MentorChain: An AI-Powered Career Recommendation and Blockchain-Based Verification Platform

This paper presented MentorChain, an AI-powered career recommendation and verification platform that combines psychometric assessment, ensemble machine learning, automated report generation, and blockchain-based verification to support informed career decision-making. The platform utilizes a 29-question psychometric assessment to evaluate student interests, aptitude, personality traits, and career preferences.   A feature engineering pipeline was employed to transform 18 raw assessment attributes into 45 engineered features, enabling a richer representation of student profiles. The recommendation engine integrates Random Forest and XGBoost classifiers through a calibrated soft-voting ensemble approach to generate personalized career recommendations across thirteen professional domains.   Experimental evaluation demonstrated strong predictive performance, achieving an overall classification accuracy of 97.71%, a weighted F1-score of 97.71%, and a log-loss value of 0.0975. The system further enhances transparency through confidence-based recommendations and specialized role suggestions aligned with individual strengths and interests.   To ensure report authenticity and integrity, cryptographic hashes of generated PDF reports are securely recorded on the blockchain using Solidity smart contracts. This approach enables tamper-evident verification while preserving user privacy.   The proposed platform demonstrates that the integration of Artificial Intelligence and Blockchain technologies can provide secure, explainable, and personalized career guidance. MentorChain offers a practical solution for educational institutions and students seeking trustworthy career recommendation systems while contributing to the advancement of intelligent educational technology.

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