In this activity, students learn how to use AI as a coding partner to design and build their own Connections-style word game. They will explore how to prompt an AI assistant, iterate on feedback, and reflect on the process of coding with AI support.
In this activity, students learn the basics of prompt engineering by experimenting with AI chatbots. They will compare simple and detailed prompts, refine their inputs through iteration, and reflect on how prompt design affects AI outputs.
In this activity, students explore how AI models learn from training data through hands-on simulations. They will see how examples shape AI predictions, test their own training sets, and reflect on how data quality affects accuracy and fairness.
In this activity, students explore how AI can recognize and respond to different communication systems. They will test AI models trained to recognize letters and sign language, then create and train their own communication system using Teachable Machine.
In this activity, students build, train, and test a machine learning model to classify music into genres using Teachable Machine. They explore how the amount and variety of training data impact the model’s accuracy, then reflect on the strengths and limitations of AI in music classification.
Students will learn how AI uses data to make decisions by practicing sorting with simple rules. They will create and test their own decision trees to classify objects.
Students learn how AI recognizes patterns in data by training themselves to identify aliens called “blorgs.” They then create and share their own alien species to practice how machines (and people) learn from examples.
In this activity, students learn about different machine learning approaches and explore how computers use data to recognize patterns and make predictions. They practice classification by building and testing decision trees.
Participants explore how generative AI responds to different prompts and learn strategies for effective prompt engineering. They practice with tools like Google’s Say What You See and then create their own “AI-as-Historical-Figure” dialogue, refining their prompts to improve outputs and reflecting on results.
Participants explore the fundamentals of artificial intelligence by learning the difference between generative and predictive AI, experiencing real-world examples, and creating their own image classification model with Teachable Machine. They leave with a stronger foundation in what AI is, how it works, and how it creates outputs.
Participants learn what a prototype is and use Bool, an AI-powered app builder, to turn their own idea into a working app. Through brainstorming, building, and refining, they see how AI supports creativity, problem-solving, and rapid design.
In this lesson, students will use Tynker's AI (artificial intelligence) body tracking model to create a fun, interactive project where they can control the weather using gestures.
In this lesson, students will use Tynker's AI (artificial intelligence) body tracking model to create an interactive game where the player balances a ball on a platform using their face. Let's see how long your students will last before the ball falls to the floor.
In this lesson, students will use Tynker's AI (artificial intelligence) hand tracking model to tickle a monster with their finger.
In this lesson, students will create an interactive project where they'll slice fruit on their screen by moving their right pointer finger.
Parents and community members explore how to build programs with natural language prompts, refine and debug AI outputs, and design an app.
Teachers learn about vibe coding and explore best practices for meaningful use of AI as a tool in the computer science classroom.
Hour of AI highlights engaging, 60-minute AI-focused activities from CodeHS and partners to introduce students to artificial intelligence concepts.
