Ai-Driven Recruitment Systems and Talent Acquisition Quality of Paramilitary Organizations in North-East, Nigeria

This study investigated the relationship between AI-driven recruitment systems and talent acquisition quality among paramilitary organizations in North-East Nigeria, with a focus on three core dimensions: Automation in Sourcing and Screening, Predictive Analytics and Profiling, and Bias Mitigation Features. Guided by the positivist paradigm and a cross-sectional research design, the study utilized a structured closed-ended questionnaire administered to 258 top management personnel selected through snowball sampling, ensuring direct engagement with individuals responsible for recruitment decisions. The instrument comprised demographic items, fifteen statements measuring the three AI proxies, and five items assessing talent acquisition quality. All 258 copies of the questionnaire were returned and analyzed using the Spearman Rank Order Correlation Coefficient. The findings revealed that all three AI-driven recruitment dimensions significantly and positively influenced talent acquisition quality. Automation in sourcing and screening enhanced recruitment efficiency, predictive analytics improved candidaterole alignment, and bias-mitigation features demonstrated the strongest effect by reinforcing fairness, transparency, and merit-based selection. The study concludes that AI integration meaningfully strengthens recruitment outcomes within paramilitary contexts by improving precision and reducing subjectivity, but also requires ongoing oversight, ethical safeguards, and capacity development to ensure sustainable impact. Accordingly, the study recommends enhancing automation capabilities through advanced applicant-tracking tools, deepening the use of predictive analytics supported by robust historical data, and prioritizing bias-mitigation mechanisms such as anonymized screening and algorithmic fairness audits.