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You Ready?                                                                   Use your camera app and join us at the table! 

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You Ready?                                                                   Use your camera app and join us at the table! 

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Need More ?                                                                     We are just a click away.Follow us at: www.equitymattersconsulting.org 

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Need More ?                                                                     We are just a click away.Follow us at: www.equitymattersconsulting.org 

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Equity Matters Consulting

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This book was designed to support teachers and instructional leaders with tools and strategies to address unfinished learning through whole-child acceleration strategies that ensure success for every child. Link to Serving Educational Equity: A Five-Course Framework for Accelerated Learning can be found below.

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This book was designed to support teachers and instructional leaders with tools and strategies to address unfinished learning through whole-child acceleration strategies that ensure success for every child. Link to Serving Educational Equity: A Five-Course Framework for Accelerated Learning can be found below.

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Here is the link to Corwin's site:

https://us.corwin.com/en-us/nam/serving-educational-equity/book285463

Please add SAVE20 for 20% off!

The book is also now available on Amazon here:

https://www.amazon.com/Serving-Educational-Equity-Five-Course-Accelerated/dp/1071909479/

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Creates content for an LMS (think Canvas) based on the information you provide.

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NOLEJ is a decentralized skills platform powered by an AI engine, that automatically generates interactive courseware & global knowledge graph

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Takes meeting notes for you via Zoom, Google Meet and Teams

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Otter.ai - AI Meeting Note Taker & Real-time AI Transcription

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Otter.ai uses an AI Meeting Assistant to transcribe meetings in real time, record audio, capture slides, extract action items, and generate an AI meeting summary.

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Create trending videos and automatically add relevant emojis to highlight valuable content

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AI Emoji & Keyword

Our AI analyzes your video to automatically add relevant emojis and highlight keywords, which increase views by over 42%.

Trusted by 1 million video creators

Our website uses cookies. By continuing, we assume your permission to deploy cookies as detailed in our Privacy Policy.

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Opus Clip - AI-powered Video Repurposing

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Opus Clip turns long videos into high-quality viral clips for you to share on TikTok, YouTube Shorts, and Reels to increase social media reach.

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Reads articles, videos, and your past Parlay discussions to generate higher order discussion prompts and follow-up questions in seconds.

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Parlay is an AI-powered instructional platform that helps teachers facilitate meaningful, measurable, and inclusive class discussions. Join over a million teachers and students. Start using Parlay for free in your class today.

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Create a new document and edit with others at the same time -- from your computer, phone or tablet. Get stuff done with or without an internet connection. Use Docs to edit Word files. Free from Google.

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by Pati Ruiz and Judi Fusco

This glossary was written for educators to reference when learning about and using artificial intelligence (AI). We will start with a definition of artificial intelligence and then provide definitions of AI-related terms in alphabetical order. This glossary was last updated on July 19, 2023

Artificial Intelligence (AI): AI is a branch of computer science. AI systems use hardware, algorithms, and data to create "intelligence" to do things like make decisions, discover patterns, and perform some sort of action. AI is a general term and there are more specific terms used in the field of AI. AI systems can be built in different ways, two of the primary ways are: (1) through the use of rules provided by a human (rule-based systems); or (2) with machine learning algorithms. Many newer AI systems use machine learning (see definition of machine learning below).

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Algorithm: Algorithms are the "brains" of an AI system and what determines decisions in other words, algorithms are the rules for what actions the AI system takes. Machine learning algorithms can discover their own rules (see Machine learning for more) or be rule-based where human programmers give the rules.

Artificial General Intelligence (AGI): Artificial general intelligence has not yet been realized and would be when an AI system can learn, understand, and solve any problem that a human can.

Artificial Narrow Intelligence (ANI): AI can solve narrow problems and this is called artificial narrow intelligence. For example, a smartphone can use facial recognition to identify photos of an individual in the Photos app, but that same system cannot identify sounds.

Chat-based generative pre-trained transformer (ChatGPT): A system built with a neural network transformer type of AI model that works well in natural language processing tasks (see definitions for neural networks and Natural Language Processing below). In this case, the model: (1) can generate responses to questions ( Generative); (2) was trained in advance on a large amount of the written material available on the web ( Pre-trained); (3) and can process sentences differently than other types of models ( T ransformer).

