How AI assistance impacts the formation of coding skills

This post presents a randomized controlled trial investigating the impact of AI assistance on software developers' skill acquisition in coding. It found that participants using AI scored significantly lower on a quiz assessing mastery of a new Python library compared to those coding by hand. The study identified different interaction patterns with AI, categorizing them into low-scoring (AI delegation, progressive reliance, iterative debugging) and high-scoring (generation-then-comprehension, hybrid code-explanation, conceptual inquiry) behaviors. The findings suggest that while AI can increase efficiency, it may hinder skill development, particularly in debugging and conceptual understanding, if not used intentionally to build comprehension. The research emphasizes the importance of designing AI tools and workplace policies that foster both productivity and learning.