Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The primary keyword for this post is AI-enabled qualitative analysis for feasibility studies, and this article shows how that approach would be applied to the PLOS One protocol "Moving Kindergartens: Protocol for a 10-week feasibility study". According to Bandak et al. in PLOS One (published August 10, 2026), the Moving Kindergartens feasibility study runs for 10 weeks from March 2, 2026, to May 8, 2026, and aims to test acceptability, recruitment, data collection methods, and implementation strategies in 10 Danish kindergartens.
Key Takeaways
According to the PLOS One protocol, the Moving Kindergartens feasibility study is a 10-week mixed-methods test across 10 kindergartens designed to assess acceptability, recruitment, measurement feasibility, and implementation strategies (PLOS One).
- Bandak et al., PLOS One (published August 10, 2026) plan to recruit 10 kindergartens from November 15, 2025, to January 31, 2026, representing an estimated 450–650 children aged 3–6 years.
- Bandak et al., PLOS One (protocol) schedule primary data collection during the intervention from March 2, 2026, to May 8, 2026, and expect results by December 31, 2026.
- Bandak et al., PLOS One (methods) use mixed qualitative methods including semi-structured interviews, participatory field observations, implementation logs, and thematic analysis of interview transcripts.
- Bandak et al., PLOS One (data procedures) state that interviews will be transcribed using the AI-transcription tool Good Tape and then manually corrected.
What happened and how the protocol is structured
Answer: The PLOS One protocol by Bandak et al. describes a co-designed, 10-week feasibility study that tests both an intervention and the evaluation methods before a planned cluster RCT.
According to Bandak et al., PLOS One (published August 10, 2026), the intervention was co-designed with stakeholders between January and December 2025 and will be delivered in 10 kindergartens during March 2, 2026 to May 8, 2026.
Bandak et al., PLOS One (methods) classify intervention elements into four categories: formal project establishment, activity components integrated into kindergarten routines, organizational and staff training, and inspirational materials and plans.
Bandak et al., PLOS One (participants) expect each kindergarten to have 25–100 children and estimate a total sample of approximately 450–650 children, 80–120 pedagogical staff, and 10 leaders for feasibility assessment.
Bandak et al., PLOS One (data collection) combine objective measures (accelerometers), structured observation (SOPLAY), surveys using validated AIM/IAM/FIM instruments, implementation logs, and semi-structured interviews transcribed via Good Tape for thematic analysis.
Findings snapshot: numeric facts to extract
| Date / Period | Metric | Value | Implication |
|---|---|---|---|
| Published Aug 10, 2026 | Protocol publication | PLOS One article by Bandak et al. | Public protocol allows reproducibility and pre-specified progression criteria |
| Recruitment Nov 15, 2025 to Jan 31, 2026 | Kindergartens to recruit | 10 kindergartens | Feasibility of recruitment strategy will be assessed |
| Intervention Mar 2, 2026 to May 8, 2026 | Intervention length | 10 weeks | Short test period mirroring planned procedures for RCT |
| Sample estimate (protocol) | Children (approx.) | 450–650 children aged 3–6 years | Provides scope for infant-level feasibility testing in 2–3 kindergartens |
| Data completion target | Results expected | By Dec 31, 2026 | Timeline for reporting feasibility outcomes |
Implications for qualitative researchers and trialists
Answer: The PLOS One protocol demonstrates how mixed-methods feasibility studies can combine co-design, structured qualitative collection, and objective measures to inform an RCT.
Bandak et al., PLOS One (discussion) argue that co-design increases acceptability and contextual fit, which is relevant for researchers designing interventions that must operate within routine early childhood settings.
Bandak et al., PLOS One (methods) exemplify pragmatic constraints that qualitative teams must plan for: recruitment windows (Nov 15, 2025 to Jan 31, 2026), resource-light implementation without extra staff, and feasibility testing of battery-powered accelerometers taped to children for seven days.
Bandak et al., PLOS One (data processes) show one operational practice that impacts qualitative pipelines: interviews will be AI-transcribed using Good Tape, then manually corrected before thematic analysis, creating hybrid human+AI workflows that require explicit QA steps.
How Evidano helps: mapping problems in feasibility qualitative work to AI-enabled solutions
Problem: Large interview volume and slow coding
Solution: Evidano automates high-quality AI-assisted transcription and generates thematic, frequency, and cross-segment analyses to accelerate synthesis.
