AI-Assisted Learning Gaps: Addressing Foundational Programming Skills for Independent Problem-Solving

Expert Analysis: The AI-Assisted Learning Gap in Programming Education Main Thesis: Students who learn programming primarily through AI assistance often lack foundational understanding of core programming concepts, hindering their ability to debug, optimize, and problem-solve independently. This comparative analysis examines the trade-offs between AI-first and traditional learning approaches, highlighting the long-term implications for skill development and employability in the tech industry. Mechanisms of Learning Disparity Mechanism 1: AI-Generated Code Bypassing Fundamental Understanding Impact: Students produce functional code rapidly, but at the cost of foundational knowledge. Internal Process: AI tools translate natural language into executable code, abstracting syntax and low-level logic. Observable Effect: Students deliver working solutions without engaging with programming fundamentals, leading to a superficial grasp of core concepts. Intermediate Conclusion: While AI accelera