Design and CIPP Evaluation of an AI-Powered Personalized Learning Platform for Cross-Border E-Commerce

This research addresses the pain points of homogeneous learning content, standardized learning paths, and the inability to meet individual learner needs prevalent in the current cross-border e-commerce training landscape. It proposes and designs an AI-powered personalized learning platform for cross-border e-commerce [1]. This platform innovatively integrates Artificial Intelligence (AI) technologies such as Natural Language Processing (NLP) and Machine Learning (ML) to construct core functional modules including intelligent learning needs identification, dynamic knowledge graph construction, adaptive learning path planning, intelligent content recommendation, and real-time learning feedback and evaluation [2]. By deeply analyzing learners' knowledge base, learning habits, interest preferences, and career development goals, the platform can accurately match and push customized learning content and learning paths, achieving a personalized learning experience tailored to each individual, significantly improving learning efficiency and effectiveness. This study employs the CIPP (Context, Input, Process, Product) evaluation model to systematically assess the platform's design concept, resource investment, development process, and anticipated outcomes. The aim is to validate the platform's feasibility, effectiveness, and potential impact, providing theoretical support and practical reference for building an efficient and intelligent talent development system for cross-border ecommerce.