A hands-on interactive course covering ethical principles, privacy techniques, and best practices for responsible generative AI.
Activities for the WSQ course Responsible Generative AI Basics (TGS-2025060472).
This repository contains the activities for the WSQ course Responsible Generative AI Basics (TGS-2025060472), providing a foundational understanding of responsible generative AI practices through 14 interactive web-based activities across three Learning Units. Learners explore ethical principles, privacy techniques, and best practices for developing and deploying generative AI systems responsibly.
- 14 interactive activities — hands-on simulators, quizzes, explorers, and case study tools
- Dark/Light theme toggle across all activities
- No installation required — runs entirely in the browser
- AI-powered demos using Google Gemini API for data anonymisation simulations
- Covers real-world topics — prompt injection, differential privacy, XAI (LIME/SHAP), resource allocation ethics
| Activity | Description | Link |
|---|---|---|
| Ethical Dilemma Simulator | Realistic AI ethical dilemma scenarios across healthcare, hiring, journalism, and education | Launch |
| AI Principles Matching Quiz | Match ethical principles to definitions, examples, and violation scenarios | Launch |
| Prompt Injection Playground | Attack simulated AI systems with 3 levels of increasing defences | Launch |
| Decision Framework Tool | Step-by-step ethical decision-making framework for AI scenarios | Launch |
| Scepticism Checker | Evaluate AI-generated content with a structured checklist and scepticism score | Launch |
| Cognitive Threat Matrix | AI risk scenario generator exploring generative AI threats through cognitive biases (Gemini-powered) | Launch |
| Activity | Description | Link |
|---|---|---|
| Data Anonymisation Demo | Interactive demo of anonymisation techniques powered by Google Gemini API | Launch |
| Python Anonymisation Pipeline | Build a step-by-step anonymisation pipeline using Python techniques | Launch |
| Privacy Policy Generator | Create a comprehensive AI data handling policy with an interactive wizard | Launch |
| Activity | Description | Link |
|---|---|---|
| AI Comparison Matrix | Compare AI systems across IP compliance, privacy, environmental impact with radar charts | Launch |
| Differential Privacy Explorer | Experiment with Laplace mechanism, randomised response, and privacy budgets | Launch |
| Explainable AI (XAI) Explorer | Understand LIME and SHAP through an interactive loan approval model | Launch |
| AI Resource Allocation Simulator | Simulate Utilitarian, Egalitarian, and Prioritarian allocation paradigms | Launch |
| Ethics Case Study Analyzer | Analyse real-world AI ethics case studies across multiple domains | Launch |
| Layer | Technology |
|---|---|
| Frontend | HTML5, CSS3, Vanilla JavaScript |
| AI Integration | Google Gemini API (gemini-2.0-flash) |
| Theming | CSS Custom Properties (dark/light) |
| Deployment | GitHub Pages |
| Typography | Georgia / Times New Roman serif stack |
┌─────────────────────────────────────────────┐
│ GitHub Pages (CDN) │
├─────────────────────────────────────────────┤
│ index.html (root) │
│ (Course Hub - Main Page) │
├──────────┬──────────┬───────────────────────┤
│ LU1 │ LU2 │ LU3 │
│ Ethical │ Privacy │ Best Practices │
│Principles│Techniques│ │
├──────────┼──────────┼───────────────────────┤
│ 6 apps │ 3 apps │ 5 apps │
└──────────┴────┬─────┴───────────────────────┘
│
┌───────▼────────┐
│ Gemini API │
│ (client-side) │
└────────────────┘
TGS-2025060472--AI-Ethics/
├── index.html # Course Hub (main landing page)
├── lu1-ethical-principles/ # Learning Unit 1
│ ├── ethical-dilemma/
│ ├── principles-quiz/
│ ├── prompt-injection/
│ ├── decision-framework/
│ ├── skepticism-checker/
│ └── cognitive-threat-matrix/ # Uses Gemini API
├── lu2-privacy/ # Learning Unit 2
│ ├── dataprivacy/ # Uses Gemini API
│ ├── anonymizer-python/
│ └── privacy-policy/
├── lu3-best-practices/ # Learning Unit 3
│ ├── ai-comparison/
│ ├── differential-privacy/
│ ├── xai-explorer/
│ ├── ai-resource-allocation/
│ └── ethics-case-study/
├── course_activities.md # Full course content & learning objectives
└── CLAUDE.md # AI assistant guidance
No build step or installation required. All activities run directly in the browser.
Option 1: Live Demo
Visit the Course Hub
Option 2: Run Locally
git clone https://github.com/alfredang/TGS-2025060472--AI-Ethics.git
cd TGS-2025060472--AI-Ethics
python3 -m http.server 8000Then open http://localhost:8000 in your browser.
Note: The Data Anonymisation Demo and Cognitive Threat Matrix require a Google Gemini API key entered at runtime.
- Fork the repository
- Create your feature branch (
git checkout -b feature/new-activity) - Commit your changes (
git commit -m 'Add new activity') - Push to the branch (
git push origin feature/new-activity) - Open a Pull Request
Tertiary Infotech Academy Pte. Ltd.
- Google Gemini API for AI-powered data generation
- GitHub Pages for hosting
- Course framework aligned with Singapore's AI Governance Framework and EU AI Act
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