AI qualitative analysis of education policy

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to PLOS Sustainability and Transformation, published August 18, 2026, the paper "From generating human capital to transformative education" traces three discursive periods in Spain’s education policy (1975–1990, 1990–2018, 2019–present) and combines a document genealogy with seven interviews conducted between February and September 2025. This post shows how AI-enabled qualitative research can extract the paper's actionable signals, speed synthesis, and preserve traceability for researchers, policy teams, and education advocates.

Key Takeaways

According to PLOS Sustainability and Transformation, published August 18, 2026, Spain’s education policy discourse has shifted through three clear periods (1975–1990; 1990–2018; 2019–present) and the 2020 LOMLOE law creates openings for transformative sustainability education while remaining constrained by entrenched neoliberal governance. The paper includes seven in-depth interviews completed between February and September 2025 and a document analysis of national education laws dating from 1980 onward, according to PLOS Sustainability and Transformation.

1) According to PLOS Sustainability and Transformation, the authors identify three periods: democratic transition and pedagogical renewal (1975–1990), neoliberal consolidation shaped by OECD discourse (1990–2018), and emergent openings for transformative sustainability education (2019–present).
2) According to PLOS Sustainability and Transformation, the LOMLOE (2020) explicitly frames schools as places for stewardship, quoting that “schools must become a place of stewardship and care for our environment.”
3) According to PLOS Sustainability and Transformation, the study triangulates national law texts with seven interviews carried out February–September 2025, showing both discursive openings and implementation constraints such as lack of teacher training and bureaucratic burdens.
4) According to PLOS Sustainability and Transformation, sustainability in policy is a contested or “floating signifier” that risks co-optation unless governance and resources change to enable whole-school, experiential pedagogies.

What Happened and how the paper measures it

According to PLOS Sustainability and Transformation, the authors apply discourse analysis to Spain’s national education laws from 1980 to the present and triangulate that document work with seven in-depth interviews performed between February and September 2025.

According to PLOS Sustainability and Transformation, the authors code law preambles and parliamentary minutes using NVivo and use genealogical and articulation concepts (Foucault, Hajer, Howarth) to trace how human-capital and transformative paradigms are constructed and contested.

According to PLOS Sustainability and Transformation, the analysis identifies barriers to implementing transformative sustainability education including teacher workload, insufficient training, and the continued use of accountability metrics inherited from OECD frameworks.

Findings Snapshot

Date / Period	Metric or Evidence	Value or Example	Implication for practice
Published Aug 18, 2026	Paper type	Peer-reviewed research article in PLOS Sustainability and Transformation	Use as citable, open-access evidence for policy briefs
1975–1990	Discursive period	Emancipatory, pedagogical renewal	Search for qualitative indicators of relational pedagogy in archives and interviews
1990–2018	Discursive period	Neoliberal/OECD influence (PISA-driven)	Expect quantitative metrics to dominate policy text and assessors
2019–present	Discursive opening	LOMLOE (2020) legitimises sustainability and transdisciplinarity	Track implementation gaps: teacher training (deadline 2025 unmet) and regional roll-out
Feb–Sep 2025	Interviews	Seven in-depth interviews	Use mixed document+interview coding to validate emergent themes

Implications for AI qualitative researchers

AI-enabled qualitative research can accelerate replication of the paper’s approach by automating document ingestion, coding, triangulation, and traceable audit trails, according to the methods described in PLOS Sustainability and Transformation.

According to PLOS Sustainability and Transformation, researchers should combine historical document genealogy with targeted interviews to identify dislocatory events and 'chains of equivalence'; AI tools can surface candidate dislocatory text spans and suggest interviewees who appear across documents.

Design decisions: According to PLOS Sustainability and Transformation, include law preambles and parliamentary minutes in the corpus because the authors found preambles explicitly frame purposes and signifiers.
Sampling: According to PLOS Sustainability and Transformation, thematic saturation was achieved with seven interviews in this policy-focused study, so combine selective purposive interviews with broad document scraping for balance.
Validation: According to PLOS Sustainability and Transformation, triangulate automated thematic coding with manual checks and store original transcript excerpts for auditability.

