AI insights: qualitative analysis of coach learning

For coach educators and qualitative researchers, AI qualitative analysis of coach learning offers faster synthesis, reproducible coding, and clearer cross-study comparisons. The primary keyword for this post, AI qualitative analysis coach learning, describes the use of machine-assisted thematic and content analysis on interview transcripts, open-ended survey responses, and program documentation to surface patterns and exceptions. This post refracts the PLoS One scoping review of youth participation coach learning through the lens of AI-enabled qualitative research and shows practical steps to convert the review's descriptive findings into actionable study designs and program improvements.

Key Takeaways

According to the PLoS One scoping review (Tan et al., 2026) PLoS One, eleven empirical studies were included after screening 3, 224 records up to April 28, 2025, and AI qualitative analysis can accelerate thematic synthesis of such bodies of literature while preserving reproducibility.

3224 records were returned and screened and 1337 duplicates removed in the database search conducted up to April 28, 2025, as reported in the PLoS One review (Tan et al., 2026).
Eleven studies were included in the review and mapped; of these, nine (81.8%) focused on team sports and six (54.5%) were conducted in Europe, according to the PLoS One scoping review (Tan et al., 2026).
Ten of the eleven studies (90.9%) evaluated coach education programs (CEPs) and most CEPs used online or blended delivery, which the PLoS One review (Tan et al., 2026) identifies as an opportunity for digital data collection and automated transcript analysis.
Coaches in the review described online resources as enabling “time efficiency” and flexible, self-paced reflection, and one coach said, “I really enjoyed the freedom to go and search and learn about things that I wanted to learn about without being told I had to learn certain things, ” as cited in Tan et al. (2026).

What Happened and how the review was measured

What happened: Tan et al.'s PLoS One scoping review (Tan et al., 2026) mapped empirical literature on youth participation coach learning published between January 2010 and April 28, 2025.

How it was measured: The PLoS One review (Tan et al., 2026) searched six databases (SPORTDiscus, ERIC, Scopus, Web of Science, APA PsycINFO, Medline), imported 3, 224 records into Covidence, removed 1, 337 duplicates, screened 1, 887 titles/abstracts, assessed 82 full texts, and included 11 studies for analysis.

Constraints and focus: The PLoS One review (Tan et al., 2026) limited inclusion to English-language peer-reviewed empirical work after 2010, resulting in a western-country bias (all included studies were from Europe or North America) and a predominance of team-sport contexts (nine of eleven studies, 81.8%).

Findings Snapshot

Date / Period	Metric	Value	Implication
searches through April 28, 2025	Records identified	3, 224	Large initial yield; need scalable synthesis methods (PLOS One, Tan et al., 2026)
after de-duplication (Apr 2025)	Duplicates removed	1, 337	Standard preprocessing required before qualitative coding (PLOS One, Tan et al., 2026)
screening completed by Apr 2025	Full texts assessed	82	Manual screening bottleneck highlights value of AI-assisted triage (PLOS One, Tan et al., 2026)
final inclusion (published Aug 18, 2026)	Studies included	11	Small corpus for deep qualitative synthesis but diverse designs recommend structured coding (PLOS One, Tan et al., 2026)
studies' contexts	Team-sport focus	9 studies (81.8%)	Findings may not generalise to individual sports (PLOS One, Tan et al., 2026)
CEPs delivery	Formal CEP structure	10 of 11 studies (90.9%)	Most evaluations concern formal programs, enabling pre/post designs and digital capture (PLOS One, Tan et al., 2026)

Implications for researchers studying coach education

AI-assisted qualitative workflows answer the review's call for larger, more diverse samples by making coding and cross-segment analysis scalable, as recommended in the PLoS One review (Tan et al., 2026).

Researchers designing CEP evaluations should plan mixed-methods longitudinal designs and capture audio and open-text artifacts, because the PLoS One review (Tan et al., 2026) found that seven studies used mixed methods and seven studies were longitudinal to validate behavioural change.

Researchers should explicitly collect metadata for context (country, sport type, coach status) because the PLoS One review (Tan et al., 2026) showed geographic and sport-type skews that limit generalisability.

How Evidano helps: map from problem to AI-enabled solution

Evidano definition

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

Use case: When a scoping review yields thousands of records like the 3, 224 returned in the PLoS One search (Tan et al., 2026), research teams can use Evidano to ingest transcripts and CEP artifacts, run thematic and frequency analyses, and export reproducible codebooks.

Problem: screening and synthesis bottlenecks → Solution: AI triage and thematic clustering

Problem: The PLoS One review (Tan et al., 2026) screened 1, 887 titles/abstracts and 82 full texts, a time-consuming manual process.

Solution: Evidano's ingestion and AI triage can prioritize likely-relevant documents and surface candidate themes for human review, reducing initial screening time while preserving reviewer control via human-in-the-loop workflows.

Related feature: See the platform features for thematic coding and document ingestion in the Evidano feature overview: Evidano Features.

Problem: small heterogeneous samples limit cross-study comparison → Solution: harmonized coding and cross-segment analysis

Problem: The PLoS One review (Tan et al., 2026) included 11 heterogeneous studies, limiting aggregation.

Solution: Evidano produces hierarchical codebooks and cross-segment frequency matrices that let researchers compare themes by sport type, geography, or coach experience, making it easier to test whether findings (for example, autonomy-supportive coaching uptake) vary across subgroups.

Problem: audio and remote CEP data need accurate transcripts → Solution: contextual transcription and PII redaction

Problem: The PLoS One review (Tan et al., 2026) highlights that many CEPs are delivered online or blended, generating audio and video artifacts that require transcription.

Solution: Evidano provides transcription with custom dictionaries and PII redaction to produce analysable text for downstream AI qualitative analysis, see the speech-to-text capability: Evidano Speech-to-Text.

FAQ: AI qualitative analysis coach learning

How can AI help synthesize small corpora like the 11 studies in the PLoS One review?

AI can accelerate coding and surface candidate themes, but final interpretation should be human-led.

Specifically, the PLoS One review (Tan et al., 2026) mapped four qualitative topics (learning enablers, learning challenges, outcomes, and factors influencing practice), and AI can reproduce initial topic maps and quantify co-occurrence across documents for rapid comparison.

Can AI identify context mismatches that coaches reported as a challenge?

Yes, AI-driven cross-segment analysis can flag themes that are overrepresented in particular contexts, enabling targeted follow-up.

For example, the PLoS One review (Tan et al., 2026) documents content mismatch in some CEPs and AI can help quantify which coach subgroups reported that issue most frequently.

Is AI appropriate for analyzing coach education program outcomes?

AI is appropriate for organizing and summarising qualitative outcomes but should be paired with mixed-methods validation.

The PLoS One review (Tan et al., 2026) found mixed evidence of behavior change in longitudinal mixed-methods studies, indicating that AI summaries should be triangulated with observations or quantitative measures.

Conclusion & Next Steps

The PLoS One scoping review (Tan et al., 2026) surfaces a compact but information-rich set of 11 studies and identifies clear gaps in geography, sport type, and measurement of practice change; AI qualitative analysis provides a practical pathway to scale synthesis while preserving interpretive rigor.

Researchers should capture audio and open-text artifacts in CEP evaluations and plan metadata collection so AI-assisted tools can produce cross-segment analyses that answer who benefits, when, and why, as recommended by Tan et al. (2026).

To accelerate your own coach education research workflows and produce reproducible thematic and cross-segment results, Try Evidano for free.

Topics

AI qualitative analysis coach learning
qualitative analysis of coach learning
coach education qualitative synthesis
AI thematic analysis coaching

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