Feasibility Studies: AI Qualitative Analysis

Bandak et al. (2026) in PLOS One present the Moving Kindergartens protocol, a 10-week mixed-methods feasibility study designed to test implementation and measurement approaches in 10 Danish kindergartens.

• According to Bandak et al. (2026) in PLOS One, the feasibility intervention runs for 10 weeks from March 2, 2026, to May 8, 2026.
• According to Bandak et al. (2026) in PLOS One, the study aims to recruit 10 kindergartens and an estimated 450–650 children and 80–120 pedagogical staff for feasibility testing.
• According to Bandak et al. (2026) in PLOS One, mixed methods include semi-structured interviews, participatory observations, accelerometer data, staff and parent surveys, and a daily implementation log.
• Bandak et al. (2026) in PLOS One write that the study uses co-design and the MRC framework and state, "The primary aim of this study is to evaluate the feasibility of a proposed intervention, " and that a "co-design approach in close collaboration... will ensure contextual relevance and shared ownership."

What happened: Bandak et al. (2026) in PLOS One published a protocol describing a co-designed, 10-week feasibility trial called Moving Kindergartens that tests both an intervention and the measurement strategy.

According to Bandak et al. (2026) in PLOS One, the intervention was developed during January–December 2025 with stakeholders from five kindergartens through workshops and design experiments, and the feasibility phase was scheduled from March 2, 2026, to May 8, 2026.

According to Bandak et al. (2026) in PLOS One, the feasibility outcomes include acceptability, appropriateness, feasibility, recruitment rates, consent rates, adoption and fidelity, and data quality metrics for measures such as accelerometry and the BOT-2 short form.

According to Bandak et al. (2026) in PLOS One, qualitative data collection includes on-site group interviews with staff, individual leader interviews, telephone interviews with 4–5 parents, and participatory field observations in all 10 kindergartens with in-depth observations in two sites.

According to Bandak et al. (2026) in PLOS One, interviews will be transcribed using the AI-transcription tool Good Tape and then manually corrected before thematic analysis guided by feasibility outcomes.

For qualitative researchers, Bandak et al. (2026) in PLOS One show that feasibility studies generate multiple complementary qualitative sources that need coordinated analysis.

According to Bandak et al. (2026) in PLOS One, semi-structured interviews, participatory observations, implementation posters, and free-text survey responses are all core data streams that should be triangulated against fidelity logs and objective accelerometer measures.

According to Bandak et al. (2026) in PLOS One, the protocol anticipates transcription and thematic analysis, which means researchers should plan for consistent codebooks, inter-coder checks, and time-stamped linking between transcripts and implementation-log dates.

According to Bandak et al. (2026) in PLOS One, progression criteria are predefined, which means qualitative signals of acceptability and adoption must be summarized into decision-ready evidence for whether to proceed to an RCT.

Bandak et al. (2026) in PLOS One provide a detailed, co-designed protocol that makes explicit which qualitative signals inform feasibility decisions and when to collect them.

Qualitative researchers can extract higher value from feasibility data by time-linking transcripts, observation notes, and implementation logs and by using AI to speed transcription, coding, and synthesis while maintaining human verification.

Evidano supports these steps with secure AI transcription, thematic and cross-segment analyses, and audit-ready exports; teams running feasibility pilots can get started quickly and reproducibly.

To pilot an AI-enabled qualitative workflow for your next feasibility study, Try Evidano for free.