Generative AI: Introduction and Applications Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to Generative AI

Estimated time: 5 hours

• What is Generative AI
• Comparison with traditional AI models
• History and key breakthroughs in generative AI
• Identifying common generative AI tools: ChatGPT, DALL·E, Midjourney

Module 2: Generative AI in Action

Estimated time: 5 hours

• Use cases in content creation and marketing
• Applications in healthcare diagnostics
• Real-world problem solving with generative AI
• Evaluating industry-specific implementations

Module 3: Tools & Technologies

Estimated time: 5 hours

• Large language models (LLMs) and how they work
• Diffusion models and image generation
• Transformers and multimodal AI systems
• Exploring platforms for text, image, and code generation

Module 4: Ethical Considerations & Responsible Use

Estimated time: 5 hours

• Understanding deepfakes and misinformation risks
• Copyright and intellectual property concerns
• Bias in generative AI models
• Practices for responsible AI deployment

Module 5: The Future of Generative AI

Estimated time: 5 hours

• Evolving AI architectures and research trends
• Emerging tools and creative applications
• Impact on automation and human work
• Predicting industry transformations

Module 6: Final Project

Estimated time: 10 hours

• Develop a use case proposal for generative AI in your field
• Analyze ethical implications of your proposed application
• Present strategies for integrating AI tools into workflows

Prerequisites

• Familiarity with basic digital tools
• No prior technical or coding experience required
• Curiosity about AI and innovation

What You'll Be Able to Do After

• Explain the core concepts of generative AI clearly
• Identify key generative AI tools and their applications
• Evaluate real-world use cases across industries
• Apply ethical reasoning to AI deployment scenarios
• Integrate generative AI insights into professional workflows