically appropriate constructs from a literature-anchored database and leverages LLMs to generate semantically coherent, domain-specific items, supporting human-AI co-refinement. Its key innovation lies in the deep coupling of LLMs with an evidence-validated construct–item relational database, shifting scale development from experience-driven practice toward evidence-enhanced collaborative measurement. Experiments show a 62% reduction in design time, a 3.1× increase in item reuse, and significantly improved theoretical fidelity; expert evaluations across multiple rounds confirm ≥92% contextual appropriateness. The system has been integrated into a prototype HCI research workflow.

This study investigates whether large language models (LLMs) can bridge the gap between UX experts and non-experts in authoring user scenarios. In a controlled experiment, both groups authored scenarios with LLM assistance; outputs were evaluated via mixed methods—structured scoring and qualitative coding—assessing structural completeness, expressive clarity, and audience orientation. Results demonstrate, for the first time empirically, that LLMs significantly enhance non-experts’ performance: their scenarios achieve structural and clarity levels comparable to experts’, and—remarkably—surpass experts in articulating user perspectives. The findings validate LLMs as effective, democratized tools for requirements analysis and reveal their unique capacity to augment empathic user-centered expression. This work advances accessible UX practice by lowering barriers to rigorous scenario-based design.

A critical challenge in explainable AI (XAI) and human-AI collaboration is determining *when* to provide explanations—i.e., real-time identification of genuine explanation needs—yet existing approaches rely on static, subjective assumptions and fail to dynamically capture users’ contextual demands. Method: We propose the first holistic, real-time explanation-need recognition framework integrating user behavior, system events, and physiological-emotional signals. Through systematic literature synthesis and empirical validation, we identify and verify 39 measurable, generalizable, and triggerable user-side indicators. We organize them into a cross-dimensional taxonomy (behavioral, system-event, and affective/physiological) and develop a demand-type mapping model. Contribution/Results: Grounded in online experiments, self-reports, and qualitative coding, we establish a structured indicator catalog comprising 17 behavioral, 8 system-event, and 14 affective/physiological measures, and design its runtime telemetry integration. The framework enables dynamic, precise, and temporally appropriate automated explanation triggering, validated in both prototype and production environments.

Existing computational tools for qualitative data analysis often fall short in effectively supporting causal exploration due to insufficient contextual awareness, limited trustworthiness, or overly complex outputs. To address these limitations, this work proposes QualCausal, the first interactive causal analysis system grounded in user research–driven design principles. Developed through formative user studies, QualCausal integrates context-aware processing, cognitive scaffolding, and explainability mechanisms to facilitate efficient exploration and validation of causal hypotheses within qualitative datasets. The system enables researchers to extract causal relationships, construct interactive causal networks, and examine findings through coordinated multi-view visualizations. User evaluations demonstrate that QualCausal significantly reduces analytical burden, provides robust cognitive support, and prompts critical reflection on how computational tools can be meaningfully integrated into social science research practices, thereby bridging the gap between computational assistance and qualitative inquiry paradigms.

Traditional questionnaires struggle to simultaneously capture qualitative depth and quantitative structure, limiting comprehensive understanding of complex social phenomena. This study proposes a dynamic survey platform powered by large language models (LLMs) that, for the first time, enables real-time semantic clustering of open-ended responses. Through an interactive feedback mechanism, users can rate, rank, and reflect on these clusters, generating visual reports that integrate qualitative insights with quantitative analysis. Innovatively embedding LLMs within a closed-loop data collection framework, the approach facilitates dynamic comparisons between individual perspectives and group-level trends. Empirical validation across two field studies involving 93 participants demonstrates that the platform significantly enhances data richness and user engagement compared to conventional survey tools, while effectively fostering collaborative sensemaking.

This study addresses the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

Learning analytics often suffers from low user trust and intervention acceptance due to opaque reasoning processes. To address this, we propose a novel “transparency-through-exploration” paradigm, developed via iterative human-centered design (n=15), resulting in a self-service metric editor that enables end-users—particularly instructors—to interactively construct, inspect, and refine analytical metrics. This tool grants users direct agency over both the logic and generation process of learning metrics. Empirical evaluation demonstrates significant improvements in system transparency, user trust, satisfaction, and adoption willingness. Our key contribution is the first systematic integration of exploratory metric construction into learning analytics practice, shifting transparency from post-hoc explanation to real-time, participatory engagement through an operationalizable tool design. This advances human-centered, trustworthy, and usable learning analytics.

This study addresses the current lack of a systematic understanding of user interaction mechanisms with large language model–driven computer-use agents and the key design factors influencing their user experience (UX). Through a two-stage approach, the authors construct and empirically validate a UX design space for such agents. First, they synthesize findings from a literature review and expert interviews to develop a taxonomy encompassing dimensions such as user prompting, explainability, and user control. Second, they conduct a Wizard-of-Oz experiment across normal, error, and high-risk scenarios to observe user behaviors, revealing interdependencies among design dimensions and the diversity of user needs. This work presents the first systematically formulated and empirically validated UX design framework for LLM-driven agents, offering developers a structured and actionable foundation for design decisions.