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<p>Artificial intelligence (AI) is increasingly used in recruitment processes to enhance efficiency and improve hiring decisions. This systematic literature review examines the opportunities and challenges of AI in employee recruitment across various organizational settings. Analyzing 49 peer-reviewed articles sourced from the Web of Science database published between 2018 and 2025, this review summarizes the current knowledge of AI's impact, highlighting its potential to increase productivity (e.g., through automated resume parsing and chatbot-led initial screening), improve candidate quality (e.g., via predictive analytics for job-fit and AI-assisted video interview analysis), and potentially reduce human bias by standardizing initial evaluations, though it also addresses critical ethical considerations such as the risk of algorithmic bias stemming from training data. However, it also addresses ethical considerations, including algorithmic bias and the need for transparency. The review concludes with recommendations for future research focused on legal frameworks, industry-specific applications, and mitigating the risks associated with AI in recruitment.</p>

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Role of artificial intelligence in employee recruitment: systematic review and future research directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月5日

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  • Sherzodbek Murodilla Ugli Dadaboyev,
  • Jasmina Abdullayeva,
  • Naval Abbosova,
  • Afina Suleymenova,
  • Komila Mamadjanova

摘要

Artificial intelligence (AI) is increasingly used in recruitment processes to enhance efficiency and improve hiring decisions. This systematic literature review examines the opportunities and challenges of AI in employee recruitment across various organizational settings. Analyzing 49 peer-reviewed articles sourced from the Web of Science database published between 2018 and 2025, this review summarizes the current knowledge of AI's impact, highlighting its potential to increase productivity (e.g., through automated resume parsing and chatbot-led initial screening), improve candidate quality (e.g., via predictive analytics for job-fit and AI-assisted video interview analysis), and potentially reduce human bias by standardizing initial evaluations, though it also addresses critical ethical considerations such as the risk of algorithmic bias stemming from training data. However, it also addresses ethical considerations, including algorithmic bias and the need for transparency. The review concludes with recommendations for future research focused on legal frameworks, industry-specific applications, and mitigating the risks associated with AI in recruitment.