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Introduction to AI for High School

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Intro to AI
Description

In this lesson, students will explore the concept of intelligence, engaging with various resources to make their own determination on the level of intelligence of AI models.

Objective

Students will be able to:

  • Define intelligence as it will be used in this course
  • Describe the Turing Test, its uses, and where it falls short
  • Engage with various resources to gauge the intelligence level of AI tools
Description

In this lesson, students will explore the difference between generative and predictive AI by engaging with different AI tools and reflecting on the output they provide.

Objective

Students will be able to:

  • Describe the difference between generative and predictive AI tools
  • Explain the boundaries of generative and predictive AI tools
Description

In this lesson, students learn about the underlying technology that generative AI applications use - Large Language Models.

Objective

Students will be able to:

  • Describe what Large Language Models and Multimidal Models are at a high level
  • Students will be able to prompt quality outputs from LLMs
  • Students will be able to use LLMs to produce code
Description

In this lesson, students will learn about prompting techniques that can help maximize the quality of outputs by AI.

Objective

Students will be able to:

  • Describe what prompt engineering is and common techniques
  • Apply common prompt engineering techniques to produce better outputs
Description

In this lesson, students will explore the leadership of AI companies, examine the consequences of exposing biases in AI, and engage in reflective discussions.

Objective

Students will be able to:

  • Explain the diversity issues that surround the AI industry
  • Explain how the data used to train models is labeled
  • Engage in meaningful discourse about the state of AI and who is allowed to work on the technology
Machine Learning
Description

In this lesson, students explore machine learning and visualize how neural networks work.

Objective

Students will be able to:

  • Define machine learning, neural networks, and convolutional neural networks
  • Understand the process of a convolutional neural network and its inner workings
Description

In this lesson, students will explore and engage with the supervised learning method through the use of Google’s Teachable Machine.

Objective

Students will be able to:

  • explain how supervised learning is used to train an AI model
  • explain the benefits of using a supervised learning method
Description

In this lesson, students will explore and engage with the unsupervised learning method of machine learning. They will get a chance to act as a computer and will explore 3 different Google Experiments to explore how unsupervised learning can be used to categorize large amounts of data.

Objective

Students will be able to:

  • explain how unsupervised learning is used to train an AI model
  • explain the benefits of using an unsupervised learning method
Description

In this lesson, students will learn about the reinforcement learning method of machine learning through both on an offline activities.

Objective

Students will be able to:

  • explain how reinforcement learning is used to train an AI model
  • explain the benefits of using a reinforcement learning method
Training AI Models
Description

In this lesson, students will engage with the actual training of AI models. They will use Teachable Machine to create a model that will then be used inside a program in the CodeHS editor.

Objective

Students will be able to:

  • Explain how AI models are created from training data
  • Use Teachable Machine to train a model that will be used in an HTML program
Description

In this lesson, students will explore the use cases for AI in various industries by using image and audio data sets to create Teachable Machine models.

Objective

Students will be able to:

  • Explore the use cases of AI in industries such as healthcare, fashion, animal sciences, sales, and music
  • Use Teachable Machine to engage with data in various ways
Description

In this lesson, students will explore how biased training data can affect an AI model’s output.

Objective

Students will be able to:

  • Explain the concept of biased training data
  • Describe how the use of biased training data can affect an AI model
Risks of AI
Description

In this lesson, students discuss important ethical issues related to the development of Artificial Intelligence.

Objective

Students will be able to:

  • Articulate their position on ethical issues in AI.
  • Explain how humans can be biased and the impact that they have.
Description

In this lesson, students will explore the security risks that AI poses, such as image prompt injections and hallucinations.

Objective

Students will be able to:

  • Explain what hallucinations are and their dangers
  • Describe what prompt injections are and how they can cause harm
  • Demonstrate how AI safeguards can be bypassed
Description

In this lesson, students will learn how deepfakes are created and how they can be used to spread misinformation.

Objective

Students will be able to:

  • Define and better identify deepfakes
  • Explain the challenges that deepfakes present
Description

In this project, students engage in a debate to explore and express their views on the wider societal implications of AI.

Objective

Students will be able to:

  • Discuss the legal challenges that surround AI
  • Demonstrate the ability to take a stance on AI and make arguments