CareerMap: A Comparative AI Framework for Resume Parsing, Skill Gap Analysis, and Intelligent Interview Assistance
Year: 2026
Type: article
Abstract: 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.
Language: English
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204453115
FWCI:
Cites:
Cited by:
Related to: