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Create your own teaching materials with AI
With CodeRoom’s AI-powered content creation, you can automatically generate new teaching materials based on professional topic formulations. Starting from a brief description – for example, introducing a programming concept or applying it in a real-world scenario – a structured prompt is generated that is tailored to programming language, skill level, and didactic context.
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Overview of presented contents
Below you will find a selection of the most popular teaching modules and materials that can be directly used in computer science class. The content is modularly structured and supports step-by-step competence development – from basic programming concepts to complex applications and modeling tasks.
Coffee Slalom [Content]
If-Statements with Random Values in Python. Linus navigates the cafeteria, checks coffee quantities and decides under 500ml threshold.
City Traffic and Pedestrian Simulation [Content]
Prototype-First Project for City Traffic and Pedestrian Simulation with HTML/CSS/JS, State Logic, Measurement Values, and Step-by-Step AI-Assisted Expansion.
Space Invaders with frameCraft – Arcade Game Step by Step [Template]
Basics of frameCraft Games with Space Invaders
Data Analytics for Business – Company Key Figures with Python [Template]
Analysis and Visualization of Company Key Figures with Python.
Data Science Toolbox – Climate Data & City Analysis [Template]
Analysis and Visualization of Climate Data with Python Libraries such as pandas, numpy, and matplotlib.
Say the correct answer with if else [Content]
Else instead of double if: An existing solution is converted into a clear if-else decision and thus structured case distinction is practiced.
Understanding Simple Models in Everyday Life [Template]
Learn Class Modeling and Implementation.
Coding Lab: Interactive Simulations Build [Template]
Develop Interactive Web Projects with HTML/CSS/JS.
Histogram of Dice Rolls [Content]
Interactive Sandbox for Visualizing Dice Roll Frequencies and Relative Histograms.
Who do I write the task for? [Content]
Target specific groups in prompts and analyze the influence on language and understandability of AI responses.
Breakout / Arkanoid – Object Management and Collision [Content]
Breakout Game in Pygame with Block Management, Ball-Block Collision Logic, and Conditions for Game Over and Level End.
Intelligent Inquiry – The AI Workshop [Series]
Children learn to write clear AI requests, critically evaluate answers, and interact safely with AI.
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Prompting: Verify facts instead of just believing [Content]
Structure prompts so that AI statements are marked as [FACT] or [OPINION] and facts contain a verification option.
Basic Principles [Series]
Course for Optimizing AI Prompts: Structure, Word Choice, Uncertainty, Facts/Opinions, Learning Support, and Data Protection.
Dice Histogram: From Simulation to Interaction [Template]
Course on Chance, Counting, and pygame Histograms
Simulating Dynamic Systems with ODEs (Euler → RK4 → Ecosystem → Stability)
Numerical Simulation of ODEs using Euler/RK4 and Analysis of Stability.
Frogger: Autos datengetrieben erzeugen [Inhalt]
Automated enemy setup using lane_data, name schema, and nested loops for moving multiple cars.
Function Plotter – Sandbox and Step-by-Step Setup [Template]
Interaktiver Funktionsplotter mit Zoom, Pan und Achsenzeichnung.
AI Game Lab BuildFromZero: Classic Games from Scratch [Template]
Building Projects for Classic Games with frameCraft: Develop well-known game ideas like Snake, Pac-Man, Bomberman light, Space Invaders, and Tower Defense step by step from a small starting point.
Analyze company key figures from CSV [Content]
Analyze CSV data with pandas, calculate average values, determine the most profitable month, and visualize key figures with matplotlib.