Bandak et al., PLOS One (methods) transcribe interviews using an AI tool and then manually correct them; Evidano offers integrated speech-to-text with custom dictionaries and PII redaction to reduce manual cleanup time and maintain GDPR-compliant workflows.
Problem: Keeping qualitative context while summarizing many sources
Solution: Evidano produces thematic coding with hierarchical codes and excerpt-level evidence linking back to original transcripts so teams preserve context for implementation decisions.
Bandak et al., PLOS One (analysis plan) rely on thematic analysis to evaluate acceptability and adoption; Evidano supports thematic workflows and visualizations that make implementation barriers and local adaptations visible to trial teams.
Problem: Comparing staff, leader, and parent perspectives across sites
Solution: Evidano enables cross-segment analysis by role, site, and time, producing comparative tables and co-occurrence networks so researchers can operationalize Bandak et al.'s progression criteria.
Bandak et al., PLOS One (progression criteria) require site-level decisions; Evidano’s cross-segment tools shorten the path from raw transcripts to actionable progression judgments.
Problem: Tight timelines for feasibility reporting
Solution: Evidano combines automated transcription, rapid thematic extraction, and AI chat over your dataset so teams can answer reviewers or stakeholders with evidence-backed quotes and counts.
Bandak et al., PLOS One (timeline) expect results by December 31, 2026; using Evidano can compress synthesis time while preserving audit trails and verbatim quotes required by journals.
Relevant Evidano links
Learn more about relevant features on the Evidano features page.
See Evidano’s AI transcription capabilities on the speech-to-text page.
FAQ: AI-enabled qualitative analysis for feasibility studies
How can AI transcription be used safely in feasibility study interviews?
Answer: AI transcription can be used safely if transcripts are reviewed, PII is redacted, and data handling complies with ethics approvals and GDPR.
Bandak et al., PLOS One (ethics) state that personal information will be collected through secure, encrypted platforms and stored on password-protected servers; Evidano’s transcription workflow supports PII redaction and encrypted storage to align with those requirements.
Will automated thematic coding miss nuanced implementation barriers?
Answer: Automated thematic coding can surface patterns quickly but requires human validation to capture nuanced, context-specific barriers.
Bandak et al., PLOS One (analysis) plan manual adjustments after AI transcription and thematic analysis; Evidano’s platform is designed for iterative human-in-the-loop coding so researchers can refine themes while keeping reproducible codebooks.
What immediate outputs should a feasibility study provide for an RCT decision?
Answer: A feasibility study should provide recruitment rates, data completion rates, acceptability and feasibility scores, fidelity logs, and site-level qualitative summaries that map to pre-specified progression criteria.
Bandak et al., PLOS One (progression criteria) include recruitment and AIM/FIM/IAM scores as decision triggers; Evidano produces tabulated counts and verbatim quote bundles tied to these measures to support RCT progression decisions.
Can AI tools produce quotable, auditable evidence for publications?
Answer: Yes, when AI outputs are linked to original audio and manually validated excerpts provide the audit trail required by journals.
Bandak et al., PLOS One (reporting) include verbatim interview quotations and a plan for manual correction of AI transcripts; Evidano preserves source-audio links and a version history so every quoted excerpt is auditable.
Conclusion & Next Steps
The PLOS One protocol by Bandak et al. (published August 10, 2026) provides a clear, mixed-methods template for feasibility testing that pairs co-design with structured qualitative and objective measures.
AI-enabled qualitative analysis workflows reduce turnaround time for transcription, thematic coding, and cross-site comparisons while preserving the audit trail needed for progression decisions in feasibility studies.
If you plan to run a feasibility study like Moving Kindergartens and want to shorten synthesis time without losing rigor, consider integrating an AI-assisted platform designed for qualitative trials.
Get started by exploring Evidano’s features and speech-to-text pages, and Try Evidano for free.
"The primary aim of this study is to evaluate the feasibility of a proposed intervention, " Bandak et al., PLOS One (2026).
Topics
- AI-enabled qualitative analysis for feasibility studies
- qualitative analysis of feasibility studies
- AI transcription for qualitative research
- thematic analysis for feasibility studies
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