Exploring the Application of Knowledge-Enhanced Large Language Models in Automotive Marketing Education: A Case Study of ERNIE Bot
Year: 2024
Type: article
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 forskilled automotive marketing professionals equipped with both practical expertise and cross-cultural competence. This studyexplores 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 enhancecross-cultural sensitivity, we investigate ERNIE Bot’s effectiveness in preparing students for global industry challenges. Casestudies illustrate ERNIE Bot’s role in guiding students through culturally tailored virtual marketing scenarios, emphasizing theimportance of cultural adaptation in customer engagement and international sales. The findings suggest that knowledgeenhancedlanguage 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.
Authors: Hong Kun Lu, Fei Song, Muhamad Mat Noor, Bingli Wu
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4405946232