AI as a Co-Researcher in Qualitative Data Analysis

WCQR2026 Training Day

Please note: Attendance at the Training Day is not included in the WCQR2026 conference registration and requires separate enrollment. Please visit WCQR2026 Training Day - WCQR

This short course offers an original methodology for qualitative data analysis that incorporates natural language processing (NLP) and artificial intelligence (AI). The methodology, developed by the researcher, combines traditional sociological approaches with advanced digital tools, creating new opportunities for social research, particularly in the context of narrative analysis and thematic analysis. 

Content

This short course provides insight into the researcher’s program, advancing the paradigm of digital qualitative sociology and offering a new approach to the relationship between the researcher, data, and analytical tools in qualitative research in the digital age.

Structure

The short course introduces techniques for leveraging GenAI to support algorithmic data extraction methods, enhancing the researcher’s ability to identify theoretically rich segments of empirical material. A key aspect of this process is the iterative relationship between the researcher’s emergent conceptualization and the algorithmic identification of patterns in the data.

It also covers AI-assisted coding methods, especially lexical-semantic and generative coding, as well as the process of identifying descriptive and interpretative codes in thematic analysis.

Goals

Participants will learn how to design digital research prompts that incorporate inductive, deductive, and abductive logic within the research process.

Moreover, the short course presents methods for algorithmic support in transitioning from codes to conceptual categories and from codes to themes in thematic analysis. The digital qualitative sociology approach treats GenAI systems as tools that assist in abductive reasoning, where algorithmic pattern identification inspires the researcher to formulate theoretical explanations.