Transformer models: Used in ChatGPT (the T stands for Transformer), transformer models are a type of language model. They are neural networks and also classified as deep learning models. They give AI systems the ability to determine and focus on important parts of the input and output using something called a self-attention mechanism to help.

Self-attention mechanism: These mechanisms, also referred to as attention help systems determine the important aspects of input in different ways. There are several types and were inspired by how humans can direct their attention to important features in the world, understand ambiguity, and encode information.

Computer Vision: Computer Vision is a set of computational challenges concerned with teaching computers how to understand visual information, including objects, pictures, scenes, and movement (including video). Computer Vision (often thought of as an AI problem) uses techniques like machine learning to achieve this goal.

Critical AI: Critical AI is an approach to examining AI from a perspective that focuses on reflective assessment and critique as a way of understanding and challenging existing and historical structures within AI. Read more about critical AI.

Data: Data are units of information about people or objects that can be used by AI technologies.

Training Data: This is the data used to train the algorithm or machine learning model. It has been generated by humans in their work or other contexts in their past. While it sounds simple, training data is so important because the wrong data can perpetuate systemic biases. If you are training a system to help with hiring people, and you use data from existing companies, you will be training that system to hire the kind of people who are already there. Algorithms take on the biases that are already inside the data. People often think that machines are "fair and unbiased" but this can be a dangerous perspective. Machines are only as unbiased as the human who creates them and the data that trains them. (Note: we all have biases! Also, our data reflect the biases in the world.) ¹ ​​

Foundation Models: Foundation Models represent a large amount of data that can be used as a foundation for developing other models. For example, generative AI systems use large language foundation models. They can be a way to speed up the development of new systems, but there is controversy about using foundation models since depending on where their data comes from, there are different issues of trustworthiness and bias. Jitendra Malik, Professor of Computer Science at UC Berkeley once said the following about foundation models: " These models are really castles in the air, they have no foundation whatsoever. "

Human-centered Perspective: A human-centered perspective sees AI systems working with humans and helping to augment human skills. People should always play a leading role in education, and AI systems should not replace teachers.

Intelligence Augmentation (IA): Augmenting means making something greater; in some cases, perhaps it means making it possible to do the same task with less effort. Maybe it means letting a human (perhaps teacher) choose to not do all the redundant tasks in a classroom but automate some of them so they can do more things that only a human can do. It may mean other things. There's a fine line between augmenting and replacing and technologies should be designed so that humans can choose what a system does and when it does it.

Intelligent Tutoring Systems (ITS): A computer system or digital learning environment that gives instant and custom feedback to students. An Intelligent Tutoring System may use rule-based AI (rules provided by a human) or use machine learning under the hood. By under the hood we mean the underlying algorithms and code that an ITS is built with. ITSs can support adaptive learning.

Adaptive Learning: Subject or course material is adjusted based on the performance of the learner. The difficulty of material, the pacing, sequence, type of help given, or other features can be adapted based on the learner's prior responses.

Interpretable Machine Learning (IML): Interpretable machine learning, sometimes also called interpretable AI, describes the creation of models that are inherently interpretable in that they provide their own explanations for their decisions. This approach is preferable to that of explainable machine learning (see definition below) for many reasons including the fact that we should understand what is happening from the beginning in our systems, rather than try to "explain" black boxes after the fact.

Black Boxes: We call things we don't understand, "black boxes" because what happens inside the box cannot be seen. Many machine learning algorithms are "black boxes" meaning that we don't have an understanding of how a system is using features of the data when making their decisions (generally, we do know what features are used but not how they are used)There are currently two primary ways to pull back the curtain on the black boxes of AI algorithms: interpretable machine learning (see definition above) and explainable machine learning (see definition below).

Machine Learning (ML): Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. These algorithms build a model for decision making as they go through data. (You will sometimes hear the term machine learning model.) Because they discover their own rules in the data they are given, ML systems can perpetuate biases. Algorithms used in machine learning require massive amounts of data to be trained to make decisions.

It's important to note that in machine learning, the algorithm is doing the work to improve and does not have the help of a human programmer. It is also important to note three more things. One, in most cases the algorithm is learning an association (when X occurs, it usually means Y) from training data that is from the past. Two, since the data is historical, it may contain biases and assumptions that we do not want to perpetuate. Three, there are many questions about involving humans in the loop with AI systems; when using ML to solve AI problems, a human may not be able to understand the rules the algorithm is creating and using to make decisions. This could be especially problematic if a human learner was harmed by a decision a machine made and there was no way to appeal the decision.

