Beyond the end(s) of qualitative data analysis

2026/04/24 by Sunze Yu · 1 voice
Social Sciences · #Data Analysis and Archiving #Qualitative Research Methods and Applications #Qualitative Research Methods and Ethics

paper · doi:10.1080/00187259.2026.2649886

openalex publication_date 2026/04/24 · openalex created_date 2026/04/25 · openalex updated_date 2026/07/22

Abstract

In response to Duke’s (Citation2026) op ed, “Artificial Intelligence and the End(s) of Qualitative Data Analysis,” published in Human Organization (85, 1), and my own experience with AI-mediated research in Islamic finance, I engage with the ongoing dialogue on AI tools and qualitative research. I argue that AI tools ought to be viewed as reflexive collaborators rather than threats to interpretive rigor. I suggest that the key challenge for qualitative researchers in the Human-AI era lies in preserving autonomy and responsibility, especially for those who have yet to develop a deep understanding of their field.

Citations

• Achieving Rigor in Qualitative Analysis: The Role of Active Categorization in Theory Building
• Artificial intelligence and the end(s) of qualitative data analysis
• Universities are embracing AI: will students get smarter or stop thinking?
• Effects of LLM use and note-taking on reading comprehension and memory: A randomised experiment in secondary schools
• AI Can Code… But Can It Care? Exploring Automation in Qualitative Research
• Artificial intelligence tools expand scientists’ impact but contract science’s focus
• Exploring the Use of AI in Qualitative Data Analysis: Comparing Manual Processing with Avidnote for Theme Generation

Discussions

• Available now from Human Organization: Beyond the End(s) of Qualitative Data Analysis by Sunze Yu doi.org/10.1080/0018... #SfAA #SocialScience #Anthropology #AppliedAnthropology #HumanOrganization [bsky, 0 points, 0 comments]