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AI evidence extraction

Generative Artificial Intelligence and Research Integrity: A Responsibility-Centred Framework

José António C. Santos, Miguel Puig-Cabrera, Margarida Custódio Santos, Alfonso Vargas-Sánchez, Antonio Juan Briones-Peñalver · Tourism & Management Studies · 2026

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

9/10
Relevance
1/4
Quality (LMQS)
I
Evidence
0
Citations
0.00
FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.18089/tms.20260203

Methodology & findings

Study design

Integrative narrative review and conceptual synthesis of a large interdisciplinary corpus of Scopus-indexed literature on AI in academic research, publishing ethics, and editorial decision-making.

Main result

The study identifies that "human epistemic responsibility as the central ethical concern, showing that risks arise not from AI itself but from the displacement or obscuring of accountable scholarly judgment." The analysis systematises AI-assisted scholarly practices by distinguishing among integrity-compromising uses, ethically responsible uses, and persistent grey zones in which norms remain contested.

Reports effect sizes.

Research paradigm

Interpretivist/hermeneutic

Author conclusions

The paper "contributes a framework for the responsible integration of GenAI into scholarly research and publishing" and emphasizes that a "human-centred, context-sensitive governance perspective" is needed to guide authors, reviewers, and editors in navigating ethical challenges.

Risk of bias

Narrative review format (non-systematic) carries inherent selection bias risk in literature identification; No explicit search strategy reported for Scopus corpus; Potential for subjective classification of uses as 'integrity-compromising', 'ethically responsible', or 'grey zones'; No inter-rater reliability measures mentioned for conceptual synthesis

Data: not_statedCode: not_statedExtracted from: pdfAgreement 93%

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