2025/12/04 by Li Fang, Li, Fang
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Genetics, Bioinformatics, and Biomedical Research #Machine Learning in Materials Science #Teaching and Learning Programming
paper · pdf · doi:10.48550/arxiv.2512.05167
openalex publication_date 2025/12/04 · openalex created_date 2025/12/09 · openalex updated_date 2026/07/28
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.