Scaffolding In-Service Science Teachers for Developing Epistemic Empathy
Authors: Valentina Bologna (Teaching and Learning Centre) and Caterina Bembich (Department of Humanistic Studies), University of Trieste, Italy.
Published in: Education and New Developments 2025, the proceedings of the END 2025 international conference in Budapest.
Method: Mixed-methods evaluation of a three-module training programme for 75 primary and lower-secondary teachers, with inductive thematic analysis of the end-of-programme survey.
Disclosed, named, and cited
The authors named Evidano directly in their methods section, with full attribution. An example of transparent, traceable AI use in a published research workflow.
How Evidano fit the workflow
- 1Qualitative input
Open-ended survey responses from the participating teachers.
- 2Inductive thematic analysis in Evidano
Themes identified across the entire dataset.
- 3Reported output
Findings synthesized into a report on the program’s effectiveness.
“The analysis was conducted using AILYZE software (www.ailyze.com, 2025, AILYZE Inc.), employing an inductive approach to identify themes across the entire dataset.”
“This method avoids the limitations of traditional coding by capturing broader themes and complex discussions, ensuring consistent theme detection and speeding up the analysis process.”
Why researchers are looking
Slow, line-by-line coding. Risk of inconsistent theme detection. Harder to capture complex discussions.
Inductive themes across the whole dataset. Consistent theme detection. Faster, with the authors synthesising the final report.
Benefits above reflect how the publication describes the analysis method.