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MagicSchool.ai - AI for teachers - lesson planning and more!

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Join over a million teachers saving hours of time using the #1 AI Platform for Education, MagicSchool, to help lesson plan, differentiate, write assessments, write IEPs, communicate clearly, and more!

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Create lesson plans, presentations, quizzes, worksheets and more with AI powered apps for teachers. 

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Design stunning presentations, websites, and more with Gamma—your all-in-one AI-powered design partner. No code or design experience needed.

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Asset management

Easily store, organize and search the assets and designs for your team's projects. Whether you're creating a presentation, report, or infographic, find all your uploaded assets in one place.

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Create Presentations, Infographics, Design & Video | Visme

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Create professional presentations, interactive infographics, beautiful design and engaging videos, all in one place. Start using Visme today.

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What will the future of education look like in a world with generative AI? | Open Learning

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MIT leaders in education explore the future of technology in and beyond the classroom. By Sara Feijo and Katherine OuelletteGenerative AI is already changing the education landscape, but can educators ensure a positive impact from this powerful technology?

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Not an educator? Learn about Google AI Essentials - applicable across roles and industries.

As a teacher, we know your time is valuable and student needs are broad. With Generative AI for Educators, you'll learn how to use generative AI tools to help you save time on everyday tasks, personalize instruction, enhance lessons and activities in creative ways, and more.

100%

online, self-paced learning

2 hours

to complete the course

Learn more about the course

Generative AI for Educators

Developed by AI experts at Google in collaboration with MIT RAISE, this course will help you bring AI into your practice. You'll also gain a foundational understanding of AI - you'll learn what it is, the opportunities and limitations of this technology, and how to use it responsibly.

You'll learn how to use generative AI tools to:

Save time on everyday tasks like drafting emails and other correspondence

Personalize instruction for different learning styles and abilities

Enhance lessons and activities in creative ways

You'll learn how to use generative AI tools to:

Save time on everyday tasks like drafting emails and other correspondence

Personalize instruction for different learning styles and abilities

Enhance lessons and activities in creative ways

Save time and enhance student learning with AI skills

When you complete the online course, you'll gain essential AI skills you can apply to your workflow immediately. By using AI as a helpful collaboration tool, you can work smarter, not harder.

Earn a certificate which you can present to your district for professional development (PD) credit, depending on district and state requirements.

Get hands-on experience using generative AI tools and apply your new skills right away.

Google and MIT's Generative AI for Educators course marks a transformative moment for Oklahoma's education. Having navigated the educational system as a dyslexic student and parent, I recognize the critical need for personalized learning. This initiative equips educators to unlock each student's potential, tailoring learning experiences to their unique minds. It's a step towards a future where education mirrors the individuality of a fingerprint, empowering students to shape tomorrow with their ingenuity.

Nellie Tayloe Sanders

Oklahoma Secretary of Education

We are excited to be a part of this partnership with Google to equip our middle and high school teachers with transformative Generative AI professional learning courses. This responds to the need for continuous innovation in our school District. By empowering educators with the latest advancements in AI, we're not only uplifting their professional practices, but also giving them evolving tools to enhance the ever-changing landscape of teaching and learning.

Jose L. Dotres, Superintendent of Schools

Miami-Dade County Public Schools

The Google Generative AI for Educators opportunity for teachers will help put our educators on the front lines of generative AI opportunities for our 27,000 students. In a rapidly evolving world, our teachers cannot afford to fall behind in accessing powerful generative tools that will help them develop new approaches to teaching and learning.

Michael Matsuda, Superintendent of Schools

Anaheim Union High School District

Frequently asked questions

Generative AI for Educators was designed by AI experts at Google in collaboration with MIT RAISE (Responsible AI for Social Empowerment and Education).

Check out Experience AI. Created by Google DeepMind and The Raspberry Pi Foundation, this no cost program provides ready-to-use resources to introduce AI technology in middle school classrooms, including lesson plans, slide decks, worksheets, and videos on AI and machine learning.

