Frontiers in Educational Research, 2025, 8(10); doi: 10.25236/FER.2025.081008.
Chenrui Wu, Yao Huang, Zhenzhong Chu
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
Large language models in artificial intelligence are profoundly transforming work methodologies across various domains. Undergraduate education must accelerate pedagogical reform to adapt to this technological transformation and cultivate high-quality professionals capable of proficiently utilizing artificial intelligence tools. This paper examines the application potential of large language models in three key aspects: knowledge graph construction, instructional implementation, and engineering practice, using embedded systems courses as a case study. Through the integration of large model technology, more intelligent knowledge point recommendations and personalized learning pathway planning can be achieved. The introduction of real-time interaction and intelligent question-answering systems in classroom instruction facilitates enhanced student engagement and comprehension capabilities. In engineering practice components, large models can be leveraged to generate rich case studies and simulation scenarios, thereby strengthening students' hands-on competencies. This paper further discusses the implementation pathways and developmental trends of AI large language models in embedded systems education.
Large Language Model, Teaching Application, Embedded System, Knowledge Graph
Chenrui Wu, Yao Huang, Zhenzhong Chu. Research on the Application of Large Language Models in Embedded Systems Education. Frontiers in Educational Research (2025), Vol. 8, Issue 10: 53-60. https://doi.org/10.25236/FER.2025.081008.
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