Neural Networks (NN): Neural networks also called artificial neural networks (ANN) and are a subset of ML algorithms. They were inspired by the interconnections of neurons and synapses in a human brain. In a neural network, after data enter in the first layer, the data go through a hidden layer of nodes where calculations that adjust the strength of connections in the nodes are performed, and then go to an output layer.

Deep Learning: Deep learning models are a subset of neural networks. With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted, but as mentioned earlier there are more layers and more calculations that can make adjustments to the output to determine each decision. The decisions by deep learning models are often very difficult to interpret as there are so many hidden layers doing different calculations that are not easily translatable into English rules (or another human-readable language).

Natural Language Processing (NLP): Natural Language Processing is a field of Linguistics and Computer Science that also overlaps with AI. NLP uses an understanding of the structure, grammar, and meaning in words to help computers "understand and comprehend" language. NLP requires a large corpus of text (usually half a million words).

NLP technologies help in many situations that include: scanning texts to turn them into editable text (optical character recognition), speech to text, voice-based computer help systems, grammatical correction (like auto-correct or grammarly), summarizing texts, and others.

Robots: Robots are embodied mechanical machines that are capable of doing a physical task for humans. "Bots" are typically software agents that perform tasks in a software application (e.g., in an intelligent tutoring system they may offer help). Bots are sometimes called conversational agents. Both robots and bots can contain AI, including machine learning, but do not have to have it. AI can help robots and bots perform tasks in more adaptive and complex ways.

User Experience Design/User Interface Design (UX/UI): User-experience/user-interface design refers to the overall experience users have with a product. These approaches are not limited to AI work. Product designers implement UX/UI approaches to design and understand the experiences their users have with their technologies.

Explainable Machine Learning (XML) or Explainable AI (XAI): Researchers have developed a set of processes and methods that allow humans to better understand the results and outputs of machine learning algorithms. This helps developers of AI-mediated tools understand how the systems they design work and can help them ensure that they work correctly and are meeting requirements and regulatory standards.

It is important to note that the term "explainable" in the context of explainable machine learning or explainable AI, refers to an understanding of how a model works and not to an explanation of how the model works. In theory, explainable ML/AI means that an ML/AI model will be "explained" after the algorithm makes its decision so that we can understand how the model works. This often entails using another algorithm to help explain what is happening as the "black box." One issue with XML and XAI is that we cannot know for certain whether the explanation we are getting is correct, therefore we cannot technically trust either the explanation or the original model. Instead, researchers recommend the use of interpretable models.

Thank you to Michael Chang, Ph.D., a CIRCLS postdoctoral scholar, for reviewing this post. We appreciate your work in AI and your work to bring educators and researchers together on this topic.

Educator CIRCLS posts are licensed under a Creative Commons Attribution 4.0 International License. If you use content from this site, please cite the post and consider adding: "Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)."
Suggested citation format: [Authors] ([Year]). [Title]. Educator CIRCLS Blog. Retrieved from [URL]

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¹​​​​Fusco, J. (2020). Book Review: You Look Like a Thing and I Love You. CIRCLEducators Blog. Retrieved from https://circleducators.org/review-you-look-like-a-thing/

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Glossary of Artificial Intelligence Terms for Educators

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by Pati Ruiz and Judi Fusco This glossary was written for educators to reference when learning about and using artificial intelligence (AI). We will start with a definition of artificial intelligence and then provide definitions of AI-related terms in alphabetical order.

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Input some reading, and the AI will write essential questions, learning objectives, and aligned multiple choice questions.

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AI question generator. Input a reading and align standards or learning outcomes so the AI writes multiple choice, fill in the blank, short answer, and essential questions that can be exported to Kahoot, Quizziz, Google Forms, Microsoft Forms, Gimkit, Moodle, Canvas, Blooket, and Quizlet.

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Have realistic conversations with historical figures.

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Hello History - Chat with AI Generated Historical Figures

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With the help of modern AI & machine learning, we’ve brought historical figures back to life. Now is your chance to ask the questions you’ve always wanted to ask.

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Creates summaries of youtube videos (lecures, government meetings, etc.)

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Speechify is a text-to-speech tool.

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Speechify: Free Text to Speech & Voice Typing AI Assistant | 55M+ Users

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Speechify reads anything aloud to you. Listen to books, PDFs, or web pages anytime with natural voices. Try Speechify free.

