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# System prepended metadata

title: Secondary school AI

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**Curriculum Overview: Introduction to Generative AI for Secondary School Students**

**Grade Levels:** Form 3 - Form 5

**Duration:** 20 weeks (10 sessions per semester, 1 sessions per week)

**Subject:** Computer Science & Creative Arts

**Prerequisites:** Basic computer skills

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### Semester 1: Fundamentals of Python and Introduction to Generative AI (10 Weeks)

#### Week 1-2: Introduction to Python
- **Objective**: Understand the basics of Python programming.
- **Topics**: Variables, data types, operators, basic input/output.
- **Activities**: Write simple Python scripts, practice exercises on data types and operators.
- **Assessment**: Quiz on Python basics.

#### Week 3-4: Control Structures and Functions in Python
- **Objective**: Master control structures and functions in Python.
- **Topics**: Conditional statements, loops, function definition, scope, and parameters.
- **Activities**: Developing small functions, practicing loops and conditionals with Python challenges.
- **Assessment**: Project on creating a calculator or similar utility using functions.

#### Week 5-6: Data Structures and File Handling
- **Objective**: Learn to use lists, dictionaries, sets, and file operations.
- **Topics**: Data structures operations, reading and writing to files, understanding file formats (like CSV, 
JSON).
- **Activities**: Assignments involving data manipulation and file processing.
- **Assessment**: Project on data analysis from a given dataset.

#### Week 7: Introduction to AI and Machine Learning
- **Objective**: Introduce AI, machine learning, and neural networks.
- **Topics**: Overview of AI, difference between AI and machine learning, introduction to neural networks.
- **Activities**: Discuss practical examples of AI in everyday life, interactive AI demonstrations.
- **Assessment**: Short essay on potential impacts of AI.

#### Week 8-9: Basics of Generative AI
- **Objective**: Understand the concept of generative models.
- **Topics**: Generative vs. discriminative models, introduction to Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Transformer Models.
- **Activities**: Interactive sessions with simple generative AI applications, exploratory activities using online tools.
- **Assessment**: Group discussions evaluating different generative models.

#### Week 10: Python Libraries for Generative AI
- **Objective**: Introduce Python libraries and API useful in Generative AI.
- **Topics**: MidJourney, Stable Diffusion, GPT-4, Gemini-Pro.
- **Activities**: Install libraries, explore pre-existing models, initial experimentation.
- **Assessment**: Worksheet on functions and common uses of each library.

### Semester 2: Practical Applications of Generative AI and Agent-Based Modeling (10 Weeks)

#### Week 1-2: Generating Images with Generative AI
- **Objective**: Learn to generate images using generative models.
- **Topics**: Advanced prompt writing techniques for image generation, using public APIs.
- **Activities**: Work on projects to generate art and images.
- **Assessment**: Create a portfolio of generated images.

#### Week 3-4: Generating Text with Generative AI
- **Objective**: Grasp the processes for generating human-like text.
- **Topics**: Text generation using public GPT models, introduction to transformers.
- **Activities**: Experiment with text-generation libraries and APIs.
- **Assessment**: Creative writing assignment using AI-generated content.

#### Week 5-6: Understanding Agent-Based Models
- **Objective**: Understand what agent-based GPTs are and how they are used.
- **Topics**: Introduction to agent-based models (ABMs), agents, environments, and rulesets.
- **Activities**: Simple simulations with agents, analysis of ABM examples.
- **Assessment**: Essay on the importance and applications of ABMs.

#### Week 7-8: Implementing a Basic Agent-Based Model
- **Objective**: Develop a basic agent-based simulation.
- **Topics**: Programming agents and environments, simulating interactions, collecting results, and iterative improvement techniques.
- **Activities**: Create a basic ABM simulation in Python.
- **Assessment**: Presentation and demonstration of ABM projects.

#### Week 9: Ethical Considerations and the Future of Generative AI
- **Objective**: Discuss the ethical implications of using Generative AI.
- **Topics**: Biases in AI, privacy concerns, the future possibilities of Generative AI.
- **Activities**: Debates and discussions on AI ethics.
- **Assessment**: Write and present an argumentative essay on an ethical issue in Generative AI.

#### Week 10: Capstone Project and Review
- **Objective**: Integrate the knowledge and skills developed during the course into a final project.
- **Topics**: Review of key concepts, project design, and planning.
- **Activities**: Working in teams to create a proposal and develop a functioning Generative AI project.
- **Assessment**: Presentation and demonstration of capstone projects.

Throughout both semesters, homework and reading assignments should also be provided to reinforce lessons and extend knowledge. The balance between theory and practical application is essential, as hands-on experience is pivotal in understanding such advanced topics. Continuous formative assessments such as quizzes, class participation, and coding exercises are recommended to monitor progress. Bloom's taxonomy is addressed by starting with basic understanding and advancing to creating, which involves the highest levels of cognitive processes.