AI Qualitative Analysis in SEN Education

Context matters because the PLOS One authors found that special educational needs settings require adaptations for diverse cognitive, communication, sensory, physical, and behavioural profiles.

The study reported specific requirements including adaptive equipment, additional one-to-one support, flexible activities, specialist collaboration, and safe physical activity environments. AI-assisted analysis can surface these requirements across transcripts, but researchers must interpret them within each school's local context.

AI can support framework-based coding by grouping passages under predefined domains such as CFIR 2.0, ERIC, COM-B, NPT, and implementation outcomes.

The PLOS One researchers used deductive coding and team discussion to map workshop evidence to theoretical constructs. An AI workflow can make candidate coding and retrieval faster, while human review remains necessary when a passage fits multiple constructs or falls outside the framework.

Researchers should measure implementation, school-level, and pupil-level outcomes separately because the 2026 PLOS One study identified all three layers in its implementation research logic model.

The study found that reach, acceptability, and sustainability were among the most frequently identified implementation outcomes, while pupil enjoyment and engagement were prominent pupil-level outcomes. Frequency can show what appeared often in workshop discussions, but the authors cautioned that frequency should not be treated as relative importance.

AI analysis should remain human-led because qualitative meaning depends on context, participant voice, theoretical assumptions, and power relationships.

The PLOS One authors stated that the implementation frameworks were most useful as "flexible guides rather than as prescriptive approaches." Researchers should therefore use AI to accelerate organization and comparison, not to replace reflexive interpretation, participant validation, or ethical review.

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

Researchers can bring transcripts, workshop notes, papers, and reports into one analysis workspace, then use thematic and content analysis to identify recurring barriers, facilitators, strategies, and outcomes. Researchers can explore Evidano's qualitative research features when planning a framework-based education study.

Solution: Cross-segment analysis can compare evidence from parents, teachers, school leaders, authorities, and community partners.

For a study modeled on the November 2024 workshops, researchers could label data by stakeholder role, school, geography, or pupil context and then examine where perspectives converge or diverge.

Solution: Hierarchical codes and subcodes can represent structures such as determinants, strategies, mechanisms, and outcomes, while AI chat over documents can help researchers retrieve supporting passages.

Researchers can use visualizations such as co-occurrence networks to inspect relationships between barriers, strategies, and outcomes before confirming the interpretation through human review.

Solution: Evidano supports PII redaction, encrypted data handling, and a policy in which customer data is not used to train third-party models.

Researchers working with children, families, disability-related information, or school records should still obtain appropriate consent, follow institutional requirements, and avoid treating AI output as a diagnostic or clinical conclusion.