Qualitative Research with Artificial Intelligence

Responsible Methods for Research Design, Analysis, and Teaching
1. Auflage 2027
ISBN: 978-1-041-43473-3
Verlag: Taylor & Francis Ltd

Buch, Englisch, 226 Seiten, Format (B × H): 174 mm x 246 mm

Qualitative Research with Artificial Intelligence offers a comprehensive, accessible, and pedagogically rich guide to conducting qualitative research in an era shaped by generative AI, automated analysis, and human–AI collaboration. The book argues that AI can extend analytic visibility, but interpretation, ethics, representation, and responsibility must remain human-led.

The text introduces students and researchers to the philosophical, ethical, methodological, and pedagogical foundations of AI-assisted qualitative inquiry. It covers research design, data collection, transcription, coding, natural language processing, human–AI co-coding, interpretation, reflexivity, trustworthiness, audit trails, writing, and future methodological directions. Throughout, practical boxes, toolkits, reflection prompts, real-world researcher examples, and companion workbook activities help readers translate concepts into accountable research practice. The book emphasizes transparency, data provenance, algorithmic bias, participant protection, and methodological fit, showing how AI can be used critically without reducing qualitative inquiry to automation.

This book is designed for graduate and doctoral qualitative research methods courses, dissertation seminars, research design courses, and scholars seeking practical guidance for ethically integrating AI into qualitative inquiry.

Zielgruppe

Postgraduate

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein

Inhaltsverzeichnis

FRONT MATTER Dedication Preface How to Use This Book Acknowledgments About the Author PART I. RETHINKING QUALITATIVE RESEARCH IN THE ERA OF ARTIFICIAL INTELLIGENCE 1. The Evolving Landscape of Qualitative Inquiry in the Age of AI 2. Philosophical and Ethical Foundations 3. Designing AI-Enhanced Qualitative Studies PART II. METHODS AND PRACTICES FOR HUMAN–AI COLLABORATION IN QUALITATIVE RESEARCH 4. Data Collection and AI Integration 5. Coding, Categorizing, and Pattern Recognition 6. Interpretation and Reflexivity in AI Contexts 7. Representation, Writing, and AI-Supported Composition PART III. ADVANCING QUALITY, PEDAGOGY, AND THE FUTURE OF AI-ASSISTED INQUIRY 8. Validation, Rigor, and Trustworthiness in the AI Era 9. Teaching and Learning Qualitative Research with AI 10. The Future of Qualitative Inquiry and AI Collaboration BACK MATTER Appendices: AI Transparency, Reflexivity, and Auditability in Qualitative Inquiry Glossary of Key Terms References Author Index Subject Index

Über Autor(innen)

John Johnston is an independent scholar and higher education researcher based in the United States, with doctoral training in management, technology, education, and qualitative inquiry. His work focuses on AI-assisted research methods, higher education leadership, and the ethical integration of artificial intelligence into teaching, learning, and scholarly practice