How Evidano helps: AI-enabled qualitative workflows

Problem: Large document corpora and hand-coding slow synthesis

Solution: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents and it ingests laws, parliamentary minutes, and interview transcripts to auto-suggest themes and code co-occurrences.

Feature mapping: Use automated ingestion and thematic clustering to surface candidate 'floating signifiers' such as "sustainability" and then run frequency, co-occurrence, and timeline analyses to map when those signifiers appear across 1975–2024 documents.

Problem: Traceability and quotations must be preserved for policy citation

Solution: Evidano preserves original text spans and linked metadata (source, date, paragraph), enabling the export of exact quotations such as “schools must become a place of stewardship and care for our environment, ” which the paper highlights in the LOMLOE preamble as reported in PLOS Sustainability and Transformation.

Feature mapping: Use Evidano’s transcript linking and citation export to generate evidence-backed policy briefs with embedded, source-coded quotes.

Problem: Interview coding and cross-validation are time-consuming

Solution: Evidano supports transcription with custom dictionaries and PII redaction and lets you run cross-segment analyses (region, actor type, time period) like the Spain study’s Feb–Sep 2025 interview set, so you can compare policy-maker language to grassroots actor language quickly.

Learn more: See the Evidano features page for workflow examples and export formats.

FAQ: AI qualitative analysis of education policy

How does AI change qualitative discourse analysis for education policy research?

Answer: AI accelerates coding, thematic clustering, and traceable quotation extraction while keeping human oversight, according to the methods used in the Spain case in PLOS Sustainability and Transformation.

Supporting detail: According to PLOS Sustainability and Transformation, combining automated text-mining with purposive interviews (seven in their study) produced robust triangulation of discursive shifts.

Which data should I collect to map discursive openings like LOMLOE?

Answer: Collect law preambles, parliamentary debate minutes, curricula documents, and purposive interviews, because the Spain study used those sources to identify shifts, according to PLOS Sustainability and Transformation.

Supporting detail: According to PLOS Sustainability and Transformation, preambles are particularly diagnostic for signifier changes and should be prioritized in document scraping.

Can AI tools preserve methodological rigor for policy citation?

Answer: Yes, when AI outputs are anchored to original text spans, timestamps, and human-verified codes, following the Spain paper’s triangulation approach reported in PLOS Sustainability and Transformation.

Supporting detail: According to PLOS Sustainability and Transformation, the authors combined NVivo coding with interview checks; an AI platform that stores raw excerpts and coding provenance supports the same standards.

What risks should researchers watch for when using AI on policy texts?

Answer: Researchers should avoid over-reliance on automated labels and should audit for co-optation or 'floating signifier' interpretations, as the Spain study warns about sustainability being contested, according to PLOS Sustainability and Transformation.

Supporting detail: According to PLOS Sustainability and Transformation, human reflexivity and triangulation are necessary to prevent simplified, decontextualized outputs.

Conclusion & Next Steps

According to PLOS Sustainability and Transformation, Spain’s case shows how discursive openings for transformative sustainability education can appear in law while implementation remains constrained by institutionalized neoliberal logics.

According to PLOS Sustainability and Transformation, researchers and policy teams planning similar studies should combine historical document genealogy with purposive interviews and use AI-enabled platforms to speed coding, preserve quotes, and run cross-segment analyses.

If you want to prototype an AI-enabled qualitative pipeline for mapping policy discourses like the Spain study, start by collecting law preambles and parliamentary minutes, then upload transcripts to a platform that preserves source provenance and supports thematic and cross-segment analysis; learn more on the Evidano features page.

Ready to try it? Try Evidano for free.

Topics

AI qualitative analysis of education policy
AI-enabled qualitative research
qualitative discourse analysis Spain
transformative sustainability education

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