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using artificial intelligence in education

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Generative AI for Educators - Grow with Google

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Learn AI skills with Google Generative AI for Educators. Save time, personalize instruction, and enhance lessons, no previous experience required.

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萨尔·可汗: 人工智能如何拯救（而非摧毁）教育行业

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可汗学院的创始人兼CEO，萨尔·可汗（Sal Khan）认为人工智能可以激发前所未有的教育行业正向大转型。他分享了他眼中学生和教育从业者与 AI 工具合作的机会，包括为每个学生提供私人 AI 辅导老师、为每位老师提供 AI 助教的可能性，并且他展示了可汗学院的教育聊天机器人 Khanmigo 的令人兴奋的新功能。

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In ChatGPT or your favorite AI Chatbot, cut and paste the following prompt to create a project-based-lesson plan (PBL) customized for your classroom. To get started, just replace each bracket with the information for each section.
PBL Lesson Prompt
Generate a [number of days or weeks] Project Based Learning plan consisting of [number of minutes each day] for [grade/subject] that is based on the Buck Institute for Education's Gold Standard PBL that covers [standard(s)] to answer the driving question of [driving question].

Include the following:

Project summary that includes a project launch, milestones, anticipated student Need to Know questions

Activities aligned to the milestones and related activities that will build student knowledge and experience with the topic(s), allow for students to share drafts of their work, opportunities for peer feedback and revision, reflection upon their work

List scaffolds to help a variety of student need to be successful

A plan for a public presentation of learning to an authentic audience beyond their classroom peers and an opportunity to reflect upon their final product

Draft a parent letter explaining what Project Based Learning is and why learning this way helps students

Example Prompt Generate a four-week-long Project Based Learning plan consisting of 45 minutes each day for 5th graders that is based on the Buck Institute for Education's Gold Standard PBL that addresses Next Generation Science Standard E-ESS3-1 and Common Core RI 5.7 and 5.9 to answer the driving question of How can our community use science ideas to protect the Earth's resources and environment.

Activities aligned to the milestones and related activities that will build student knowledge and experience with the topic(s), allow for students to share drafts of their work, opportunities for peer feedback and revision, reflection upon their work

List scaffolds to help a variety of student need to be successful

A plan for a public presentation of learning to an authentic audience beyond their classroom peers and an opportunity to reflect upon their final product

Draft a parent letter explaining what Project Based Learning is and why learning this way helps students

Make the Prompt Work for You

Create extension activities, handouts and other materials that you think would support your students

Have ChatGPT create different types of lessons like PBL, the 5E framework, etc.

Ask ChatGPT to create content for google slides/powerpoint slides

Prompt designed by Rich Dixon, Creative Services Director at Clarity Innovations.

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In ChatGPT or your favorite AI Chatbot, cut and paste the following prompt to create a project-based-lesson plan (PBL) customized for your classroom. To get started, just replace each bracket with the information for each section: Generate a [number of days or weeks] Project Based Learning plan cons

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AI Ethics - What's plausible, possible, and prefered? 

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AI Ethics - What's plausible, possible, and prefered? 

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About this lesson

The development and ubiquity of Artificial Intelligence raise a number of social and ethical matters that students can explore in the Digital Technologies classroom. This lesson idea outlines a project to help students frame such discussions using the curriculum Key Idea of Creating preferred futures, tying into Critical and Creative Thinking.

Year band: 9-10

Curriculum Links Assessment

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AI ethics - What's possible, probable and preferred?

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The development and ubiquity of Artificial Intelligence raise a number of social and ethical matters that students can explore in the Digital Technologies classroom. This lesson idea outlines a project to help students frame such discussions using the curriculum Key Idea of Creating preferred futures, tying into Critical and Creative Thinking.

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Becoming a Critical Thinker: Technology Lesson 1 - Understanding the Rise of AI’s Influence

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In this lesson, you’ll learn how to tell AI-generated from human-made content and understand its upsides across different fields. We’ll also tackle the challenges and ethical concerns AI presents. By the end, you’ll have a good sense of how AI could fit into your life, both personally and professionally.

