This article is part of a series on the Sens-AI Framework—practical habits for learning and coding with AI.
AI gives novice developers the ability to skip the slow, messy parts of learning. For experienced developers, that can mean getting to a working solution faster. Developers early in their learning path, however, face what I call the cognitive shortcut paradox: they need coding experience to use AI tools well, because experience builds the judgment required to evaluate, debug, and improve AI-generated code—but leaning on AI too much in those first stages can keep them from ever gaining that experience.
I saw this firsthand when adapting Head First C# to include AI exercises (Stellman and Greene 2024). The book’s exercises are built to teach specific development concepts like object-oriented programming, separation of concerns, and refactoring. If new learners let AI generate the code before they’ve learned the fundamentals, they miss the problem-solving work that leads to those “aha!” moments where understanding really clicks.
With AI, it’s easy for new learners to bypass the learning process completely by pasting the exercise instructions into a coding assistant, getting a complete program in seconds, and running it without ever working through the design or debugging. When the AI produces the right output, it feels like progress to the learner. But the goal was never just to have a running program; it was to understand the requirements and craft a solution that reinforced a specific concept or technique that was taught earlier in the book. The problem is that to the novice, the work still looks right—code that compiles and produces the expected results—so the missing skills stay hidden until the gap is too wide to close.
Evidence is emerging that AI chatbots can boost productivity for experienced workers but have little measurable impact on skill growth for beginners (Humlum et al. 2025). In practice, the tool that speeds mastery for seniors can slow it for juniors, because it hands over a polished answer before they’ve had the chance to build the skills needed to use that answer effectively.
The cognitive shortcut paradox isn’t just a classroom issue. In real projects, the most valuable engineering work often involves understanding ambiguous requirements, making architectural calls when nothing is certain, and tracking down the kind of bugs that don’t have obvious fixes (Stellman and Greene 2005). Those abilities come from wrestling with problems that don’t have a quick path to “done.” If developers turn to AI at the first sign of difficulty, they skip the work that builds the pattern recognition and systematic thinking senior engineers depend on.
Over time, the effect compounds. A new developer might complete early tickets through vibe coding, feel the satisfaction of shipping working code, and gain confidence in their abilities. Months later, when they’re asked to debug a complex system or refactor code they didn’t write, the gap shows. By then, their entire approach to development may depend on AI to fill in every missing piece, making it much harder to develop independent problem-solving skills.
The cognitive shortcut paradox presents a fundamental challenge for how we teach and learn programming in the AI era. The traditional path of building skills through struggle and iteration hasn’t become obsolete; it’s become more critical than ever, because those same skills are what allow developers to use AI tools effectively. The question isn’t whether to use AI in learning, but how to use it in ways that build rather than bypass the critical thinking abilities that separate effective developers from code generators. This requires a more deliberate approach to AI-assisted development, one that preserves the essential learning experiences while harnessing AI’s capabilities.
