GAIDeT – Making AI Use in Research Visible, Comparable and Reusable

Q: What does this research mean for the field?

GAIDeT provides a structured, machine-readable approach for disclosing generative AI use in research, enabling its integration as reusable metadata in scholarly infrastructures. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Serhii Nazarovets; Yana Suchikova; Natalia Tsybuliak; Jaime A. Teixeira da Silva

doi:10.5281/zenodo.22670653

September 9, 2026Open Access

GAIDeT – Making AI Use in Research Visible, Comparable and Reusable

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Key Points

To introduce GAIDeT, a structured framework designed to convert narrative generative AI disclosures into machine-readable, standardized metadata for academic literature.
Conceptualized a standardized data schema to replace free-text, narrative descriptions of generative AI usage in scientific research.
Designed integration workflows to embed machine-readable AI disclosure metadata into existing scholarly communication infrastructures.
Evaluated applications of structured metadata profiles for citation analysis, scientometrics, and institutional research evaluation frameworks.
Demonstrates that GAIDeT enables the systematic tracking and comparison of generative AI usage across scientific disciplines.
Establishes a mechanism for scholarly databases to ingest, index, and reuse standardized descriptions of AI contributions.
Provides contextual metadata necessary for modern scientometric assessment without relying exclusively on traditional citation metrics.

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Implication

Methodological proposal introduces structured generative AI disclosures for scholarly metadata, suggesting improved visibility and comparability in research evaluation.

Abstract

This poster was presented at the 30th Annual International Conference on Science and Technology Indicators (STI-ENID 2026), held in Antwerp, Belgium, 9–11 September 2026. The poster presents GAIDeT as an approach for moving from narrative generative AI-use disclosures towards structured, machine-readable research information. It illustrates how GAIDeT-based descriptions could become reusable metadata within scholarly infrastructures and provide contextual information for scientometrics and research evaluation. This poster accompanies the conference paper From citation metrics to LLM judgement: a GAIDeT-based framework for structured AI disclosure.

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Cite This Study

Nazarovets et al. (2026) studied this question.

synapsesocial.com/papers/6aa27c4958559d80afc75f8e https://doi.org/10.5281/zenodo.22670653

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GAIDeT – Making AI Use in Research Visible, Comparable and Reusable

Key Points

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Implication

Abstract

Discussion

Cite This Study

GAIDeT – Making AI Use in Research Visible, Comparable and Reusable

Key Points

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Implication

Abstract

Discussion

Cite This Study

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