Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era

2026-01-16

AI Summary

This study addresses the inconsistent and poorly governed application of large language models (LLMs) in qualitative research, stemming from a lack of clear distinction between levels of meaning-making and modeling commitments. To resolve this, the paper proposes a 4×4 landscape framework that systematically integrates four meaning-making levels—description, categorization, explanation, and theorization—with four modeling commitments: static structure, sequential staging, causal pathways, and feedback dynamics. Through systematic literature mapping and multidimensional categorization, the analysis reveals that current LLM applications predominantly occupy lower-order meaning-making and low-commitment modeling, with scant engagement in explanatory or theoretical inference and dynamic mechanisms. This work establishes the first unified framework to delineate LLM capabilities and gaps in qualitative data analysis, offering a novel agenda and methodological foundation for governable AI-assisted qualitative research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

LLMs are increasingly used to support qualitative research, yet existing systems produce outputs that vary widely--from trace-faithful summaries to theory-mediated explanations and system models. To make these differences explicit, we introduce a 4$\times$4 landscape crossing four levels of meaning-making (descriptive, categorical, interpretive, theoretical) with four levels of modeling (static structure, stages/timelines, causal pathways, feedback dynamics). Applying the landscape to prior LLM-based automation highlights a strong skew toward low-level meaning and low-commitment representations, with few reliable attempts at interpretive/theoretical inference or dynamical modeling. Based on the revealed gap, we outline an agenda for applying and building LLM-systems that make their interpretive and modeling commitments explicit, selectable, and governable.

Problem

• qualitative data analysis
• large language models
• interpretive inference
• theoretical modeling
• human-machine representation

Innovation

• qualitative data analysis
• large language models
• interpretive modeling
• meaning-making landscape
• governable AI