PrepMate: A Multi-Stage Transformer-based Framework for Automated Interview Preparation and Semantic Response Evaluation

Interview preparation has become an essential component of career development across technical and non-technical domains. Increasing competition in employment environments has created a demand for structured training platforms capable of providing realistic interview practice and meaningful performance feedback. Traditional interview preparation approaches rely heavily on static learning resources and manual evaluation methods that lack scalability and personalization. Limited availability of structured feedback, inconsistent evaluation standards, and absence of adaptive learning mechanisms often reduce the effectiveness of existing preparation methods. These limitations create difficulties for candidates seeking objective assessment and targeted improvement during preparation stages. The proposed work introduces PrepMate, an intelligent interview preparation framework designed to support automated response evaluation and adaptive training processes. The framework utilizes contextual semantic analysis to assess candidate responses against expected knowledge representations and generate structured evaluation scores. The system architecture incorporates stages including data collection, preprocessing, contextual representation learning, response quality assessment, and automated feedback generation. Structured datasets containing interview questions, reference responses, and candidate answers enable effective training and evaluation of the framework. Experimental evaluation demonstrates reliable prediction of response quality through contextual understanding of candidate answers and question intent. The proposed framework provides consistent scoring mechanisms that reflect semantic completeness, clarity, and relevance of interview responses. Automated evaluation capability enables scalable interview preparation without dependence on manual reviewers. Integration within training platforms supports adaptive learning environments where feedback improves communication quality and conceptual understanding. The framework contributes toward intelligent career development tools by enabling automated assessment, structured feedback generation, and scalable interview preparation support for diverse professional domains.