Conventional interview processes are often resource-intensive and may result in inconsistent candidate assessments. This paper proposes an AI-Based Multilingual Interview Detection System that automates interview workflows using artificial intelligence and language technologies. The framework generates personalized interview questions from resume content, job roles, and candidate skills. It supports multilingual communication through automated translation and enables voice interaction using Speech-to-Text and Text-to-Speech modules. Candidate responses are evaluated using relevance, clarity, and semantic metrics. In addition, the system incorporates behavioral and process-level monitoring to identify possible AI-assisted responses during online interviews. The proposed framework improves efficiency, accessibility, and fairness in digital recruitment environments.
Saini et al. (Sun,) studied this question.