Bridging the Gap: Enhancing Coding Education to Balance AI Tool Use with Fundamental Understanding

The AI-Driven Coding Education Paradox: A Hiring Committee’s Perspective Over the past 18 months, hiring committees have observed a striking shift in the performance of junior developer candidates. While resumes and portfolios showcase polished, functional projects, technical interviews reveal a troubling gap: candidates increasingly struggle to explain their code, handle edge cases, or demonstrate intuition about its behavior. This discrepancy is not coincidental but a direct consequence of the growing reliance on AI tools in coding education and practice. Below, we dissect the mechanisms driving this phenomenon, their unintended consequences, and the stakes for the software industry. Mechanisms of the AI-Driven Coding Education Impact Mechanism 1: *AI-Generated Code Submission* Impact → Internal Process → Observable Effect: Impact: Candidates increasingly rely on AI tools to generate code. Internal Process: Candidates describe desired functionality to AI, accept the output, and submi