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.
computational constraints
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: Breaking Computational Barriers in K12 Learning
This article explores how K12 artificial intelligence courses can overcome computational resource limitations, enabling students to explore AI with minimal hardware requirements.
