This document includes a set of activities, teacher guides, assessments, materials, and more to assist educators in teaching about the ethics of artificial intelligence. These activities were developed at the MIT Media Lab to meet a growing need for children to understand artificial intelligence, its impact on society, and how they might shape the future of AI.
The goal of this curriculum is to teach middle school children about Creative Machine Learning techniques and how they can partner with AI to create art. We will explore tools and techniques such as Neural Networks and GANs across various forms of media, such as text, images, music, and videos. We frame this curriculum as an exploration of creativity, such that children’s creative and imaginative capabilities can be enhanced by innovative technologies. Further, we aim to have discussions throughout the workshop to highlight important ethical issues around generative AI, such as ownership of art and generation of hyperrealist fake media. This course is meant to be hands-on and encourages the explorative creation of art with and without AI tools.
Related: Pix2 Pix Image Transfer Activities https://mitmedialab.github.io/GAN-play/
Dancing with AI
https://dancingwithai.media.mit.edu/curriculum
Physical movement is one of the most engaging ways to interact with AI systems, but it’s rare today to see motion integrated with K-12 AI curricula. Beyond that, many middle schoolers have passionate interests in dance, art, physical movement in sports, and video games that involve physical motion (Beat Saber, Just Dance) which aren’t easy to build on in the typical creative learning environments found in classrooms. Dancing with AI is a week-long workshop curriculum in which students conceptualize, design, build, and reflect on interactive physical-movement-based multimedia experiences. Students will learn to build interactive AI projects using two new Scratch Extension tools developed for this curriculum: (1) hand/body/face position-tracking and expression-detecting blocks based on the machine learning models PoseNet & MediaPipe from Google and Affectiva’s face model, and (2) Teachable Machine blocks that allow students to train their own image- and pose-recognition models on Google’s Teachable Machine and use them as part of their projects.
AI and Data Privacy Curriculum for Middle School
https://docs.google.com/document/d/17x1vwEAS-NT5OoFjaxwPliUrmhrt2LChN0xtWntRr68/edit
Our goal is to make the technical and contextual concepts related to data privacy approachable and engaging for youth. Today, regulators are trying to determine what privacy considerations they should account for in the many products and services that are created for youth