Abstract

Qualitative research is a multifaceted process involving data collection, transcription, organization, coding, and thematic analysis. As qualitative data analysis (QDA) tools gain popularity, artificial intelligence (AI) is increasingly integrated to automate aspects of this workflow. This study investigates researchers’ QDA practices, their experiences with existing tools, and their perspectives on AI involvement in qualitative research. We introduce ChromaScribe, a prototype QDA tool, and gather feedback to assess whether it addresses the limitations of current tools. Through interviews with 16 qualitative researchers, we examine their preferences for traditional coding, AI-initiated coding, and human-initiated coding, exploring factors such as ownership, trust and bias. While participants valued QDA tools for streamlining analysis and data management, concerns about data confidentiality and steep learning curves deterred widespread adoption. AI-initiated coding was preferred alongside traditional coding, with participants emphasizing final human oversight. Additionally, human-AI collaboration emerged as a promising approach to mitigating bias. Finally, text-based analysis remained dominant, with limited integration of audio and video data.

Publication Date

4-2025

Document Type

Master's Project

Student Type

Graduate

Degree Name

Computer Science (MS)

Department, Program, or Center

Information, School of

College

Golisano College of Computing and Information Sciences

Advisor

Hidy Kong

Advisor/Committee Member

Roshan Peiris

Campus

RIT – Main Campus

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