AI coding tools can help students write programs more quickly. They can suggest the next line, explain an error and even generate an entire solution in seconds.
However, faster coding does not always mean faster learning.
Some students use AI tools and continue to develop their programming skills. Others produce working code but become less able to explain, adapt or debug it. The worrying part is that these students may still feel as though they understand what they have created.
This creates an important question:
Does AI help every beginner equally, or can it make the gap between students wider?
Two students, one programming task
Imagine two students completing the same assignment in an introductory programming course. Both are allowed to use an AI assistant and code completion tools.
The first student has little previous programming experience. She reads the task and begins to type, but an autocomplete tool immediately suggests a long block of code. The suggestion interrupts her thinking and makes her unsure about what to do next.
She opens an AI chatbot and asks for step-by-step guidance rather than a complete answer. This sounds like a sensible approach. However, after reading the first part of the explanation, she begins looking for a faster solution and copies some generated code.
The code does not quite match the problem. It also contains unfamiliar syntax, creating even more confusion. She repeatedly moves the program between the chatbot and her development environment, asking the AI to fix each new problem.
Eventually, the code passes the tests.
The assignment is complete, but the student has only a limited understanding of how the program works.
When asked whether the AI was useful, she describes it as being like a personal tutor. She believes it gave her the support she needed.
The problem is not simply that she used AI. The problem is that she does not recognise how little of the solution she understood.
She has developed an illusion of competence.
Her program works, so she feels that she has learned. However, she may struggle to answer questions such as:
- Why was this algorithm chosen?
- What would happen with a much larger input?
- Why is this data structure suitable?
- How could the program be changed?
- What caused the original error?
Passing the tests is not necessarily the same as understanding the solution.
The second student
Now consider another student completing the same task.
She has some previous experience of learning to code. She still finds programming difficult, but she knows that confusion and mistakes are a normal part of the process.
She reads the problem carefully and pauses before writing any code. She identifies the values she will need to store and writes comments describing the main stages of her solution.
The autocomplete tool offers frequent suggestions. She ignores most of them because she already has a plan. When she accepts a suggestion, it is small and deliberate, such as completing a bracket or a simple conditional statement.
She makes several mistakes, but she works through them methodically. She reads the error messages, checks her assumptions and corrects the program herself.
Her final solution also passes the tests.
When asked about the AI, she says that it helped her work more quickly. However, she credits her own planning and problem-solving for producing the solution.
The same tool was available to both students, but it played a very different role.
For the first student, AI replaced much of the thinking.
For the second student, AI supported thinking that was already taking place.
AI can amplify existing differences
These examples were based on students observed during research into the use of generative AI in an introductory programming course. The researchers used techniques including eye tracking and asked students to explain their thinking aloud while they worked.
Their observations suggest that AI coding tools may not benefit all beginners equally.
Students with stronger foundations are more likely to:
- plan before prompting
- judge whether a suggestion is appropriate
- notice when generated code does not match the task
- reject unnecessary suggestions
- debug errors independently
- explain why a solution works
Students with weaker foundations may be less able to judge the quality of an answer. This can lead to a cycle:
- The student does not know how to begin.
- AI generates a solution.
- The generated solution contains unfamiliar ideas.
- The student asks AI to explain or repair it.
- The program eventually works.
- The student mistakes successful output for understanding.
AI has not necessarily caused the original difference between the students, but it may amplify it.
The student who already knows how to think like a programmer becomes faster. The student who needs to develop those thinking skills may avoid practising them.
The danger of not knowing what you do not know
A beginner cannot always recognise a poor solution.
Generated code may appear convincing because it is neatly formatted, uses technical vocabulary and produces the expected output. However, it might also be inefficient, unnecessarily complicated or based on assumptions that are not true.
Without enough knowledge, a student may not know which questions to ask.
This is why an AI assistant cannot automatically act as a good tutor. A tutor does more than provide correct information. A tutor checks understanding, chooses an appropriate level of challenge and notices when a learner is following instructions without understanding them.
An AI tool may offer an explanation, but it cannot force the learner to stop, think and engage with it.
Even asking for “step-by-step help” does not guarantee learning. A student can still scroll past the explanation and copy the answer.
