Year: 2026 | Month: June | Volume 14 | Issue 1
Artificial Intelligence in Bibliometrics: Defining Objectives, Limits, and Ethical Dimensions
Harendra Nath Roy1* and Jayati Lahiri Dey2
DOI:10.30954/2322-0465.01.2026.12
Abstract:
The rapid expansion of digital scholarly communication has intensified the demand for advanced, automated bibliometric systems capable of moving beyond simple counting exercises. Traditional bibliometrics remains limited in extracting semantic, contextual, and interdisciplinary insight from scholarly texts, relying instead on citation counts, h-index values, and co-authorship statistics that overlook the intent and quality of scholarly influence. Artificial Intelligence (AI), particularly Natural Language Processing (NLP), deep learning, and graph-based learning, enhances bibliometric evaluation by enabling citation intent classification, thematic trend analysis, semantic similarity mapping, and predictive scientometrics. This paper defines the objectives, operational boundaries, and ethical challenges associated with integrating AI into bibliometric research. It reviews contemporary literature, outlines a structured methodological approach built around transformer architectures, topic modelling, and network-based learning, and presents conceptual workflow visualizations. Ethical, technical, and computational considerations, including bias, transparency, reproducibility, scalability, and responsible use, are examined in depth. The study concludes by proposing principles for responsible AI-enabled bibliometrics suitable for research evaluation within Scopus-indexed academic environments and beyond, offering researchers, institutions, and policymakers a structured, balanced understanding of both the promise and the limitations of AI-driven scholarly assessment, while
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