AI Tools for Qualitative Analysis of School Sports

Evidano is an AI-powered qualitative data analysis platform that helps you run reproducible analyses of school sports transcripts and turn findings into decision-ready outputs. Use multi‑stakeholder focus groups to reveal role-based divergences, then align schedules and supports to realize academic and psychosocial benefits. A two-week pilot workflow can move audio to stakeholder-ready briefs with representative quotes and visuals.

• Taş et al. (Published June 29, 2026) used 3 focus groups (n = 18) and found perceived gains in motivation, time management, and school belonging, with parents noting fatigue and academic balance concerns.
• Design analyses to compare segments (students vs teachers vs parents) so you avoid misleading generalizations and highlight operational levers like scheduling and coach-teacher coordination.
• A two-week pilot (upload, auto‑transcribe, code, compare, visualize, brief) produces decision-ready outputs for school leaders and policymakers.

Fast take: Taş et al. (Published June 29, 2026) found school sports are perceived to boost motivation, time management, and school belonging, while parents raised concerns about student fatigue and balancing academics.

Read the original study: PLOS ONE.

• Why it matters: multi‑stakeholder qualitative data reveal convergent benefits and role‑based divergences that schools must coordinate to realize academic gains.
• Payoff for you: reproducible themes, segment comparisons (student vs parent vs teacher), and visualizations that support policy or schedule changes.

The study ran separate 45 to 60 minute focus groups for students, teachers, and parents to capture how school sports are experienced and interpreted.

The transcripts were analyzed with inductive content analysis to surface recurring patterns and differences across stakeholder groups.

• Data: verbatim Turkish audio transcriptions, translated for reporting.
• Analysis orientation: descriptive, then inductive coding, then categories, then higher‑order themes.
• Quality checks: peer debriefing, independent coding by a second researcher, audit trail.

The paper’s practical takeaways are immediately actionable for program managers and researchers who run school sport interventions.

• Perceived benefits: increased academic motivation, discipline, time planning, self confidence, and school belonging.
• Stakeholder divergence: students and teachers emphasize motivation and organization, parents focus more on fatigue and academic priority.
• Conditionality: benefits appear when sports are embedded in supportive teacher and parental practices, and when institutional coordination exists.
• Operational implication: scheduling, flexible academic arrangements during competition periods, and monitoring systems reduce risk.

Follow this minimal two‑week plan to replicate a study like the PLOS ONE example and produce decision‑ready outputs.

• Day 1–2: Upload audio and consent forms to Evidano, set custom dictionary for sports terms and proper names.
• Day 3–5: Auto‑transcribe, validate transcripts, and run initial topic extraction.
• Day 6–8: Generate inductive codes with AI suggestions, import or edit codebook, and apply across segments.
• Day 9–11: Run cross‑segment frequency and co‑occurrence analyses, pull representative quotes per theme and stakeholder.
• Day 12–13: Create visual reports such as code hierarchy and network, and a one‑page executive brief.
• Day 14: Stakeholder review meeting, using clickable quotes and visuals exported from Evidano to align teachers, coaches, and parents.

Evidano compresses weeks of manual work into reproducible pipelines that produce themes, segment comparisons, and publishable visuals.

• Run a two‑week pilot with your school or research corpus and produce a stakeholder brief before the next term.
• See how quickly you can move from audio to policy by starting a pilot: Try Evidano for free.