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

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 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.

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

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

Host Brenna Clarke Gray (Thompson Rivers University) and guest Autumm Caines (University of Michigan - Dearborn) explore the pedagogical implications of generative AI in this conversation in honour of Open Education Week. They ask such questions as:
- What happens when we leap into new technologies without first pausing to imagine harms, such as surveillance, bias, and discrimination?
- Can recentering the core values of the open education movement—equity, inclusion, transparency, and social justice—in our pedagogy help us move forward in a good way?
- How do we introduce these considerations to our students and empower them to make informed decisions with new technologies?

The LLM course is divided into three parts:
🧩 LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks.
🧑‍🔬 The LLM Scientist focuses on building the best possible LLMs using the latest techniques.
👷 The LLM Engineer focuses on creating LLM-based applications and deploying them.

تُعنى هذه الدورة التدريبية بتقديم المفاهيم الأساسية للذكاء الاصطناعي وتطبيقاته في قطاع التعليم. حيث ستزود المشاركين بفهم معمق لأسس الذكاء الاصطناعي وكيفية استثماره في دعم التعليم الشامل وتعزيز مبدأ تكافؤ الفرص التعليمية للجميع دون استثناء.
ستتناول الدورة بشكل مفصل أهم المبادئ والآليات التي يقوم عليها الذكاء الاصطناعي، إلى جانب استعراض أبرز تطبيقاته العملية في المجال التعليمي، وكيف يمكن توظيفها لخلق بيئات تعليمية شاملة وفعالة تلبي احتياجات جميع الطلبة بمختلف قدراتهم.
سيكتسب المشاركون من خلال هذه الدورة المعرفة الكافية لفهم دور الذكاء الاصطناعي وأهميته في تحقيق التعليم الشامل، والاطلاع على أحدث الأدوات والتقنيات المستخدمة في هذا المجال، مما يمكنهم من تطبيقها لاحقًا في بيئاتهم التعليمية الخاصة.