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On the vulnerability of citation metrics in the era of generative artificial intelligence
Citation Link: https://doi.org/10.15480/882.17366
Publikationstyp
Journal Article
Date Issued
2026-04-11
Sprache
English
TORE-DOI
Volume
14
Issue
2
Article Number
23
Citation
Publications 14 (2): 23 (2026)
Publisher DOI
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Large language model (LLM) chatbots, as a widely used form of generative artificial intelligence, have reduced the marginal cost of producing publication-style manuscripts and have expanded feasible routes for manipulating citation metrics within the publishing ecosystem. Citation-based indicators (e.g., the h-index, the i10-index, and total citation counts) remain embedded in research evaluation and are sensitive to indexing practices of bibliographic databases, with Google Scholar providing broad coverage combined with comparatively limited curation. In this study, a systematic literature review is conducted to synthesize reported mechanisms of citation-metric manipulation and to examine limitations of citation-metric use, including evidence reported in civil engineering. A Google Scholar proof-of-concept case study examines whether the indexing of LLM-assisted, non-peer-reviewed documents with concentrated references to a target author is associated with changes in author-level citation metrics under platform-specific conditions. After indexing, a stepwise increase in author-level metrics is observed, demonstrating the feasibility of citation-metric manipulation under the platform-specific conditions. Finally, this paper discusses the implications for research integrity and citation manipulation in the era of generative artificial intelligence. It also presents recommendations for researchers, academic institutions and evaluation committees, publishers and editors, bibliographic database providers, and funding institutions and policymakers.
Subjects
large languagemodels
generative artificial intelligence
citationmetrics
h-index
Google Scholar
bibliographic databases
publishing ecosystem
research evaluation
DDC Class
020: Library and Information Sciences
Funding(s)
I
Publication version
publishedVersion
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Name
publications-14-00023.pdf
Type
Main Article
Size
1.42 MB
Format
Adobe PDF