Probabilistic Single-Valued Neutrosophic Operator for Optimizing Teaching Outcomes in University Career Planning Courses Using AI Evaluation Models

Abstract

 AI integration into university career planning courses has emerged as a crucial step 
toward individualized and successful student development in the rapidly changing higher 
education landscape. This project investigates using AI-driven assessment models to optimize 
teaching results in career planning at the university level. The efficacy of several teaching 
strategies was evaluated using eight major criteria, such as engagement, AI-driven feedback, 
career target clarity, and flexibility to meet the requirements of individual students. Data was 
gathered from a variety of instructional options, including AI-enhanced virtual simulations and 
conventional lectures. These choices were ranked and evaluated using neutrosophic set. The 
single valued neutrosophic set (SVNS) is used to solve uncertainty information. We combine 
Probabilistic with SVNS to deal with uncertainty information. The findings show that AI
integrated teaching models perform noticeably better than traditional approaches in terms of 
providing individualized career counseling, raising student happiness, and coordinating 
education with the needs of the labor market. The results give educators and policymakers a 
framework for using intelligent technology to improve the caliber and effectiveness of career 
planning education. 

DOI: 10.5281/zenodo.16734192