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This research aims to conduct a systematic bibliometric analysis of AI applications in recruitment systems to understand trends and development in the field.

May 10, 2026Open Access

Artificial Intelligence in Recruitment Systems: A Comprehensive Bibliometric Analysis

Key Points

This research aims to conduct a systematic bibliometric analysis of AI applications in recruitment systems to understand trends and development in the field.
Bibliometric analysis of publications from major academic databases
Identification of key trends, influential authors, and collaboration patterns
Examination of emerging themes and research gaps in AI-driven recruitment
Rapid growth in AI recruitment research output in recent years
Identification of critical gaps, including ethical concerns and algorithmic bias
Increased focus on collaboration patterns within the domain.

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.

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