AI-DRIVEN RESUME ANALYSIS AND PLACEMENT SUPPORT

Abstract: The rapid adoption of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) has expanded the use of automated techniques for resume analysis, recruitment matching, job recommendation, skill development, and interview preparation. This paper presents a comprehensive review of existing research on AI- driven resume analysis and placement support across five interconnected research areas: resume intelligence; semantic matching and Applicant Tracking System (ATS) evaluation; career and skill development; Large Language Model (LLM) and conversational support; and interview intelligence. The reviewed studies employ traditional NLP and machine- learning techniques such as regular expressions (regex), keyword matching, Term Frequency–Inverse Document Frequency (TF-IDF), Bag-of-Words (BoW), Word to Vector (Word2Vec), cosine similarity, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression and fuzzy logic, together with Named Entity Recognition (NER), Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), distilled Bidirectional Encoder Representations from Transformers (DistilBERT), attention-based neural networks, collaborative filtering, retrieval-augmented generation (RAG), and LLM-based architectures. The literature shows a methodological progression from lexical and rule-based processing toward contextual, semantic, and generative approaches, with several studies reporting strong task-specific performance. At the same time, the evidence remains functionally fragmented: resume parsing and matching are extensively studied, while ATS evaluation, skill-gap analysis, learning support, conversational assistance, and AI-based interview preparation are addressed by smaller and more specialized groups of studies. The review identifies an integration gap in the literature, together with limitations related to dataset diversity, evaluation comparability, changing job requirements, explainability, fairness, privacy, and real-world validation. The findings provide a consolidated view of current methods, reported outcomes, and research opportunities in AI-supported resume analysis and placement preparation.

Keywords: Artificial Intelligence, Machine Learning, Natural Language Processing, Resume Parsing, ATS, Semantic Matching, Skill-Gap Analysis, Job Recommendation, Large Language Models, Mock Interview, Placement Assistance.