Using AI to Develop Case Studies in Qualitative Research: A Practical Workflow

Case study research is one of the most demanding qualitative designs. It requires defining the case and its boundaries, selecting an appropriate case type, assembling rich contextual detail, integrating multiple data sources, and maintaining a chain of evidence from data to conclusions. Each of those tasks is exactly the kind of structured, context-heavy work where generative AI can help — and exactly where unguided AI can do damage. The difference, as with thematic analysis, is process.

The same architecture, applied to design

The published process follows the architecture established in the AI-assisted thematic analysis paper: context before content, instruction step by step, criteria-based evaluation, and complete documentation. The researcher provides the AI with the research aim and questions, the phenomenon of interest, the case context and boundaries, participants or organisations involved, and the theoretical underpinnings — before asking it to contribute to the case study’s development. The AI then supports the researcher through the stages of building the case study, with the researcher reviewing and directing at every stage.

Where AI genuinely helps in case study work

In practice, the AI’s contribution concentrates in four areas. It helps structure the case: turning scattered contextual material into an organised case description. It supports consistency: applying the same analytical questions across multiple cases in multi-case designs. It accelerates drafting: producing first versions of case narratives that the researcher then corrects against the data. And it strengthens transparency: because every instruction is documented, the path from raw material to finished case study is auditable in a way traditional case writing rarely is.

Where the researcher must stay in charge

Boundary decisions — what counts as the case and what does not — are theoretical decisions, not clerical ones. So are case selection logic, the interpretation of evidence, and the theoretical contribution. The process keeps these firmly with the researcher: the AI proposes and drafts; the researcher decides, corrects and owns. An AI-developed case study without researcher oversight is not a shortcut — it is a validity problem.

A complete AI-assisted qualitative toolkit

Read together, the two 2025 papers form an end-to-end published toolkit: use the case study process to design and develop your case research, and the six-step AI-assisted STA process to analyse the qualitative data it generates — with the PRICE model to demonstrate saturation, and the original STA framework as the methodological foundation. Every element is peer-reviewed and citable, which means your methods section can reference a published process at every stage where AI touched your research.