Cognitive Hybrid AI Model for Intelligent Career Counselling and Decision Support
Keywords:
Association Rule Mining (ARM), Artificial Intelligence (AI), Career counselling, Hybrid learning, Machine Learning (ML)Abstract
Career selection is a crucial decision that shapes a student’s academic and professional future. Traditional counselling methods like aptitude tests and manual interviews are helpful but limited due to subjectivity, time consumption, and inability to support large populations. With the advancing job market and diverse career options, automated, data-driven career guidance has become essential.
Artificial Intelligence (AI) and Machine Learning (ML) now enable intelligent analysis of student interests, skills, academic performance, and behavioural factors to suggest suitable careers. ML supports accurate prediction, while Association Rule Mining (ARM) identifies meaningful relationships between subjects, skills, and achievements. However, using ML or ARM alone results in prediction accuracy without explanation or insights without predictive strength. Hybrid AI models address these gaps by combining to provide accuracy, interpretability, and transparent recommendations supported through Explainable AI (XAI).
Such systems can guide students, identify missing skills, and suggest personalized upskilling courses. This paper reviews hybrid AI research for career counselling, highlighting trends, challenges, and future directions. The findings show that hybrid AI-driven counselling can enhance fairness, scalability, and personalized decision-making bridging the gap between student capabilities and industry needs.
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