What the study analyzed
Part of the journal’s running series on AI in educational research, the article is a methods demonstration built around eight interview transcripts on the challenges and coping strategies of middle-level leaders in public elementary schools, answering six research questions. The publication walks through a thematic analysis workflow run entirely in Evidano.
How Evidano fit the workflow
From raw transcripts to a researcher-editable report.
- 1Upload transcripts
Eight interview transcripts were loaded into Evidano for analysis.
- 2Themes and codebook
Thematic analysis produced a structured codebook of 3 themes and 12 subthemes.
- 3Quotes and frequency
Key quotes were linked by viewpoint and topic, with frequency charts and per-file analysis.
- 4Review and export
Researchers edited the outputs, then exported the report as DOCX or XLSX.
“Through basic operational demonstrations, this research showcases how AILYZE facilitates researchers' work in authentic educational research settings, providing an in-depth examination of its operational mechanisms, potential benefits, and limitations.”
“...positioning AI as 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.”
Manual vs AI-assisted
Manual coding across every transcript. Slow theme identification. Quotes pulled by hand.
Draft codebook generated, then refined. Themes surfaced for researcher review. Quotes linked automatically by topic.
The publication argues for a human-AI collaborative model where researchers keep interpretive control.
Themes, quotes and summaries can be revised, with sources linked back to transcripts.
Summaries, frequency charts and per-file analysis support interviews, focus groups and open-ended responses.
A 2026 conservation preprint cites this article when describing its own Evidano interview analysis.