# Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation

arXiv: https://arxiv.org/abs/2509.12503 · HTML: https://mlsanity.com/paper/2509.12503/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2025.

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

- [The Cultural Mapping and Pattern Analysis (CMAP) Visualization Toolkit: Open Source Text Analysis for Qualitative and Computational Social Science](https://mlsanity.com/paper/2510.16140/) (2025)
- [Can machines perform a qualitative data analysis? Reading the debate with Alan Turing](https://mlsanity.com/paper/2512.04121/) (2025)
- [Integrating Computational Methods and AI into Qualitative Studies of Aging and Later Life](https://mlsanity.com/paper/2512.17850/) (2025)

## Abstract (arXiv)

Computational developments--particularly artificial intelligence--are reshaping social scientific research and raise new questions for in-depth methods such as ethnography and qualitative interviewing. Building on classic debates about computers in qualitative data analysis (QDA), we revisit possibilities and dangers in an era of automation, Large Language Model (LLM) chatbots, and 'big data.' We introduce a typology of contemporary approaches to using computers in qualitative research: streamlining workflows, scaling up projects, hybrid analytical methods, the sociology of computation, and technological rejection. Drawing from scaled team ethnographies and solo research integrating computational social science (CSS), we describe methodological choices across study lifecycles, from literature reviews through data collection, coding, text retrieval, and representation. We argue that new technologies hold potential to address longstanding methodological challenges when deployed with knowledge, purpose, and ethical commitment. Yet a pragmatic approach--moving beyond technological optimism and dismissal--is essential given rapidly changing tools that are both generative and dangerous. Computation now saturates research infrastructure, from algorithmic literature searches to scholarly metrics, making computational literacy a core methodological competence in and beyond sociology. We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand--rather than displace--the irreducible insights of qualitative research.