Decoding Complexity: Exploring Human-AI Concordance in Qualitative Coding

TL;DR

This paper investigates how Large Language Models can assist in qualitative coding, highlighting challenges and potential benefits, and emphasizes the need for task-specific evaluation of LLM integration in qualitative data analysis.

Contribution

It provides initial insights into the use of LLMs for qualitative coding, emphasizing the importance of task complexity and context in their effective application.

Findings

LLMs face challenges with extensive codebooks and context complexity.

Task-specific evaluation is crucial for effective LLM integration.

Both human and AI coders encounter difficulties with complex coding tasks.

Abstract

Qualitative data analysis provides insight into the underlying perceptions and experiences within unstructured data. However, the time-consuming nature of the coding process, especially for larger datasets, calls for innovative approaches, such as the integration of Large Language Models (LLMs). This short paper presents initial findings from a study investigating the integration of LLMs for coding tasks of varying complexity in a real-world dataset. Our results highlight the challenges inherent in coding with extensive codebooks and contexts, both for human coders and LLMs, and suggest that the integration of LLMs into the coding process requires a task-by-task evaluation. We examine factors influencing the complexity of coding tasks and initiate a discussion on the usefulness and limitations of incorporating LLMs in qualitative research.