In an era where AI can generate code with a single prompt, the temptation to lean on tools like ChatGPT or Claude Code is strong. But for beginners, relying on AI too early can come at a cost: weakened problem-solving skills, shallow understanding, and lost creative autonomy. Learning to code without AI may feel harder, but it builds the foundation for long-term success.
The Illusion of Mastery with AI
AI-assisted coding tools are incredibly efficient. GitHub Copilot, for example, can autocomplete functions, suggest modules, and even refactor code. Yet this efficiency can create an illusion of mastery, where learners assume they understand the code simply because it works.
A case study from the University of Patras found that students using AI in introductory programming courses often skipped critical thinking, relying on AI-generated solutions without grasping the logic (Avouris et al., 2025). This cognitive offloading may save time, but in the context of learning, it can stunt growth in debugging, algorithmic thinking, and syntax fluency (Risko & Gilbert, 2016).
Avoiding Shallow Learning
Programming is more than writing code, it’s understanding systems, predicting edge cases, and designing solutions. Beginners who let AI do the heavy lifting miss the chance to wrestle with complexity.
Research from MDPI shows that novice programmers who over-relied on AI tools retained fewer concepts and struggled to apply knowledge to new problems (Bozkurt et al., 2024). AI may generate correct syntax, but it can also produce logically flawed code, which beginners may not detect, reinforcing bad habits.
Learning from Real-World AI Examples
Georgia Tech’s “Jill Watson” AI teaching assistant answered thousands of student questions with 97% accuracy (Axon Park, 2023). But this system works best when students already understand the basics. For beginners, hands-on struggle and human mentorship remain irreplaceable.

The Value of Struggle
Struggle isn’t a bug, it’s a feature of learning. Wrestling with a broken loop or a misfired function develops resilience, attention to detail, and debugging skills. These cognitive muscles shape how developers think. When AI removes the friction, it also removes the growth opportunity.
How to Use AI Wisely as a Beginner
AI will inevitably play a role in software development, but beginners should use it intentionally. Instead of generating full solutions, AI can:
- Explain concepts clearly
- Offer hints or guidance
- Validate your logic
In other words, use AI to support, not replace, your learning process. As Bozkurt et al. (2024) argue, “We must reformulate traditional assessment methods and advocate for critical and reflective pedagogy on AI use.”
Final Thoughts
Learning to code is about cultivating a mindset, not just producing output. By embracing the struggle, avoiding shortcuts, and thinking critically, beginner programmers build the foundation for long-term success. AI can be a powerful ally, but only once that foundation is solid.
References
- Axon Park. (2023). How Effective is AI in Education? 10 Case Studies and Examples. https://axonpark.com/how-effective-is-ai-in-education-10-case-studies-and-examples/
- Avouris, N., Sgarbas, K., Caridakis, G., & Sintoris, C. (2025). Teaching Introduction to Programming in the Times of AI: A Case Study of a Course Redesign. Proceedings of the 12th Panhellenic Conference of Computer Science Education.
- Bozkurt, A., et al. (2024). The Good and Bad of AI Tools in Novice Programming Education. Education Sciences, 14(10), 1089. https://www.mdpi.com/2227-7102/14/10/1089
- Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676–688.
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