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Qualitative Data Analysis With AI
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Qualitative Data Analysis With AI
Theory, Methods, and Practice

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October 2026 | 304 pages | SAGE Publications, Inc
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This text provides a broad, interdisciplinary overview of the emerging field of artificial intelligence (AI) in qualitative research. It is neither a manifesto for AI adoption nor a warning against it. The book explores what AI tools can do, where they fall short, and what they demand of researchers who choose to use them. Each chapter is grounded in real analytic work: the authors describe their actual encounters with AI-assisted analysis, including false starts, methodological adjustments, and moments where human judgment proved irreplaceable.
 
Preface
 
Acknowledgement
 
Editors
 
List of Contributors
 
Introduction
Sharlene Hesse-Biber
The AI-Cyborg Researcher: A Human-Centered Approach to Qualitative Data Analysis in the Era of Artificial Intelligence,
Introduction

 
The Coming of a New Renaissance: The Rise of Artificial Intelligence and Generative Technologies

 
The Paradigm Shift from Manual Coding and Computer-Assisted Coding to Prompting

 
The Rise of the AI-Cyborg Researcher

 
The Cyborg Researcher Guides AI in Feminist Principles of Praxis

 
Future Directions: Expanding the AI-Cyborg Researcher Model of Meaning-Making Framework.

 
Conclusion

 
AI Sandbox: Reflection

 
References

 
Christina Silver
Chapter 2: The Five-Level QDA Method in the Gen-AI Era: Rethinking Qualitative Pedagogy and Practice
CAQDAS Pedagogy: The Five-Level QDA Method

 
Experiences and Ethos

 
Learners’ Uncertainties and Expectations

 
Pedagogic Aims and Instructional Frameworks

 
The Whether-When-How Debate

 
Encouraging Critical Reflection

 
Contexts Framing Discussion of GenAI for QDA

 
Enacting Analytic Tasks via the use of GenAI Tools

 
GenAI Conversing as an Example of Tactics Informing Strategies

 
Discussion

 
Conclusion

 
AI Sandbox: Reflection

 
References

 
Stefan Rädiker and Udo Kuckartz
Chapter 3: Integrating AI into QDA Software: The Example of MAXQDA
Introduction

 
Software and AI in Qualitative Data Analysis

 
Overview of AI Features in MAXQDA

 
AI in Practice: Support for Qualitative Content Analysis and Grounded Theory

 
Integrated AI in MAXQDA vs. External AI Tools like ChatGPT

 
Conclusion

 
AI Sandbox: Practice

 
References

 
Jessica Parker, Veronika Richard and Susanne Friese
Chapter 4: An Experiment: Can Consumer Chatbots Analyze Open-Ended Survey Responses?
Introduction

 
Traditional Coding Workflows in Qualitative Survey Analysis

 
The value and limits of traditional approaches

 
From Human Coding to AI Assisted Coding

 
Why This Is Not a Straw-Man Experiment

 
The Sample Data Set

 
Why Automated Coding Falls Short

 
Implications: From Coding to Dialogic Analysis

 
Conclusion

 
AI Sandbox: Practice

 
References

 
Appendix: Initial prompt for code frame development

 
Susanne Friese
Chapter 5: Beyond Coding: Conversational AI for Qualitative Analysis with QInsights
Towards a New Perspective on Qualitative Analysis

 
The Origins of Coding: A Historical Perspective

 
The Emergence of AI and LLMs in Qualitative Analysis

 
Understanding and Working with LLMs

 
A New Workflow: Engaging with Data Through Questions

 
Exemplary Analysis with QInsights

 
Methodological Adaptation

 
Discussion

 
AI Sandbox: Practice

 
References

 
Jonas Wibowo & Hendrik Wiese
Chapter 6: Productivity and Quality of using AI for Qualitative Data Analysis in One Research Project
Introduction

 
Productivity Promises of Generative AI

 
Problematic Dimensions in QDA using GenAI

 
Project Description

 
Categorical Qualitative Data Analysis as an Analytic Framework

 
Study Design for Testing GenAI Supported Categorical QDA

 
The Final Procedure

 
A Framework for GenAI-Assisted Categorical QDA

 
Discussion

 
AI Sandbox: Reflection

 
References

 
Appendix

 
Uwe Krähnke, Thorsten Dresing, and Thorsten Pehl
Chapter 7: Hybrid interpretation of text-based data with dialogically integrated LLMs. On the use of generative AI in qualitative research
Introduction

