What the study analysed
Interviews, focus groups, document analysis, and social media data on the adoption of open educational resources in community schools, collected across the Lusaka and Chipata districts of Zambia.
How Evidano was used in the study
The researchers describe a dual coding process that combined manual coding with AI-assisted tools, including Evidano.
The publication reports the AI-assisted tools supported: keyword extraction and categorisation; pre-processing of large transcription volumes; and identifying initial themes through inductive and deductive coding.
The study states the manual coding was cross-referenced with the AI-generated codes, keeping researcher judgement at the centre of the analysis.
AI assisted. Researchers decided.
The publication sets out a clear split between AI support and researcher judgement.
AI-assisted tools: generated initial code ideas and categories; sped up keyword extraction and pre-processing; offered alternative interpretations and labels.
Researcher judgement: selected and validated the most relevant codes; applied theory, literature, and prior expertise; validated patterns across multiple data sources.
Researcher bias was also addressed through reflexivity, triangulation, and member checking.
Study reference
Lufungulo, E. S., Jia, J., & Mwila, K. (2025). Exploring factors of open educational resources (OER) in Zambian community schools: A qualitative study. Social Sciences & Humanities Open, 11, 101465. Open access under CC BY-NC. Evidano is referenced as one of the AI-assisted tools in the study’s data analysis, and the paper has since been cited as precedent by a Springer study that added an Evidano pass to its own manual coding.
How Evidano fit the workflow
- 1Multi-source data
34 interviews, 2 focus groups, documents, and social media content.
- 2AI-assisted processing
Tools including Evidano extracted keywords, categorised, and pre-processed large transcripts.
- 3Cross-referenced coding
AI-generated codes checked against manual coding.
- 4Codebook to themes
Researchers built the codebook, then interpreted and validated the themes.
“leveraged the strengths of both human and artificial intelligence in uncovering meaningful patterns”
In the study, AI-assisted tools helped pre-process large transcription volumes.
AI codes were cross-referenced with manual coding and human judgement.
Interviews, focus groups, documents, and social media in one workflow.
The tool use was described openly in the study’s methodology.