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arXiv · 2601.11739

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

Xinyu Pi·Qisen Yang·Chuong Nguyen·Hua Shen
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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.

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Xinyu Pi, Qisen Yang, Chuong Nguyen, Hua Shen. 2026-01-16. Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era. https://arxiv.org/abs/2601.11739

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