While talent acquisition is a critical organizational function, traditional lexical filtering methods exhibit limited efficacy in extracting high-dimensional semantic signals from unstructured applicant data. This review addresses the gap in existing literature regarding recent advancements in AI by proposing a systematic framework connecting these technologies to specific recruitment stages. We synthesized cross-disciplinary literature published between 2020 and 2025 and surveyed contemporary AI-driven recruitment tools to capture the early-stage transition from discriminative to generative applications.
To align computational capabilities with human resource requirements, this paper contributes a comprehensive taxonomy organized by the recruitment lifecycle — encompassing job posting, candidate matching, and assessment. Our synthesis centers on an end-to-end recruitment pipeline that orchestrates diverse artificial intelligence techniques to enable robust semantic representation and bi-directional person-job fit. We analyze how these integrations optimize data-intensive processes while exposing systemic challenges such as algorithmic bias and limited explainability.
We conclude that the optimal division of labor — where automated systems handle quantitative scoring and screening while human experts focus on high-entropy tasks like cultural assessment and complex negotiations — remains an open research question.
The review searched the ACM Digital Library, IEEE Xplore, the ACL Anthology, and Google Scholar, prioritizing peer-reviewed computer-science venues. Findings are organized by a six-stage recruitment lifecycle rather than by algorithm type, mapping AI applications across job posting, candidate matching, and assessment.