# Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive Qualitative Coding Results

arXiv: https://arxiv.org/abs/2411.06316 · HTML: https://mlsanity.com/paper/2411.06316/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [A Computational Method for Measuring "Open Codes" in Qualitative Analysis](https://mlsanity.com/paper/2411.12142/) (2024)
- [Processes Matter: How ML/GAI Approaches Could Support Open Qualitative Coding of Online Discourse Datasets](https://mlsanity.com/paper/2504.02887/) (2025)
- [Toward LLM-Supported Automated Assessment of Critical Thinking Subskills](https://mlsanity.com/paper/2510.12915/) (2025)

## Abstract (arXiv)

Inductive qualitative methods have been a mainstay of education research for decades, yet it takes much time and effort to conduct rigorously. Recent advances in artificial intelligence, particularly with generative AI (GAI), have led to initial success in generating inductive coding results. Like human coders, GAI tools rely on instructions to work, and how to instruct it may matter. To understand how ML/GAI approaches could contribute to qualitative coding processes, this study applied two known and two theory-informed novel approaches to an online community dataset and evaluated the resulting coding results. Our findings show significant discrepancies between ML/GAI approaches and demonstrate the advantage of our approaches, which introduce human coding processes into GAI prompts.