In this lesson, students will create stories of their own with GAN text and art tools. Students will learn to use GAN-based generation tools to generate texts and form them into stories.
This lesson introduces students to how GANs work as a result of the interplay between generator and discriminator neural networks. Students will learn how the generator and discriminator compete with one another to generate text, images, videos, and more.
In this lesson, students will learn that GANs can generate art such as photographs, paintings, handwritten poetry, music, and jokes (that are kind of funny! Maybe.)
In this activity, students will learn about what STEM jobs are, why they should consider STEM jobs, and why it is important for everyone to participate in STEM jobs.
In this activity, students will learn about Holland’s work personality types and examples of jobs favored by people with each type. This lesson requires the students to use the Internet to answer a survey and explore a website.
In this lesson, students experience the process of generation and classification as they mix colors on an online platform and observe that they can create a varied palette of colors with a few input colors. Students will learn about examples of AI systems that perform classification and generation and practice distinguishing between generating and classifying AIs.
This lesson introduces students to a specific type of supervised learning called neural networks. Students will learn how neural networks work from a game in which they role-play different components of a neural network.
This lesson introduces students to how supervised machine learning can be trained to classify complex datasets based on labeled data. Students will train their teachable machine models and learn that AI can learn from labeled data. They will also revisit the idea that training AI systems with an increasing amount of data does not necessarily mitigate bias if there’s not enough diversity in the data.
In this lesson, students will daydream about what a typical work day is going to be like in 30 years. The instructor will read a pre-written script to help students meditate and guide them to share their answers.
This lesson shows students that unfairly trained AI systems can be far from objective and neutral. Students will recognize that AI systems can be unfairly trained and that there are several strategies that AI designers can use to mitigate biases in AIs.
This lesson introduces students to how decision trees, a basic form of neural networks, can be designed in multiple ways to classify a complex dataset. Students will create their own decision trees that can be used to classify various types of pastas and to classify clothing as suitable for winter or not.
This lesson further shows students that different algorithms can have different purposes for different stakeholders and that such relationships can be visually represented using an ethical matrix. Students will create their own ethical matrices for their best PB&J sandwich algorithms.
This lesson introduces students to what an algorithm is, using the making of peanut butter jelly sandwiches as an example. Students will learn that an algorithm is like a recipe and that different people tend to prefer different algorithms based on their varied interests and goals.
This unit focuses on how AI is used to create and generate content, including text and images. It discusses GANs (Generative Adversarial Networks) and AI's impact on future jobs.
This unit introduces many of the basic concepts of AI, including supervised machine learning, neural networks, classifying AI vs. Generating AI, and starts to introduce students to thinking about careers in AI.
This lesson introduces students to what an algorithm is, using the making of peanut butter jelly sandwiches as an example. Students will learn that an algorithm is like a recipe and that different people tend to prefer different algorithms based on their varied interests and goals.
Most types of AI comprise three parts - a dataset, a learning algorithm, and a prediction - each of which can be influenced by different types of bias (such as algorithmic bias) to prioritize the values of some stakeholders over others.
