Dear BTE & DRE-ians,
First of all, congratulations on completing Step 7 of your Slider Game project! You’ve successfully created your own Python game — an achievement that shows how far you’ve come in learning to code.
Now, let’s take a step forward into an exciting new experience — learning to code with AI.
In this session, we explored how Artificial Intelligence can support us as a learning partner — not to code for us, but to help us think, debug, and create better. Throughout today’s activity, we focused on four different roles of AI in programming.
1. AI for Flowchart Generation and Code Understanding
We began by revisiting the completed Step 7 of the Slider Game. Using GPT-based tools, students explored how to comprehend the logic behind their Python code and then derive a flowchart from it.
Flowcharting is a crucial part of computational thinking — it helps us visualize abstract logic and understand the sequence of decisions and actions within our program. By having AI explain the code flow, students learned how to map their code into structured diagrams that represent real-world logic.
2. AI for Troubleshooting and Debugging
Next, students explored how AI can assist in debugging. In Step 7, we noticed two common issues:
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The score counter was upcounting continuously.
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The collision detection was adding multiple scores at once.
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By prompting AI for guidance, students learned how to correct the logic — ensuring the game counts down properly and increases the score by only one per collision.
This activity demonstrated how AI can serve as a learning buddy, guiding students to identify, understand, and fix programming errors while reinforcing their knowledge of conditional statements, loops, and Boolean flags.
3. AI for Code Generation and Modification
In the third task, students practiced AI-assisted code generation. They were challenged to modify their existing Slider Game without changing its core gameplay mechanics.
By using AI to suggest new features — such as different movement behaviors, boundary limits, or score displays — students learned how to prompt effectively, evaluate the AI-generated code, and integrate improvements meaningfully.
This step emphasized creativity with control — learning how to enhance existing code while maintaining logical integrity.
4. Coding for AI and with AI
The key takeaway from today’s activity is to encourage you to learn to code with AI, not just getting codes by AI.
While AI can generate code, meaningful learning happens when students engage with the logic — understanding why and how it works. AI becomes a partner in exploration, enabling students to think critically, problem-solve, and apply what they learn to real-world challenges.
Today’s session introduced a new dimension of programming — blending Python logic with AI literacy. Students discovered that AI isn’t just a shortcut; it’s a tool for concept reinforcement, debugging, and idea expansion.
As we move forward, remember: the goal isn’t just to write code — it’s to understand it, modify it, and make it better. And with AI as your learning partner, that journey becomes even more exciting.
See you all next week =)


































