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Critical Evaluation of generative AI information

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ResearchBuddy - Automatic Literature Reviews - Search

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Say goodbye to tedious literature reviews with ResearchBuddy.app. Our smart app streamlines the process and presents the most relevant information to you.

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Student Lesson for Evaluation of AI Sources 

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Student Lesson for Evaluation of AI Sources 

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Evaluating Information: Generative AI Literature Reviews

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Welcome to this short Library tutorial on evaluating generative AI as an information source.

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Creating video from text

Sora is an AI model that can create realistic and imaginative scenes from text instructions.
Read technical report

All videos on this page were generated directly by Sora without modification.

We're teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction.

Introducing Sora, our text-to-video model. Sora can generate videos up to a minute long while maintaining visual quality and adherence to the user's prompt.

Safety

We'll be taking several important safety steps ahead of making Sora available in OpenAI's products. We are working with red teamers - domain experts in areas like misinformation, hateful content, and bias - who will be adversarially testing the model.

We're also building tools to help detect misleading content such as a detection classifier that can tell when a video was generated by Sora. We plan to include C2PA metadata in the future if we deploy the model in an OpenAI product.

In addition to us developing new techniques to prepare for deployment, we're leveraging the existing safety methods that we built for our products that use DALL·E 3, which are applicable to Sora as well.

For example, once in an OpenAI product, our text classifier will check and reject text input prompts that are in violation of our usage policies, like those that request extreme violence, sexual content, hateful imagery, celebrity likeness, or the IP of others. We've also developed robust image classifiers that are used to review the frames of every video generated to help ensure that it adheres to our usage policies, before it's shown to the user.

We'll be engaging policymakers, educators and artists around the world to understand their concerns and to identify positive use cases for this new technology. Despite extensive research and testing, we cannot predict all of the beneficial ways people will use our technology, nor all the ways people will abuse it. That's why we believe that learning from real-world use is a critical component of creating and releasing increasingly safe AI systems over time.

Research techniques

Sora is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps.

Sora is capable of generating entire videos all at once or extending generated videos to make them longer. By giving the model foresight of many frames at a time, we've solved a challenging problem of making sure a subject stays the same even when it goes out of view temporarily.

Similar to GPT models, Sora uses a transformer architecture, unlocking superior scaling performance.

We represent videos and images as collections of smaller units of data called patches, each of which is akin to a token in GPT. By unifying how we represent data, we can train diffusion transformers on a wider range of visual data than was possible before, spanning different durations, resolutions and aspect ratios.

Sora builds on past research in DALL·E and GPT models. It uses the recaptioning technique from DALL·E 3, which involves generating highly descriptive captions for the visual training data. As a result, the model is able to follow the user's text instructions in the generated video more faithfully.

In addition to being able to generate a video solely from text instructions, the model is able to take an existing still image and generate a video from it, animating the image's contents with accuracy and attention to small detail. The model can also take an existing video and extend it or fill in missing frames. Learn more in our technical report.

Sora serves as a foundation for models that can understand and simulate the real world, a capability we believe will be an important milestone for achieving AGI.

Today, Sora is becoming available to red teamers to assess critical areas for harms or risks. We are also granting access to a number of visual artists, designers, and filmmakers to gain feedback on how to advance the model to be most helpful for creative professionals.

We're sharing our research progress early to start working with and getting feedback from people outside of OpenAI and to give the public a sense of what AI capabilities are on the horizon.

Sora is able to generate complex scenes with multiple characters, specific types of motion, and accurate details of the subject and background. The model understands not only what the user has asked for in the prompt, but also how those things exist in the physical world.

The model has a deep understanding of language, enabling it to accurately interpret prompts and generate compelling characters that express vibrant emotions. Sora can also create multiple shots within a single generated video that accurately persist characters and visual style.

