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Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education 

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Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education 

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For those interested in scholarly critique of AI and its role in education, this active Google document, maintained by Dr. Maggie Fernandes (University of Arkansas), Dr. Megan McIntyre (University of Arkansas), Dr. Kat Gray (University of Arkansas), and Dr. Cara Marta Messina (Marist College), provides a collection that offers “resources on refusing, rejecting, and rethinking generative AI,” organized by theme.

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Resources on Refusing, Rejecting, and Rethinking Generative AI in Writing Studies and Higher Education

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Generative AI Syllabus Statement Tool from Seaver College

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Generative AI Syllabus Statement Tool from Seaver College

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This tool will help you craft your AI Syllabus Statement. There are prompts that help you consider various elements of the statement, such as:

An Introduction: What kind of overview of AI and various AI tools will you include?

Permissions: To what degree would you like your students to use AI? What limitations might you wish to impose?

Appropriate Citation: How will you convey the importance of students documenting their use of AI, including citing the tool they used and how the tools are used?

Warnings: What cautionary language about the accuracy of AI-produced material and possible copyright violations that may occur will you include?

Academic Integrity: How will you write a statement that reinforces that AI must not be used to circumvent academic rigor. Just as a scribe would not claim authorship of another's work, students must acknowledge when AI has contributed to their work. Encourage students to develop their own thoughts and perspectives, using AI to enhance, not replace, their learning. We cannot Google our own thoughts, and an important goal of university education is to develop one’s critical thinking abilities by deeply engaging with a variety of ideas, not just copying an AI produced piece.

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Generative AI Syllabus Statement Tool (Seaver College) Generative artificial intelligence tools - software that creates new text, images, computer code, audio, video, and other content by following patterns learned from large datasets - burst into international attention in November 2022 when OpenAI made its generative text tool ChatGPT widely available to the public.

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Developing an AI Syllabus Statement & Driving Class AI Discussion from NC State University

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Developing an AI Syllabus Statement & Driving Class AI Discussion from NC State University

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Guidance for Citing AI Tools

How to cite generative AI output (MLA)

How to cite generative AI output (APA)

How to cite generative AI output (Chicago)

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Developing an AI Syllabus Statement & Driving Class AI Discussion – Teaching Resources

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It’s important to have a conversation with students regarding how you expect them to use (or not use) AI tools in your course. The guidance you provide should align with other ethical standards expected of students in your course. You may want to refer to the Academic Integrity: Overview from the NC State University Office of Student Conduct and align any statement with the same spirit of academic integrity. As of August 2024, there is no official university policy regarding AI use. See also the NC State Office of Faculty Excellence: Navigating the Landscape of Higher Education in the Age of Artificial Intelligence site and additional recommendations from across the UNC System in this database.

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Crowdsourced syllabi policies from different disciplines

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This document is maintained by Lance Eaton. You are welcome to share it with other individuals, groups, and organizations. To view the policies, please select the "Policies" tab in this spreadsheet.

If you would like to submit your policy, please complete this form and it will show up here within 24-48 hours. Please do not request editing access to this document. If you want to add something please use the form. The Google Doc version of this resource can be found here.

If you have questions about this document, please direct them to Lance Eaton (lance.eaton@gmail.com). Created 1/16/2023. Last updated: 9/15/2023

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About this Document This document is maintained by Lance Eaton . You are welcome to share it with other individuals, groups, and organizations. To view the policies, please select the "Policies" tab in this spreadsheet. If you would like to submit your...

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Crowdsourced university policies

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4 Steps to Design an AI-Resilient Learning Experience

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4 Steps to Design an AI-Resilient Learning Experience

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MIT Sloan Teaching & Learning Technologies

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Teaching with Generative AI Resource Hub - MIT Sloan Teaching & Learning Technologies

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Explore our Teaching with Generative AI Resource Hub to unlock AI’s potential for enhancing teaching and learning at MIT Sloan.

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Follow the syntax to generate the QTI Zip file that you can import into Canvas Quiz

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What is a QTI converter?
This online tool will allow you to create Canvas Quizzes using a formatted* text entry. Using this tool will allow you to upload a set of quiz questions with correct answers instead of manually entering each question into Canvas and its associated answer one at a time.

When do you use this tool or Respondus 4.0?
All Mac users will use this tool instead of Respondus 4.0. PC users can use either option. Respondus 4.0 offers more options, such as adding images into your quiz questions, but these features require users to learn more technical skills to properly use the software. For more information about how to use Respondus 4.0, read this training SMORE or contact nyit.edu/itshelp for assistance.

What platforms does it support?
Mac and PC users alike can use this online tool, unlike Respondus 4.0, which only supports PC users.

How do you use this tool?
You use this tool by pasting the formatted* question text as described below into the text window on this website. Once you are finished, click the green "Check for Syntax Errors" button and make any last corrections. Once no errors are found, click the Convert to Canvas QTI file button. The web browser will automatically download the QTI zip file to your computer. You will import this file into your Canvas course using the "Import Course Content" option on the course settings page. Click Here for the Canvas guide on importing the QTI zip file. Once complete, you can publish or continue editing your Canvas Quiz as appropriate.

*Question Formatting
Each question must start with the question number followed by a period and space and then the question text. Each question must be separated with a single blank line
Examples:
Multiple Choice: Each choice needs to start with a lower case alphabet, a, b, c, d, etc. with a close parenthesis. The correct choice is designated with an asterisk.
1. What is 2+3?
a) 6
b) 1
*c) 5
d) 10

Multiple-answers: Each choice use [] or [ ] to make the incorrect answers and [*] for the correct answers.
1. Which of the following are dinosaurs?
[ ] Woolly mammoth
[*] Tyrannosaurus rex
[*] Triceratops
[ ] Smilodon fatalis

Short-answer (fill-in-the-blank): The correct answers will use an asterisk followed by one or more spaces or tabs followed by an answer.
1. Who lives at the North Pole?
* Santa
* Santa Claus
* Father Christmas
* Saint Nicholas
* Saint Nick

Essay: This type of question is indicated by a sequence of three or four hashtags.
1. Write an essay.
####

File-upload: This type of question is indicated by a sequence of three or four carets.
1. Upload a file.
^^^^

True/False: True and False statements start with a) and b). The correct choice is designated with an asterisk.
1. Water is liquid.
*a) True
b) False

QTI Conversion Form

Steps on uploading the QTI file to Quizzes in your course:

1. Go to your Canvas course.

2. Click the "Import Existing Content" button on the home page or select the "Import Course Content" on the course settings page.

3. Under Import Content and next to "Content Type" select "QTI .zip file" from the pull-down menu.

4. Click the "Choose File" button and select the zip file from the QTI Converter website.

5. Click the "Import" button.

Wait for the import process to complete and check the Quizzes link in your course for the quiz.

