Exploring the application of knowledge-enhanced large language models in automotive marketing education: A case study of ERNIE bot
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Akademia Baru
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/43494/ http://umpir.ump.edu.my/id/eprint/43494/1/J%202024%20ARFMT%20LuHK%20M.M.Noor%20Automotive%20Edu%20Case%20Study.pdf |
| Summary: | 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’ |
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