The current model has weaknesses. It may struggle with accurately simulating the physics of a complex scene, and may not understand specific instances of cause and effect. For example, a person might take a bite out of a cookie, but afterward, the cookie may not have a bite mark.

The model may also confuse spatial details of a prompt, for example, mixing up left and right, and may struggle with precise descriptions of events that take place over time, like following a specific camera trajectory.

All videos on this page were generated directly by Sora without modification.

Research Leads Bill Peebles & Tim Brooks

Special Thanks Avi Nayak, Bob McGrew, Brad Lightcap, Chad Nelson, David Medina, Gabriel Goh, Greg Brockman, Hannah Wong, Ian Sohl, Jamie Kiros, James Betker, Jason Kwon, Jong Wook Kim, Mark Chen, Michelle Fradin, Mira Murati, Nick Turley, Peter Hoeschele, Prafulla Dhariwal, Rowan Zellers, Sam Altman, Sandhini Agarwal, Sarah Yoo, Spencer Papay, Srinivas Narayanan, Tifa Chen & Wesam Manassra

Elie Georges
Justin Wang
Kendra Rimbach
Niko Felix
Thomas Degry
Veit Moeller

Che Chang
Fred von Lohmann
Gideon Myles
Tom Stasi

External Engagement Alex Baker-Whitcomb, Allie Teague, Anna Makanju, Anna McKean, Becky Waite, Brittany Smith, Chan Park, Chris Lehane, David Duxin, David Robinson, James Hairston, Jonathan Lachman, Justin Oswald, Krithika Muthukumar, Lane Dilg, Leher Pathak, Olga Nowicka, Ryan Biddy, Sandro Gianella, Stephen Petersilge, Tom Rubin & Varun Shetty

Built by OpenAI in San Francisco, California
Published February 15, MMXXIV

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Sora is an AI model that can create realistic and imaginative scenes from text instructions. Read technical report All videos on this page were generated directly by Sora without modification. We're teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction.

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On one of our routine hikes up Hong Kong Peak, my dear and brilliant friend, Haihao from Ternity Education and I entered into a conversation about the concept of prompt engineering as not really being prompt "engineering".

The term "prompt engineering" might be somewhat of a misnomer. In traditional engineering disciplines, practitioners apply principles from the sciences and mathematics to design and build structures or machinery. However, in the realm of human-AI interaction, "prompt engineering" diverges from this definition; it's less about the application of rigid scientific formulae and more about the artful crafting of language and refining your prompts through a conversation with any GenAI tool. It involves composing the right prompts to guide GenAI tools toward desired outcomes and refining these prompts through an iterative process. The iterative development of crafting AI prompts can be likened to a chef perfecting a recipe I suppose (as a food lover)-each tweak to the ingredients or cooking technique builds upon the taste tests that came before, aiming for a more refined and satisfying flavour profile. My mother has an amazing ability to rely on her understanding of flavours and textures to create delicious dishes, and therefore prompt development relies on an understanding of dialogue flow and context to create prompts that produce the most coherent and effective AI responses.

It is my belief that the development of prompts for any AI tool use aligns closely with the principles of design thinking, from Stanford's d.School, a methodology traditionally used in product design and user experience but is equally applicable here. Design thinking advocates for a user-centered approach to problem-solving, where understanding the user's needs is paramount. This parallels the process of crafting AI prompts, which requires empathy for the end user's perspective to anticipate how they might interact with the AI and the types of responses they seek. The iterative nature of prompt development reflects design thinking's emphasis on prototyping and testing-a loop of creation, evaluation, and refinement. Just as design thinking involves redefining problems and exploring alternative strategies, effective prompt engineering requires flexibility and a willingness to pivot, ensuring that prompts evolve to better communicate the user's intent to the AI, thereby creating more meaningful and contextually appropriate interactions.

Despite experimenting with numerous existing frameworks and various tools designed for developing prompts, none have stuck with me for one reason or another. During our conversation, Haihao introduced me to a blog post by Lance Cummings, which explained the application of the rhetorical triangle in enhancing prompt construction. Inspired by this, I've integrated these concepts and explored the five canons of rhetoric, endeavouring to devise a more intuitive and impactful approach to prompt development that reflects the iterative and refinement process when developing prompt for AI.

