Artificial Intelligence in Recruitment Systems: A Comprehensive Bibliometric Analysis

Artificial Intelligence (AI) has emerged as a transformative force in modern recruitment practices, enabling organizations to automate and optimize hiring processes. Companies increasingly rely on AI-driven tools such as Applicant Tracking Systems (ATS), machine learning algorithms, and natural language processing to enhance efficiency, reduce hiring time, and improve decision-making. Most existing studies have focused on the technological capabilities of AI, recruitment efficiency, and candidate screening mechanisms. However, limited attention has been given to understanding the overall research structure, thematic evolution, and intellectual development of AI-driven recruitment systems. The existing literature is fragmented and lacks a comprehensive review of trends, key contributors, and emerging research areas in this domain. Furthermore, there are relatively few bibliometric studies that systematically examine AI applications in recruitment, particularly in the context of developing economies where digital adoption and HR practices vary significantly. This study aims to provide a systematic bibliometric analysis of research on AI in recruitment systems. By analyzing publications from major academic databases, this research identifies key trends, influential authors, prominent journals, collaboration patterns, and evolving themes within the field. The findings reveal a rapid growth in research output in recent years, driven by advancements in machine learning and data analytics. The study also highlights critical research gaps, including ethical concerns, algorithmic bias, and limited global collaboration. This research contributes to the existing body of knowledge by offering a structured understanding of AI-driven recruitment and its implications for organizational effectiveness. The insights generated will assist researchers, HR professionals, and policymakers in making informed decisions and guiding future research directions in this evolving field.

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Publication Details

Journal
INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH
Published
2026-05-01
DOI
https://doi.org/10.56975/ijedr.v14i2.307222
Primary Topic
Employer Branding and e-HRM
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence in Recruitment Systems: A Comprehensive Bibliometric Analysis

Abinaya M, Dr. Rahini S
INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH
Employer Branding and e-HRM
article

Artificial Intelligence in Recruitment Systems: A Comprehensive Bibliometric Analysis

Abinaya M, Dr. Rahini S
article en

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in modern recruitment practices, enabling organizations to automate and optimize hiring processes. Companies increasingly rely on AI-driven tools such as Applicant Tracking Systems (ATS), machine learning algorithms, and natural language processing to enhance efficiency, reduce hiring time, and improve decision-making. Most existing studies have focused on the technological capabilities of AI, recruitment efficiency, and candidate screening mechanisms. However, limited attention has been given to understanding the overall research structure, thematic evolution, and intellectual development of AI-driven recruitment systems. The existing literature is fragmented and lacks a comprehensive review of trends, key contributors, and emerging research areas in this domain. Furthermore, there are relatively few bibliometric studies that systematically examine AI applications in recruitment, particularly in the context of developing economies where digital adoption and HR practices vary significantly. This study aims to provide a systematic bibliometric analysis of research on AI in recruitment systems. By analyzing publications from major academic databases, this research identifies key trends, influential authors, prominent journals, collaboration patterns, and evolving themes within the field. The findings reveal a rapid growth in research output in recent years, driven by advancements in machine learning and data analytics. The study also highlights critical research gaps, including ethical concerns, algorithmic bias, and limited global collaboration. This research contributes to the existing body of knowledge by offering a structured understanding of AI-driven recruitment and its implications for organizational effectiveness. The insights generated will assist researchers, HR professionals, and policymakers in making informed decisions and guiding future research directions in this evolving field.

INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCHVol. 14(2)
SASTRA University (IN)
Openalex Percentile: Top 4%
Employer Branding and e-HRM
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