Reporting LLM-Assisted Qualitative Data Analysis: Literature-Informed COREQ-AI and SRQR-AI Extensions

Large language models (LLMs) are increasingly used in qualitative data analysis, introducing reporting considerations not explicitly addressed by COREQ or SRQR. This study used a structured literature review–informed methodological synthesis to develop preliminary COREQ-AI and SRQR-AI extensions. Of 2,127 records identified, the synthesis included 65 studies. The proposed extensions address model specification, prompt transparency, algorithmic reflexivity, human-in-the-loop validation, AI auditability, data governance, and ethical accountability. Five independent researchers appraised the extensions; 80% considered them relevant and clear, 100% applicable, and 60% indicated that further refinement was needed. Illustrative applications further demonstrated their potential to identify AI-specific reporting information not captured by the original guidelines. COREQ-AI and SRQR-AI are preliminary, literature-informed reporting extensions intended to improve transparency and critical appraisal of LLM-assisted qualitative analysis; they are not validated or consensus-derived standards. Further empirical evaluation, stakeholder consultation, and consensus development are required.