Qualitative research using artificial intelligence: Methods, techniques, challenges, and future directions

Abstract

The introduction of artificial intelligence into qualitative research is a massive revolution in the extraction, interpretation, and analysis of non-numerical data by researchers. Although the traditional approaches of qualitative research are deeply informative and focused, they have the major problems such as lengthy analytic procedures, researcher biasness, absence of scaling and varying interpretation. This is a literature review that covers the way the AI technologies are transforming the qualitative research methods of various fields. The article examines the new AI-related methods such as natural language processing, machine learning algorithms, deep learning models, and network architectures that support automated coding, thematic analysis, sentiments, and pattern recognition of qualitative data. This study points out the essential applications of PRISMA-based systematic analysis that include interview transcription, focus group analysis, ethnographic observation, narrative inquiry, and case study research. The review shows that the opportunities provided by the integration of AI are unprecedented, providing improved chances to be more accurate, efficient, and scalable, but researchers are faced with significant challenges concerning the bias in the algorithms and interpretive validity, ethics, data privacy, and maintenance of human contextual awareness. The results confirm that the use of hybrid ways to include AI capacities with human skills results in better results than automated ones or conventional manual methods.