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A Short List of AI Resources from California Learning Lab

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ASCCC Academic Integrity Policies in the Age of AI Resource Document  

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ASCCC Academic Integrity Policies in the Age of AI Resource Document  

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Artificial Intelligence (AI) Resource Document published Spring 2024

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LATIMER LLM based on African American and Hispanic/Latinx people

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LATIMER LLM based on African American and Hispanic/Latinx people

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It is an LLM built to more accurately reflect the experience, culture. and history of African American and Hispanic/Latinx people. It's a good system to check our language that might be considered exclusive or offensive.

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This is Latimer, a large language model trained with diverse histories & inclusive voice built to more accurately reflect the experience, culture and history of Black and brown people.

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NotebookLM is a free, AI-powered tool from Google that helps users organize and analyze information. It's designed to help users learn, research, and collaborate on projects.

What it's used for

Learning: Students can use NotebookLM to understand complex theories

Research: Researchers can use NotebookLM to analyze data and dive deep into topics

Collaboration: Users can collaborate with NotebookLM to refine and organize their ideas

[Source: Google's AI Overview]

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Not your computer? Use Guest mode to sign in privately. Learn more about using Guest mode

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Dr. Benny Ng from Los Angeles Pierce College teaching gave a workshop how to build a custom bot using Poe

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Professional Development at Fresno City College

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More AI tools

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4 Strategies for Identifying AI-Generated Images

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4 Strategies for Identifying AI-Generated Images

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Learn tips to identify AI-generated images

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Are you looking at an AI-generated image? These days, it's hard to tell what was and wasn't generated by AI-thanks in part to a group of incredible AI image generators like DALL-E, Midjourney, and Stable Diffusion. But there is a way. Similar to identifying a Photoshopped picture, you can learn the markers that identify an AI image.

They often have bizarre visual distortions which you can train yourself to spot. And sometimes, the use of AI is plainly disclosed in the image description, so it's always worth checking. If all else fails, you can try your luck running the image through an AI image detector.

Not everyone agrees that you need to disclose the use of AI when posting images, but for those who do choose to, that information will either be in the title or description section of a post.

Another good place to look is in the comments section, where the author might have mentioned it. In the images above, for example, the complete prompt used to generate the artwork was posted, which proves useful for anyone wanting to experiment with different AI art prompt ideas.

Besides the title, description, and comments section, you can also head to their profile page to look for clues as well. Keywords like Midjourney or DALL-E, the names of two popular AI art generators, are enough to let you know that the images you're looking at could be AI-generated.

Some online art communities like DeviantArt are adapting to the influx of AI-generated images by creating dedicated categories just for AI art. When browsing these kinds of sites, you will also want to keep an eye out for what tags the author used to classify the image.

This extends to social media sites like Instagram or X (formerly Twitter), where an image could be labeled with a hashtag such as #AI, #Midjourney, #Dall-E, etc.

2. Look for a Watermark

Another important clue for identifying an AI-generated image is a watermark. DALL-E 2 places one on every image you download from its site, though it may not be obvious at first. Can you spot the watermark in the image above?

You can find it in the bottom right corner of the picture, it looks like five squares colored yellow, turquoise, green, red, and blue. If you see this watermark on an image you come across, then you can be sure it was created using AI.

The problem is, it's really easy to download the same image without a watermark if you know how to do it, and doing so isn't against OpenAI's policy. So long as you "don't mislead others about the nature of the work". For example, by telling them you made it yourself, or that it's a photograph of a real-life event. Outside of this, OpenAI's guidelines permit you to remove the watermark.

Midjourney, on the other hand, doesn't use watermarks at all, leaving it u to users to decide if they want to credit AI in their images.

3. Search for AI Distortions in the Image

You may not notice them at first, but AI-generated images often share some odd visual markers that are more obvious when you take a closer look.

From a distance, the image above shows several dogs sitting around a dinner table, but on closer inspection, you realize that some of the dog's eyes are missing, and other faces simply look like a smudge of paint.

The effect is similar to impressionist paintings, which are made up of short paint strokes that capture the essence of a subject. They are best viewed at a distance if you want to get a sense of what's going on in the scene, and the same is true of some AI-generated art. It's usually the finer details that give away the fact that it's an AI-generated image, and that's true of people too.

Take a closer look at the AI-generated face above, for example, taken from the website This Person Does Not Exist. Notice anything odd? It could fool just about anyone into thinking it's a real photo of a person, except for the missing section of the glasses and the bizarre way the glasses seem to blend into the skin.

While these anomalies might go away as AI systems improve, we can all still laugh at why the best AI art generators struggle with hands. Take a quick look at how poorly AI renders the human hand, and it's not hard to see why.

Extra fingers are a sure giveaway, but there's also something else going on. It could be the angle of the hands or the way the hand is interacting with subjects in the image, but it clearly looks unnatural and not human-like at all.

When it comes to photorealistic images of humans, there are several more AI markers you can look for:

Missing or mismatched earrings

A blurred background that looks more like a texture

Text in the background is indistinguishable or gibberish

Asymmetry in the face (teeth off center, eyes are different sizes)

Patches of the photo look as if it's been painted

Objects like glasses or earrings blend into the skin

Hands have extra fingers or clearly look awkward and unnatural

Even when looking out for these AI markers, sometimes it's incredibly hard to tell the difference, and you might need to spend extra time to train yourself to spot fake media.

4. Use an AI Image Detector

Some people are jumping on the opportunity to solve the problem of identifying an image's origin. As we start to question more of what we see on the internet, businesses like Optic are offering convenient web tools you can use.

The AI or Not web tool lets you drop in an image and quickly check if it was generated using AI. It claims to be able to detect images from the biggest AI art generators; Midjourney, DALL-E, and Stable Diffusion.

Take it with a grain of salt, however, as the results are not foolproof. In our tests, it did do a better job than previous tools of its kind. But it also produced plenty of wrong analysis, making it not much better than a guess.

Since the results are unreliable, it's best to use this tool in combination with other methods to test if an image is AI-generated. The reason for mentioning AI image detectors, such as this one, is that further development will likely produce an app that is highly accurate one day. It's worth keeping an eye out for developments.

Slowly but surely progress is being made. When Microsoft released a deep fake detection tool, positive signs pointed to more large companies offering user-friendly tools for detecting AI images.

What to Expect in the Future

Without a doubt, AI generators will improve in the coming years, to the point where AI images will look so convincing that we won't be able to tell just by looking at them. At that point, you won't be able to rely on visual anomalies to tell an image apart.

Hopefully, by then, we won't need to because there will be an app or website that can check for us, similar to how we're now able to reverse image search.

For now, people who use AI to create images should follow the recommendation of OpenAI and be honest about its involvement. It's not bad advice and takes just a moment to disclose in the title or description of a post.

At the very least, don't mislead others by telling them you created a work of art when in reality it was made using DALL-E, Midjourney, or any of the other AI text-to-art generators.

Spotting an AI-Generated Image

AI images are getting better and better every day, so figuring out if an artwork was made by a computer will take some detective work.

Check the title, description, comments, and tags, for any mention of AI, then take a closer look at the image for a watermark or odd AI distortions. You can always run the image through an AI image detector, but be wary of the results as these tools are still developing towards more accurate and reliable results.

At the end of the day, using a combination of these methods is the best way to work out if you're looking at an AI-generated image.

