qualitative research

This study examines the tension between efficiency gains and researcher autonomy arising from AI-assisted analysis in qualitative research. Through in-depth interviews with 16 qualitative researchers, it comparatively analyzes acceptance and underlying mechanisms across three coding paradigms: fully manual, human-initiated AI-assisted, and AI-initiated. The study innovatively conceptualizes AI explicitly as a “supporter”—neither collaborator nor supervisor—and identifies three core determinants of adoption: efficiency enhancement, attribution of interpretive ownership, and algorithmic trust. Findings indicate broad acceptance of AI for accelerating coding and thematic analysis, yet strong consensus on human primacy in meaning-making and interpretive authority. Enhancing procedural transparency, researcher control, and structured human–AI collaboration significantly strengthens trust and mitigates bias risks. The work provides theoretical grounding and actionable guidelines for developing human-centered, accountable AI-augmented qualitative research workflows.

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.

This study explores the effective integration of large language models (LLMs) into qualitative and mixed-methods social network analysis to augment—rather than replace—the deep analytical capacities of human researchers. Focusing on core issues such as relational meaning, narrative interpretation, and identity construction, the work proposes an LLM application paradigm oriented toward enhancing methodological rigor. This paradigm emphasizes human–AI collaboration, reflexive practice, and ethical accountability. By incorporating LLM-assisted data coding, theory generation, and abductive reasoning, the project develops a methodologically innovative yet practically feasible framework for qualitative social network analysis. The approach significantly improves analytical efficiency while upholding scholarly standards and ethical compliance.

This study addresses the lack of systematic understanding regarding the types and motivations of visual representations in qualitative research. Building upon and extending Verdinelli & Scagnoli’s (2013) work through a data-driven literature review, it conducts a content analysis of articles and their visualizations published between 2020 and 2022 in three leading qualitative methods journals. Integrating epistemological stance classification with visualization-type coding, the study innovatively combines correspondence analysis and cognitive network analysis for the first time. Findings indicate that while visualizations remain underutilized in qualitative research, their typological diversity is increasing, and the choice of graphical representation appears largely independent of the authors’ epistemological positions. These results offer both empirical grounding and methodological innovation for integrating interdisciplinary visualization tools into qualitative inquiry.

HCI has long evaluated qualitative research through a positivist lens, overemphasizing quantifiable metrics and neglecting its interpretive nature. Method: Drawing on epistemological critique, this paper systematically distinguishes positivist and interpretivist paradigms, exposing the fundamental limitations of quantification in understanding human behavior. It then proposes the first non-quantitative quality assessment framework specifically designed for HCI qualitative research. Contribution/Results: The framework introduces five interpretivist quality criteria—credibility, transferability, dependability, confirmability, and resonance—grounded in qualitative logic rather than numerical standards to ensure rigor and contextual appropriateness. It shifts evaluation away from positivist assumptions toward interpretivist principles, enhancing methodological fidelity to qualitative inquiry. The framework has been preliminarily adopted in the ACM Transactions on Computer-Human Interaction (TOCHI) and CHI conference review guidelines, marking a substantive step toward an interpretivist reorientation in HCI methodology.

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 limitations of conventional interview analysis, which relies heavily on researchers’ specialized qualitative skills, is difficult to scale, and often overlooks participants’ own interpretive logics. To overcome these challenges, the paper proposes the Documentary Mode of Interpretation (DMI), a membership-based method grounded in ordinary people’s natural language competencies. DMI identifies endogenous themes within interview texts to uncover the collective reasoning processes participants employ in making sense of the research topic. Crucially, this approach requires neither predefined coding frameworks nor formal training in social theory, thereby departing from traditional qualitative paradigms by generating themes bottom-up from participants’ perspectives. By significantly lowering the technical and epistemic barriers to qualitative analysis, DMI offers non-specialist researchers an accessible, low-threshold pathway to effectively interpret the collective meaning structures embedded in interview data.

This study addresses key limitations in gerontological qualitative research—namely, constraints in data scale, pattern detection, and methodological integration. Methodologically, it pioneers the embedding of machine learning and natural language processing (NLP) techniques directly into qualitative workflows, enabling systematic indexing, multi-scale textual analysis, and reproducible management of participatory observation and in-depth interview data. It integrates open science platforms with qualitative data management systems to support synergistic analysis of large-scale secondary datasets (e.g., the American Voices Project) and original ethnographic fieldwork (e.g., DISCERN dementia ethnography). Contributions include: (1) substantially enhancing qualitative data processing efficiency and analytical transparency; (2) achieving a principled integration of humanistic depth with computational breadth; and (3) advancing aging research toward a multimodal, scalable, and verifiable mixed-methods paradigm.

This study examines the applicability and contested boundaries of generative AI in qualitative research. It innovatively distinguishes between “small-q” positivist and “big-Q” non-positivist paradigms, using this dichotomy as a central criterion to develop a multidimensional decision framework for AI adoption that incorporates researcher expertise, ethical considerations, and personal preferences. Through a comprehensive literature review and theoretical analysis, the paper clarifies the conditions under which generative AI is methodologically justifiable or constrained within each paradigm. The findings offer valuable methodological guidance and practical insights for conducting qualitative research—particularly in fields such as software engineering—where the integration of AI tools raises both opportunities and epistemological challenges.

Software engineering (SE) practices are often too complex for quantitative methods to fully capture, limiting empirical understanding of developer behavior, team collaboration, and organizational contexts. To address this, this study conducts multiple expert focus groups and applies qualitative content analysis and thematic synthesis—yielding the first structured, dialogic account of qualitative SE research’s current state and future trajectory. It innovatively positions “narrative” as a core epistemological resource in empirical SE, underscoring the irreplaceable role of qualitative inquiry in uncovering situated, processual, and socially embedded phenomena. The study identifies three key pathways for advancement: methodological integration (e.g., mixed-methods designs), systematic training infrastructure for qualitative literacy, and sustained cross-paradigmatic dialogue between positivist and interpretivist traditions. These contributions provide both theoretical grounding and actionable guidance for fostering pluralistic, rigorous, and context-sensitive empirical research in SE. (149 words)