By: Dr. Chetankumar M. Patel
(Author, Programming Logic and Problem Solving)
(A personal note supported by recent research on AI-generated code – Vibe Coding)
I was thinking to myself about one question: In this AI era, to what extent are the fundamentals of coding, or rather the foundation of logic building, still necessary?
After reflecting on it, I arrived at the following conclusions.
The output generated by AI depends on the prompts given to the system. The quality of those prompts, in turn, depends entirely on the user's understanding of the domain. In the case of coding, this means having a strong foundation, supported by the developer's experience.
At the same time, the quality of AI-generated output depends on proper verification and validation. To verify whether the generated code is correct, efficient, and reliable, one again needs strong domain knowledge. AI can certainly write code, but it cannot judge whether the solution is truly appropriate for the problem.
The same applies to vibe coding. A developer's fundamental programming skills and experience play a major role in deciding whether the AI-generated solution is actually usable. Looking at both the input (prompts) and the output (validation) of AI-assisted coding, I realized that having a solid foundation in programming logic is not optional—it is essential.
Another question came to mind: Is there any research that supports this conclusion?
To find the answer, I explored the available research and its findings. I came across several recent studies, and their conclusions were remarkably consistent.
| Research | Source | Findings |
|---|---|---|
| The Impact of Generative AI on Programming Education: A Systematic Literature Review (2025) | ScienceDirect – International Journal of Educational Research Open | Reviewing 40 empirical studies, the authors concluded that while Generative AI improves learning efficiency, students still require strong programming fundamentals, critical thinking, and structured instruction to avoid superficial learning. |
| Learning to Debug AI- Generated Code: Challenges for Novice Programmers (2025) | ScienceDirect – International Journal of Artificial Intelligence in Education | The study found that beginners could use AI to generate programs but often struggled to identify logical errors and incorrect algorithms. Programming fundamentals remained essential for validating AI- generated code. |
| Generative AI in Programming Education: Developing Higher-Order Thinking Skills (2026) | Frontiers in Computer Science | Students who combined AI with structured learning of algorithms, problem decomposition, and debugging developed significantly better programming logic and higher-order thinking than those relying solely on AI-generated answers. |
| Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency (2026) | University-based research published on arXiv | Students with stronger computer science knowledge consistently performed better in AI-assisted ("vibe") coding tasks. Programming fundamentals were a stronger predictor of success than general intelligence alone. |
| From Prompting to Verification: How Experience Shapes Vibe Coding Practices (2026) | Multi-university research published on arXiv | Experienced programmers were significantly better at verifying, debugging, and improving AI-generated code. The study concluded that AI-assisted development increases—not decreases—the importance of programming knowledge and logical reasoning. |
Now I can confidently say that there is strong research evidence supporting the importance of programming fundamentals for every computer programmer. For a novice coder, learning the fundamentals of coding and logic building is probably the most valuable investment for building a successful career.
This is exactly where this book becomes useful. It focuses on the very fundamentals of programming logic and problem-solving. More importantly, it teaches how to think, not just how to write code.
The book is filled with solved examples, colorful flowcharts, algorithms, pseudocode, and trace tables, making the learning process practical, engaging, and enjoyable.
Programming has never been just about writing syntax. At its core, it is about solving problems logically. AI may generate the code, but someone still has to define the problem correctly, evaluate the solution, and decide whether the result is actually right. In many ways, AI has made programming fundamentals even more valuable than before.
The better the prompts we design, the better our thinking, and ultimately the better the output. Prompt engineering, therefore, does not replace programming knowledge—it builds upon it.
The book begins with the fundamentals of computer engineering and gradually covers input and output operations, decision-making, loop control, modular programming, arrays, and file management. Every chapter is designed to take the reader one step further without creating unnecessary learning pressure. The colorful presentation also makes the learning experience more engaging and enjoyable.
Anyone new to coding should read this book. It will be especially useful for students of B.Tech (Computer Science), BCA, MCA, and MSc (IT). Even students from non- computer disciplines who wish to learn programming will find it an excellent starting point.
I would also encourage faculty members who teach introductory programming courses to make use of the colorful teaching resources provided with this book as free online material. These resources can help instructors prepare classroom presentations and laboratory sessions more quickly and effectively.
Learning the fundamentals given in this book makes the novice or the faculty member not only AI-independent but also enhances their AI-supported coding.
About the Author
Dr. Chetankumar M. Patel is a Professor, researcher, trainer, and author with more than 26 years of teaching experience. His interests include programming logic, AI-assisted learning, engineering education, and problem-solving methodologies.
References:
•ScienceDirect – The Impact of Generative AI on Programming Education (2025)
•ScienceDirect – Learning to Debug AI-Generated Code (2025)
•Frontiers in Computer Science (2026)
•arXiv – Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency (2026)
•arXiv – From Prompting to Verification (2026)
