CareerNova: A Proposed Integrated AI-Powered Framework for Resume Generation, ATS Analysis, Personalized Interview Coaching, And Job Recommendation

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The transition from academic education to professional employment requires candidates to address multiple interconnected tasks, including resume creation, Applicant Tracking System (ATS)-oriented optimization, interview preparation, and identification of suitable job opportunities.Existing career-support tools generally address these tasks independently, requiring candidates to use multiple platforms and repeatedly provide the same information.This paper presents CareerNova, a proposed integrated artificialintelligence-based career assistance system that combines four modules: an automated resume maker, ATS-oriented resume evaluation and improvement, personalized interview coaching, and job recommendation.The proposed system maintains a structured candidate profile that is reused across the modules to enable consistent personalization.The resume module employs structured information extraction, large language models, and template-based document generation to create professionally organized resumes from candidate-provided information.The ATS module combines deterministic resume analysis, keyword and structural matching, and semantic similarity using sentence embeddings to evaluate resume-job-description alignment and generate actionable improvement recommendations.The interview module uses resume and job-description information to generate personalized questions and supports text-and speech-based mock interviews, with responses evaluated using speech recognition, natural language processing, and AI-assisted semantic analysis.The job recommendation module combines semantic embeddings with structural matching of skills, education, experience, and keywords to generate job-fit scores and interpretable skill-gap information.The proposed framework demonstrates how deterministic analysis and generative AI can be integrated within a unified career-support pipeline.The proposed architecture is designed to provide candidate-specific resume guidance, ATS-oriented compatibility analysis, interview preparation, and explainable job recommendations.The framework provides a foundation for future prototype development and empirical evaluation involving larger-scale user studies and performance validation.