Comprehensive Review of Existing Chatbot Systems for Career Assistance, Resume Support, and ATS-Aware Guidance

Data science

**Abstract—** This paper presents a comprehensive review of existing chatbot systems used for career assistance, resume support, and Applicant Tracking System (ATS)-aware guidance. The study focuses on the historical evolution of conversational technologies, beginning with rule-based systems and progressing through retrieval-based models, neural sequence architectures, and transformer-based Large Language Models (LLMs). Unlike implementation-oriented research, this work is purely literature-based and does not propose a new system or experimental framework. The review analyzes various supporting technologies integrated into modern chatbot ecosystems, including multimodal document processing, voice interaction modules, resume builders, and ATS optimization platforms. Existing systems are compared based on multiple factors such as language quality, context retention, reliability, explainability, fairness, privacy, usability, and deployment complexity. Architecture-level and workflow-level comparisons are presented to identify recurring strengths, weaknesses, and trade-offs among different chatbot generations. The study further examines challenges related to hallucination, bias, privacy concerns, interpretability, and operational reliability that continue to affect AI-assisted career support systems. Additionally, limitations in current evaluation methodologies are discussed, highlighting the need for standardized benchmarks and long-term usability assessments. The findings indicate that although modern LLM-based systems provide significant improvements in conversational quality and personalization, several issues remain unresolved, particularly in maintaining factual accuracy, transparency, and user trust. This review establishes a strong academic foundation for future research by clearly separating historical analysis from implementation claims and providing a structured understanding of existing career-assistance chatbot technologies.

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