Applied Artificial Intelligence

Overview and Time Arrangement

Within the Artificial Intelligence module, students learn the conceptual ideas, ethical considerations, real life applications, challenges and future careers of Artificial Intelligence (AI). Students begin with a series of content knowledge presentations, then progress into open discussions, NeuroMaker kit applications and finally open challenges.

This module is separated into 12 sessions of approximately 50 minutes each with corresponding lesson guides, presentations, demo code and other learning resources. Educational goals of this unit reflect the essential AI instructional goals of the AI4K12 learning standards.

Note: NeuroMaker kits must be assembled prior to the start of this unit. Additionally, it is recommended that students complete the Introduction to Programming unit prior to the beginning of this module.

Big Ideas

• What is Artificial Intelligence and what isn’t?
• What ethical considerations are there for AI and how can I start a career in this field?
• How does AI interact with real world inputs like audio and video input?
• How can I apply AI to control a mechanical hand to perform specific motions, build out sign
  language and other challenges?

Module Goals

By the end of this unit, students will be able to differentiate between AI and other kinds of technologies, and will also have gained an understanding of how to use Machine Learning in their own computer programs.

Students will start by learning about Machine Learning (ML),  the process by which AI products use data to make predictions. They will learn about algorithms, how ML algorithms work, and how data is used and manipulated to generate new insights.

Building on this foundational knowledge, students will be encouraged to think about how AI technology is used and will or could impact individuals and society at large.   What are the long term effects?  Is there a possibility for misuse?  Who are the stakeholders?

In the AI Careers lesson, students will learn about professional fields that develop and use AI technologies, and explore the pathways to get there.

In the Sensors Provide Perception and AI Representation & Reasoning lessons, students will explore the process of data acquisition & data storage.

In the Types of Machine Learning lesson, students will be introduced to the definitions of supervised, unsupervised, and reinforcement ML algorithms. The lesson will focus on supervised learning, and students will explore the impact of training their ML algorithm on unbalanced datasets (skewed towards specific data types) and the limits of supervised learning.

Activities in this module include a class-wide AI Jeopardy game, class-wide ethic debates,  paper based activities,  where students will “dissect” & “reverse engineer” existing AI products, and mBlock programming activities, where students will apply their knowledge to create ML algorithms to address image, text, speech, video, recognition needs.

The module completes with 3 different AI challenges that challenge students to integrate AI into real life situations like training their mechanical hand to play rock paper scissors and display different words in sign language.