Researchers at leading universities have used Evidano in their published work — and agreed to share how.
Annalisa Raymer
Senior Lecturer & Director of CLASPCornell University
Dr. Raymer uploaded the transcript of an in-house CLASP film into Evidano to summarize four stakeholder groups’ perspectives and run a thematic analysis, then compared the AI’s reading with feedback from adult-education colleagues at the AAACE conference.
“With Evidano, I feel that I have a straightforward thinking partner, rather than an internet pet eager to please.”
Cheng-Ta Wu
Distinguished Professor, College of EducationNational Chengchi University, Taiwan
His team’s published article is itself a case study of Evidano: the authors demonstrate its full qualitative workflow — summaries, themes, codebook, frequency analysis, and key quotes — on elementary-school leadership interview data. They propose a human–AI collaborative model for qualitative research.
“A powerful assistant that not only enhances the efficiency of qualitative data processing but also empowers researchers to concentrate on higher-level interpretation, theory construction, and nuanced analysis.”
Siv M. Gamlem
Professor of EducationVolda University College, Norway
After two researchers completed the initial inductive thematic analysis of 11 interviews, Professor Gamlem’s team ran the anonymised transcripts through Evidano and compared the results with their manual analysis for triangulation and validation.
“Evidano supported our research process by offering an additional lens on the interview data, helping us identify patterns that could be compared with our initial manual analysis. Its value was not in replacing interpretation, but in strengthening the transparency and robustness of our analytic decisions.”
Shawn S. Savage
Assistant ProfessorUniversity of North Carolina Wilmington
After multiple rounds of human coding of narrative student evaluations, Dr. Savage and Dr. Parker used Evidano to interrogate the data from a different analytic vantage point, surfacing gendered comparative distinctions they then verified through collaborative analysis.
“Evidano was most helpful because it encouraged Dr. Parker and me to revisit the data from a different analytic vantage point. It didn’t replace our interpretation; it expanded the kinds of questions we asked.”
Lawrence Meda
Director of Research & Associate ProfessorSharjah Education Academy, UAE
After translating, transcribing, and manually coding nine teacher interviews, Professor Meda’s team used Evidano to categorize participant quotes into researcher-defined themes — with every allocation manually verified by the research team.
“AI-supported tools like Evidano can enhance qualitative research when used carefully and transparently.”
Adriana Cunha Neves
Researcher, Department of Applied ScienceSouth East Technological University, Ireland
Her team used Evidano for AI-assisted thematic analysis of focus group discussions about starch-protein bioplastics, helping mitigate unintentional researcher bias during interpretation.
“Evidano is particularly valuable for researchers who lack extensive experience in qualitative data analysis, offering an efficient way to organize large datasets while minimizing the influence of pre-existing knowledge and researcher bias.”
Munn Hong Lam
Researcher, Faculty of Accountancy and ManagementUniversiti Tunku Abdul Rahman, Malaysia
In a sequential mixed-methods pilot on AI literacy and employment prospects, his team entered the qualitative interview data into Evidano and interpreted it thematically to add depth to their survey results.
“The qualitative element of our research was supported by Evidano through assistance with thematic organisation and interpretation of interview data. This was helpful in our research workflow, particularly in identifying patterns within the responses received during the interviews.”
Patient-centered obesity consultations require consent-based initiation, neutral language, tailored information, and structured follow-up, according to the International Journal of Obesity study published 23 June 2026. The primary keyword for this post is patient-centered obesity consultations and the audience is qualitative researchers and clinical teams who need reproducible methods to capture patient expectations. According to the International Journal of Obesity article, a purposive sample of 20 adults with obesity were interviewed by Zoom between October and December 2024, producing detailed narratives that yielded five core themes. This post translates those findings into AI-enabled qualitative research tactics you can apply immediately, including transcript processing, thematic coding, frequency matrices, and cross-segment comparisons.
In this article
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Analyze Vancouver's Indigenous transit pilot using AI-enabled qualitative research methods and evidence from National Observer (July 23, 2026). Learn methods and try Evidano.
AI-enabled qualitative analysis of menstrual health: turn PLOS One interviews (18 IDIs, 5 KIIs, Sep–Oct 2025) into actionable themes for researchers and programs.
Evidence-based guide to mHealth for task-sharing in Africa: functions, barriers, and research-ready methods for program teams, plus data tools and next steps.
How AI qualitative analysis accelerates insight from the WHiSE 2.0 cohort (356 participants, published July 24, 2026). Practical steps for researchers and a demo CTA.
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