Quintuple-Valued Neutrosophic Offset for Quality Evaluation of Cross-Border E-Commerce Talent Training Based on Artificial Intelligence

Abstract

As global trade continues its rapid digitization, the demand for professionals skilled in 
cross-border e-commerce has grown exponentially. In this context, artificial intelligence (AI) is 
reshaping the landscape of talent training by enhancing communication, streamlining operations, 
and personalizing learning experiences. This study conducts a quality evaluation of cross-border 
e-commerce talent training programs that incorporate AI technologies. A Neutrosophic approach 
is employed to assess training effectiveness based on six critical dimensions: language and 
communication proficiency, digital marketing and data analytics integration, platform operation 
skills, cross-cultural and regulatory awareness, AI-based customer service simulation, and 
adaptability to emerging technologies. Seven alternative training programs are comparatively 
analyzed using a structured evaluation model. We use the Quintuple-Valued Neutrosophic Offset 
to solve the uncertainty problem. The findings reveal that AI-integrated platforms with 
immersive and personalized content outperform traditional training methods. This evaluation 
provides a strategic framework for educational institutions, training providers, and policymakers 
to enhance the relevance, scalability, and global competitiveness of their e-commerce talent 
development initiatives.

DOI: 10.5281/zenodo.15864964