What the study analysed
The convergent mixed-methods dissertation examined how undergraduates describe and interpret holistic wellbeing. The quantitative strand validated a new eight-dimension wellbeing survey with a final analytic sample of 251 full-time undergraduates. The qualitative strand followed 18 students enrolled in a yoga-based general education seminar.
Each participant produced a compiled document of roughly 10 to 25 pages of single-spaced journal text, alongside a 30 to 60 minute semi-structured interview. Transcripts were de-identified and labelled with pseudonyms before any analysis began.
A deductive frame, kept open to new codes
The researcher entered a set of predetermined codes drawn from the eight dimensions of holistic wellbeing and from contemplative learning constructs such as self-awareness, emotional regulation, and perspective shifts, then instructed the platform to stay open to codes emerging directly from participants' language.
Three analyses were run and compared: one over journals, one over interview transcripts, and one combining both sources for overarching themes. A second pass over the interview data with revised prompts produced an alternative five-theme structure, which the researcher weighed against the first.
How Evidano fit the workflow
AI organised the volume; interpretation stayed with the researcher.
- 1Data compiled and de-identified
One document per participant, combining welcome survey responses and every journal submission, with interviews transcribed and manually corrected.
- 2Structured prompts and predetermined codes entered
Prompts were aligned to the research questions and to the eight wellbeing dimensions, while leaving room for emergent codes.
- 3Three datasets analysed independently
Journals, interviews, and a combined set, each uploaded separately to preserve the context of the source.
- 4Every code reviewed and interpreted by the researcher
Initial codes and suggested themes were refined and read back against the research questions.
“I used AILYZE, an AI-assisted qualitative analysis platform, to support the coding process by organizing large volumes of text data, identifying preliminary patterns, and generating initial thematic structures.”
“Based on these inputs, AILYZE generated initial codes and suggested themes for each dataset, which I then reviewed, refined, and interpreted in relation to my research questions.”
“While tools like AILYZE cannot replace the interpretive role of the researcher, they may offer valuable support in organizing large datasets, identifying preliminary patterns, and facilitating iterative cycles of coding and thematic development.”
Why AI-assisted over manual coding alone
Comparing alternative coding structures across hundreds of pages of journals and transcripts is slow enough that most researchers only get one pass.
Multiple rounds of thematic exploration across both data sources, so alternative structures can be compared rather than assumed.
The platform is named and cited in the data analysis chapter and the reference list.
Predetermined codes were entered up front, with emergent codes still welcome.
Transcripts were de-identified and pseudonymised before upload.
Every code was reviewed, refined, and interpreted by the author.
About Evidano (previously AILYZE)
Evidano is AI-assisted qualitative data analysis software for interviews, focus groups, open-ended survey responses, and documents. It supports AI-assisted thematic analysis with your own or an AI-suggested codebook, transcription and translation in 100+ languages, clickable quotes and citations, and exportable visual reports. End-to-end encryption, no third-party data sharing, and your data is not used to train AI. From USD 50/month for unlimited analysis.
Run the first pass in days, not weeks
Keep the judgment with you. Let Evidano do the heavy lifting on your interviews, focus groups, open-ended responses, and documents.
More case studies
AcademiaResearchers at Oxford and Melbourne used AI to analyse their interview data
Evidano helped analyse interview transcripts in a peer-reviewed study, then was checked against manual coding.
AcademiaA Stanford-led study used AI to cross-check its qualitative coding
A published mixed-methods study uploaded deidentified transcripts into Evidano to cross-check themes. Every AI code was reviewed by the researcher.
AcademiaResearchers used AI for qualitative analysis. Human experts reached the same conclusions.
A published Cornell study used Evidano (previously AILYZE) for thematic analysis, and human experts reached the same conclusions.