AI Mock Interviewer

Interview performance is influenced by domain knowledge, the ability to organize evidence, communication clarity, and familiarity with interview formats. Access to repeated, individualized practice can be limited by the cost and availability of human interview coaches. This paper proposes an AI Mock Interviewer, a practice-oriented system that simulates role-specific interviews and generates structured, actionable feedback. The framework combines a candidate profile and job-description parser, a competency-based question engine, text or speech input, speech-to-text processing, natural-language analysis, a transparent scoring rubric, and a feedback module. Rather than predicting employability or making hiring decisions, the proposed system is designed to support low-stakes learning. The evaluation plan specifies measures for feedback agreement with trained human raters, scoring reliability, response relevance, usability, accessibility, and fairness across user groups. A weighted rubric is presented as an initial design choice and is explicitly subject to empirical validation. Prior research on automated interview assessment indicates potential benefits in standardization and scale, while also identifying concerns about reliability, validity, bias, disability access, and applicant perceptions. Accordingly, the framework incorporates consent, data minimization, uncertainty communication, user control over recordings, and human review for consequential decisions. Since no implementation data are supplied in this paper, the contribution is a research proposal and system design, not a report of experimentally demonstrated performance.