DEVELOPMENT AND EVALUATION OF THE EFFECTIVENESS OF AN INTELLIGENT VIDEO INTERVIEW SYSTEM FOR ASYNCHRONOUS RECRUITMENT
DOI:
https://doi.org/10.20998/2413-3000.2025.11.6Keywords:
asynchronous recruiting, asynchronous video interview, DeepFace, GPT-4o, OpenAI API, emotion recognition, computer vision, natural language processing, artificial intelligence, candidate evaluation, objective assessment, HR automation, machine learning, recruitment optimization, nonverbal behavior analysis, semantic analysisAbstract
The article is focused on the development and evaluation of an intellectual system for asynchronous video interviews aimed at providing objective candidate assessments and significantly reducing the time costs for recruiters. By combining advanced artificial intelligence technologies, the system analyzes candidates' responses using the GPT-4o language model from OpenAI for content and logic evaluation, while also performing emotional recognition through the DeepFace neural network, which analyzes candidates' nonverbal behavior. This combination of technologies enables not only the automation of recruitment processes but also enhances the objectivity and accuracy of hiring decisions. As a result, the implementation of this system allows companies to substantially reduce time and resource expenditures for candidate evaluation, while improving the quality of recruitment. The article discusses the main functional and non-functional requirements of the system, as well as the technical stack used for its implementation. Experimental research demonstrates that the asynchronous video interview format enables recruiters to quickly access structured analytics without the need to review all video recordings, significantly speeding up the decision-making process. Test results show high accuracy in both emotional recognition and content evaluation of candidates' responses, which increases the efficiency of recruitment and reduces the risks of subjective errors.
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