Toward AI-facilitated Learning Cycle in Integration Course Through Pair Programming with AI Agents
Zhengyuan Wei, Albert Ting Leung Lee, Victor C. S. Lee, W.K. Chan · 2024
We propose a new methodology that harnesses recent advancements in AI techniques to formulate an AI-facilitating code learning cycle for students. The approach builds on an existing learning process and innovatively incorporates pair programming into the learning cycle. It first transforms the example code into scaffold code as exercises through an instructor-AI pairing. The scaffold code serves as an exercise for students to complete and debug on a hardware platform iteratively with an expert AI assistant. This design alleviates instructors' burden of crafting new exercises for new scenarios and offers students the advantage of interactive learning with scenario diversity. We evaluate the methodology using a suite of example codes and assess the semantic similarity among different code versions produced by AI assistants. The case study shows promising results of the methodology. We further discuss our findings and outline future work for the proposed methodology.