This is a free, foundational online learning series for any teacher and educator interested in the groundbreaking world of artificial intelligence (AI) and its transformative potential in education. Partners Code.org, ETS, ISTE and Khan Academy are offering engaging sessions with renowned experts that will demystify AI, explore responsible implementation, address bias, and showcase how AI-powered learning can revolutionize student outcomes. Join us on this journey of exploration and empowerment, and unlock the future of teaching with and about AI.

This lesson centers around the How AI Works: Equal Access and Algorithmic Bias video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson, students will practice cropping images to uncover the bias underlying the Twitter cropping algorithm. Then, students will read and watch a video about the discovery of this biased algorithm. Finally, students will discuss ways to recognize and reduce bias along with analyzing Twitter's response to the allegations of bias in their cropping algorithm.

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

The following is a Generative AI instructional framework that seeks to warn up-and-coming professionals, corporations, and organizations of the potential social dangers of the widespread usage of generative artificial intelligence (AI), while also providing a framework for safeguarding digital racial and gender justice at the institutional level. 

This lesson centers around the How AI Works: Chatbots and Large Language Models video from the How AI Works video series. Watch this video first before exploring the lesson plan.

Large Language Models (LLMs) generate new text. The text LLMs generate looks like a human might have written it because large language models are built based on large bodies of text, such as Wikipedia. In this lesson, students learn what an LLM is and how it works, then use an LLM to co-create a text with AI. Finally, the class wraps up with a discussion about whether or not LLMs are intelligent or creative.

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

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

Students learn how computer vision works. They first look at optical illusions to identify the features of the drawing that their eyes noticed. Students watch a video explaining computer vision and how a computer "sees". They design an algorithm that uses a network to decide what number the seven segment display is displaying. Finally, students test their algorithm.

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

The onset of new, more accessible, artificial intelligence (AI) technologies marks a significant turning point for libraries, ushering in a period rich with both unparalleled opportunities and complex challenges. In this era of swift technological transformation, libraries stand at a critical intersection. To effectively chart this transition, two quick polls were conducted among members of the Association of Research Libraries (ARL).

The first poll, which ran in April 2023, provided an initial snapshot of the AI landscape in libraries. The second poll, carried out in December 2023, continued this inquiry, offering a comparative perspective on the evolving dynamics of AI use and possibilities in library services. This study analyzes and juxtaposes the outcomes of these two surveys to better understand how library leaders are managing the complexities of integrating AI into their operations and services. It specifically seeks to capture changing perspectives on the potential impact of AI, assess the extent of AI exploration and implementation within libraries, and identify AI applications relevant to the current library environment.

The insights derived from this comparative analysis shed light on the role of libraries in an increasingly AI-driven era, providing strategic directions and highlighting practices in research libraries.

This Open Educational Resource (OER) was produced for educators who wish to find positive and productive ways to incorporate generative artificial intelligence (AI) tools into their work. This includes:

- using AI tools to develop courses, lesson plans, activities, assessments, and rubrics;
- leveraging AI tools to enhance existing in-class activities and assignments;
- teaching students how to engage with AI effectively, ethically, and responsibly;
- utilizing AI tools to efficiently complete administrative tasks.

This resource is focused on how AI tools can be used in polytechnic education. However, much of the content will also be relevant to educators in other educational contexts, like university or high school. The term ‘instructional staff’ is used widely in this resource and is meant to include instructors, professors, lecturers, teachers, educational assistants, and tutors.

This lesson centers around the How AI Works: Creativity and Imagination? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

Diffusion models generate images. Diffusion AI converts an image to noise, and trains an AI to reverse the process. In this lesson, students learn how AI can generate images, then explore a diffusion AI widget. Finally, the class wraps up with a discussion about whether or not these models are creative.

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

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.

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.

Peer-reviewed articles in this special issue:

- “Responsible AI Practice in Libraries and Archives: A Review of the Literature” by Sara Mannheimer, Natalie Bond, Scott W. H. Young, Hannah Scates Kettler, Addison Marcus, Sally K. Slipher, Jason A. Clark, Yasmeen Shorish, Doralyn Rossmann, and Bonnie Sheehey. The authors explore the existing literature to identify and summarize trends in how libraries have (or have not) considered AI’s ethical implications.
- “It Takes a Village: A Distributed Training Model for AI-based Chatbots” by Beth Twomey, Annie Johnson, and Colleen Estes, discusses the steps taken at their institution to develop and implement a library chatbot powered by a large language model, as well as lessons learned.
- “‘Gimme Some Truth’ AI Music and Implications for Copyright and Cataloging” by Adam Eric Berkowitz, details modern developments in AI-assisted music creation, and the resultant challenges that these surface regarding copyright and cataloging work.
- “Adapting Machine Translation Engines to the Needs of Cultural Heritage Metadata” by Konstantinos Chatzitheodorou, Eirini Kaldeli, Antoine Isaac, Paolo Scalia, Carmen Grau Lacal, and Mª Ángeles García Escrivá provides an overview of the process used to hone general-use machine translation engines to improve their outputs when translating cultural heritage metadata in the Europeana repository from one language to another.
- “Exploring the Impact of Generative Artificial Intelligence on Higher Education Students' Utilization of Library Resources: A Critical Examination” by Lynsey Meakin applies the Technological Acceptance Model to higher education students’ perceptions and adoption of tools using generative AI models.

Recurring content:
- Public Libraries Leading the Way: “Activating Our Intelligence: A Common-Sense Approach to Artificial Intelligence” by Dorothy Stoltz

- ITAL &: “The Jack in the Black Box: Teaching College Students to Use ChatGPT Critically” by Shu Wan

The Foundations of Applied AI course is an introductory exploration into the world of artificial intelligence (AI), designed for undergraduates with no prior experience in AI. This online, 3-credit course offers a deep dive into AI's core concepts, applications, and the ethical implications of deploying AI technologies across various industries. Through multimedia lectures, case studies, and hands-on projects, students will gain a comprehensive understanding of how AI can transform business, healthcare, and more, while also navigating the ethical considerations vital for responsible AI use.By the end of the course, students will be equipped to identify AI opportunities, understand AI's potential impacts, and discuss the importance of ethical frameworks in AI development. This course lays the groundwork for a future in AI, preparing students for further specialization or to apply AI insights in their fields.

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

On the eve of the CC Global Summit, members of the CC global community and Creative Commons held a one-day workshop to discuss issues related to AI, creators, and the commons. The community attending the Summit has a long history of hosting these intimate discussions before the Summit begins on critical and timely issues.

Emerging from that deep discussion and in subsequent conversation during the three days of the Summit, this group identified a set of common issues and values, which are captured in the statement below. These ideas are shared here for further community discussion and to help CC and the global community navigate uncharted waters in the face of generative AI and its impact on the commons.

A list of Microsoft Generative AI Resources.  Not an exhaustive list but it'll get you started with generative AI on the Microsoft Azure Platform.  

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

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