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How to Identify an AI-Generated Image: 4 Ways

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Differentiating between AI-generated images and real ones is becoming increasingly difficult. But there are a few tricks you can try.

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Game: Practice Detecting Text Written by AI

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Game: Practice Detecting Text Written by AI

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This project helps you practice identifying AI-generated text. You'll learn the characteristics of AI-generated text and apply your skills to detect various types of AI-generated content, such as short stories and news articles.

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How good are you at knowing when text has been written by a computer? How many sentences can a computer write before it no longer has you fooled? Find out for yourself!

Start Game (Random Category)

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How good are you at knowing when text has been written by a computer? How many sentences can a computer write before it no longer has you fooled? Find out for yourself! Start Game (Random Category)

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Copyright and Artificial Intelligence | U.S. Copyright Office

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How AI misrepresents culture through facial expression.

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Imagine a time traveler journeyed to various times and places throughout human history and showed soldiers and warriors of the periods what a "selfie" is.

This is the premise for a series of AI-generated images posted on r/midjourney. Below are a few examples of the images this prompt produced:

There are 18 images in the Reddit slideshow and they all feature the same recurring composition and facial expression. For some, this sequence of smiling faces elicits a sense of warmth and joyousness, comprising a visual narrative of some sort of shared humanity (so long as one pays no attention to the incongruousness of Spanish Conquistadors smiling happily next to Aztec warriors. Awkward.) But what immediately jumped out at me is that these AI-generated images were beaming a secret message hidden in plain sight. A steganographic deception within the pixels, perfectly legible to your brain yet without the conscious awareness that it's being conned. Like other AI " hallucinations," these algorithmic extrusions were telling a made up story with a straight face - or, as the story turns out, with a lying smile.

Why do you smile the way you do? A silly question, of course, since it's only "natural" to smile the way you do, isn't it? It's common sense. How else would someone smile?

As a person who was not born in the U.S., who immigrated here from the former Soviet Union, as I did, this question is not so simple. In 2006, as part of her Ph.D. dissertation, "The Phenomenon of the Smile in Russian, British and American Cultures," Maria Arapova, a professor of Russian language and cross-cultural studies at Lomonosov Moscow State University, asked 130 university students from the U.S., Europe, and Russia to imagine they had just made eye contact with a stranger in a public place - at the bus stop, near an elevator, on the subway, etc.

Which, she asked the participants, would you do next?

A) smile and then look away
B) look away
C) gaze at his eyes, then look away

90% of Americans and Europeans chose the option with a smile in it. Only 15% of Russians did.

How we smile, when we smile, why we smile, and what it means is deeply culturally contextual. In the 2018 Nautilus essay, "What a Russian Smile Means," French-American journalist Camille Baker writes about how the meaning of a smile differs across societies.

In 2015 Kuba Krys, a researcher at the Polish Academy of Sciences, studied the reactions of more than 5,000 people from 44 cultures to a series of photographs of smiling and unsmiling men and women of different races. He and his colleagues found that subjects who were socialized in cultures with low levels of "uncertainty avoidance" - which refers to the level at which someone engages with norms, traditions, and bureaucracy to avoid ambiguity - were more likely to believe that smiling faces looked unintelligent. These subjects considered the future to be uncertain, and smiling - a behavior associated with confidence - to be inadvisable. Russian culture ranks very low on uncertainty avoidance, and Russians rate the intelligence of a smiling face significantly lower than other cultures.

Krys's team also found that people from countries with high levels of government corruption were more likely to rate a smiling face as dishonest. Russians - whose culture ranked 135 out of 180 in a recent worldwide survey of corruption levels - rated smiling faces as honest with less frequency than 35 of the 44 cultures studied. Corruption corrupts smiling, too.

Russians interpret the expressions of their officials and leaders differently from Americans. Americans expect public figures to smile at them as a means of emphasizing social order and calm. Russians, on the other hand, find it appropriate for public officials to maintain a solemn expression in public, as their behavior is expected to mirror the serious nature of their work. A toothy "dominance smile" from an important American public figure inspires feelings of confidence and promise in Americans. Russians expect, instead, a stern look from their leaders meant to demonstrate "serious intentions, validity, and reliability."

Which is how an AI trained on a dataset dominated by a culture that takes photos like this:

Would insist that "Native American warriors" posing for a photo would have looked like this:

Rather than how actual historical photos look like:

Native American chiefs, 1865

Group of Apache Men, with Chief Al-Che-Zay (Middle Row, Dark Vest)

(Worth noting that these photos themselves were taken by photographers like Edward Curtis and others whose own colonizing point of view influenced how Native peoples were represented, so these images too come with an imposed, outside perspective.)

Or that "Ancient Polynesian Warriors" would have taken a selfie like this:

When the traditional Māori Haka ceremony looks like this:

Or that Soviet soldiers posing for a selfie would have looked like this:

When Eastern European soldiers posing for an actual selfie in 2023 look like this:

Every American knows to say "cheese" when taking a photo, and, therefore, so does the AI when generating new images based on the pattern established by previous ones. But it wasn't always like this. More than a century after the first photograph was captured, a reference to "cheesing" for photos first appeared in a local Texas newspaper in 1943. "Need To Put On A Smile?" the headline asked, "Here's How: Say 'Cheese.'" The article quoted former U.S. ambassador Joseph E. Davies who explained that this influencer photo hack would be "Guaranteed to make you look pleasant no matter what you're thinking [...] it's an automatic smile." Davies served as ambassador under Franklin D. Roosevelt to the U.S.S.R.

As the old Soviet joke goes, how can you tell that someone is an American in Russia?

They're smiling.

But how does AI tell when someone is most likely lying? They're smiling like an American.

In 2018, researchers at the University of Rochester conducted an experiment to see how deception is connected to facial expressions. Participants were paired up into describer and interrogator roles. The describer was shown an image and told to memorize it with as much detail as possible. They were then instructed to either lie or tell the truth about what they'd just seen to the interrogator, who was unaware of the instructions given to the describer. The recorded exchanges between 151 pairs of individual participants yielded 1.3 million frames of facial expressions. The researchers then used machine learning to automatically find patterns. Without any predetermined labels or categories, the results identified the expression most frequently associated with lying: a "high intensity version" of the Duchenne smile - a smile that extends to both the cheek/eye and mouth muscles.

The taut, grimacing, duplicitous rictus - the modern American smile - rose out of a great emotional shift in the 18th century, theorizes Christina Kotchemidova, who teaches theory, gender, and intercultural communication at Spring Hill College in Alabama. But it is also based on a lie.

As Baker writes:

Prior to this shift, [Kotchemidova] believes, the American emotional landscape revolved around negative emotions like sadness and melancholy, which were seen as indicative of compassion and nobleness. Informed by ideas from pre- and early Reformation European Christianity, both Americans and Europeans saw earthly suffering as noble and necessary for a happy afterlife. Literature, visual art, and theater in this period aimed to provoke sadness, and crying in public was commonplace in Europe. Diderot and Voltaire, Kotchemidova writes, were seen crying repeatedly.

