SCOPE- Situational Competency and Opportunity Pathway Engine

The contemporary labor market is experiencing an increasing disparity between the needs of the employers and the abilities of individuals who are looking or seeking employment and hence career guidance is now becoming more difficult. The conventional guidance systems are based on non-portable signs like academic achievement and personality assessments that do not reflect on situational judging, transferable competencies and shifting industries. This paper will present an AI-based career guidance system that will solve these limitations by providing dynamic and data-based evaluation system. The system combines situational judgment tests, analysis of skills gap based on semantic similarity and open-ended response analysis based on a large language model. Individuals are assigned to more than five hundred career pathways and offered individual and time bound learning plans which contain applicable abilities and accreditations and objective positions. The site has three tiers design which include; web-based interface, backend services which are scalable and hybrid data storage. A test run with student users showed better results as career matches were much higher than those made through traditional techniques. The system effectiveness was also confirmed when high user satisfaction levels were observed and confidence in career choices. The findings show that multi modal career guidance systems using AI to enable a system have a better accuracy and personalization advantage. Future developments would be towards real time labor market integration, adaptive learning and advanced features of interview preparation.