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.”
AI qualitative analysis of WHiSE 2.0 explains how researchers and program teams can use AI-enabled methods to extract themes, frequency counts, and cross-segment patterns from the WHiSE 2.0 cohort profile. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One cohort profile (Verma et al., 2026), WHiSE 2.0 enrolled 356 Indigenous participants across Thunder Bay, Sault Ste. Marie, and Sudbury and published baseline results on July 24, 2026 ([PLOS One](https://journals.plos.org/plosone/article? id=10.1371%2Fjournal.pone.0354477)). This post gives a short method playbook and concrete examples (using the WHiSE 2.0 numbers reported in PLOS One) to show how AI-enabled qualitative research can speed synthesis while respecting Indigenous data governance.
In this article
Use AI-enabled qualitative analysis to extract actionable insights from a PLOS ONE study on menstrual health in Khulna slums. Read findings, quotes, and research-ready next steps.
How mHealth supports task-sharing for NCDs in Africa and how AI-enabled qualitative research speeds synthesis into action. Practical methods, stats, and tools.
How AI-enabled qualitative research accelerates culturally safe analysis of WHiSE 2.0 interviews and surveys; practical steps, stats from PLOS One, and Evidano tools to scale analysis.
Practical AI-enabled qualitative analysis of digital health task-sharing in Africa, using PLOS Global Public Health findings to guide program design and evidence synthesis.
Turn a PLOS ONE qualitative study into AI-enabled thematic insights: best practices for qualitative analysis of menstrual health, methods, and tools to speed synthesis.
How mHealth supports task-sharing for NCD care in Africa and how AI-enabled qualitative research accelerates insight. Learn methods, findings, and next steps.
Learn how AI-enabled qualitative analysis clarifies mHealth task-sharing for NCDs in Africa, with stats from the PLOS review (Jul 24, 2026) and practical research workflows.
Evidence-led guide to mHealth task-sharing in Africa: 2026 scoping review data, barriers, and how AI-enabled qualitative research speeds synthesis and program design.
See how AI-enabled qualitative research can accelerate analysis of WHiSE 2.0 (n=356, published July 24, 2026) to surface themes, timelines, and culturally grounded recommendations.
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