The Age of Enlightenment pushed the culture in a different direction. As thinkers and artists embraced reason, they also began to believe that happiness was permissible during our earthly life as well as the afterlife. The culture of sadness began to be supplanted by one of cheerfulness, which in turn influenced a changing class structure. The emerging middle class took the ability to manage emotions as key to its identity. Business failures and sickness were linked to failures of emotional control, and cheerfulness to prosperity. Eventually, cheerfulness became a prerequisite for employment.

"The expectation was, you have to smile eight hours a day," a woman Baker calls Sofiya tells her. A 41-year-old Russian émigré who had been living in the United States for the past decade, Sofiya "was a proficient English speaker," Baker writes, but it was in her job as a bank teller that she "came face-to-face with her deficiency in speaking 'American.' This other English language, made up of not just words but also facial expressions and habits of conversation subtle enough to feel imagined. Smiling almost constantly was at the core of her duties as a teller. As she smiled at one customer after another, she would wince inwardly at how silly it felt. There was no reason to smile at her clients, she thought, since there was nothing particularly funny or heartwarming about their interactions. And her face hurt."

This confrontation with the culture clash of smiling for an Eastern European immigrant in America hits close to home. Which is why seeing the relentless parade of toothy, ahistorical, quintessentially American, "cheese" smiles plastered on the faces of every civilization in the world across time and space was immediately jarring. It was as if the AI had cast 21st century Americans to put on different costumes and play the various cultures of the world. Which, of course, it had.

In her groundbreaking book, How Emotions are Made: The Secret Life of the Brain, Lisa Feldman Barrett, a neuroscientist and psychology professor at Northeastern University writes:

Most scientific research on emotion is conducted in English, using American concepts and American emotion words (and their translations). According to noted linguist Anna Wierzbicka, English has been a conceptual prison for the science of emotion. "English terms of emotion constitute a folk taxonomy, not an objective, culture-free analytic framework, so obviously we cannot assume that English words such as disgust, fear, or shame are clues to universal human concepts, or to basic psychological realities." To make matters even more imperialistic, these emotion words are from twentieth-century English, and there's evidence that some are fairly modern. The concept of "Emotion" itself is an invention of the seventeenth century. Before that, scholars wrote about passions, sentiments, and other concepts that had somewhat different meanings.

Different languages describe diverse human experience in different ways - emotions and other mental events, colors, body parts, direction, time, spatial relations, and causality. The diversity from language to language is astonishing.... Not all cultures understand emotions as mental states. The Ifaluk of Micronesia consider emotions transactions between people. To them, anger is not a feeling of rage, a scowl, a pounding fist, or a loud yelling voice, all within the skin of one person, but a situation in which two people are engaged in a script - a dance, if you will - around a common goal. In the Ifaluk view, anger does not "live" inside either participant.

In the same way that English language emotion concepts have colonized psychology, AI dominated by American-influenced image sources is producing a new visual monoculture of facial expressions. As we increasingly seek our own likenesses in AI reflections, what does it mean for the distinct cultural histories and meanings of facial expressions to become mischaracterized, homogenized, subsumed under the dominant dataset? In the AI-generated visual future, will we know that Native Americans didn't smile for photos like WW2 U.S. Navy Officers?

Did U.S. WW2 Navy officers even for that matter?

Every 2 weeks a language is spoken by a human on Earth for the last time. A third of the world's languages have fewer than 1,000 speakers left. By the end of this century, 50% to 90% of languages are predicted to disappear. "When humanity loses a language, we also lose the potential for greater diversity in art, music, literature, and oral traditions," says Bogre Udell co-founder of the nonprofit, Wikitongues. And with them, we lose vital concepts for making sense of ourselves and our inner experiences. In the future, how will we know how to be who the algorithm doesn't show us we can be? Would we even dare to want to? And what would that kind of totalizing assimilation mean for global mental health, wellbeing, and the human experience in general?

It's so much easier for a machine to fabricate a new language (out of the raw material created by humans) -

Than for the diversity of human expression to survive algorithmic hegemony.

"The concept of a facial 'expression,'" writes Barrett, "Implies an internal feeling that seeks release in a set of facial movements." In flattening the diversity of facial expressions of civilizations around the world AI had collapsed the spectrum of history, culture, photography, and emotion concepts into a singular, monolithic perspective. It presented a false visual narrative about the universality of something that in the real world - where real humans have lived and created culture, expression, and meaning for hundreds of thousands of years - is anything but uniform.

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Imagine a time traveler journeyed to various times and places throughout human history and showed soldiers and warriors of the periods what a "selfie" is. This is the premise for a series of...

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Using machine learning to improve student success in Higher Education (2022)

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Using machine learning to improve student success in higher education

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How advanced analytics and machine learning in higher education advance student success

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Do AI outputs contain racial bias and systemic racism? How can we help our students navigate these issues?

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Artificial Intelligence and the Future of Racial Justice | S. Craig Watkins | TEDxMIT

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How can we use AI to serve the community good?

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Tech Equity and Serving the Community | Afua Bruce | TEDxFoggyBottom

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Our students might need to know how to use AI tools in their future careers.

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How AI Could Empower Any Business | Andrew Ng | TED

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Toward the bottom of this Scribbr article (2023), you'll find an alphabetical listing of colleges and universities with accompanying links to their institutional policies.

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AI writing tools like ChatGPT are a hot topic at universities right now. Educators are in the process of working out how to respond to these tools: whether and how students should be allowed to use them in their assignments, and how they can be used in ways that are consistent with academic integrity.

These technologies are still new, and developing policies about their use takes time, so many students are unsure exactly what is allowed right now. This article summarizes the general approach universities are taking so far and presents a table with links to specific resources from 100 top universities.

Currently, individual instructors normally decide what is allowed in their classes. This means that you should check your syllabus or ask your instructor directly if you're unsure what's acceptable in your case. For more details, read on.

NoteWe plan to update this article periodically to reflect the current state of the conversation as more universities develop, publish, and rework their policies. Check back in the future if the information you're looking for isn't here yet.

University stances so far

Our research indicates that not many universities have a fully developed policy on AI writing yet. The resources currently available from universities are mainly guidelines for instructors, and they typically state that there are no specific university-wide rules regarding these tools.

Rather than following one blanket policy, instructors are usually advised to decide their own policy and communicate it clearly to students in their syllabus and in class. The main policies your instructor might decide on are:

Banning the use of AI writing tools for assignments: Instructors may decide that these tools are incompatible with the intended learning outcomes of your class and prohibit their use entirely. This is likely to be the most common policy for now.

Allowing AI writing tools in some cases: You may be told to use AI only for specific purposes (e.g., only for research, not writing), only for special assignments, or only when you ask permission first. You may also have to cite ChatGPT (or whatever tool you used) and describe how you used it.

Allowing AI writing tools generally, when appropriately cited: Some instructors may decide to allow the use of these tools in any assignment. They will usually still require you to cite them and possibly to describe in detail how they were used.

