Generative AI

Students will be able to understand and apply generative AI techniques to solve real-world problems, culminating in the creation of a generative AI-driven project relevant to industry needs.

Module 1: Introduction to Generative AI

Lesson Plan:

• Learning Objectives: Understand what generative AI is and its applications.
• Real World Example: Generative AI in content creation (e.g., text, images, music).
• Activities:
  1. Watch a video introduction to generative AI.
  2. Discuss examples of generative AI in various industries.
  3. Explore a simple generative AI tool (e.g., ChatGPT for text generation).
• Discussion Questions:
  • How can generative AI be used to address real-world challenges?
  • What are some ethical considerations of using generative AI?
• Ways to Expand Learning:
  • Research different generative AI models and their uses.
  • Join online forums or groups focused on AI innovation.

Module 2: Fundamentals of Generative AI Models

Lesson Plan:

• Learning Objectives: Learn about different types of generative AI models and their functions.
• Real World Example: Generative Adversarial Networks (GANs) for image synthesis.
• Activities:
  1. Read an article on GANs and their applications.
  2. Watch a tutorial on creating a simple GAN model.
  3. Discuss how GANs can be applied in various industries.
• Discussion Questions:
  • How do GANs differ from other generative models?
  • What are the limitations of GANs in practical applications?
• Ways to Expand Learning:
  • Experiment with pre-built GAN models.
  • Explore advanced topics in generative AI research.

Module 3: Designing a Generative AI Project

Lesson Plan:

• Learning Objectives: Design a project plan for a generative AI application.
• Real World Example: Creating a chatbot for customer service.
• Activities:
  1. Define the problem your project will address.
  2. Outline the steps needed to develop your AI solution.
  3. Create a project proposal with goals, research, and implementation strategies.
• Discussion Questions:
  • What are the key components of a successful AI project proposal?
  • How can you ensure your project aligns with industry needs?
• Ways to Expand Learning:
  • Review case studies of successful generative AI projects.
  • Seek feedback from industry experts on your project proposal.

Module 4: Developing and Training Generative AI Models

Lesson Plan:

• Learning Objectives: Understand the development and training processes of generative AI models.
• Real World Example: Training a text generation model for content creation.
• Activities:
  1. Follow a tutorial on training a generative model using a dataset.
  2. Experiment with adjusting model parameters and evaluating results.
  3. Document your development process and findings.
• Discussion Questions:
  • What challenges might you face while training a generative AI model?
  • How can you improve the performance of your model?
• Ways to Expand Learning:
  • Participate in AI development workshops.
  • Explore additional resources on model optimization.

Module 5: Evaluating Generative AI Solutions

Lesson Plan:

• Learning Objectives: Evaluate the effectiveness and impact of generative AI solutions.
• Real World Example: Assessing the performance of an AI-generated marketing campaign.
• Activities:
  1. Use evaluation metrics to assess your AI model’s performance.
  2. Collect feedback from test users and analyze results.
  3. Prepare a report summarizing your findings and recommendations.
• Discussion Questions:
  • What metrics are most useful for evaluating generative AI solutions?
  • How can you incorporate user feedback into improving your project?
• Ways to Expand Learning:
  • Study advanced evaluation techniques.
  • Network with professionals who specialize in AI evaluation.

Module 6: Presenting Your Generative AI Project

Lesson Plan:

• Learning Objectives: Develop and deliver a presentation of your generative AI project.
• Real World Example: Creating a presentation for a pitch to potential investors.
• Activities:
  1. Design a presentation highlighting your project’s objectives, process, and results.
  2. Practice delivering your presentation to peers for feedback.
  3. Revise your presentation based on feedback and prepare for the final presentation.
• Discussion Questions:
  • What are the key elements of an effective project presentation?
  • How can you effectively communicate the value of your AI solution?
• Ways to Expand Learning:
  • Attend presentations or webinars on AI innovation.
  • Join a public speaking group to improve presentation skills.