AI Qualitative Analysis for SEN Research

The PLoS One study, “Embedding behavioural and implementation science in a whole school approach to promote physical activity among children and young people with disabilities”, published on November 13, 2026, found that co-development workshops in Northern Ireland identified 31 implementation determinants for whole-school physical activity in special educational needs settings.

AI qualitative analysis can support this kind of research by organizing transcripts, applying theory-informed codes, comparing stakeholder perspectives, and tracing connections between barriers, strategies, mechanisms, and outcomes. AI outputs should remain subject to researcher review, especially when findings concern children, disability, and health.

• The 2026 study included 16 participants from six special schools and stakeholder organizations, including parents, teachers, classroom assistants, school leaders, education authority representatives, and community professionals.
• The researchers identified 18 facilitators, 12 barriers, and one factor that functioned as both a barrier and a facilitator across 31 implementation determinants.
• The study used two workshops held in November 2024 and produced eight pragmatic implementation strategies, eight causal mechanisms, 11 implementation outcomes, seven school-level outcomes, and six pupil-level outcomes.
• The most frequently identified implementation outcomes were reach, mentioned 11 times, acceptability, mentioned nine times, and sustainability, mentioned four times, according to the study’s workshop analysis.
• The researchers concluded that the Implementation Research Logic Model should be treated as a flexible framework rather than a prescriptive model, because local stakeholders must adapt it to their own special education contexts.

The 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, the research team recruited participants from 39 special schools across Northern Ireland and ultimately included six participating schools representing five of the region’s 11 local council areas. The two workshops lasted approximately four hours each and took place at university campuses in Northern Ireland.

The researchers used deductive content analysis on workshop transcripts, flipchart mind maps, and fieldnotes. Two researchers coded the material, resolved disagreements through discussion, and then refined the categories with the wider research team.

The analysis was theory-informed. The researchers used CFIR 2.0 to identify determinants, the ERIC taxonomy to categorize 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 wrote that the model can help implementers “explain how and why the WSA to physical activity is implemented.” The statement describes the value of connecting qualitative evidence to an explicit causal pathway rather than reporting themes without implementation logic.

AI qualitative analysis is most useful in SEN implementation research when it accelerates organization and comparison without replacing stakeholder-informed interpretation.

The 2026 PLoS One study shows that qualitative evidence can serve several analytical purposes at once: identifying barriers and facilitators, selecting implementation strategies, explaining mechanisms, and defining outcomes. Researchers can use AI to maintain these relationships across a large evidence base instead of treating each theme as an isolated finding.

• Researchers can use thematic analysis to identify recurring concerns such as staff training, competing priorities, specialist equipment, family engagement, and flexible adaptation, provided that each AI-generated theme is checked against the source data.
• Researchers can use cross-segment analysis to compare how parents, teachers, school leaders, education authorities, and community organizations describe the same implementation problem.
• Researchers can use hierarchical codes to distinguish broad constructs such as barriers and facilitators from subcodes such as staff confidence, resources, leadership, and pupil support.
• Researchers can use frequency analysis to identify how often an outcome or concern appeared in workshop data, while following the study’s warning that frequency should not be interpreted as importance.
• Researchers can use AI chat over documents to ask evidence-traceable questions about which excerpts support a proposed mechanism, strategy, or outcome.

The study’s authors reported that pupil voices were not directly included in the co-development workshops, although pupil experiences were considered through wider project involvement and parent representatives. That limitation demonstrates why AI analysis cannot correct gaps in sampling or substitute for direct participation by affected groups.

Because the source concerns disability, children, physical activity, and health-related outcomes, AI-assisted analysis should be used for research support rather than diagnosis or individual clinical decision-making.

The 2026 PLoS One study demonstrates how carefully analyzed stakeholder discussions can produce a practical implementation model for physical activity in special educational needs settings.

AI qualitative analysis can make similar research workflows more manageable by organizing documents, comparing stakeholder segments, surfacing recurring themes, and linking evidence to theory-informed codes. Human researchers must still protect participant privacy, assess sampling limitations, validate AI outputs, and interpret findings in context.

Researchers who want to reduce manual synthesis time while keeping qualitative evidence organized can try Evidano for free.