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AI Is Changing Coding: Why Learners Still Need Strong Foundations

Updated: 1 day ago

Artificial intelligence is changing how developers write, debug, test, and review code. AI coding assistants can suggest lines of code, explain unfamiliar functions, help generate tests, answer coding questions, and support code review workflows. GitHub’s Copilot documentation describes coding assistance across areas such as syntax, programming concepts, test cases, debugging, code explanations, suggested improvements, and code fixes.


For beginners, this can feel exciting. It can also feel confusing.


If AI can help write code, do learners still need to understand programming properly?


The answer is yes.


A learner reviewing AI-generated code beside notes showing logic, debugging, testing and security checks.

AI can be a useful coding assistant, but it does not remove the need for strong programming knowledge. In fact, the more AI becomes part of software development, the more important it becomes for learners to understand what code is doing, why it works, and when it may be wrong.


How AI Is Changing Coding


AI coding tools are now part of many developer workflows. Stack Overflow’s 2025 Developer Survey reports that 84% of respondents are using or planning to use AI tools in their development process, and 51% of professional developers use AI tools daily.


GitHub’s Octoverse 2025 report also describes generative AI as becoming standard in development, with more than 1.1 million public repositories using an LLM SDK and many new developers using Copilot early in their GitHub journey.


AI can support developers by:

  • Reducing repetitive typing

  • Generating starter code

  • Explaining unfamiliar syntax

  • Suggesting alternative approaches

  • Helping create tests

  • Supporting early code review feedback

  • Helping developers investigate errors


Used well, AI can make coding smoother. But it should not be treated as an automatic source of correct code.


AI Can Help You Write Code Faster


One common use of AI in coding is code generation. A learner or developer may describe a task, and the AI tool may suggest a function, form handler, test structure, or data-processing example.


This can be helpful because many coding tasks include repeated patterns. For example, AI may help generate a starter function, explain a loop, suggest a unit test, or show a simpler version of a solution.


But faster is not the same as correct.


GitHub’s responsible-use documentation warns that AI-generated code may appear valid while still being incorrect, incomplete, or insecure, and that developers should carefully review and test AI-generated code before using it.


So the real question is not:

Did the AI give me code?

The better question is:

Do I understand this code well enough to test, change, and explain it?

AI Can Support Debugging, But It Cannot Replace Understanding


Debugging is one of the most valuable skills a programmer can build. AI tools can help by explaining error messages, suggesting likely causes, and pointing to code that may need attention. This is especially helpful for beginners who often get stuck on small syntax errors or confusing messages.


But debugging is not only about removing the visible error. It is about understanding the root cause.


A learner still needs to ask:

  • Why did this error happen?

  • Does this fix solve the real problem?

  • Could this change break another part of the program?

  • Have I tested the behaviour properly?

  • Can I explain the fix in my own words?


AI can suggest. The learner must verify.


AI in Code Review and Quality Checks


AI is also becoming more visible in code review. GitHub Copilot code review can scan code changes and provide feedback comments or suggested changes, but GitHub’s documentation also warns that Copilot review can produce false positives, misunderstand code, or suggest code that still needs human review and testing. That distinction matters.


AI review can help spot possible issues earlier, but it should not replace human judgement. Human reviewers understand user needs, business goals, project context, team standards, security expectations, and long-term maintainability. For learners, this is an important lesson: good programming is not just about getting code to run. It is about building code that is understandable, testable, safe, and fit for purpose.


Why Core Programming Skills Still Matter


AI can generate code, but it cannot take responsibility for your understanding. Every learner still needs a strong foundation in core coding concepts.


Logic and Control Flow


Learners need to understand how programs make decisions, repeat actions, and respond to different inputs. Without this, it is difficult to judge whether AI-generated logic is correct.


Variables and Data Types


A programmer must know how information is stored, changed, passed between functions, and displayed to users.


Data Structures


Arrays, lists, objects, dictionaries, maps, queues, trees, and other structures help developers organise data properly. AI may suggest a structure, but the learner needs to know whether it fits the problem.


Problem-Solving


Coding is not only typing syntax. It is breaking a problem into smaller steps, choosing a sensible approach, and checking whether the result works.


Testing and Debugging


Developers need to test what they build. AI can help generate test ideas, but humans still need to decide what should be tested and whether the results make sense.


