From Workshops to Action: AI Qualitative Data Analysis

The November 13, 2026 PLoS One study found that co-development workshops with 16 stakeholders generated a practical implementation research logic model for physical activity in Northern Irish special educational needs settings.

AI qualitative data analysis for education research can help organize large volumes of workshop and interview evidence, but the study demonstrates that stakeholder context, theoretical frameworks, and researcher review remain essential.

• According to the November 13, 2026 PLoS One study, researchers identified 31 implementation determinants: 18 facilitators, 12 barriers, and one factor that was both a barrier and a facilitator.
• According to the November 13, 2026 PLoS One study, the research team used two workshops involving 16 participants from six special schools and several education, family, and community organizations.
• According to the November 13, 2026 PLoS One study, the findings were translated into eight pragmatic implementation strategies, eight causal mechanisms, 11 implementation outcomes, seven school-level outcomes, and six pupil-level outcomes.
• According to the November 13, 2026 PLoS One study, the most frequently identified implementation outcomes were reach with 11 references, acceptability with nine references, and sustainability with four references.
• The authors of the November 13, 2026 PLoS One study wrote that the model "illustrates causal mechanisms between implementation determinants and implementation strategies to achieve intended outcomes."

The November 13, 2026 PLoS One study used collaborative co-development workshops to understand how a whole school approach to physical activity could be implemented in special educational needs settings.

According to the study, recruitment took place between October 5, 2024, and November 29, 2024, and the final sample included parents, teachers, classroom assistants, school leaders, Education Authority Northern Ireland representatives, and community-sector professionals.

According to the study, researchers analyzed workshop transcripts, flipchart mind maps, and fieldnotes using deductive content analysis. Two researchers conducted the initial coding, resolved disagreements through discussion, and then refined the categories with the wider research team.

According to the study, the researchers used CFIR 2.0 to identify determinants, the ERIC taxonomy to select implementation strategies, the Implementation Outcomes Framework to classify outcomes, and COM-B and Normalization Process Theory to explain mechanisms of change.

The study's authors stated that "frameworks were most valuable when used as complementary and flexible guides rather than as prescriptive approaches." This finding is important for AI-assisted analysis because automated coding should support, not replace, contextual interpretation.

The November 13, 2026 PLoS One study suggests that education researchers should connect qualitative themes to context, action, mechanisms, and outcomes rather than reporting themes as isolated topics.

According to the study, special educational needs settings require flexible interventions that account for differences in pupils' cognitive, communication, sensory, physical, and behavioral profiles.

According to the study, participants identified staff training, physical activity champions, collaborative networks, family engagement, adaptive equipment, and individual planning as important implementation considerations.

AI qualitative data analysis can support this workflow by grouping recurring barriers, comparing stakeholder segments, surfacing contradictory views, and linking evidence to a researcher's codebook. AI-generated themes still require human review because the study reported that stakeholder experiences did not always fit neatly into predefined theoretical constructs.

• For education researchers, preserve the source context for every coded excerpt, including stakeholder group, school setting, and workshop date.
• For implementation teams, distinguish barriers from facilitators and connect each major finding to a feasible strategy.
• For evaluators, separate implementation outcomes from school-level and pupil-level outcomes so that success is not reduced to a single measure.
• For research leaders, treat AI as an analysis accelerator and audit the evidence behind important conclusions before publication or policy use.

The November 13, 2026 PLoS One study shows how carefully structured qualitative analysis can turn diverse stakeholder perspectives into implementation determinants, strategies, mechanisms, and outcomes.

AI qualitative data analysis can make similar education research workflows faster and more transparent by supporting transcription, thematic coding, cross-segment comparison, and evidence retrieval.

Researchers should use AI alongside a clear codebook, theoretical framework, human review process, and appropriate data-governance safeguards.

To analyze education interviews, workshops, surveys, and documents with AI-assisted qualitative methods, Try Evidano for free.