Bug Arena Learning Outcomes
A page for Educators & Parents
The Bug Arena skillmap introduces students to Artificial Intelligence (AI) through a fun, iterative, game-based learning approach.
In this set of activities, students will code their own AI Bug using a variety of different algorithms. They can then compete against other AI bugs to cover the screen with paint.
This skillmap is designed for students between the ages of 8 and 15 years old and takes approximately 45 minutes (including game play) to complete. No previous coding background is required and students will not be exposed to an AI chatbot through this experience.
There are 3 learning paths available for students to choose from. At the end of the learning path, students will receive a certificate of completion and a badge.
| Tutorial | Duration | Difficulty | Description |
|---|---|---|---|
| Random Algorithm | 8 minutes | easy | Students learn how to code a Random algorithm for their bug. (solution) |
| Squares Algorithm | 10 minutes | medium | Students learn how to code an algorithm for their bug to move in concentric squares. (solution) |
| Back-and-Forth Algorithm | 11 minutes | hard | Students learn how to code an algorithm for their bug to move back and forth across the screen. (solution) |
| Tower Battle | 2 minutes | game play | Students will take the AI algorithms they have created and compete with other AI bugs in the Tower. |
Note - duration is approximate based on time to follow instructions as written. Providing extra time for creativity, debugging and game play is encouraged!
AI Skills Acquisition
The Bug Arena skillmap teaches AI in the classical sense. The earliest AI systems were built as algorithms that learned to solve problems like navigating mazes and competing in simple games. In Bug Arena, students do exactly that by designing strategies and iterating on them through observation and experimentation.
As a type of video game, Bug Arena does not use Generative AI or Machine Learning AI - rather, it focuses more on Game AI to simulate strategic decision making by non-player characters.
The learning objective of the Bug Arena lesson is to build AI literacy. This means that students must analyze how their opponent’s algorithm behaves, infer how it is “thinking”, and adapt their own strategy in response. These are the same critical skills used today to evaluate real AI systems understanding behavior, identifying bias, and reasoning about how models make decisions.
Some historical references can provide a background in these classical AI systems and strategies:
| Early AI System | Skill in Bug Arena | Earliest AI System (Link) |
|---|---|---|
| Maze-solving mice | Path strategy | Theseus (1950–1951) |
| Checkers AI | Opponent prediction | Arthur Samuel’s Checkers Program (1952) and IBM’s Deep Blue (1995)) |
| Search algorithms | Best-move selection | Depth/Breadth-First Search (1950s) |
| Shakey robot | Planning before acting | Shakey the Robot (1966–1972) |
Learning Objectives
This activity was designed to align with the AI Literacy Framework empowering students to engage and create with AI in positive, meaningful ways that further their technical skills and understanding in order to thrive in a world influenced by AI.
This activity covers the following competencies of AI Literacy:
Engaging with AI
- Evaluate whether AI outputs should be accepted, revised, or rejected
- Examine how predictive AI systems can inform and limit perspectives
Creating with AI
- Use AI systems to explore new perspectives and approaches that build upon original ideas
- Collaborate with AI systems to elicit feedback, refine results, and reflect on thought processes
- Explain how AI systems perform tasks using precise language that avoids anthropomorphism
Designing AI
- Describe how AI systems can be designed to support a solution to a community problem
- Evaluate AI systems using defined criteria, expected outcomes, and user feedback
- Describe an AI model’s purpose, intended users, and its limitations
Lesson Materials
Materials available to use for this lesson include:
- Educator Guide
- PowerPoint Presentation with talking points
- Student worksheet to print
- Student worksheet example solution
- Certificate to print
All materials are posted at: https://aka.ms/bug-arena-educator
Code definitions
On Start
The code inside the On Start block will run as soon as the program starts.
Every
The code inside the Every block will run on a specified millisecond (ms) time interval.
On Bump Wall
The code inside the On Bump Wall block will run when the bug bumps into a wall.
Turn
Will turn the bug a specified number of degrees from its current direction. A positive number is clockwise, a negative number is counter-clockwise.
Face Towards
Will turn the bug to face a specific angle in degrees where: 0 = right, 90 = down, 180 = left, 270 = up
Run After
Will wait a specified amount of time in milliseconds (ms) before running the code inside it.
Distance to Color
Will return the distance in pixels from the front of the bug to its own paint color.
Pick Random
Will return a random whole number between a minimum and maximum value.
What’s Next?
When students are finished with Bug Arena consider encouraging them to complete another skillmap.