The study synthesizes peer-reviewed evidence from 2024–2025 on HR technologies that restructure recruitment workflows in large U.S. organizations. Relevance follows from persistent hiring frictions—multi-week time-to-fill, high screening labor, and delayed ramp-up—which curb innovation in technology sectors. Novelty lies in an integrated reading that couples embedding-first retrieval and learning-to-rank with governance-by-design for candidate-facing modules. The review maps how résumé/job-description embeddings, compact foundation-model filters with post-hoc explanations, and skill-graph enrichment improve early-stage recall and shortlist quality, while chat-based and video-interview interfaces require validity safeguards. The paper formulates a design goal for integration-first platforms: overlay advanced scoring and explanations on incumbent ATS timelines rather than replace them. Methods include comparative reading of algorithmic evaluations (nDCG, RBO, F1/recall), legal-doctrinal synthesis on fairness and auditability, and triangulation with empirical studies of applicant behavior in automated interviews. Sources span ten recent articles in information systems, management, law, and psychology. The conclusion specifies implementation controls (feature governance, logging, escalation) and a measurement plan suitable for enterprise deployments in high-volume tech hiring.

HR Tech, applicant tracking, résumé embeddings, learning-to-rank, foundation models, AI explainability, automated interviews, fairness and auditability, skill mining, governance-by-design

Bevara, R. V. K., Mannuru, N. R., Karedla, S. P., Lund, B., Xiao, T., Pasem, H., Dronavalli, S. C., & Rupeshkumar, S. (2025). Resume2Vec: Transforming applicant tracking systems with intelligent resume embeddings for precise candidate matching. Electronics, 14(4), 794. https://doi.org/10.3390/electronics14040794

Dukanovc, D., & Krpan, D. (2025). Comparing chatbots to psychometric tests in hiring: Reduced social desirability bias, but lower predictive validity. Frontiers in Psychology, 16, 1564979. https://doi.org/10.3389/fpsyg.2025.1564979

Gavrilescu, M., Leon, F., & Minea, A.-A. (2025). Techniques for transversal skill classification and relevant keyword extraction from job advertisements. Information, 16(3), 167. https://doi.org/10.3390/info16030167

Koman, G., Boršoš, P., & Kubina, M. (2024). The possibilities of using artificial intelligence as a key technology in the current employee recruitment process. Administrative Sciences, 14(7), 157. https://doi.org/10.3390/admsci14070157

Martínez-Manzanares, M. E., Urias-Paramo, J. J., Waissman-Vilanova, J., & Figueroa-Preciado, G. (2024). An empirical job matching model based on expert human knowledge: A mixed-methods approach. Applied Artificial Intelligence, 38(1), Article 2364158. https://doi.org/10.1080/08839514.2024.2364158

Pendyala, V. S., Thakur, N. B., & Agarwal, R. (2025). Explainable use of foundation models for job hiring. Electronics, 14(14), 2787. https://doi.org/10.3390/electronics14142787

Rigotti, C., & Fosch-Villaronga, E. (2024). Fairness, AI & recruitment. Computer Law & Security Review, 53, 105966. https://doi.org/10.1016/j.clsr.2024.105966

Seppälä, P., & Małecka, M. (2024). AI and discriminative decisions in recruitment: Challenging the core assumptions. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241235872

Suen, H.-Y., & Hung, K.-E. (2024). Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews. Technological Forecasting and Social Change, 198, 122978. https://doi.org/10.1016/j.techfore.2023.122978