🤖 AI Summary
Traditional AI education suffers from a pedagogical disconnect between classical machine learning (ML) and large language models (LLMs), hindering students’ ability to grasp the historical and conceptual evolution of AI techniques.
Method: This paper proposes a two-stage, progressive curriculum: Stage I establishes foundational ML concepts—including supervised learning and model evaluation—while Stage II advances to LLM-specific competencies such as prompt engineering, fine-tuning, and application development. The design integrates both paradigms via comparative technical analysis, historical contextualization, and cross-paradigm project-based learning.
Contribution/Results: Empirical evaluation demonstrates that the approach significantly enhances students’ holistic understanding of the AI technology ecosystem, increasing conceptual linkage by 32%. Moreover, graduates exhibit stronger practical competencies aligned with industry demand for “ML+LLM” hybrid professionals. The framework provides a reusable, evidence-based pedagogical model for AI curriculum reform.
📝 Abstract
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.