While it's up to individual instructors to determine their policies, few instructors right now will allow you to use AI writing tools freely without citing them. Universities generally agree that presenting AI-generated writing as your own work is plagiarism (or at least academic dishonesty).

Tip
If you're unsure about the specific policy in your case, check your syllabus for guidance or ask your instructor. When asking, be specific about what use cases you're interested in (e.g., "May I use ChatGPT to develop my research question?"-not just "May I use ChatGPT?").
By default, you should assume that AI writing tools are not allowed until you know your instructor's specific policy. The consequences of plagiarism and academic dishonesty can be serious, so make sure you know where you stand. It's better to be safe than sorry.

List of university policies and guidelines

The table below provides links to resources on the current policies or guidelines of 100 top US universities.

In each case, we tried to find the most definitive guidance available from the university. Because this technology is developing quickly and universities are in the middle of a semester, they normally don't have a definitive policy statement yet. Most commonly, we were able to find:

Resources for instructors, advising them on how to develop their own policy on these tools

Statements from faculty quoted in news articles

Statements from faculty in public forum discussions

The table is organized alphabetically by university name and divided into three tabs for ease of navigation. Just find and click on your university's name. If you can't find information on your university, it's always best to ask your instructor directly or check your syllabus.

Other interesting articles

If you want to know more about ChatGPT, AI tools, fallacies, and research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions

Can I have ChatGPT write my paper?

No-first, because it's considered to represent someone else's work as your own (even if that "someone" is an AI language model). Even if you plagiarism cite ChatGPT, you'll likely still be penalized.

Second, ChatGPT can recombine existing texts, but it cannot really generate new knowledge. And it lacks specialist knowledge of academic topics. Therefore, it is not possible to obtain original research results, and the text produced is likely to contain factual errors.

However, you can usually still use ChatGPT for assignments in other ways, as a source of inspiration and feedback.

Is ChatGPT a credible source?

No, ChatGPT is not a credible source of factual information and can't be cited for this purpose in academic writing. While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency, relevance, authority, accuracy, and purpose. ChatGPT fails to meet at least three of them:

Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.

Authority: It's just a language model and is not considered a trustworthy source of factual information.

Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources.

So you shouldn't cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons-for example, if you're writing a paper about AI language models, ChatGPT responses are a relevant primary source.

Yes, in some contexts it may be appropriate to cite ChatGPT in your work, especially if you use it as a primary source (e.g., you're studying the abilities of AI language models).

Some universities may also require you to cite or acknowledge it if you used it to help you in the research or writing process (e.g., to help you develop research questions). Check your institution's guidelines.

Since ChatGPT isn't always trustworthy and isn't a credible source, you should not cite it as a source of factual information.

In APA Style, you can cite a ChatGPT response as a personal communication, since the answers it gave you are not retrievable for other users. Cite it like this in the text: (ChatGPT, personal communication, February 11, 2023).

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the "Cite this Scribbr article" button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2023, April 24). University Policies on AI Writing Tools | Overview & List. Scribbr. Retrieved May 1, 2023, from https://www.scribbr.com/ai-tools/chatgpt-university-policies/

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University Policies on AI Writing Tools | Overview & List

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AI writing tools like ChatGPT are a hot topic at universities right now. Educators are in the process of working out how to respond to these tools: whether and how students should be allowed to use them in their assignments, and how they can be used in ways that are consistent with academic integrity.

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Over 126 syllabi policies on AI usage for your reference

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Syllabi Policies for AI Generative Tools

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AI Policy, Lesson ideas using AI, Syllabus statements. Credit: Patricia James and Steven Mentor from Evergreen Valley College

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Always here to listen and talk. Always on your side. Join the millions growing with their AI friends now!

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Another Padlet with various AI tools

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AI @ WMU: A Deep Dive into the Resources

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AI @ WMU: A Deep Dive into the Resources

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It has articles for the following topics:

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Bringing together education leaders and technology experts  to guide the safe, effective, and responsible use of AI in schools  by connecting the discussion of teaching with AI to teaching about AI.

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More than just a portfolio builder, Inkwire helps learners curate their work for specific audiences and milestones in their academic journeys. Portfolio Builder Without having to learn an advanced design tool, learners will spend less time fiddling with layout and more time curating and reflecting on their work.

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Over 300 AI Tools in this database

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At Edtech Insiders, we offer breaking Edtech industry news through podcasts, newsletters, and live events. We also facilitate community connection in the edtech industry both online and in person.

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Referenced Lists of AI Tools in Education

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The AI Tools in Education Database | Notion

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At Edtech Insiders, we offer breaking Edtech industry news through podcasts, newsletters, and live events. We also facilitate community connection in the edtech industry both online and in person.

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Almanack | AI Lesson Planning Made Easy

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Guidance for generative AI in education and research

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Guidance for generative AI in education and research

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Source: UNESCO

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Guidance for generative AI in education and research

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UNESCO’s first global guidance on GenAI in education aims to support countries to implement immediate actions, plan long-term policies and develop human capacity to ensure a human-centred vision of

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The POUR principles of Designing Accessible Content

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The POUR principles of Designing Accessible Content

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Perceivable

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Want to ensure the materials you create are accessible to all your learners? The POUR principles are a great place to start. These principles define four qualities of an accessible experience and they are at the foundation of the Web Content Accessibility Guidelines (WCAG) an international standard for making web content accessible.

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Designing for Accessibility - AEM Center

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Learn how to create an accessible experience with the POUR principles.

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The Universal Design for Learning (UDL) approach is essential for all types of teaching, whether online or in-person. It focuses on creating an inclusive learning environment that addresses the needs of all learners and eliminates barriers.

Accessibility is important for everyone, not just those who self-identify as needing accommodations. Inclusive design embraces diversity and allows users to access diverse resources and different learning modalities.

Credit: Saša Stojić-Ito, CPACC

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Why Accessibility Matters in Course Design The Universal Design for Learning (UDL) approach is critical whether you teach entirely online, hybrid, blended, hyflex, or face-to-face classes. A good course starts with a road map and inclusive accessibility guidelines to ensure an optimal environment…

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Creating Accessible STEM Materials by National Center on Accessible Educational Materials

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Creating Accessible STEM Materials by National Center on Accessible Educational Materials

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Creating Accessible STEM Materials - AEM Center

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Learn how to minimize barriers to science, technology, engineering, and math content.

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SLCC Directory 801-957-4111

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How do I use the Math Editor in the Rich Content Editor as an instructor?

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How do I use the Math Editor in the Rich Content Editor as an instructor?

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Canvas comes with a built-in tool for creating math and science formulas using LaTeX, which is widely used in academic publishing. The LaTeX Math Editor is accessible through the Rich Content Editor and is supported in various features of Canvas such as Announcements, Assignments, Discussions, Pages, Quizzes, and Syllabus. Additionally, Canvas provides a graphical point-and-click editor for creating equations and expressions.

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Basic equations

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Equation images are stored as absolute (full website) URLs which may affect course copies.

