Extending AI-assisted methodology from analysis into research design.
Case study research demands careful boundary-setting, rich contextualisation and a defensible chain of evidence. This paper shows how generative AI can support that work — while transparency, theoretical grounding and researcher oversight preserve the rigour that qualitative case studies require. Together with the AI-assisted thematic analysis paper, it forms a complete published toolkit for AI-supported qualitative research: the case study process shapes the research design, and the six-step AI process analyses the data it produces.
The same principles carry across both papers: give the AI the research aim, questions, context, participants and theoretical underpinnings before it works; instruct it step by step rather than in one request; evaluate its outputs against published criteria; and document every prompt so the process is transparent and auditable.
The thematic analysis paper focuses on analysing data through the six STA steps. This paper focuses on the case study method itself — how AI can support the development of case studies in qualitative research from design through to write-up.
Doctoral researchers and academics using case study methodology who want a published, citable process for incorporating generative AI without compromising methodological rigour.