Curriculum

Students learn a basic definition of Artificial Intelligence and look at of AI in their world.

Students look at different types of tasks/games that AI may play, and consider how successful AI would be in each.

Students define what sensors are, and look at some examples of them in use. Students then learn how data from sensors can be used to do image recognition. Finally, students bring together their knowledge of image recognition and sensors to consider how a fictional human-like robot could learn to do a task.

Students design a robot that uses sensors and AI capabilities to help people in their class, school, home, or community.

Students are introduced to a common definition of Artificial Intelligence, and play a game to help discern what is considered AI and what is not.

Students learn about the process of machine learning, starting with data and datasets. They then play with Google Quick, Draw! and apply what they learned about the machine learning process to understanding how Quick, Draw! was created.

Students learn the definition for an algorithm, and then engage in an experiential activity in which they write directions for their teacher in the form of an algorithm.

Students use Teachable Machines to train a machine to recognize certain images.

Students learn what algorithmic bias is, and consider how the data used to train a model impacts its results.

Students learn how robots learn to do a sequence of tasks on their own through an experiential activity in which they act as "robots" using a learning algorithm.

Students about Generative Adversarial Networks (GANs) and how they are used to create different forms of media. After learning how these algorithms function, they consider the question "can machines be creative", and finish the lesson collectively creating an image book of generated pictures.

Students learn about different types of disinformation and misinformation that can be perpetuated by AI-generated media.

Students collectively write a story and consider how their creativity is expressed. Then, they observe ChatGPT write a similar story and compare it to their own.

Students learn the mechanics of how ChatGPT works, and how it is built. They then compare and contrast its capabilities with that of a search engine, and human creativity.

Students consider the positive and negative implications of ChatGPT in workplaces, and in school. They then come up with ideas for policies for how ChatGPT should and should not be used in schools.

Students create presentations on what they learned about how ChatGPT can be good and bad, and how they think it should and should not be used in schools.

Students engage in an experiential activity that helps define the difference between rights, and privileges.

Students explore primary source documents used throughout history to establish and protect human rights. Then, they make generalizations from what they have read to discuss how technological advances like AI have changed what rights need to be protected.

Students learn what an algorithm is through a recipe making activity. They then discuss how bias can be embedded in algorithms that are used in technology today.

Students engage in a drawing activity in which they guess who someone is based on data they are given about a person. They then consider how information stored about people online relates to privacy, and discuss privacy as a human right.

Students create their own ads, being challenged to be both persuasive and transparent. They then reflect on ads they encounter online, and whether those ads should be more transparent or not.

Students use the Ethical Matrix to think about how to responsibly design social media systems.

Students engage in a simulated senatorial debate in which they propose what policies should be included in an AI Bill of Rights

Students engage in a simulated test with different resources at their disposal to consider what is "fair" and "unfair" for school rules.

Students collectively write a story and consider how creativity is expressed. Then, they observe ChatGPT write a similar story and compare it to their own.

Students learn the mechanics of how ChatGPT works, and how it is built. They then learn about Reinforcement Learning, and how it is used to build Large Language Models.

Students learn how AI is used in 6 different government and business sectors today, and think about how it may be used sometime in the future. They then consider what careers they might be interested in.

Students learn different examples of algorithmic bias, before thinking critically about how bias could show up in their own lives.

Students discuss the pros and cons of the different ways AI may be used by each of a school's constituents: students, teachers, and administrators. They consider these costs and benefits as they begin thinking about the goals behind rules that should be put in place regarding the use of AI in their school.

Students learn how bills are written in government and the different needs of different stakeholders.

Students think about the implications of AI for the different school stakeholders and work together to write a comprehensive draft of an AI bill for their school.

Students work to agree on one AI bill to put forward to the teachers.

Students consider the importance of negotiation, the inevitability of compromise, and imperfect solutions. They do this through amending their bill (if needed) to get teacher approval before presenting their ideas to school leadership.

Students evaluate their AI bill and determine how well they think it will work. They then get a chance to make any last changes to the bill before learning how to present and advocate for legislation.

Students try to persuade school leadership to approve their AI Bill to set the rules for the use of AI in the school.

Students share their bill with other students and schools, and contribute to a database that will showcase youth’s general opinion on AI regulation in schools.

Students go outside and make quantitative observations about the natural world driven by their own interest and curiosity.

Students compare what they observed with each other. Students discover misalignment across observations limiting their ability to make comparisons, and consequently make new observation plans in groups.

Students take the observation plans they made in the previous lesson and implement them outside.

Student groups aggregate their observations into tables of data, and then graph or chart the data on paper or in Google Sheets.

Students share their graphs and charts with each other, and consider whether different observations have some form of relationship to each other. They then consider if their graphs indicate anything about the natural world.

Students learn the definitions for Climate Change and Global Warming. Then they learn how their own process of data collection relates to data collection for Climate observation.

Students learn how clustering algorithms work, before acting out the part of a data scientist creating "recommendation systems" based on clusters. They then consider the implications of recommendations systems and learn about "filter bubbles".

Students learn about different types of misinformation, how they manifest, and consider how recommendation systems might contribute to the spread of misinformation.