The quality of learning depends partly on the behaviour of the learner.
A useful rule for beginners
A helpful rule is:
Do not ask AI to write code that you could not reasonably write or explain yourself.
This does not mean every programmer must memorise every function or piece of syntax. Professional developers regularly use documentation, examples and other forms of support.
The important question is whether you remain in control of the solution.
Before using generated code, you should be able to explain:
- what each important line does
- why the approach solves the problem
- what inputs might cause it to fail
- how you would test it
- how you could modify it
- what alternatives could have been used
When you cannot answer these questions, the code may be working for you rather than being understood by you.
Why writing code yourself still matters
Writing code manually can feel slower, but that difficulty has value.
When you plan a solution, make a mistake and then correct it, you are building a mental model of how the program works. You are learning to recognise patterns, predict behaviour and connect errors to their causes.
These experiences also develop confidence.
There is a major difference between thinking:
“The AI managed to solve it.”
and thinking:
“I struggled with this, but I eventually solved it.”
The second experience provides evidence that you can overcome future problems.
Programming also contains moments of genuine satisfaction. After spending time investigating an error or developing an algorithm, seeing the program finally work can feel rewarding.
Generating a solution instantly may remove much of the struggle, but it can also remove the feeling of achievement that follows it.
For beginners, these small successes are especially important. They provide motivation during a stage when programming can otherwise feel confusing and frustrating.
Learning and producing are different goals
There is still a reasonable case for using AI when the main goal is to produce something rather than learn a particular skill.
A programmer working on a personal project may not have time to study every unfamiliar library or framework in depth. AI can help create a prototype, explain documentation or generate repetitive code.
Programmers used similar shortcuts long before modern AI tools. They searched online, copied examples, adapted code from documentation and learned by repairing things when they broke.
This approach can work, but it involves a trade-off.
You might finish the project sooner while gaining less understanding of one part of it. That may be an acceptable decision, provided you recognise the risk. Problems arise when students believe they are practising programming while consistently outsourcing the parts that require programming thought.
Before using AI, ask:
Am I trying to learn this, or am I trying to finish it?
The answer should influence how you use the tool.
Better ways to use AI while learning
AI does not have to provide the solution. It can support learning in more productive ways.
You could ask it to:
- give you a small hint without writing code
- ask you questions about your planned solution
- explain an error message in simple language
- generate additional test data
- compare two approaches after you have attempted the problem
- check your explanation of an algorithm
- create a similar practice question
- identify one area of your code that could be improved
- quiz you on code you have already written
You should also sometimes close the AI tools completely.
Try solving a problem with only the task description, your notes and official documentation. This reveals what you can actually do independently.
A simple AI learning routine
When completing a programming task, try this process:
1. Read
Read the entire problem before opening an AI tool.
Identify the required inputs, processes and outputs.
2. Plan
Write comments, pseudocode or a flowchart.
Decide which variables, conditions, loops and data structures may be needed.
3. Attempt
Write a first version yourself.
It does not need to be correct. An incomplete attempt gives you something specific to investigate.
4. Test
Use simple test data and predict the result before running the program.
Compare your prediction with what actually happens.
5. Ask
Use AI only after identifying a particular problem.
Ask focused questions such as:
“My loop stops one item too early. Help me understand why, but do not rewrite the whole program.”
6. Explain
After using AI, explain the solution in your own words.
Remove the generated code and see whether you can reproduce the important parts without looking.
7. Extend
Change the problem slightly.
Add another condition, support a new input or rewrite one section using a different method. Being able to adapt the solution is stronger evidence of understanding than simply running it.
The pause before the prompt
The most important habit may be a simple pause.
When you feel the impulse to immediately ask an AI tool, stop and consider:
- What do I already know?
- What have I tried?
- What exactly is confusing me?
- Could I test one small idea first?
- Am I looking for help, or am I trying to escape the difficulty?
Difficulty is not always evidence that something has gone wrong. In learning, difficulty is often where the learning is happening.
AI can be a valuable programming tool, but beginners need to use it carefully. It should help them think, not protect them from ever having to think.
A working program is useful.
A programmer who understands why it works is much more powerful.
