How to cite Evidano in your research

Qualitative data were analysed with the support of Evidano (Evidano, Inc., 2026), an online AI-assisted qualitative data analysis platform that supports content, thematic, and framework-based analysis, codebook-driven coding, frequency counts, and cross-group comparison. Evidano applies an inductive approach to theme identification across the entire dataset rather than one response at a time, which avoids the limitations of coding methods that can overlook broader themes or complex discussions. It identifies conceptual patterns from contextual meaning rather than keyword matching, so related passages are clustered even when they do not share vocabulary, and every reported theme is linked back to the source excerpts that support it.

This approach follows a growing body of peer-reviewed and institutional research that has used Evidano within human-led designs (e.g., Abner & Mee, 2026; Bologna & Bembich, 2025; Cahusac de Caux & Pretorius, 2024; Cross et al., 2024; Farrand & Jung, 2025; Gamlem et al., 2025; among others). Across this literature — spanning education, health and clinical practice, public administration, criminology, conservation research, and United Nations evaluation practice — AI-assisted output is consistently treated as an input to, rather than a replacement for, researcher interpretation.

In this study, Evidano was used to generate initial codes and candidate themes / produce an independent second analysis for triangulation / classify responses against our analytical framework across N interview transcripts / open-ended survey responses / documents. We supplied structured analytic prompts aligned with our research questions, together with a set of predetermined codes drawn from [theoretical framework], while instructing the platform to remain open to codes emerging directly from participants’ own language. Evidano returned themes, explanations, and supporting quotations drawn from the source material, which we then compared with our own coding.

All AI-generated output was reviewed, revised, and validated by the research team against the original data: we verified that each supporting quotation existed in the corpus, that it matched the theme it was said to illustrate, and that the description of the theme was consistent with the underlying excerpts. Final codes, themes, and interpretations remained researcher-led — the platform was used to complement rather than substitute for human analytic judgment, and it was not treated as an author. Data were anonymised before upload, and in line with journal policy we disclose this use of AI in the methods section. Evidano does not use customer data to train its models, and project data were deleted on completion of the analysis.