Ai-Powered Personalized Career Coach: Experimental Evaluation and Performance Analysis of an Adaptive Career Guidance System

This study presents the experimental evaluation and performance analysis of a fully implemented AI-powered personalized career coaching system. Building upon the architectural framework proposed in our previous work, this paper demonstrates the practical effectiveness of the system through rigorous quantitative experimentation on a dataset of 185,000+ records. The implemented system integrates a hybrid machine learning pipeline — comprising Random Forest, Gradient Boosting, and BERT-based NLP components — with a standardized prompt engineering module and a guided React.js user interface. Experimental results demonstrate that the proposed system achieves a career recommendation accuracy of 87.4%, a skill gap identification precision of 91.2%, and a user satisfaction score of 4.3/5.0, outperforming baseline systems including traditional rule-based portals, standalone ML methods, and non-personalized platforms by margins of 23–41%. The system also achieves an average API response time of 1.8 seconds and supports concurrent multi-user load with 94.6% uptime under stress testing. The standardized prompt engineering module achieved 95.3% response consistency across 10,000 test interactions. Results validate the hypothesis that combining ML, NLP, standardized prompt templates, and a guided user interface significantly improves the quality, consistency, and accessibility of career guidance over existing approaches.