Security Awareness


AI-written code can contain bugs or vulnerabilities. OpenSSF guidance recommends assuming AI-written code can contain bugs or security issues, including outdated dependencies, weak error handling, exposed secrets, and vulnerable package suggestions.


The Risk of Blindly Trusting AI-Generated Code


The biggest risk is not that AI writes code. The bigger risk is that a learner accepts code they do not understand.


AI-generated code may appear polished, but it can still:

  • Ignore edge cases

  • Use outdated patterns

  • Introduce security weaknesses

  • Misunderstand the project requirement

  • Work in one example but fail elsewhere

  • Add unnecessary complexity

  • Hide mistakes behind confident explanations


OpenAI’s own guidance notes that ChatGPT can produce incorrect or misleading outputs and may sound confident even when wrong.


Security guidance also matters. OWASP’s LLM Top 10 includes risks such as insecure output handling, where failing to validate LLM output can lead to downstream security issues, including code execution risks and data exposure.


For learners, this is not a reason to avoid AI. It is a reason to use AI carefully.


Questions to Ask Before Using AI-Generated Code


Before accepting code from an AI assistant, ask:

  • Do I understand what every important line does?

  • Have I tested the code with normal and edge-case inputs?

  • Could this code expose private data or create a security risk?

  • Does this code match the project requirement?

  • Is there a simpler or clearer way to write it?

  • Can I explain this solution to another person?


If the answer is no, pause and learn the concept before moving forward.


How Beginners Should Use AI While Learning to Code


AI can be a helpful learning partner when used properly. The key is to use it to improve understanding, not to avoid understanding.


A beginner can use AI to:

  • Explain an error message in plain English

  • Compare two possible solutions

  • Generate practice exercises

  • Suggest test cases

  • Explain unfamiliar syntax

  • Review code for clarity

  • Ask for a simpler version of a solution


A beginner should avoid using AI to:

  • Complete every assignment without understanding it

  • Copy code directly into a project without testing

  • Skip learning loops, functions, data structures, and debugging

  • Assume the first answer is correct

  • Ignore security and quality checks


A practical rule is:

Use AI to ask better questions, not to stop thinking.

A Better Way to Learn Coding With AI


A better way to learn coding with AI looks like this:

  1. Learn the concept.

  2. Write your own version.

  3. Use AI to compare or explain.

  4. Test the code.

  5. Refactor and improve it.

  6. Explain what changed and why.


This builds confidence, not dependency.


AI may help you move faster, but foundations help you move correctly.


Final Thoughts


AI is becoming a normal part of coding. It can help developers write code, debug problems, generate tests, and review changes. But AI does not remove the need for human knowledge.


The best programmers will not be the ones who blindly accept every suggestion. They will be the ones who understand the problem, evaluate the output, test carefully, and use AI responsibly.


For beginners, the message is simple: learn the foundations. Practise problem-solving. Build projects. Test your work. Then use AI as a helpful assistant, not as a replacement for your own thinking.


Want to build coding confidence the right way? Explore ITLearnner’s beginner-friendly programming and software development learning pathways, where learners practise core coding skills, project thinking, debugging, testing, and responsible use of modern tools.


For more details refer to the below links:

Building Confidence in Coding Skills


In the evolving landscape of technology, building confidence in coding skills is essential. Many learners face anxiety about career breaks and skill gaps. It is crucial to address these concerns head-on.


Overcoming Career Break Anxiety


Taking a break from a career can be daunting. However, it is important to remember that skills can be refreshed and new ones can be acquired. Engaging in structured learning pathways can help bridge the gap.


Emphasizing Certifications


Certifications such as ISTQB, Agile, and automation are valuable in today’s job market. They not only enhance knowledge but also improve employability. Many learners have successfully transitioned into tech roles after pursuing these certifications.


Success Stories


Real-life success stories can inspire and motivate learners. Many individuals have overcome challenges and achieved their career goals through dedicated learning and mentorship.


Flexible Learning Options


Flexible evening and weekend learning options are available to accommodate busy schedules. This makes it easier for learners to balance their education with work and personal commitments.


Expert Mentorship


Having access to expert mentorship can make a significant difference. Mentors can provide guidance, support, and industry insights that are invaluable for career development.


By focusing on practical skills and real career outcomes, learners can build the confidence they need to succeed in the tech industry.

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