By Devakumar A/L Robert William, Senior Executive, BAC College

Most articles on this topic are written by experienced engineers. This one is written by a complete beginner. I am learning Python from scratch, and I use generative AI almost every day. I also work at UNIMY, where I am surrounded by people who are very good at this. Seeing the changes from my position has taught me as much as the headlines.

What Is Changing in Software Development?
Generative AI tools can now write basic code, standard functions and repetitive sections of code in seconds. These tools assist developers with coding, while making skills such as system design, problem-solving, debugging and code review increasingly important. AI can produce code with bugs, security weaknesses or even functions that do not exist. Developers still need to understand, test and verify what AI produces. Human judgement remains essential.

Developers also use AI agents that can handle tasks involving multiple steps, such as refactoring a module and writing tests for it. To get useful results, they must describe problems clearly and precisely. That is a skill in itself.

AI has made some tasks, such as writing simple scripts and basic web pages, easier to complete. However, the skills needed for software development vary across companies and roles. Depending on the job, developers may need a strong understanding of system design, debugging, software integration and security. AI can help speed up certain development tasks, but it does not guarantee that an entire project will be completed faster. Testing, maintenance and good software design remain important to ensure that the code is reliable, secure and manageable.

How I Use AI as a Beginner
Setting up my tools. Many beginners get stuck before writing a single line of code while installing VS Code, Python and extensions. I ask Claude or ChatGPT to guide me step by step. When an error message appears, I paste it in and ask what it means.

Learning from YouTube, then practising. I watch tutorials and try the exercises myself. When I do not understand a concept, I ask AI to explain it more simply, with examples. I also ask it to guide me through practice exercises instead of giving me the answers.

This last part matters. If AI writes everything, I learn nothing. The value comes from trying first, getting stuck and then asking AI for help, like a patient tutor who is always available.

Where AI Meets Practical Experience
AI has a limitation that beginners can easily overlook. I cannot always tell when an explanation is wrong, too simplistic or different from how things are done in professional software development. Learning to evaluate AI output takes time, practice and a growing understanding of the subject.

This is where my experience at UNIMY has helped me most.

Take Osama Radi Al Assme, a graduate research assistant at UNIMY and a UNIMY computer engineering graduate. He is much younger than me, yet I find his knowledge truly inspiring. I have watched him set up virtual machines, work in Linux with confidence, write Python code and build Internet of Things (IoT) projects. What stands out is that he understands why these things work, not just how to do them.

When I am stuck, learning from someone with practical experience can turn a confusing error into a lesson. AI helps me explore possibilities and understand concepts, while guidance from people with hands-on experience helps me develop a deeper understanding of how those concepts work in practice.

His journey also shows how university learning can extend beyond the classroom. Here is a graduate who has progressed into research and can work with real systems, connecting technical knowledge with practical applications.

The wider environment at UNIMY has also made a difference. My colleagues are experienced, and they explain things patiently and give their time freely. I also get to see student projects at UNIMY’s Innoverse. Watching students turn ideas into working prototypes helps me connect what I study to things that actually run. A loop or a function stops being just an exercise and becomes part of something real.

What This Means for Learners

• Use AI as a tutor, not a shortcut. Try first, then ask for help.
• Ask for explanations, not just answers. “Why does this work?” is one of the most useful questions you can ask.
• Verify what you learn. Run the code, test it and check it against reliable sources.
• Learn alongside people who build things. Mentors, researchers and student projects can help you develop practical skills and understanding that complement AI-assisted learning.

Conclusion
Generative AI makes it easier to begin learning software development. It can help beginners overcome initial obstacles, explore unfamiliar concepts and practise new skills. But learning the fundamentals, gaining practical experience and receiving guidance from people who understand the technology remain important.

For me, AI helps me get started, while the people and projects at UNIMY help me understand what I am learning and how it works in practice. Combining AI-assisted learning with hands-on experience and mentorship is a valuable way to prepare for a changing software development landscape.

About the Writer
Devakumar is a Senior Executive reporting to the Chief Growth Officer, Mr. Shaun Tan, at the BAC Education Group. He has been with the group for over a decade, starting in Human Resources before moving into IT, and holds a Bachelor of Science in Biotechnology from Monash University Malaysia. Along the way, he has been fortunate to support a wide range of work, from technology operations and events to content and website initiatives, and he is grateful to the colleagues and mentors who have guided him.