Getting Started with Artificial Intelligence Assisted Qualitative Analysis: An Introductory Guide to Qualitative Research Approaches with Exploratory Examples from Reflexive Content Analysis
Dados Bibliográficos
| AUTOR(ES) | |
|---|---|
| AFILIAÇÃO(ÕES) | School of Psychology, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia, Adelaide Business School, Faculty of Arts, Business, Law and Economics, The University of Adelaide, Adelaide, SA, Australia |
| ANO | 2025 |
| TIPO | Artigo |
| PERIÓDICO | International Journal of Qualitative Methods |
| ISSN | 1609-4069 |
| E-ISSN | 1609-4069 |
| DOI | 10.1177/16094069251354863 |
| ADICIONADO EM | 2025-08-18 |
Resumo
Rapid advances in artificial intelligence (AI) technologies, especially in consumer-available large language models (LLMs), have spurred efforts to automate qualitative data analysis. For researchers new to qualitative research, existing work rarely explains how research values and the different approaches to qualitative research shape AI-assisted data analysis methods. We aim to open a discussion about the role of qualitative research values and approaches in AI-assisted data analysis and how this may shape the application of these methods. We begin by outlining the two approaches to qualitative research in the literature ('Small-q' and 'Big-Q') and how these guide the alignment between research values and the use of methods. We then highlight the relevance of these approaches for developing AI-assisted analysis methods by reviewing seminal work that uses LLMs for qualitative analysis. Building on the two approaches to qualitative research, we propose an approach-based model that can be used to understand the alignment between the values of qualitative research and the use of methods. Additionally, we position AI within this model and propose questions to help understand AI-assisted analysis within these approaches. Next, using exploratory examples, we underscore the importance of these considerations by highlighting how alignment with the different approaches and their values may shape the application of AI in assisting reflexive content analysis. These examples are not intended as definitive guides; rather, they illustrate the influence of aligning with each approach. Thus, to support researchers interested in applying these AI-assisted analysis methods in practice, we provide references to further literature. To conclude, we present a summary of ethical considerations and future directions. While AI-assisted analysis will not be suitable for all qualitative research, this paper initiates a discussion about the theoretical challenges of applying AI technologies in qualitative analysis and demonstrates how these considerations change the use of AI-assisted analysis.