The five canons or tenets of rhetoric, as articulated by the Roman orator Cicero, are Inventio (Invention), Dispositio (Arrangement), Elocutio (Style), Memoria (Memory), and Pronuntiatio and Actio (Presentation and Delivery). Originally these tenets were created to focus on public speaking and the iterative nature of writing that incorporates the stages of drafting, writing, and rewriting as a cyclical process.

From Wikipedia:

"Rhetorical education focused on five canons. The Five Canons of Rhetoric serve as a guide to creating persuasive messages and arguments:

inventio (invention) the process that leads to the development and refinement of an argument.

dispositio (disposition, or arrangement) used to determine how an argument should be organized for greatest effect, usually beginning with the exordium

elocutio (style) determining how to present the arguments

memoria (memory) the process of learning and memorizing the speech and persuasive messages

pronuntiatio (presentation) and actio(delivery) the gestures, pronunciation, tone, and pace used when presenting the persuasive arguments-the Grand Style

Based on this idea (literally), I have created an acronym that may help with AI prompt development that operates in two stages to reflect the iterative refinement nature of crafting prompts. The initial steps of input involve inquiring, designing, and engaging. Once a response is received, the next phase is to adapt and synthesize.

Employing the I.D.E.A.S. acronym as a guide can streamline the prompt crafting process, ensuring that your prompts are intentionally designed yet not burdensome to create, and are more apt to elicit valuable and intelligible replies from AI tools.

On one of our routine hikes up Hong Kong Peak, my dear and brilliant friend, Haihao from Ternity Education and I entered into a conversation about the concept of prompt engineering as not really being prompt "engineering".

The term "prompt engineering" might be somewhat of a misnomer. In traditional engineering disciplines, practitioners apply principles from the sciences and mathematics to design and build structures or machinery. However, in the realm of human-AI interaction, "prompt engineering" diverges from this definition; it's less about the application of rigid scientific formulae and more about the artful crafting of language and refining your prompts through a conversation with any GenAI tool. It involves composing the right prompts to guide GenAI tools toward desired outcomes and refining these prompts through an iterative process. The iterative development of crafting AI prompts can be likened to a chef perfecting a recipe I suppose (as a food lover)-each tweak to the ingredients or cooking technique builds upon the taste tests that came before, aiming for a more refined and satisfying flavour profile. My mother has an amazing ability to rely on her understanding of flavours and textures to create delicious dishes, and therefore prompt development relies on an understanding of dialogue flow and context to create prompts that produce the most coherent and effective AI responses.

It is my belief that the development of prompts for any AI tool use aligns closely with the principles of design thinking, from Stanford's d.School, a methodology traditionally used in product design and user experience but is equally applicable here. Design thinking advocates for a user-centered approach to problem-solving, where understanding the user's needs is paramount. This parallels the process of crafting AI prompts, which requires empathy for the end user's perspective to anticipate how they might interact with the AI and the types of responses they seek. The iterative nature of prompt development reflects design thinking's emphasis on prototyping and testing-a loop of creation, evaluation, and refinement. Just as design thinking involves redefining problems and exploring alternative strategies, effective prompt engineering requires flexibility and a willingness to pivot, ensuring that prompts evolve to better communicate the user's intent to the AI, thereby creating more meaningful and contextually appropriate interactions.

Despite experimenting with numerous existing frameworks and various tools designed for developing prompts, none have stuck with me for one reason or another. During our conversation, Haihao introduced me to a blog post by Lance Cummings, which explained the application of the rhetorical triangle in enhancing prompt construction. Inspired by this, I've integrated these concepts and explored the five canons of rhetoric, endeavouring to devise a more intuitive and impactful approach to prompt development that reflects the iterative and refinement process when developing prompt for AI.

