AI evidence extraction

Tres escenarios para la IA en educación: del apoyo responsable a la cocreación

Francisco José García‐Peñalvo · Education in the Knowledge Society (EKS) · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

2.58

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.14201/eks.32932

Methodology & findings

Study design

Conceptual framework development and normative analysis.

Main result

The paper proposes that "tres escenarios graduados por autonomía, agencia y riesgo (apoyo responsable, colaboración guiada y cocreación con declaración reforzada)" (three graduated scenarios by autonomy, agency and risk: responsible support, guided collaboration and co-creation with reinforced declaration) can convert broad AI principles into verifiable and traceable teaching decisions across the teaching cycle. The framework emphasizes that "la inteligencia artificial como complemento bajo juicio académico, nunca sustituto" (artificial intelligence as a complement under academic judgment, never a substitute) with transparency, external verification of facts and citations, and equity and inclusion by design.

Reports effect sizes.

Research paradigm

Pragmatist/Interpretive

Author conclusions

The authors conclude that "El resultado es un mapa operativo para marcar, verificar y documentar con proporcionalidad al riesgo, que permite convertir la inteligencia artificial en oportunidad pedagógica sin ceder en rigor, justicia y responsabilidad" (The result is an operational map to mark, verify and document with proportionality to risk, which allows converting artificial intelligence into a pedagogical opportunity without compromising rigor, justice and responsibility). The framework provides instruments including a transversal rubric and task-specific checklists to facilitate adoption and homogeneous evaluation.

Risk of bias

Not applicable. This is a conceptual and normative paper without empirical data collection or human subjects research.

Open questions raised

The paper identifies the need for practical operationalization of AI governance in higher education, developing tools and instruments that translate broad policy principles (UNESCO, AI Act, Safe AI in Education Manifesto, SAFE framework) into verifiable teaching decisions and traceable implementation across all phases of the teaching cycle.
The paper identifies the need for operationalized frameworks that translate broad AI principles into verifiable teaching decisions and traceable documentation across the teaching cycle (planning, material creation, support, and evaluation). It addresses gaps in alignment between policy guidance (UNESCO, AI Act, SAFE framework) and practical classroom implementation.
The paper does not explicitly identify future research directions or gaps in the available text.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 70%

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Responsible AI Use AI Governance Conceptual