AI POWERED RESUME SCREENING SYSTEMS
Abstract
ABSTRACT
The study presents the design and development of an AI-powered résumé screening system aimed at improving the efficiency, accuracy, and fairness of recruitment processes in Nigeria. Adopting the Agile Software Development Life Cycle (SDLC) model, the system was built using modern technologies such as Python, React Native, Firebase, and NLP-based machine learning algorithms. The research employed a Design Science Research (DSR) approach, combining qualitative and quantitative methods including interviews, surveys, and secondary data analysis. The system automatically parses résumés, extracts key candidate information, and ranks applicants based on relevance to job requirements through hard and soft match scoring. Testing and evaluation revealed significant improvements in screening speed and consistency, with 90% accuracy alignment to human HR judgments. Ethical measures, including data encryption, anonymization, and compliance with the Nigerian Data Protection Regulation (NDPR, 2019), ensured privacy and fairness. Overall, the system demonstrated that AI integration in recruitment can streamline candidate selection while reducing human bias and operational workload.
Keywords: Artificial Intelligence, Résumé Screening, Recruitment Automation
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