AI evidence extraction

GAIDeT (Generative AI Delegation Taxonomy): A taxonomy for humans to delegate tasks to generative artificial intelligence in scientific research and publishing

Yana Suchikova, Natalia Tsybuliak, Jaime A. Teixeira da Silva, Serhii Nazarovets · Accountability in Research · 2025

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

10/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

10.78

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/08989621.2025.2544331

Methodology & findings

Study design

Iterative consensus-building approach informed by existing contributor role taxonomies (CRediT, NIST AI Use Taxonomy) and peer-reviewed literature.

Primary method

Iterative consensus-building; informed by existing taxonomies (CRediT, NIST AI Use Taxonomy) and peer-reviewed literature

Main result

GAIDeT provides a structured framework for documenting GAI's role in scholarly research. The taxonomy "classifies research activities into key domains - conceptualization, literature review, methodology, data analysis, writing, supervision, and ethical review - ensuring transparency and human accountability." Additionally, "a GitHub-based interactive tool - the GAIDeT Declaration Generator - was developed to help researchers document delegation choices transparently."

Research paradigm

Design science / Normative framework development

Author conclusions

The authors conclude that "By standardizing GAI task delegation, GAIDeT enhances research integrity and transparency." They further state that "Future work should focus on empirical validation, cross-disciplinary adaptability, and policy implications for GAI governance."

Risk of bias

Limited details on consensus-building process and potential groupthink; No information on disciplinary representation in framework development; Lack of empirical validation noted for future work

Open questions raised

Empirical validation of the taxonomy, cross-disciplinary adaptability of GAIDeT, and policy implications for GAI governance in research contexts.
Empirical validation of the taxonomy, cross-disciplinary adaptability of GAIDeT, and policy implications for GAI governance in research contexts
The authors identify the need for empirical validation, cross-disciplinary adaptability testing, and investigation of policy implications for GAI governance in research contexts.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 72%

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Institutional Policy AI Governance Design Science