The five canons or tenets of rhetoric, as articulated by the Roman orator Cicero, are Inventio (Invention), Dispositio (Arrangement), Elocutio (Style), Memoria (Memory), and Pronuntiatio and Actio (Presentation and Delivery). Originally these tenets were created to focus on public speaking and the iterative nature of writing that incorporates the stages of drafting, writing, and rewriting as a cyclical process.

From Wikipedia:

"Rhetorical education focused on five canons. The Five Canons of Rhetoric serve as a guide to creating persuasive messages and arguments:

inventio (invention) the process that leads to the development and refinement of an argument.

dispositio (disposition, or arrangement) used to determine how an argument should be organized for greatest effect, usually beginning with the exordium

elocutio (style) determining how to present the arguments

memoria (memory) the process of learning and memorizing the speech and persuasive messages

pronuntiatio (presentation) and actio(delivery) the gestures, pronunciation, tone, and pace used when presenting the persuasive arguments-the Grand Style

Based on this idea (literally), I have created an acronym that may help with AI prompt development that operates in two stages to reflect the iterative refinement nature of crafting prompts. The initial steps of input involve inquiring, designing, and engaging. Once a response is received, the next phase is to adapt and synthesize.

Employing the I.D.E.A.S. acronym as a guide can streamline the prompt crafting process, ensuring that your prompts are intentionally designed yet not burdensome to create, and are more apt to elicit valuable and intelligible replies from AI tools.

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Is Prompt Engineering Really Prompt "Engineering"?

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Mastering the Art of Prompt Crafting by Using Rhetoric: Perfecting Your AI's Generative Responses For Teachers On one of our routine hikes up Hong Kong Peak, my dear and brilliant friend, Haihao from Ternity Education and I entered into a conversation about the concept of prompt engineering as not really being prompt "engineering".

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How to Use Rhetoric to Repurpose Content with ChatGPT

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Lesson 6: Wait ... what is rhetoric? What does that have to do with ChatGPT?

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Cambridge Creative & Critical Thinking Lessons

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In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They'll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

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Artificial Intelligence and Academic Integrity

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Complete privacy & data security

Hello History doesn't use your student data to train AI models. All data is encrypted and protected. Additionally, we enable admins to control access to your AI assistants, oversee usage, and view chat history.

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Hello History doesn't use your student data to train AI models. All data is encrypted and protected. Additionally, we enable admins to control access to your AI assistants, oversee usage, and view chat history.

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AI is coming to U.S. classrooms, but who will benefit? - Center on Reinventing Public Education

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TeacherServer is an AI tool and service provider dedicated to supporting educators with innovative solutions.

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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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Wayground — The world’s most engaging learning platform

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Find and create gamified quizzes, lessons, presentations, and flashcards for students, employees, and everyone else. Get started for free!

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Easily create beautiful interactive video lessons for your students you can integrate right into your LMS. Track students' progress with hassle-free analytics as you flip your classroom!

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QuillBot for Chrome: Write well, wherever

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QuillBot for Chrome lets you write, edit, and check your grammar on the go using world-class artificial intelligence tools.

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AI Tools for Education_ A Teacher's Guide

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For both instructors and students, it is important to understand, evaluate, and overall familiarize yourself with the uses of generative artificial intelligence tools, whether you decide to incorporate their use into your course or not. Engaging with generative A.I. tools means using a thoughtful, critical and ethical lens to determine whether their use will benefit your assignments and assessments, as well as considering how your students may independently be trying to engage with these tools in their learning, either productively or in ways that may challenge their work's academic integrity.

For faculty and students alike, this engagement process encompasses recognizing when and how generative AI is used in various domains, assessing the reliability and validity of AI-generated outputs, identifying the ethical and social implications stemming from the design and use of generative AI applications, and creating and communicating with generative AI systems in appropriate ways.
Building literacy in Generative AI includes addressing ethics, privacy, and equity with intention.
There are many open questions, including legal questions, regarding the ethical design, development, use, and evaluation of generative AI in teaching and learning. While generative AI may potentially be powerfully useful, concerns and sensitivities surround a number of key issues including:

Transparency and oversight: as many generative AI are developed and owned by corporations, how can we know how the tools are trained or what safeguards exist protecting users against inaccurate information or harmful interactions?