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A tool that converts text into beautiful charts

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Creates quizzes from text!

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Yippity | AI Question Generator: Instant Multiple Choice, True/False, Fill-in-the-Blank Questions & More!

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Yippity: Your go-to platform for generating AI-driven questions. Convert any text into a quiz - from multiple choice to fill-in-the-blanks, effortlessly.

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Cites Sources for you!

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Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.

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Rehearse your presentation with Presenter Coach

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Speaker Coach is compatible with Microsoft Edge version 15 or later, Chrome version 52 or later, and Firefox version 52 or later. To see which version of Microsoft Edge you have, refer to Find out which version of Microsoft Edge you have. Microsoft Edge is updated with Windows. For information on updating, see Update Windows 10.

1. Open your presentation in PowerPoint for the web.

2. Sign in with a personal Microsoft account (hotmail.com, outlook.com, live.com, msn.com), or an Microsoft 365 work or school account.

3. On the Slide Show tab, select Rehearse with Coach. (If you've turned off the Simplified Ribbon, you don't have a Slide Show tab; instead use the View tab to start Speaker Coach.)

The presentation opens in a full-screen view, similar to Slide Show.

4. As you speak, Coach gives on-screen guidance (in the lower-right corner of the window) about pacing, inclusive language, use of profanity, filler words, and whether you're reading the slide text.

5. When you're through, exit the full-screen view. Coach opens a Rehearsal Report that summarizes what it observed and offers recommendations.

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Rehearse your slide show with Speaker Coach

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Speaker Coach in PowerPoint can help you refine your presentation. It lets you know if you're reading from your slides too much, using too many filler words, speaking too quickly (or too slowly) and more.

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A conversational AI system that listens, learns, and challenges

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ChatGPT is your AI chatbot for everyday use. Chat with the most advanced AI to explore ideas, solve problems, and learn faster.

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Once you've ordered the book and want to accelerate students' learning. Please partner with us to plan for equity, excellence, and accelerated learning for every student.

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Once you've ordered the book and want to accelerate students' learning. Please partner with us to plan for equity, excellence, and accelerated learning for every student.

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We will partner with you using a tailored approach to whole-child learning for acceleration that serves every child equitably! Think of us as your personal sous chefs assisting and supporting you as you expertly curate strategies that help students find success academically.

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Creates art/images from a description in natural language

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Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Engineering, Design, Product, and Prototyping
Jeff Belgum, Dave Cummings, Jonathan Gordon, Chris Hallacy, Shawn Jain, Joanne Jang, Fraser Kelton, Vishal Kuo, Joel Lehman, Rachel Lim, Bianca Martin, Evan Morikawa, Rajeev Nayak, Glenn Powell, Krijn Rijshouwer, David Schnurr, Maddie Simens, Kenneth Stanley, Felipe Such, Chelsea Voss, Justin Jay Wang
Comms, Policy, Legal, Ops, Safety, and Security
Steven Adler, Lama Ahmad, Miles Brundage, Kevin Button, Che Chang, Fotis Chantzis, Derek Chen, Frances Choi, Steve Dowling, Elie Georges, Shino Jomoto, Aris Konstantinidis, Gretchen Krueger, Andrew Mayne, Pamela Mishkin, Bob Rotsted, Natalie Summers, Dave Willner, Hannah Wong
Acknowledgments
Thanks to those who helped with and provided feedback on this release: Sandhini Agarwal, Sam Altman, Chester Cho, Peter Hoeschele, Jacob Jackson, Jong Wook Kim, Matt Knight, Jason Kwon, Anna Makanju, Katie Mayer, Bob McGrew, Luke Miller, Mira Murati, Adam Nace, Hyeonwoo Noh, Cullen O'Keefe, Long Ouyang, Michael Petrov, Henrique Ponde de Oliveira Pinto, Alec Radford, Girish Sastry, Pranav Shyam, Aravind Srinivas, Ilya Sutskever, Preston Tuggle, Arun Vijayvergiya, Peter Welinder

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DALL·E 2 is an AI system that can create realistic images and art from a description in natural language.

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A Collection Of "Best" Lists About Using Artificial Intelligence In Education

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I think it's a safe bet that Artificial Intelligence will be a prime topic of conversation in the future, including in education. Here is a collection of my related "Best" ...