Research on the Application and Practice of AI Recruitment in Improving Talent Screening Efficiency

Tianran Yuan

doi:10.71222/dswvkz88

Authors

Tianran Yuan Southeast University, Shanghai, China Author

DOI:

https://doi.org/10.71222/dswvkz88

Keywords:

ai recruitment, talent screening, machine learning, hr technology, bias mitigation, candidate assessment

Abstract

This research article investigates the application and practice of Artificial Intelligence (AI) in recruitment processes to enhance talent screening efficiency across contemporary organizational landscapes. Traditional recruitment methods often suffer from inherent human biases, procedural inconsistencies, and highly time-consuming administrative procedures, ultimately leading to suboptimal hiring decisions and increased operational costs. AI-driven recruitment tools, leveraging advanced machine learning algorithms, natural language processing, and predictive data analytics, offer robust potential solutions to effectively mitigate these persistent challenges. This comprehensive study explores the multifaceted effectiveness of AI integration across various critical stages of the recruitment lifecycle, specifically including targeted job advertisement, automated resume screening, dynamic candidate assessment, and intelligent interview automation. We systematically analyze the measurable impact of AI on essential key performance indicators, such as time-to-hire, cost-per-hire, and overall quality-of-hire metrics. Furthermore, we critically address the pressing ethical considerations and potential algorithmic biases embedded in AI systems, proposing actionable strategies for responsible, transparent, and legally compliant AI implementation in human resources. By examining diverse real-world case studies and extensive empirical data, this research provides profound insights into the current state and future trajectories of AI recruitment. It offers strategic recommendations for organizations actively seeking to optimize their talent acquisition frameworks while rigorously maintaining fairness, diversity, and equity. Ultimately, the findings significantly contribute to the evolving academic understanding of human-AI collaboration in human resources management and inform the continuous development of more effective, sustainable, and ethical AI-powered recruitment solutions.

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