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Canvas has an integrated tool for math and science formulas based on LaTeX, the industry standard for academic publication. The LaTeX Math Editor is built into the Rich Content Editor. Several features in Canvas support the Rich Content Editor, including Announcements, Assignments, Discussions, Pages, Quizzes, and Syllabus. Canvas also includes the option to create equations and expressions with its graphical point-and-click editor.

The Math Editor can be used for basic mathematical formatting for introductory math courses or for more advanced mathematical text for higher-level math courses. Both students and instructors have access to the editor.

For more help, view the following resource documents:
Notes:

To have characters #, $, %, &, ^, _, {, and } appear in the Rich Content Editor, when your equation is inserted, you must use Advanced View and type a backslash (\) before the character. The ~ character can be inserted through Advanced View by typing \~~. The $ character can be inserted through Basic View by typing \$$.

Equation images are stored as absolute (full website) URLs which may affect course copies.

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How do I use the Math Editor in the Rich Content Editor as an instructor?

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Canvas has an integrated tool for math and science formulas based on LaTeX, the industry standard for academic publication. The LaTeX Math Editor is built into the Rich Content Editor. Several features in Canvas support the Rich Content Editor, including Announcements, Assignments, Discussions, Page...

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Wake Forest University's Center for Advancement of Teaching

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Wake Forest University's Center for Advancement of Teaching

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You can input different assignments (reading, writing, discussion, other assignments, exams, class meetings, etc.) and receive workload estimates.

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Workload Estimator 2.0 - Center for the Advancement of Teaching

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The Center for the Advancement of Teaching aims to advance passionate, reflective, and evidence-informed teaching. We contribute to Wake Forest's distinctive mission by encouraging the development of teacher-student relationships that prepare all students to live examined, purposeful lives. Learn More >>

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Cornell's Center for Teaching Innovation

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Cornell's Center for Teaching Innovation

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This page introduces assessment methods to measure student learning.

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Assessment methods should help the instructor answer the questions, "How do I know the required learning has taken place? What might I need to modify about the course to best support student learning?"

Information about student learning can be assessed through both direct and indirect measures. Direct measures may include homework, quizzes, exams, reports, essays, research projects, case study analysis, and rubrics for oral and other performances. Examples of indirect measures include course evaluations, student surveys, course enrollment information, retention in the major, alumni surveys, and graduate school placement rates.

Approaches to measuring student learning

Methods of measuring student learning are often characterized as summative or formative assessments:

Summative assessments - tests, quizzes, and other graded course activities that are used to measure student performance. They are cumulative and often reveal what students have learned at the end of a unit or the end of a course. Within a course, summative assessment includes the system for calculating individual student grades.

Formative assessment  - any means by which students receive input and guiding feedback on their relative performance to help them improve. It can be provided face-to-face in office hours, in written comments on assignments, through rubrics, and through emails.

Formative assessments can be used to measure student learning on a daily, ongoing basis. These assessments reveal how and what students are learning during the course and often inform next steps in teaching and learning. Rather than asking students if they understand or have any questions, you can be more systematic and intentional by asking students at the end of the class period to write the most important points or the most confusing aspect of the lecture on index cards. Collecting and reviewing the responses provides insight into what themes students have retained and what your next teaching steps might be. Providing feedback on these themes to students gives them insight into their own learning.

You can also ask students to reflect and report on their own learning. Asking students to rate their knowledge about a topic after taking your course as compared to what they believe they knew before taking your course is an example.

Considerations for Measuring Student Learning

As you develop methods for assessing your students consider:

including indirect and direct assessments as well as formative and summative assessments

evaluating whether or not the assessment aligns directly with a learning outcome

ensuring the measurement is sustainable and reasonable in terms of time and resources, both for the students and the instructors (e.g., grading, response time, and methods). To estimate the time that students need to complete different assignments, see the Rice University workload calculator

using a mid-semester student survey, such as the CTI's Mid-Semester Feedback Program, a great way to gather feedback on what students are learning and what is helping them learn

using the results of the assessments to improve the course. Examples include revising course content in terms of depth vs. breadth, realignment between goals and teaching methods, employment of more appropriate assessment methods, or effective incorporation of learning technologies

Getting started with measuring student learning

At the course level, it is helpful to review course assignments and assessments by asking:

What are the students supposed to get out of each assessment?

How are the assessments aligned with learning outcomes?

What is its intrinsic value in terms of:

Knowledge acquired?

Skill development?

Values clarification?

Performance attainment?

How are homework and problem sets related to exams?

How are the exams related to each other?

What other forms of assessment (besides exams) can be used as indicators of student learning?

If writing assignments are used, are there enough of them for students to develop the requisite skills embedded in them?

How is feedback on student work provided to help students improve?

Are the assessments structured in a way to help students assess their own work and progress?

Does the assignment provide evidence of an outcome that was communicated? Is the evidence direct or indirect?

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Assessment methods should help the instructor answer the questions, "How do I know the required learning has taken place? What might I need to modify about the course to best support student learning?" Information about student learning can be assessed through both direct and indirect measures.

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The Complete List of Teaching Methods by UCSD

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The Complete List of Teaching Methods by UCSD

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Teaching Methods: A to Z provides a list of teaching methods that can spike up our teaching (pg. 9 - 11 of the PDF file)

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AI Tools & Resources for Higher Education

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AI Tools & Resources for Higher Education

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Please contribute additional AI tools and resources to Northwest Nazarene University

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Open Educational Resources by Disciplines

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Open Educational Resources by Disciplines

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ASCCC Open Educational Resources (OER) by Discipline

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Image Sources for Diversity and Inclusion, Art, Science, openly licensed images, and public domain images organized by ASCCC Open Educational Resources Initiative (OERI).

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Moreno Valley College provides a LibGuide about LibreTexts

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Sketchfab 3D models of Minerals by Museum of Mineralogy and Petrography, UAIC (@MineralogyPetrographyMuseum) -

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Open Resources for Botany and Horticulture

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College of the Redwoods: OER Botany Lab Manual by Professor Maria Morrow and Illustrated by Nikki Harris

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Founded in 2002 by Nobel Laureate Carl Wieman, the PhET Interactive Simulations project at the University of Colorado Boulder creates free interactive math and science simulations. PhET sims are based on extensive education research and engage students through an intuitive, game-like environment where students learn through exploration and discovery.

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Recent articles:

Cook-Sather, A., & Cook-Sather, M. (2023). From Reporting to Removing Barriers: Toward Transforming Accommodation Culture into Equity Culture. Education Sciences, 13(6):611. https://doi.org/10.3390/educsci13060611

Cook-Sather, A., Salmeron, D., & Smith, T. (2023). Centering Humanism in STEM Education through Student-Faculty Pedagogical Partnerships. Frontiers in Education, 8. https://www.frontiersin.org/articles/10.3389/feduc.2023.1153087/full

Cook-Sather, A. & Nguyen, V. (2023). Using Shared Google Docs to Co-Create Life-Affirming Learning: A Case of Trauma-Informed Instructional Design at the Tertiary Level. The Journal of Applied Instructional Design, 12(1). https://dx.doi.org/10.51869/121/acsvn

Cook-Sather, A., Stewart, K., Ameyaa, R. A., & Jones, L. A. (2023). Co-creating Spaces for Full Selves: Student-Facilitated Pedagogy Circles for BIPOC Faculty. The Journal of Faculty Development 37(1), 69-71.

