From Workshops to Logic Models: AI Qualitative Analysis

According to PLoS One, a November 2026 co-development study used qualitative workshop data to identify implementation barriers, facilitators, strategies, mechanisms, and outcomes for whole-school physical activity in special educational needs settings. The study shows that structured qualitative analysis can connect stakeholder experience to a practical implementation research logic model.

• The November 2026 study involved 16 participants from six special schools and stakeholder organizations across Northern Ireland.
• The researchers identified 31 implementation determinants in November 2026, including 18 facilitators, 12 barriers, and one factor that functioned as both.
• The researchers condensed expert-informed recommendations into eight pragmatic implementation strategies for special educational needs settings in November 2026.
• The November 2026 analysis identified eight causal mechanisms, 11 implementation outcomes, seven school-level outcomes, and six pupil-level outcomes.
• The study used deductive content analysis guided by CFIR 2.0, ERIC, the Implementation Outcomes Framework, COM-B, and Normalization Process Theory.

The November 2026 PLoS One study found that qualitative co-development workshops can translate stakeholder experience into an implementation research logic model for special educational needs schools. Researchers brought together parents, teachers, classroom assistants, school leaders, education authority representatives, and community organization professionals to discuss physical activity implementation.

The researchers held two four-hour, in-person workshops in Northern Ireland in November 2024. The first workshop explored determinants and implementation strategies, while the second examined current provision, policies, monitoring, and potential outcomes.

The study team analyzed transcripts, flipchart mind maps, and fieldnotes using deductive content analysis. According to Byrd and colleagues, two researchers initially coded the material, resolved disagreements through discussion, and then refined response categories with the wider research team.

The resulting logic model linked implementation determinants to strategies, causal mechanisms, and outcomes. The authors wrote that "The model illustrates causal mechanisms between implementation determinants and implementation strategies to achieve intended outcomes."

The researchers cautioned that the logic model should be treated as a "flexible framework rather than a prescriptive model" because the findings were specific to the Northern Irish special education context and did not include pupils directly in the workshops.

The November 2026 study indicates that implementation researchers should analyze qualitative data across several connected levels rather than treating stakeholder comments as isolated themes. The researchers considered pupils, staff, families, schools, community organizations, and wider policy or environmental conditions through complementary frameworks.

Researchers should preserve context when analyzing workshop transcripts because the study identified special education requirements such as adaptive equipment, additional one-to-one support, and flexible activities for pupils with different cognitive, communication, sensory, physical, and behavioral profiles.

Research teams should distinguish frequency from importance when reporting coded qualitative data. The November 2026 authors calculated how often outcomes appeared in workshop discussions, but they explicitly stated that frequency should not be interpreted as a measure of relative importance.

Implementation teams should connect each major theme to a decision. In the study, staff support and training were treated as a barrier, training was selected as a strategy, increased staff capability and confidence formed the proposed mechanism, and staff confidence became an outcome to monitor.

AI-assisted analysis can support this workflow by accelerating transcript organization, comparing stakeholder groups, surfacing recurring concepts, and generating auditable summaries. Human researchers must still validate codes, interpret context, protect participant confidentiality, and decide whether a proposed causal pathway is theoretically and empirically defensible.

The November 2026 PLoS One study demonstrates how carefully structured qualitative analysis can move from workshop discussion to an implementation research logic model. The study's 31 determinants, eight strategies, eight mechanisms, and 24 outcomes show the value of connecting themes to decisions rather than stopping at summary. AI qualitative data analysis can help research teams organize transcripts, compare stakeholder segments, and trace evidence across a framework, while human researchers retain responsibility for interpretation and ethics. To apply this workflow to your own interviews, workshops, surveys, or documents, Try Evidano for free.