Exploring the Application of Knowledge-Enhanced Large Language Models in Automotive Marketing Education: A Case Study of ERNIE Bot

Abstract

With the rapid evolution of intelligent and electric vehicle technologies, the automotive industry faces significant transformation,
especially in autonomous driving, connected systems, and global market integration. This shift has heightened the demand for
skilled automotive marketing professionals equipped with both practical expertise and cross-cultural competence. This study
explores the application of Baidu's ERNIE Bot as a knowledge-enhanced large language model in automotive marketing education.
Focusing on its capabilities to innovate teaching content, optimize instructional methods, expand virtual training, and enhance
cross-cultural sensitivity, we investigate ERNIE Bot’s effectiveness in preparing students for global industry challenges. Case
studies illustrate ERNIE Bot’s role in guiding students through culturally tailored virtual marketing scenarios, emphasizing the
importance of cultural adaptation in customer engagement and international sales. The findings suggest that knowledgeenhanced
language models not only enrich educational content but also improve students’ practical and global marketing skills,
offering a valuable tool for reforming vocational education in automotive marketing.