International Journal of Advanced Research in Computer and Communication Engineering

Abstract: This paper presents the design and implementation of an AI-Powered Career Intelligence Platform aimed at enhancing employability through intelligent interviewing, adaptive coding practice, automated resume optimization, and collaborative learning. The proposed system integrates Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques to deliver personalized, data-driven assistance for students and professionals seeking to improve career readiness. The platform is structured into four core modules. The first module, the AI-Based Interview System , automatically generates role-specific questions, records user responses through speech, converts them to text using multilingual speech recognition, and provides real-time, performance-based feedback using sentiment and semantic analysis. The second module, the Adaptive DSA Learning Engine, delivers context-aware coding exercises with dynamic difficulty adjustment, interactive hints, and AI-assisted code evaluation for personalized skill development. The third module, the ATS-Driven Resume Builder, optimizes resumes using real-time scoring, tone adjustment, and keyword enrichment aligned with Applicant Tracking System (ATS) standards. The fourth module, the AI-Enhanced Community Platform, employs knowledge graphs, peer endorsements, and automated discussion summarization to foster collaborative learning and professional networking. With multilingual support for Kannada, Hindi, and English, the system ensures inclusivity across diverse user groups. By combining adaptive analytics with NLP-driven insights, the platform establishes a unified, scalable, and intelligent ecosystem that bridges education, career preparation, and continuous employability enhancement.

Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Adaptive Learning, Employability Enhancement, Resume Optimization, Knowledge Graphs.

Downloads: | DOI: 10.17148/IJARCCE.2026.15135