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From manual coding to machine coding: A systematic review of AI-based coding approaches in qualitative research

artificial-intelligence
data-mining-and-machine-learning
data-science
natural-language-and-speech
text-mining

Abstract

Artificial intelligence (AI) tools have considerable potential for researchers in the process of qualitative coding. The use of AI for qualitative coding requires a thorough understanding of the various approaches and the challenges associated with its application. A systematic review was conducted using documents retrieved from the Web of Science and Scopus databases. The search was limited to articles published in English. According to the inclusion and exclusion criteria, a total of 35 articles were included in this review. The study’s results revealed five main approaches, including “comparative,” “practical guideline,” “AI as a complement,” “AI-only coding,” and “interactive coding,” each playing a distinct role in the analysis process. The advantages of using AI in coding were categorized into three stages: Pre-coding (facilitating preliminary analysis, acting as a research assistant, high accessibility for non-expert users, content summarization capability, and initial data screening), the coding process (generating rich linguistic narratives, increasing precision in code and concept generation, identifying implicit and hidden patterns, broader conceptual coverage, and alignment with human analysis), and post-coding (analytic reproducibility, cost reduction, scalability in analyzing large datasets, operational scalability, and time saving). On the other hand, limitations were also identified, including insufficient understanding of human subtleties, privacy concerns, dependence on data and prompt quality, the need for human verification, the dominance of description over interpretation, neglect of cultural and social contexts, weaknesses in affective analysis, and the requirement for large sample sizes. These findings suggest that the effective application of AI in qualitative coding necessitates the intelligent integration of technology with human expertise, as well as careful consideration of its limitations and cultural contexts.
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