AI Literacy for Everyone

Identify the key qualities that define artificial intelligence and classify examples as ai or not ai, explaining why.

Explore how autonomy and adaptivity define human and machine intelligence, and see how AI imitates intelligent behavior through technologies like search engines, while calculators lack autonomy and adaptivity.

Discover how AI changes our approach by detecting plant diseases from leaf images with Plant Village and Nuru, using 60,000 images to predict diseases autonomously and improve with more examples.

Design your own ai classifier across three lessons by choosing a theme, collecting images, and recording responses on a worksheet, with optional group work and a final presentation.

Explain what machine learning is, identify its key parts, describe what each part does and how they work together, and explain how data affects AI performance.

Machine learning builds current AI tools by teaching machines to analyze data, find patterns, and make predictions using four building blocks: model, data, training, and trained model.

Design an AI classifier with near pocket by creating a project, adding photos for two groups (scissors and colored pencils), training the model, and testing its classification.

Understand that AI tools mislabel images by patterns, not content, and that limited or unfamiliar data can produce errors—what comes in comes out, garbage in, garbage out.

Identify the items you will classify and describe how you will gather data for your chosen theme, with the option to add more photos to improve your AI classifier.

Explore how machines train, from a model analyzing data to find patterns, and explain algorithms, training steps, and how humans and machines process information differently.

Learn the describer drawer game, where two players use verbal instructions to recreate a drawing made of 3–5 basic shapes, with or without a teacher, two-minute rounds, and role swaps.

In AI literacy for everyone, the describer draw game pairs students to use verbal instructions only to recreate a simple drawing composed of three geometric shapes, challenging clarity and precision.

Explore step-by-step thinking and how it defines an algorithm by turning everyday tasks, like making tea, into clear instructions that a machine can follow.

Explore the human learning algorithm through a pattern game that starts with an educated guess, then checks results and adjusts focus to distinguish group a from group b.

Learn how machines simulate learning by converting features into numbers, calculating predictions, measuring error, and adjusting equations to train models that distinguish groups.

Identify the features that distinguish your groups, such as color, shape, and size, from your lesson data. Collect more data to refine your AI classifier.

Explain how data bias leads to wrong predictions and how humans can reduce harm. Describe simple ways to protect people's privacy and connect bias, privacy, and responsibility to responsible AI.

Build a model to distinguish bears from dogs within three minutes by selecting up to ten bears and ten dogs, then train and pass all test cases.

Explore how human bias shapes data and causes algorithmic bias in AI tools. Understand real-world consequences, from misdiagnoses to safety failures in self-driving cars, and consider who bears responsibility.

Humans are responsible for ai outcomes. Creators must use diverse data and acknowledge bias and limits to reduce harm, while users should double-check results and share feedback.

Explore data privacy in AI by distinguishing public and private data, securing permission, and protecting personal information in training examples like a bear and dog classifier.

Identify data bias and data privacy concerns and embrace your role in shaping responsible ai as a responsible creator and critical user.

Expand feature variety to reduce classifier bias, seek feedback from others, and explain and mitigate bias as you complete the final project on responsible AI.