AI evidence extraction

Ethical AI in Education: Principles, Governance, and Responsible Implementation

Igor Britchenko · PEDAGOGY AND EDUCATION MANAGEMENT REVIEW · 2025

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

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

1.77

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.36690/2733-2039-2025-4-17-30

Methodology & findings

Study design

Structured narrative review and normative synthesis integrating AI ethics and governance guidance with AI-in-education research to translate insights into implementable principles and lifecycle governance mechanisms..

Main result

The analysis shows that "generic AI ethics statements are insufficient without pedagogical grounding, because educational quality depends on developmental, relational, and legitimacy conditions that are not captured by technical metrics alone." The resulting framework prioritizes human-centered educational benefit, learner agency with meaningful oversight, fairness and inclusion, privacy and data minimization, and accountability with remedy in high-impact uses.

Reports effect sizes.

Research paradigm

Normative/interpretivist

Author conclusions

"Ethical AI in education requires institutional governance that connects values to procurement, deployment, classroom practice, monitoring, and evaluation across the AI lifecycle." The authors emphasize that domain-specific ethical principles grounded in pedagogy are essential, as generic AI ethics frameworks lack the educational context necessary for protecting learner rights and preserving assessment integrity.

Limitations

The authors identify that "future work should strengthen measurement frameworks and empirical evidence for safeguarded AI use in high-stakes contexts, and examine implementation capacity in procurement, training, and post-deployment monitoring." This indicates the current framework lacks comprehensive empirical validation and implementation guidance.

Open questions raised

The authors identify the need to strengthen measurement frameworks and empirical evidence for safeguarded AI use in high-stakes contexts, and the need to examine implementation capacity in procurement, training, and post-deployment monitoring.
Future work should strengthen measurement frameworks and empirical evidence for safeguarded AI use in high-stakes contexts, and examine implementation capacity in procurement, training, and post-deployment monitoring.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 83%

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