CHAIR: An Open-Source Tool for Inductive Qualitative Data Analysis Based on Large Language Models

CHAIR is an open-source Python library dedicated to applying large language models to qualitative data analysis in social sciences. It enables efficient inductive coding and theme extraction through a human-AI collaboration model. Its core design philosophy is "assist rather than replace", aiming to help researchers reduce repetitive work and improve research efficiency while retaining their dominance in the analysis process.

In fields such as social sciences, anthropology, and education, traditional qualitative data analysis is time-consuming and relies on researchers' subjective judgments, with the coding process often taking weeks or even months. The CHAIR (Comprehensive Helper for AI-assisted Research) project combines the text comprehension capabilities of large language models with researchers' professional knowledge to create an efficient human-AI collaborative analysis model, bringing new possibilities to this field.

As a Python library, CHAIR provides a series of intelligent tools:

1. Intelligent Coding Assistance: Learns coding rules based on initial examples, supporting open, axial, and selective coding;
2. Theme Discovery and Clustering: Identifies potential themes and clusters similar codes into high-level concepts;
3. Coding Consistency Check: Assists in detecting discrepancies among multiple researchers and provides reconciliation suggestions;
4. Iterative Analysis Workflow: Supports the full process from data import, coding, theme extraction to theory building, with decision records to ensure traceability.

The core feature of CHAIR is its "human-AI collaboration" model. Unlike fully automated tools, it positions large models as "research assistants". Researchers always hold the dominant power (deciding coding content, category definitions, etc.), while the model leverages its advantages in fast text processing and pattern recognition to expand researchers' capabilities rather than replace their judgments. This "human-in-the-loop" design balances efficiency and depth, addressing the academic community's concerns about over-reliance on AI.

CHAIR has a wide range of application scenarios:

• Graduate students/junior researchers: Lower the learning threshold for qualitative methods and quickly master coding skills;
• Experienced researchers: Handle large-scale datasets and conduct research that was previously difficult to carry out;
• Interdisciplinary collaboration: Standardized processes and transparent records facilitate team understanding and evaluation;
• Open design: Customizable workflows, and can integrate Python tools such as spaCy and NLTK.

CHAIR is developed based on Python and supports direct installation via pip. Users need to provide API keys from mainstream service providers such as OpenAI and Anthropic to call text generation capabilities. The project has clear code and complete documentation, including basic to advanced examples, and the community can contribute via GitHub. Data privacy note: When calling external APIs, users should understand the protection policies, and take preventive measures when handling sensitive data.

CHAIR has limitations: Large language models may carry biases from training data and have insufficient understanding of specific cultures or fields, so researchers need to maintain critical thinking. Future prospects: Expand multimodal analysis (interview recordings, videos), optimize prompt engineering and domain adaptation technologies, and improve analysis accuracy.

CHAIR represents the deep penetration of AI into academic research. It does not aim to replace researchers' thinking but provides powerful tools to allow researchers to focus on creative work such as theoretical construction and meaning interpretation. For qualitative research scholars, CHAIR is worth trying and paying attention to.