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

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.