将传统机器学习与大模型教学结合，帮学生理解AI发展全貌。
Bridging Traditional Machine Learning and Large Language Models: A Two-Part Course Design for Modern AI Education
- 分两阶段教学：先学基础模型，再用大模型实战
- 两期七周课程验证，学生对AI演进理解更深入
- 适合想掌握完整AI能力的本科生和从业者
本文提出一种创新的人工智能与数据科学教学方法，系统融合传统机器学习技术与现代大语言模型（LLMs）。课程分为两个前后衔接、互补的阶段：第一阶段讲授基础机器学习概念，第二阶段聚焦当代大模型应用。该设计帮助学生全面理解人工智能的发展脉络，同时掌握经典与前沿技术的实际应用能力。我们详细介绍了课程架构、实施策略、评估方式及来自两个七周暑期课程周期的教学成果。研究发现，这种整合式教学显著提升了学生对人工智能领域整体认知，并更好满足快速发展的AI产业需求。
原文摘要 · Abstract (English)
This paper presents an innovative pedagogical approach for teaching artificial intelligence and data science that systematically bridges traditional machine learning techniques with modern Large Language Models (LLMs). We describe a course structured in two sequential and complementary parts: foundational machine learning concepts and contemporary LLM applications. This design enables students to develop a comprehensive understanding of AI evolution while building practical skills with both established and cutting-edge technologies. We detail the course architecture, implementation strategies, assessment methods, and learning outcomes from our summer course delivery spanning two seven-week terms. Our findings demonstrate that this integrated approach enhances student comprehension of the AI landscape and better prepares them for industry demands in the rapidly evolving field of artificial intelligence.
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