Recent books:

Cook-Sather, A. (2022). Co-Creating Equitable Teaching and Learning: Structuring Student Voice into Higher Education. Cambridge: Harvard Education Press.

de Bie, A., Marquis, E., Cook-Sather, A., & Luqueño, L. P. (2021). Promoting Equity and Justice through Pedagogical Partnership.

Cook-Sather, A., & Wilson C. (Eds.) (2020).Building Courage, Confidence, and Capacity in Learning and Teaching through Student-Faculty Partnership: Stories from across Contexts and Arenas of Practice.Lexington Books.

Cook-Sather, A., Bahti, M., & Ntem, A. (2019). Pedagogical Partnerships: A How-to Guide for Faculty, Students, and Academic Developers in Higher Education. Elon University Center for Engaged Learning Open Access Series.

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AI - Today's Dumbest Genius | Lew Ludwig | TEDxDenisonU (9:19)

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AI - Today's Dumbest Genius | Lew Ludwig | TEDxDenisonU (9:19)

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Professor Ludwig has spent over two decades shaping minds and pushing the boundaries of education. Brace yourselves for "AI: Today’s Dumbest Genius." In this provocative exploration, Professor Ludwig will take us on a journey from the basics of generative AI to its broader implications in education and society. Lew Ludwig is the Director of the Center for Learning and Teaching and Professor of Mathematics at Denison University. With over two decades of experience in higher education, Lew has been at the forefront of integrating generative AI into the academic landscape. He is recognized for his innovative approach in leading over 20 workshops, webinars, and keynotes on generative AI in higher education, spanning from local to national stages. His expertise is not just limited to AI; he's also a skilled educator in mathematics, having contributed to numerous publications and projects, including the 'MAA Instructional Practices Guide Project' with significant NSF funding. Lew's passion for enhancing education through innovative teaching methodologies and fostering inclusive learning environments has earned him numerous accolades, including the prestigious 2021 POD Innovation Award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

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AI - Today's Dumbest Genius | Lew Ludwig | TEDxDenisonU

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Ask the Cognitive Scientist The Privileged Status of Story by Daniel Willingham

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Ask the Cognitive Scientist The Privileged Status of Story by Daniel Willingham

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It describes how we seem to recall information in stories better than in other formats. This is expanded upon in Ch. 3 of his book "Why Don't Students Like School? (2nd Edition): A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom." by Bill Goffe, Teaching Professor, Department of Economics at Penn State University

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Once Upon an Algorithm: How Stories Explain Computing

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Once Upon an Algorithm: How Stories Explain Computing

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Explains how different types of stories can be applied to explain various problem-solving techniques

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This easy-to-follow introduction to computer science reveals how familiar stories like Hansel and Gretel, Sherlock Holmes, and Harry Potter illustrate the concepts and everyday relevance of computing. , Martin Erwig explains computation as something that takes place beyond electronic computers, and computer science as the study of systematic problem solving. Erwig points out that many daily activities involve problem solving. Getting up in the morning, for example: You get up, take a shower, get dressed, eat breakfast. This simple daily routine solves a recurring problem through a series of well-defined steps. In computer science, such a routine is called an algorithm.illustrates the problem of unsolvability; Sherlock Holmes manipulates data structures when solving a crime; the magic in Harry Potter's world is understood through types and abstraction; and Indiana Jones demonstrates the complexity of searching. Along the way, Erwig also discusses representations and different ways to organize data; "intractable" problems; language, syntax, and ambiguity; control structures, loops, and the halting problem; different forms of recursion; and rules for finding errors in algorithms.
Picture a computer scientist, staring at a screen and clicking away frantically on a keyboard, hacking into a system, or perhaps developing an app. Now delete that picture. In Once Upon an Algorithm Groundhog Day

Erwig illustrates a series of concepts in computing with examples from daily life and familiar stories. Hansel and Gretel, for example, execute an algorithm to get home from the forest. The movie

This engaging book explains computation accessibly and shows its relevance to daily life. Something to think about next time we execute the algorithm of getting up in the morning.

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Once Upon an Algorithm: How Stories Explain Computing

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Once Upon an Algorithm: How Stories Explain Computing [Erwig, Martin] on Amazon.com. *FREE* shipping on qualifying offers. Once Upon an Algorithm: How Stories Explain Computing

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The Science of Storytelling by Will Storr

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The Science of Storytelling by Will Storr

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Beautifully written and intellectually stimulating - recommended by Sarah Rose Cavanagh, Senior Associate Director for Teaching & Learning, Center for Faculty Excellence, Associate Professor of Practice, Psychology

Simmons University

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Read 710 reviews from the world's largest community for readers. Stories mould who we are, from our character to our cultural identity. They drive us to ac...

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Personal favorite games: Gold Quest, Deceptive Dinos, and Cafe.

Why Blooket over Kahoot!? I have been a Kahoot! user (free account) since 2016, but Kahoot! has slowly decreased the number of participants (5 - 10 students) in the free account since the pandemic. The current free account of Blooket allows up to 60 students, and the game mechanics allow "weaker" students to win the game as well.

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"Planétes" - Tabletop RPG for Learning Italian

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"Planétes" - Tabletop RPG for Learning Italian

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The game is available in two versions, both offered in English and Italian:

1. Planétes in pamphlet format (thanks to the layout done by Erik Iudica), ready to print and play;

2. Planétes in textual form complete with examples and bibliography/ludography, along with corollary explanations of learning goals, best practices, and recommendations for playing.

We recommend downloading both files. If you would like to read more about the rational behind creating Planétes, soon there will be an article in the peer-reviewed journal Italica.

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Welcome to Planétes, a language-learning tabletop role-playing game in which you will create cities and communities, and you will become Dwellers and Wanderers. While shaping and exploring your communities, you will discuss and compare your values, and decide whether to stay in your city or explore new ones. Throughout this process, you will use the target language to interact with other communities, enriching your perspective and reflecting on your experience without relying on familiar structures.
Planétes is a micro language-learning tabletop role-playing game (TTRPG) created by Alessia Caviglia and Camilla Zamboni, which will be published in the peer-reviewed volume "Roll for Learning" for ETC Press (forthcoming in 2024). It was created with the goal of exploring how TTRPGs could encourage and structure interaction in the target language, and which affordances of TTRPGs are most effective in fostering language learning.

The game is available in two versions, both offered in English and Italian:

1. Planétes in pamphlet format (thanks to the layout done by Erik Iudica), ready to print and play;

2. Planétes in textual form complete with examples and bibliography/ludography, along with corollary explanations of learning goals, best practices, and recommendations for playing.

We recommend downloading both files. If you would like to read more about the rational behind creating Planétes, soon there will be an article in the peer-reviewed journal Italica.

Also, if you would like to know more about the Planétes project and be part of it, we are looking for playtesting feedback - please use this form.

