Academic stream and career selection continues to be one of the most consequential decisions in a student\\\'s educational journey, yet the vast majority of Indian students navigate this decision without access to objective, structured, or personalized guidance. Much like how early intervention is vital in preventive healthcare, timely and accurate academic counselling significantly reduces the risk of career mismatch, dropout, and long-term professional dissatisfaction. This paper introduces My Smart Counselling Support, a comprehensive intelligent web-based counselling platform that integrates the K-Nearest Neighbors (KNN) machine learning algorithm, a Django-powered full-stack backend, a responsive HTML/CSS/JavaScript frontend, and an AI-powered chatbot to deliver real-time, data-driven career and stream recommendations. The system evaluates a multi-dimensional student profile — comprising academic subject marks, interest survey scores across six career dimensions, twenty skill self-assessments, and behavioral pattern indicators — and processes the resulting 44-dimensional feature vector through an optimized KNN classifier (K=7, distance weighting, Euclidean metric) to recommend one of eight predefined career domains. Experimental evaluation on a dataset of 5,660 records demonstrated that the system achieves a test-set accuracy of 87.4%, weighted F1-score of 0.875, and cross-validation accuracy of 87.4% ± 0.4%, outperforming baseline Naive Bayes (81.2%), Decision Tree (83.7%), and Logistic Regression (79.4%) classifiers. An ablation study confirmed that multi-dimensional profiling outperforms academic-marks-only baselines by 9.1 percentage points, validating the framework\\\'s holistic design. A context-aware AI chatbot module handles student career queries in natural language with 91.3% intent classification accuracy. User acceptance testing with 120 students yielded a System Usability Scale score of 81.7/100 (Excellent category) and a recommendation alignment rating of 3.94/5.0, highlighting both the system\\\'s practical usability and the perceived relevance of its recommendations.
Gagandeep et al. (2026) studied this question.