AI Qualitative Analysis: Moving Kindergartens Feasibility

Researchers and qualitative teams need faster, reproducible ways to turn interviews, observations, and implementation logs into actionable decisions; the primary keyword for this post is ai qualitative analysis feasibility study. The PLoS One protocol by Bandak et al. (2026) for the 10-week "Moving Kindergartens" feasibility study provides a concrete test case for AI-enabled qualitative workflows because the protocol specifies mixed methods data sources, exact dates, sample ranges, and analytic steps. According to Bandak et al. (2026) in PLoS One, the feasibility study runs from March 2, 2026, to May 8, 2026, and will enroll 10 kindergartens with an estimated 450–650 children and 80–120 pedagogical staff. This post explains which qualitative tasks are high-value for automation, which require human judgment, and how AI platforms can accelerate the thematic analysis, transcription, and synthesis needed to decide whether to proceed to an RCT.

Key Takeaways

Bandak et al. (2026) in PLoS One present a 10-week mixed-methods feasibility protocol that runs from March 2, 2026, to May 8, 2026, and targets 10 Danish kindergartens to test intervention acceptability, recruitment, and data-collection procedures.

The study plans to recruit 10 kindergartens between November 15, 2025, and January 31, 2026, representing urban and suburban contexts, according to Bandak et al. (2026) in PLoS One.
Bandak et al. (2026) in PLoS One estimate approximately 450–650 children aged 3–6 years and 80–120 pedagogical staff across the 10 kindergartens.
Bandak et al. (2026) in PLoS One state that outcomes will include acceptability, appropriateness, feasibility, adoption, and fidelity measured with AIM, IAM, and FIM scales and qualitative interviews.
Bandak et al. (2026) in PLoS One report that interview audio will be transcribed using an AI transcription tool (Good Tape) before thematic analysis.
Bandak et al. (2026) in PLoS One set progression criteria to decide whether to proceed to a cluster RCT by December 31, 2026.

What happened and how the protocol is structured

The protocol describes a 10-week feasibility study that aims to test both the intervention and the evaluation design before a possible RCT, according to Bandak et al. (2026) in PLoS One.

Bandak et al. (2026) in PLoS One recruited 10 kindergartens in Zealand, Denmark, with recruitment scheduled from November 15, 2025, to January 31, 2026, and an intervention period from March 2, 2026, to May 8, 2026.

Bandak et al. (2026) in PLoS One co-designed the intervention during January–December 2025 with stakeholders from five kindergartens, conducting two workshops and two design-experiment phases involving 19 kindergarten staff and 2 researchers in the first workshop.

Bandak et al. (2026) in PLoS One specify mixed qualitative and quantitative data: semi-structured group and individual interviews, participatory field observations, implementation logs (wall posters), staff and parent surveys using AIM/IAM/FIM constructs, and objective child measures including accelerometers and BOT-2 short-form motor tests.

Bandak et al. (2026) in PLoS One write that who will judge feasibility is prespecified with green/amber/red progression criteria tied to recruitment, consent, data completeness, and fidelity.

Findings Snapshot

Date / Period	Metric	Value / Target	Implication
Nov 15, 2025–Jan 31, 2026	Kindergarten recruitment window	10 kindergartens (Zealand, Denmark)	Tests feasibility of institutional recruitment across urban and suburban contexts
Mar 2, 2026–May 8, 2026	Intervention delivery	10-week intervention in 10 kindergartens	Allows measurement of implementation fidelity and acceptability under real-world routines
Estimated during protocol (Bandak et al., 2026)	Sample size range	450–650 children; 80–120 staff	Gives expected scale of qualitative data from interviews and observations
By Dec 31, 2026	Planned results generation	Feasibility findings reported	Determines progression to a cluster RCT if progression criteria met

Implications for researchers and qualitative teams

Researchers should prioritize rapid synthesis of interviews, implementation logs, and observation notes because the protocol uses prespecified progression criteria that require timely decision-making, according to Bandak et al. (2026) in PLoS One.

Bandak et al. (2026) in PLoS One plan semi-structured group interviews in all 10 sites and deeper observations in two sites, which implies variable transcript volume: teams should expect dozens of hours of audio and hundreds of pages of observation notes.

Bandak et al. (2026) in PLoS One include validated survey scales (AIM, IAM, FIM) and bespoke adoption questions, so mixed-methods integration will be needed to align numeric thresholds with thematic barriers and enablers.

Bandak et al. (2026) in PLoS One used a co-design process during 2025 that produced intervention components categorized into four types: formal establishment, activity integration, staff training, and inspirational materials, which suggests qualitative coding should map to these four domains.

How Evidano Helps

Problem: High-volume transcription and slow coding

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Bandak et al. (2026) in PLoS One state that interviews will be transcribed using an AI tool (Good Tape) and then manually corrected; Evidano can ingest audio, apply a custom dictionary for local terms, and output time-coded transcripts for rapid reviewer validation.

Feature mapping: use Evidano transcription workflows and the speech-to-text page to reduce manual correction time while preserving human review for sensitive passages.

