Large Language Models as Qualitative Instruments: A Review of AI for Qualitative Research

This review examines AI for Qualitative Research: A Hands-On Guide for Management Scholars (Quevedo & Kuri, 2026), an open-access Palgrave Pivot volume published by Palgrave Macmillan (Cham). The book examines the methodological inflection in management and social science research, particularly with the normalization of large language models (LLMs) in writing, teaching, and qualitative data analysis. Rather than treating AI as either a substitute for interpretation or a threat to interpretivism, the authors argue that LLMs are computational tools that can be employed and audited responsibly. This book review summarizes two important parts. Part One introduces AI, natural language processing (NLP), and LLMs, covering their background and applications, offering a clear overview of NLP in management and social science research through a useful literature review and roadmap, along with ethical considerations. Part Two offers step-by-step guidelines for researchers with limited programming experience, including Python basics, an Application Programming Interface-based (API-based) workflow, data validation, classification, pattern modeling, and information retrieval with retrieval-augmented generation (RAG). This book review outlines the book’s key contributions, particularly regarding LLMs for corpus exploration and data selection in qualitative research, while recognizing unresolved needs for further guidelines on validation practices, disclosure, and auditing in methodology.