DESIGNING AN AI-BASED RECRUITMENT SYSTEM USING ELECTRONIC INTERVIEW ANALYSIS

Abstract

The rapid increase in candidate applications in modern labor markets necessitates the automation and optimization of recruitment processes. Traditional selection methods often face challenges such as time inefficiency, subjective decision-making, and inadequate handling of large datasets. This paper investigates the development of an AI-supported recruitment system based on filtering and analyzing electronic interviews. The study explores automated processing of candidate data, calculation of suitability scores, and optimization of decision-making mechanisms. The proposed system integrates rule-based evaluation with machine learning models to ensure more objective and reliable candidate assessments. Results indicate that the system accelerates recruitment processes, enhances decision objectivity, and improves HR efficiency.