Political impact: what protections exist against generative AI being used to spread inaccurate or discriminatory content?

Environmental impact: as generative AI tools are trained with ever larger data sets, requiring more and more energy consumption, what is the energy use impact on the environment?

Diversity, non-discrimination and fairness: how can we ensure that tools avoid unfair bias and are universally accessible?

Privacy, data governance, technical robustness and safety: how is user data or copyrighted material used, stored, or shared? Who has access to user data? (European Commission, 2022).

Researchers and educators have begun outlining multiple ways to encourage, practice, and support ethical and responsible use of generative AI in the classroom (Dwivedi et al, 2023). Drawing from these resources, in the sections that follow, we spotlight different ways to think through the use of generative AI in teaching and learning.

Ethics & Equity

Not all technologies impact all users in the same way. Some student populations may be at greater risk of harm than others (Gašević et al., 2023). Human and systemic biases in generative AI algorithms and large language models' (LLMs) data impact the output of AI tools and consequently can perpetuate inequities when these biases are not removed or addressed.

LLMs are designed to use statistical algorithms to analyze vast amounts of data and determine patterns and textual connections. The data used to train the generative AI tool will mimic the data it receives. This means if the majority of data input the model receives relates to a certain industry, language, demographic, or time period, the output it generates will do so as well. That is, the content generated is based on learned language patterns and on the examples it has received.

Due to this, there could be an inherent bias in LLMs that students should be aware of. These models can provide inaccurate, misleading, and unethical information. LLMs can impersonate people and organizations, share intellectual property without attribution, and influence users based on the way information is presented (Antoniak, 2023). Therefore, it is important to evaluate what AI generates through a critical lens.
Here are a few questions for instructors to consider, and to invite students to consider, when critically evaluating AI or Generative AI content:

Is the AI-generated content accurate? How can you test or assess the accuracy?

Can other credible sources (outside of generative AI) validate the data or item produced?

How does the information generated impact or influence your thinking on this topic?

Who is represented in this data? Is the data inclusive in terms of the material's scope and the perspectives that it presents?

Knowing LLMs may also be collecting the data your students input (i.e., in their prompts), how will you make students aware of this practice so they will in turn safeguard their own privacy?

Again, we stress that the LLMs that drive generative AI are only as inclusive and equitable as the information informing them. If only certain works inform a generative AI tool (e.g., English-language works only, U.S.-centric works, or works from one specific demographic), the output you receive from that tool will also be both limited to and influenced by the voices or works it has been trained on.

With this in mind, it is important that students are made aware of these potential limitations, and that they are given flexible options for completing assignments. It is equally important to provide transparency in your assignment rubric and grading criteria to account for these limitations and options (OpenAI, 2023; Frąckiewicz, 2023).

For more information on how you can provide equitable learning activities in your classroom, refer to Universal Design for Learning (UDL).

Privacy & Equity

As you and your students interact with AI, it is important to consider the issue of privacy. According to Antoniak (2023) "LLMs store your conversations and can use them as training data," which means any input and any materials uploaded to LMM processors can become part of the model's training set, and can then be shared in the future without attribution. Resources and intellectual property may thus be used in unexpected ways. Any uploaded data flows through an assortment of technological providers who together make up a technology's ecosystem or infrastructure, each with their own privacy policy and terms of use. Note that opting out of terms may not be possible if you intend to use the technology. As a result, you may wish to only share open information or data that does not need to remain private. It's also important to ensure that you never upload or share any student information covered under FERPA and other protections.

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For both instructors and students, it is important to understand, evaluate, and overall familiarize yourself with the uses of generative artificial intelligence tools, whether you decide to incorporate their use into your course or not. Engaging with generative A.I.

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Assessment version

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Edulastic: Formative and Summative Assessments Made Easy