CareerPathAI introduces a production-ready, MERN-stack platform that transforms traditional career counseling into scalable AI-driven guidance for students and professionals. Developed as an extension of prior reviews on AI career systems, it integrates OpenAI GPT-4o for NLP-based resume parsing, hybrid ML recommendation engines for skill-job matching, and interactive roadmap generation with progress tracking. The system processes multi-modal inputs—academic records, psychometrics, interests, and regional job trends—to deliver personalized pathways with 87% precision across 300 AKTU student evaluations. Key innovations include Hindi-English multilingual support, federated privacy measures, and cloud-native deployment achieving sub-2s latency for 1,000+ users. Compared to baselines like TF-IDF (61% F1), our hybrid model improves accuracy by 24% and user satisfaction (NPS 82 vs 45). This work bridges literature gaps in integrated prototypes, offering open-source code, empirical validation, and a maturity roadmap toward ecosystem-scale adoption in India’s competitive job market.This work establishes a unified framework for next-generation AI career coaching systems capable of delivering scalable, ethical, and future-ready personalized guidance.}

This research presented CareerPathAI as an integrated AI-driven career guidance platform designed to address the major limitations of traditional counseling approaches and fragmented digital advisory tools. The study demonstrated that effective career guidance systems must move beyond isolated features such as resume parsing or career suggestion and instead provide a connected environment that combines profile understanding, recommendation generation, skill-gap analysis, and personalized roadmap support. By adopting a modular architecture and AI-assisted processing pipeline, the proposed system establishes a practical foundation for scalable and adaptive career planning. The implementation and evaluation of CareerPathAI indicate that the platform has strong potential to support students in making informed academic and professional decisions. The generated results showed that personalized recommendation logic, when combined with structured roadmap creation, improves the clarity, relevance, and usefulness of system output. User evaluation also suggested that the platform delivers better engagement and guidance quality than generic online search methods or static counseling resources. These outcomes reinforce the growing importance of AI, NLP, and recommender systems in education-oriented decision support applications.

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