AI & Qualitative Research Blog

Gordon Abner

Associate Professor, LBJ School of Public Affairs
University of Texas at Austin

After the first author completed an inductive thematic analysis of 224 open-ended survey responses on police chief credentials, Professor Abner’s team ran an independent Evidano analysis to check the human coding for oversights, alternative frames, and researcher bias — and declined the one extra theme it proposed.

“We found that AI can strengthen qualitative research by providing an independent check on human coding, but final decisions about theme validity should remain with researchers, who must assess whether a theme is sufficiently coherent, frequent, and supported by the data.”

Chisoni Mumba

Associate Professor of Animal Health Economics
University of Zambia

In a hybrid qualitative study of six focus groups near Zambia’s game management areas, Professor Mumba’s team used Evidano to run sentiment analysis on the transcripts, then checked every theme it surfaced against the original transcripts and verbatim quotes.

“Evidano gave us a layer of insight we would have struggled to capture through manual coding alone. It picked up the emotional tone running through the transcripts, the anxiety around eating meat that might be infected, the fear tied to livestock losses, and the frustration participants expressed toward formal health campaigns.”

Annalisa Raymer

Senior Lecturer & Director of CLASP
Cornell 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 Education
National 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 Education
Volda 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 Professor
University 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 Professor
Sharjah 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 Science
South 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 Management
Universiti 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.”

Jennifer Riedl Cross

Editor of Gifted Child Quarterly & Research Professor
University of Louisiana at Lafayette

Facing more than 19,000 open-ended comments from a Mensa Foundation study of 3,443 highly intelligent adults, her team ran the responses through Evidano in batches, compared repeated analyses, and manually verified every theme and supporting quote before finalizing the findings.

“Researchers can work together with Evidano, but they have a responsibility to stay in touch with the data and take steps to confirm the output is accurate and unbiased.”

Yuliia Maslova

Professor & Head of Journalism and PR Management
National University of Ostroh Academy, Ukraine

Building a conceptual model that links fragmented digital identity with soft power, Professor Maslova’s team processed a literature sample of 51 sources with Evidano to surface recurring themes and candidate categories, then verified and interpreted every category manually during theory construction.

“Evidano helped us identify recurring themes and formulate candidate categories across a corpus of 51 literature sources. Its function was supportive rather than substitutive. The authors verified the outputs, interpreted the categories within the theoretical framework, and retained responsibility for every conceptual relation and conclusion.”

Esteban O. Mora-Martínez

Lecturer & Head of the Evaluation Section
University of Costa Rica

For a book chapter meta-evaluating Costa Rica’s National Evaluation Agenda, Dr. Mora-Martínez used Evidano to apply a single evaluation framework across ten reports spanning employment, education, health, culture, and the environment, acting as evaluator while Evidano analyzed the documents.

“Evidano helped apply the same analytical framework consistently across a diverse set of evaluation reports, making it easier to organize evidence, identify recurring patterns, and compare findings across sectors. At the same time, evaluators should remember that AI does not replace professional judgment.”

Johnnie Yang

Postdoctoral Fellow, Semel Healthy Campus Initiative Center
University of California, Los Angeles

For a convergent mixed-methods dissertation on the student wellness crisis, Johnnie Yang used Evidano to run three analyses of student journals and interview transcripts — journals alone, interviews alone, and a combined dataset — then re-read every transcript and made all final interpretations through reflexive thematic analysis.

“Evidano came in on the qualitative side, helping me organize and get an initial read on a huge amount of journal and interview data. I was also the instructor for these students, so I already had relationships and instincts going in, which meant I couldn’t just take the tool’s output at face value. I went back to every transcript myself, checked it against what I actually knew about these students, looked for the stuff that didn’t fit the pattern.”