AI-assisted juniors write code faster but struggle to debug or explain it, studies warn

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A growing concern in software engineering circles is that junior developers who rely heavily on AI coding tools can produce clean, test-passing code quickly but lack the deeper understanding needed to troubleshoot real-world problems like memory leaks. A January 2026 Anthropic study found that heavy AI users scored below 40% on code comprehension quizzes, compared to 67% for those who coded by hand, with the sharpest gap appearing in debugging tasks. A separate GitClear analysis of 211 million lines of code showed that refactoring dropped from 25% of commits in 2021 to under 10% in 2024, while duplicate code blocks surged 800% in the same period. Industry voices argue the root cause is a hiring and evaluation culture that rewards PR speed over genuine system understanding, effectively outsourcing the learning process to AI. Proposed fixes focus not on banning AI tools but on requiring developers to verbally explain their changes and independently work through complex bugs before merging code.

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