This article explores how to design interactive AI foundation projects—from perceptrons to LLMs—for undergraduate courses with limited computational resources. Through layered teaching strategies, carefully crafted micro-projects, and innovative assessment methods, educators can overcome hardware constraints while delivering high-quality AI education.
machine learning projects
Designing for Accessibility: Undergraduate AI Course Projects Under Resource Constraints
This article provides actionable recommendations for designing AI projects in foundational courses for second-year undergraduates. It explores how to demonstrate AI’s evolution from perceptrons to LLMs despite computational resource limitations, effectively bridging theory and practice in artificial intelligence education, student projects, and constrained computing environments.
AI Education, Student Projects, Computational Resource Constraints: Designing Interactive Undergraduate AI Coursework
This article explores how to design effective interactive projects for sophomore-level AI foundation courses under computational resource constraints. Presenting a tiered approach from algorithm implementation to real-world applications, it offers educators practical solutions for cultivating core AI competencies.
