This lesson is intended for classrooms that want to show the entire How AI Works video series in a single day. It is not intended to be taught in sequence with the other lessons in this unit, which introduces each video one day at a time.

Students follow along with each video by matching vocabulary from the video, then answering a reflection question about each video. The lesson plan and slides are very sparse and open-ended to allow for improvisation and customization to fit your classroom.

Background: How Neural Networks Work
Pairs with: AI & The Environment, AI & Dance, AI & Facial Recognition
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: GANs are often used when machines create new images or video content. This lesson explores how each work
Pairs with: AI & Deepfakes
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: An introductory hands-on deep dive into the technical details about how machines hold the information that they’ve learned. In the end, students will teach others what they have learned
Pairs with: Everything
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Background: How Neural Networks Work
Pairs with: AI & Ethics, AI & Drawing
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

With the release of ChatGPT in November 2022, the field of higher education rapidly became aware that generative AI can complete or assist in many of the kinds of tasks traditionally used for assessment. This has come as a shock, on the heels of the shock of the pandemic. How should assessment practices change? Should we teach about generative AI or use it pedagogically? If so, how? Here, we propose that a set of open educational practices, inspired by both the Open Educational Resources (OER) movement and digital collaboration practices popularized in the pandemic, can help educators cope and perhaps thrive in an era of rapidly evolving AI. These practices include turning toward online communities that cross institutional and disciplinary boundaries. Social media, listservs, groups, and public annotation can be spaces for educators to share early, rough ideas and practices and reflect on these as we explore emergent responses to AI. These communities can facilitate crowdsourced curation of articles and learning materials. Licensing such resources for reuse and adaptation allows us to build on what others have done and update resources. Collaborating with students allows emergent, student-centered, and student-guided approaches as we learn together about AI and contribute to societal discussions about its future. We suggest approaching all these modes of response to AI as provisional and subject to reflection and revision with respect to core values and educational philosophies. In this way, we can be quicker and more agile even as the technology continues to change.

We give examples of these practices from the Spring of 2023 and call for recognition of their value and for material support for them going forward. These open practices can help us collaborate across institutions, countries, and established power dynamics to enable a richer, more justly distributed emerging response to AI.

This lesson centers around the How AI Works: What is Machine Learning? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson students are introduced to a form of artificial intelligence called machine learning and how they can use the Problem Solving Process to help train a robot to solve problems. They participate in three machine learning activities where a robot - AI Bot - is learning how to detect patterns in fish.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

Everyone will be impacted by AI in daily life and in the workplaces of the future. It is critical for all students to have fundamental knowledge of AI and to understand AI’s potential for good and harm. The Daily-AI program will jumpstart your readiness for AI and give you the tools you need to prepare for the AI-enabled world.

The Daily-AI workshop, designed by MIT educators and experienced facilitators, features hands-on and computer-based activities on AI concepts, ethical issues in AI, creative expression using AI, and how AI relates to your future. You will experience training and using machine learning to make predictions, investigate bias in machine learning applications, use generative adversarial networks to create novel works of art, and learn to recognize the AI you interact with daily and in the world around you.

This curriculum is currently being piloted through NSF EAGER Grant 2022502. This is a joint venture between the Personal Robots Group at the MIT Media Lab, MIT STEP Lab, and Boston College.

Contents:
Unit 0: What is AI?
- What is AI
- Algorithms as Opinions
- Ethical Matrix
- Decision Trees
- Investigating Bias
Unit 1: Supervised Machine Learning
- Introduction to Supervised Machine Learning
- Neural Networks
- Classifying AI vs. Generating AI
Unit 2: GANs
- What are GANs?
- Generator vs. Discriminator
- Unanticipated Consequences of Technology
- AI Generated Art
- What are Deepfakes?
- Spread of Misinformation
- Generate a Story
Unit 3: AI + My Future
- Environmental Impact of AI
- Redesign YouTube
- Careers in AI

In this lab, students will train three simple neural networks using the AP Gridworld software and a perceptron neural network. The lab culminates when students have trained an autonomous car to drive around simple cars without crashing.

This lesson centers around the How AI Works: Neural Networks video from the How AI Works video series. Watch this video first before exploring the lesson plan.

Students learn how neural networks work. They first discuss an example of an experience that recommends things to you. They then use a widget that recommends videos based on one person. Students watch a video explaining neural networks. They use an updated widget to adjust the weights of each person. Finally, students discuss the need for diverse perspectives when creating recommendation systems.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

In CampGPT, educators experimented with generative AI-enabled tools like chatbots and image generators to learn and explore together. Their work and insights have been compiled in the Open Prompt Book from CampGPT. Throughout this prompt book, you’ll learn more about generative AI, what educators use it for, and key tips and tricks. The “Try It Out” links enable you to try the prompts in your own account (links for ChatGPT and Bard are provided). This means that, if you like an idea, you can start with the prompt in the book and then continue interacting with a chatbot to further adapt the output to your needs. In addition to the open prompts, we’ve included quotes from the educators from whom the ideas and prompts in this book were crowdsourced.