Students brainstorm about policies they think would help improve social media and recommendation systems and present their ideas.

Students learn the definition of Data Activism and discuss ways in which data can perpetuate bias.

Students engage in an identity activity called the Daisy Model, and consider the difference between how they see themselves, and how data is collected about them.

Students learn how to categorize, clean and format data by combining information from their Daisy Models into a group dataset in Google Sheets.

Students learn how to create basic graphs in Google Sheets using their datasets.

Students learn how art can represent data in more accessible and emotionally resonant ways. They then apply what they've learned to creating Data Drawings based on their data.

Students learn about systems of power in society, and consider how data can be used to uphold or confront those systems.

Students learn the basics of block-based programming by making a functioning scroller game.

Students incorporate elements of reinforcement learning into their scroller games and consider how an AI agent learns to play the game on its own.

Students share their scroller games with each other, and compare them. They then consider how to implement reinforcement learning in real world domains responsibly.

Students build their own image classifiers using AppInventor's custom interface, and embed their classifiers in an app.

Students learn about how bias can be perpetuated by image classification systems. They then reflect on this potential for bias in the context of the apps they are making.

Students create an App using App Inventor that interfaces with ChatGPT. They then consider the benefits and harms to society of using a tool like ChatGPT.

Students create an App using App Inventor that interfaces with DALL-E. They then consider the benefits and harms to society of using a tool like DALL-E.

The lesson covers the basics of voice AI and leads students through two tutorials. Students code Alexa to say “Hello, Moon!” and create a Space Trivia Generator. At the end, students dive deep into Alexa’s AI systems through an interview with Amazon’s Chief Alexa Evangelist.

Students learn what sensors are and how Micro:bits work. They then set up their Micro:bits to collect data and send it to a computer for logging.

Students consider what sensors they encounter in their every day lives, as well as constructive uses for sensors, and then play a game involving data visualizations.

Students brainstorm local issues related to the environment, and make a plan for collecting data with Micro:bits about an issue of their choice.

Students learn about different types of graphs, and what types of data they each represent. Students then use line graphs to represent time-based data.

Students import data from their Micro:bits into AppInventor, and create line graphs based on that data. They then prepare to share conclusions they reached from their data in a presentation.

Students learn the definitions for Climate Change and Global Warming. Then they learn how their own process of data collection relates to data collection on the Climate as a whole.

Students explore lake ice data in spreadsheet format to get familiar with it, before creating a graphing app in AppInventor to visualize the data.

Students add a trend line and a line of best fit to their graphing apps and start considering what lake ice will look like in the future.

Students add anomaly detection to their graphing apps, consider what the anomalies mean, and then remove them. Students then reconsider the lines of best fit after removing anomalies.

Students predict future trends in lake ice after having removed anomalies. They then incorporate an AI chatbot into their apps and explore using it as a companion for summarizing and analyzing data from their charts.

Students will learn the definition of Data Activism, and discuss ways in which data can perpetuate bias.

Students are introduced to basic syntax in Python, including object and variable types.

Students continue learning the basics of Python, and are introduced to the data science library Pandas.

Students will engage in an identity activity called the Daisy Model, and consider the difference between how they see themselves, and how data is collected about them by social media and governmental systems.

Students learn how to categorize, clean and format data by combining information from their Daisy Models into a group dataset in Google Sheets.

Students create graphs based on their data using Python and Pandas.

Students learn about how art can represent data in more accessible and emotionally resonant ways. They then apply what they've learned to create their own Data Drawings.

Students learn about systems of power in society, and consider how data can be used to uphold or confront those systems.

Students consider the importance of post secondary education in the US.

Students learn how to use the Pandas library in Python through the process of cleaning and manipulating a dataset on college admissions in the US.

Students learn how to create functions in Python. They then apply this knowledge by creating a function to map values in their college admissions dataset to new, cleaned values.

Students continue to work with functions, creating new ones that transform data from the college admissions dataset.

Students learn how to create graphs in Python. They then use what they've learned to visualize information about the college admissions dataset, and draw conclusions about post secondary education in the US from those visualizations.

Students continue working on data visualizations to draw conclusions about post secondary education in the US.

Students do a case study on Harvard University and its historical connections to structural racism in the US. They then consider how history can inform current structural barriers to accessing post secondary education.

Students learn how affirmative action in the US is structured, and how algorithmic decision making is considered in federal institutions.

Students learn more about affirmative action in the US, and consider the implications of how it operates given what they've learned from their own data analysis.

Students create stories based on data visualizations that communicate the personal impact of trends shown in the visualizations.

Using the Pandas library in Python, students explore a raw dataset and distill it into a more understandable and usable format.

Students format and visualize data using Python, then draw conclusions about the data they visualize.

Students create a predictive model trained on data they formatted in previous lessons.

Students put together the skills they learned in Lessons 2-4, applying each skill to the analysis of a new dataset.

Students construct narratives at the individual level that communicate the conclusions they have reached about the climate from their independent data analysis project.

Students learn about what a business is and the different industries in which businesses operate. They consider the industry in which they may want to work. And then finish the lesson brainstorming how AI might impact that industry and presenting their findings to their classmates.