An AI-Driven Adaptive Interview Simulator Using Transformer-Based Semantic Analysis

Interview preparation and assessment systems currently suffer from several limitations that restrict effective skill evaluation. Existing solutions largely follow static interview structures, resulting in uniform question delivery irrespective of response quality or candidate performance variation. Such rigidity limits focused assessment, causes redundant questioning, and reduces engagement. Additionally, conventional systems often struggle with evaluating open-ended responses, as semantic depth, contextual relevance, and articulation quality remain inadequately assessed. Feedback mechanisms are typically generic and fail to provide actionable insights, thereby limiting meaningful performance improvement. Scalability and consistency further remain challenging due to dependence on manual or semi-automated evaluation processes. To address these difficulties, an AI-driven adaptive interview simulator is proposed to enable dynamic, performance-aware interview assessment. The methodology emphasizes semantic understanding of responses and real-time adaptability of interview flow. Response evaluation is performed at a contextual level, allowing accurate measurement of relevance, coherence, and conceptual clarity. Based on continuous performance assessment, interview progression is dynamically adjusted by modifying question difficulty and topic focus. This adaptive behavior ensures targeted evaluation, reduces unnecessary repetition, and improves efficiency. Key features of the proposed system include dynamic interview progression, semantic response evaluation, interpretable performance feedback, and scalable automated assessment. The framework supports multiple interview domains and accommodates diverse skill levels without manual intervention. Quantitative analysis demonstrates consistent improvement in response quality and assessment reliability compared to non-adaptive interview frameworks. The proposed approach enhances realism in interview simulation and provides an effective solution for intelligent, personalized, and scalable interview assessment in academic and professional environments.