 
Fundamentals, Potentials and Current Developments of AI-supported Analysis of Text-based Empirical Data

 
Hybrid Text Interpretation with Multiple, Dialogically Integrated LLMs

 
Application Example: Functional Segmentation as a Coping Strategy

 
Discussion: Opportunities and Challenges of AI-assisted Qualitative Analysis

 
Epistemological Clarification

 
Data Protection Compliance and Research Ethics

 
Critical Reflection

 
AI Sandbox: Practice

 
References

 
Kai Dröge
Chapter 8: AI and the Co-Creation of Meaning: Using Large Language Models in Grounded Theory Research
Introduction

 
Grounded Theory and AI – An Overview

 
The Role of AI in the Research Process

 
Sycophancy: Bias Towards User Confirmation

 
Common Sense Orientation and Bias

 
The Fluid Positionality of AI

 
Putting It into Practice: Integrating AI into Grounded Theory Research

 
Coding and Memo Writing in the Age of AI

 
Close Reading and “Open Data Exploration” Memos

 
AI Assisted “Horizontal” Coding

 
Consolidating the Emerging Theory and Writing a Report

 
Conclusion

 
AI Sandbox: Practice

 
References

 
Fabio Roman Lieder
Chapter 9: Modular Prompting with the Documentary Method: Rethinking Interpretation with AI in Reconstructive Social Research
Introduction

 
Some Theoretical Considerations

 
Agency of LLMs in Distributed Interpretation

 
Meaning-Making through Modular Prompting

 
Some Basics on the Documentary Method

 
A Practical Example of Distributed Interpretation via Modular Prompting

 
Resulting Hybrid Interpretation

 
Evaluating the Result

 
Discussion and Outlook

 
AI Sandbox: Practice

 
References

 
Jessica Nina Lester and Trena M. Paulus
Chapter 10: The MERIT Framework: Guiding responsible innovation in qualitative methods
Introduction

 
Defining generative AI

 
AI and Qualitative Data Analysis Software

 
Guidelines for Responsible AI Use

 
Reporting Guidelines for Qualitative Researchers

 
A Heuristic for Generating Reporting Guidelines for Qualitative Data Analysis

 
Future Directions

 
AI Sandbox: Reflection

 
References

 
David Morgan
Chapter 11: Understanding the Adoption of an Innovation: The Case of AI in Analyzing Qualitative Data
Diffusion of Innovations

 
Conclusions

 
References

 
 
Glossary
 
References

Qualitative Data Analysis With AI the power of critical thinking and transformative insight through generative AI to challenge convention and inspire innovation.

Minghe Sun
The University of Texas at San Antonio
Minghe Sun, The University of Texas at San Antonio

This book is a great go-to for understanding the implications and use of AI in our qualitative world. Not only do I need this information for my students, but I need this information for myself as a researcher.

Kristen M. Curry
Alliant International University

This book promises to be an indispensable resource for the burgeoning field of AI in qualitative data analysis.

Steven A. Harvey
Johns Hopkins Bloomberg School of Public Health

As an expert and teacher of qualitative research to doctoral level students, I was pleasantly surprised to learn new things from this text. It is on the cutting edge of the AI curve, answering some questions, while posing many more.

Helen Runyan
Regent University

This is a great beginning for students who are at the cusp of technological advancement and learning the "traditional" fundamentals of qualitative research!

Hannah Nario Lopez
University of the Philippines-Diliman
Key features
  • Includes chapter contributors from different methodological traditions, different levels of experience with AI, and different countries to reflect the field as it is now.
  • Shows that researchers do not need to master the internal mathematics of neural networks, but it demonstrates how context limits work, how variability arises, how grounding is established, how confidence can mask uncertainty, and how governance arrangements shape data security.
  • Provides AI Sandbox: Reflection questions at the end of each chapter, to help readers engage with the material and apply it to their own research.

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