Problem: Thematic analysis tied to implementation outcomes

Bandak et al. (2026) in PLoS One plan to conduct thematic analysis mapped to feasibility constructs (acceptability, appropriateness, feasibility), so a reproducible coding frame is essential.

Feature mapping: Evidano automates initial code suggestions, supports hierarchical codebooks, and runs frequency and co-occurrence analyses so teams can rapidly test whether themes align with AIM/IAM/FIM scores.

Use the features page to learn how Evidano produces thematic summaries, exemplar quotations, and exportable codebooks suitable for reporting against prespecified progression criteria.

Problem: Integrating qualitative and quantitative progression criteria

Bandak et al. (2026) in PLoS One specify progression criteria that combine recruitment rates, consent rates, and fidelity logs with qualitative acceptability judgments; mixed-methods dashboards are needed to surface discordant signals quickly.

Feature mapping: Evidano can ingest implementation-log spreadsheets and survey scores alongside transcripts, producing cross-segment analyses (for example, adoption by kindergarten size), so teams can detect patterns such as lower fidelity in urban sites and drill into associated qualitative explanations.

Problem: Audit trail, GDPR, and secure collaboration

Bandak et al. (2026) in PLoS One require secure handling of personal data and restricted access to raw data in accordance with GDPR; platform security is therefore essential.

Feature mapping: Evidano encrypts uploads and controls user access and can be used to share coded reports without exposing identifiable raw transcripts, supporting the ethical and legal protections described in the protocol.

FAQ: ai qualitative analysis feasibility study

What qualitative data will the Moving Kindergartens feasibility study collect?

Answer: The study will collect semi-structured group and individual interviews, participatory field observations, implementation logs, staff and parental surveys, and targeted child-level observations, according to Bandak et al. (2026) in PLoS One.

Supporting detail: Bandak et al. (2026) in PLoS One specify group interviews with pedagogical staff on-site in all 10 kindergartens, individual leader interviews, telephone interviews with 4–5 parents, 1–2 days of participatory observations per site, and wall-poster implementation logs used daily during the 10-week period.

How much transcription and coding effort should teams expect?

Answer: Teams should expect dozens of recorded interviews and extensive observation notes likely totaling tens to hundreds of transcript hours, according to Bandak et al. (2026) in PLoS One.

Supporting detail: Bandak et al. (2026) in PLoS One report semi-structured group interviews (3–5 participants) at 10 sites plus individual leader interviews and parent interviews, and they explicitly plan AI-assisted transcription (Good Tape) followed by manual correction.

Can AI replace human judgment in feasibility decisions?

Answer: No, AI should accelerate coding and synthesis but not replace human judgment for progression decisions, according to the protocol logic in Bandak et al. (2026) in PLoS One.

Supporting detail: Bandak et al. (2026) in PLoS One pair quantitative thresholds with qualitative insights and plan reflection meetings with staff and experts; AI is presented as a tool for efficiency rather than a decision-maker.

Which outputs should teams prepare for progression meetings?

Answer: Prepare a mixed-methods brief that aligns AIM/IAM/FIM scores with exemplar quotations, fidelity logs, recruitment metrics, and observed barriers and enablers, according to Bandak et al. (2026) in PLoS One.

Supporting detail: Bandak et al. (2026) in PLoS One use green/amber/red progression criteria and recommend stakeholder discussion of any amber or red findings before proceeding to an RCT.

Conclusion & Next Steps

The PLoS One feasibility protocol by Bandak et al. (2026) offers a precise, mixed-methods template that shows where AI-enabled qualitative workflows add the most value: transcription, coding consistency, and cross-segment synthesis.

Bandak et al. (2026) in PLoS One set clear dates and numeric targets (10 kindergartens; March 2, 2026–May 8, 2026; estimated 450–650 children), which make the project well suited for an AI-accelerated evidence pipeline that preserves human oversight.

If your team is preparing a feasibility study and needs faster thematic coding, integrated dashboards, and secure transcript handling, consider automating routine steps while keeping human reviewers for interpretation.

To explore AI-enabled qualitative workflows that map directly to the methods used in the Moving Kindergartens protocol, Try Evidano for free.

Topics

ai qualitative analysis feasibility study
qualitative analysis of feasibility studies
AI-enabled qualitative research
feasibility study qualitative methods

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AI Qualitative Analysis: Moving Kindergartens Feasibility

Key Takeaways

What happened and how the protocol is structured

Findings Snapshot

Implications for researchers and qualitative teams

How Evidano Helps

Problem: High-volume transcription and slow coding

Problem: Thematic analysis tied to implementation outcomes

Problem: Integrating qualitative and quantitative progression criteria

Problem: Audit trail, GDPR, and secure collaboration

FAQ: ai qualitative analysis feasibility study

What qualitative data will the Moving Kindergartens feasibility study collect?

How much transcription and coding effort should teams expect?

Can AI replace human judgment in feasibility decisions?

Which outputs should teams prepare for progression meetings?

Conclusion & Next Steps

Topics

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