CareerMap: A Comparative AI Framework for Resume Parsing, Skill Gap Analysis, and Intelligent Interview Assistance

The recruitment landscape is undergoing rapid transformation as organisations increasingly rely on artificial intelligence to manage high applicant volumes, standardise candidate evaluation, and reduce hiring time and cost. Existing AI-based career tools typically address only a single stage of this process — screening resumes, running generic interview chatbots, or flagging fraudulent postings in isolation — rather than supporting a candidate end to end. This paper presents CareerMap, an integrated AI-driven framework that unifies five interconnected capabilities within one human-centred pipeline: automated resume parsing and entity extraction, embedding-based skill-gap analysis, a conversational mock-interview engine, fraud and inconsistency detection for job postings and candidate claims, and affect-aware feedback derived from behavioural and emotional signals. CareerMap combines transformer-based language models (BERT and its derivatives) with BiLSTM-CRF sequence labelling for entity recognition, cosine similarity for skill matching, a weighted fraud-scoring model, and speech- and text-based affective computing for emotion recognition. To situate CareerMap within existing work, this paper conducts a comparative review of thirty-six prior studies spanning named entity recognition, skill embeddings, explainable AI, deceptive-language detection, and bias in machine learning, showing that no existing system integrates these techniques into a single coherent pipeline. Building on this gap, the proposed methodology is described in detail, including the hybrid BERT-BiLSTM-CRF resume-parsing pipeline, the cosine-similarity formulation for skill-gap ranking, and the weighted fraud-scoring metric. Drawing on performance patterns reported across the reviewed literature, a comparative analysis across all five functional modules indicates that transformer-based contextual models consistently outperform classical rule-based and shallow statistical baselines in information-extraction accuracy, explanation quality, and user engagement, a pattern that directly motivates CareerMap's architectural choices. The paper concludes that an integrated, explainable, emotionally aware approach to career preparation offers a more trustworthy alternative to fragmented point-solutions, and outlines future work including longitudinal validation with real hiring outcomes and multilingual expansion of the fraud-detection module.