This lesson centers around the How AI Works: Privacy and the Future of Work video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In small groups, students conduct research using articles and videos that expose ethical pitfalls in an artificial intelligence area of their choice. Afterward, each group develops at least one solution-oriented principle that addresses their chosen area. These principles are then assembled into a class-wide “Our AI Code of Ethics” resource (e.g. a slide presentation, document, or webpage) for AI creators and legislators everywhere.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers have developed a new method for teaching self-driving cars how to respond to emergencies. Unlike other approaches, which teach cars to respond according to hard and fast rules, this new method trains onboard computers to react like humans do. That unique ability could make self-driving cars vastly quicker at recognizing and avoiding potential accidents. Human drivers react instinctively to road hazards—whether that’s a car that brakes suddenly or a cyclist who rushes into traffic. It’s an ability that comes from years of experience and one that’s often taken for granted. As AI experts have learned, teaching computers to do the same is notoriously difficult. Rule-based methods provide basic functionality. But they tend to be very time-consuming and can’t account for unforeseen emergencies—two tremendous liabilities for self-driving cars..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers from China have pooled together some of the most powerful techniques in machine learning to create the ultimate control system. Successfully deployed in AI-regulated hybrid electric vehicles, the framework could grant other autonomous systems unprecedented levels of control and foresight. Machine learning is booming. And arguably the most popular technique in this branch of artificial intelligence is deep reinforcement learning. Loosely modeled after our brains’ reward system, deep reinforcement learning has enabled machines to reach or even surpass human-level performance in various tasks. Those tasks range from the trivial, like playing Go or video games, to the possibly life-saving, such as detecting firearms from video. But deep reinforcement learning algorithms have their limitations. For one, they generally lack the ability to take lessons learned in one task and apply them to another..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

The People's Guide to Artificial Intelligence is an educational and speculative approach to understanding artificial intelligence (AI) and its growing impact on society. The 78-page booklet explores the forms AI takes today and the role AI-based technologies can play in fostering equitable futures. The project resists narratives of dystopian futures by using popular education, design, and storytelling to lay the groundwork for creative imaginings.

Short Description:
This book is intended to be a pragmatic guide to helping able citizen data scientists to utilize common frameworks and tools to create conversational artificial intelligence experiences for users.

Long Description:
This book is intended to be a pragmatic guide to helping able citizen data scientists to utilize common frameworks and tools to create conversational artificial intelligence experiences for users.

Word Count: 4670

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Short Description:
This book provides an overview of the field of natural language processing and recently developed methods, presuming only knowledge of computing with data structures.

Long Description:
This book allows a reader with a background in computing to quickly learn about the principles of human language and computational methods for processing it. The book discusses what natural language processing (NLP) is, where it is useful, and how it can be deployed using modern software tools. It covers the core topics of modern NLP, including an overview of the syntax and semantics of English, benchmark tasks for computational language modelling, and higher level tasks and applications that analyze or generate language. It takes the perspective of a computer scientist. The primary themes are abstraction, data, algorithms, applications and impacts. It also includes history and trends that are important for understanding why things have been done the way that they have.

Word Count: 70048

ISBN: 978-1-7376595-1-8

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Los proyectos de esta guía utilizan un enfoque centrado en los alumnos para el aprendizaje. En lugar de solo aprender acerca de la IA con videos o conferencias, los alumnos que realizan estos proyectos son participantes activos en la exploración de ella. En el proceso, los estudiantes trabajarán directamente con tecnologías innovadoras de IA, participarán en actividades no en línea para ampliar su comprensión de cómo funcionan las tecnologías de IA y crearán diversos productos auténticos
desde modelos de aprendizaje automático hasta videojuegos— para demostrar su aprendizaje.

PROYECTO 1: Programación con aprendizaje automático
PROYECTO 2: Jugadores asistidos por IA en videojuegos
PROYECTO 3: Uso de la IA para planificar movimientos robóticos
PROYECTO 4: El aprendizaje automático como un servicio

Esta guía ofrece proyectos centrados en los alumnos que pueden enseñar directamente estándares de áreas de estudio en conjunto con comprensiones fundamentales de los que es la IA, cómo funciona y cómo impacta a la sociedad. Fueron considerados varios enfoques clave para diseñar estos proyectos. Entender estos enfoques sustentará su comprensión y la implementación de los proyectos de esta guía, así como su trabajo para diseñar más actividades que integren la enseñanza sobre la IA en su plan de estudios.

PROYECTO 1: Lo que la IA hace bien y lo que no hace tan bien
PROYECTO 2: Datos de entrenamiento y aprendizaje automático
PROYECTO 3: Los sentidos comparados con los sensores
PROYECTO 4: Navegación e IA

Esta guía ofrece proyectos centrados en los alumnos que pueden enseñar directamente estándares de áreas de estudio en conjunto con comprensiones fundamentales de los que es la IA, cómo funciona y cómo impacta a la sociedad. Fueron considerados varios enfoques clave para diseñar estos proyectos. Entender estos enfoques sustentará su comprensión y la implementación de los proyectos de esta guía, así como su trabajo para diseñar más actividades que integren la enseñanza sobre la IA en su plan de estudios.

PROYECTO 1: Chatbots de IA
PROYECTO 2: Desarrollo de una mirada crítica
PROYECTO 3: Uso de la IA para resolver problemas del medio ambiente
PROYECTO 4: Leyes para la IA