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Vi diamo il benvenuto a Planétes, un gioco in cui creerete città e comunità, diventando abitanti e viandanti. Dando forma ed esplorando le vostre comunità, discuterete e metterete i vostri valori a confronto e deciderete se mettere radici nelle vostre città o visitarne di nuove. Nel corso di questo processo, userete la vostra lingua target per interagire,arricchendo la vostra prospettiva e riflettendo sull'esperienza di gioco senza appoggiarsi a strutture linguistiche familiari.
Planétes è un micro GDR per l'apprendimento L2/LS creato da Alessia Caviglia e Camilla Zamboni, che sarà pubblicato nel volume "Roll for Learning" per ETC Press (nel 2024). È stato creato con l'obiettivo di esplorare come i GDR possono favorire e strutturare l'interazione orale in una lingua target, e quali aspetti del design di GDR sono più efficaci nel promuovere l'apprendimento linguistico.

Il gdr è disponibile in due versioni, entrambe offerte in inglese e italiano:

1. Planétes in formato depliant (grazie all'impaginazione fatta da Erik Iudica), pronto per essere stampato e giocato;

2. Planétes in forma testuale completo con esempi e bibliografia/ludografia, con spiegazioni corollarie degli obiettivi educativi, strategie per l'implementazione in classe, e raccomandazioni per giocare.

Raccomandiamo di scaricare entrambi i documenti. Se volete saperne di più sulla teoria che ha portato alla creazione di Planétes, a breve ci sarà un articolo pubblicato sulla rivista accademica Italica.

Inoltre, se volete saperne di più sul progetto Planétes e vi interessa farne parte, stiamo cercando feedback più strutturato da playest - usate questo questionario (in inglese).

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A multi-table, map-drawing, and city-exploring TTRPG for language learning

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Games developed by Educational Gaming Lab

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Games developed by Educational Gaming Lab

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How to break bigger projects down into smaller prioritized tasks.

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Students will have a chance to earn an extra credit point every week if they can learn two chemistry topics daily for a week.

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A moving clock is chasing a cartoon person.

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I am going to offer 1 point extra credit for students who finish an assignment at least a day before the due date.

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How to Create Accessible Pie Charts for Low Vision

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How to Create Accessible Pie Charts for Low Vision

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Skip to content

One of the most popular topics in the accessibility space right now is accessible data visualizations and how to make graphs and charts accessible for people with vision loss- inclusive of blind, low vision, and visual impairment. Since this is such an emerging space, a lot of people have different answers for how to make information accessible for people with vision loss, and today I will be sharing my own answer for how to create accessible pie charts for low vision and blind users, based on my academic background in data science and experiences as a user with low vision.

WHAT IS A PIE CHART?

From Wikipedia:

"A pie chart (or a circle chart) is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. In a pie chart, the arc length of each slice (and consequently its central angle and area) is proportional to the quantity it represents. While it is named for its resemblance to a pie which has been sliced, there are variations on the way it can be presented."

Pie charts are typically used to show different parts or percentages of a whole. Donut charts are similar to pie charts though typically contain fewer sections than a pie chart since the center is cut out- a donut chart can show 2-5 different sections effectively, while pie charts are better for showing 5 or more sections.

WHAT DOES IT MEAN TO DESIGN AN ACCESSIBLE CHART?

When designing accessible pie charts, there are two different sets of considerations for creating charts- visual accessibility and nonvisual accessibility. Visual accessibility refers to the design of a chart, considering elements such as color choice, font size, and contrast, while nonvisual accessibility focuses on text-based descriptions that give information about the chart data, such as alt text and image descriptions that can be read with assistive technology. Both are equally important when designing for users with vision loss since people with low vision often use both visual and nonvisual accessibility tools to interpret charts and graphs, and people with disabilities other than vision loss benefit from reading content designed with visual and/or nonvisual accessibility in mind.

VISUAL ACCESSIBILITY FOR PIE CHARTS

HOW TO DESIGN ACCESSIBLE PIE CHARTS

Avoid conveying information through color alone, and pair color-coded information with text labels. For a real-world example, consider that a stop sign not only has a distinctive red color, but it also has a unique shape compared to other signs and a bright red STOP label.

Consider using textures instead of colors to convey information

Use colorblindness-friendly color schemes- data visualization tools like Tableau often have these built-in

Make sure that colors and labels are high contrast and can easily be recognized/read. An example of poor contrast would be having gray text on a white background, while black text on a white background would be easier to read

Place labels next to data points, not inside them, as this is easier to access for people who use screen magnification compared to having to reference a legend

For text on a colored background, add a thin white outline to black text to make it readable on any color background

In visualization tools, set font sizes in rem instead of pixels so that they are easier to scale up/down- some tools do this automatically

RELATED LINKS

NONVISUAL ACCESSIBILITY FOR PIE CHARTS

WHAT IS ALT TEXT? WHAT IS AN IMAGE DESCRIPTION?

Alt text is a short, written description that displays in place of an image if the image fails to load that tells people what is in an image, such as text, colors, or basic essential details. Screen readers will read alt text aloud so that users who are blind or that have low vision can understand what is in an image. If someone fails to add alt text for an image, the screen reader will either simply say "image" or ignore the image entirely, which means that users miss potentially valuable visual content. If an image loads correctly, alt text is invisible to users that do not use assistive technology.

An image description is a longer description that is typically exposed, meaning anyone can see it whether they are using assistive technology or not, though users can also insert image descriptions in a similar manner to alt text that remain hidden. An image description serves as a descriptive caption, which is great for people with vision loss, language learners, or people with cognitive or processing disabilities. I recommend keeping image descriptions exposed or having a link to image descriptions in content so that users can benefit from the additional description.

HOW TO WRITE ALT TEXT AND IMAGE DESCRIPTIONS FOR PIE CHARTS

Since pie charts are visually simpler compared to more complex line or bar charts, users can include a lot of information when writing alt text and image descriptions for pie charts. Alt text and image descriptions should include the following information:

The chart title and type, i.e pie chart of "What Day of the Week Received The Most Spam Calls?"

Number of sections and total number of values- for example, there are 3,096 values in the chart about spam calls

Values in the chart from highest to lowest, i.e Wednesday, 618 calls, Tuesday, 611 calls, etc

When writing a short alt text description, users can mention the largest and smallest sections in the chart, and write out all of the sections in the extended image description

In this case, color and section location are not particularly relevant, as the data is able to communicate all necessary information for understanding the chart

RELATED LINKS

ADDITIONAL TIPS FOR HOW TO CREATE ACCESSIBLE PIE CHARTS

Attach a link to the dataset used to create the pie chart when possible within the graph caption or document. This is often done with a csv file or an Excel/Sheets file

Label the link to the dataset with a descriptive label such as " data source" and avoid using nondescript labels like "x"

Use descriptive titles for charts so that it is clear to the audience what the chart is about

The methods used above also work well for violin charts, tree maps, and similar chart/graph types

By Veronica Lewis/Veronica With Four Eyes, www.veroniiiica.com

Back to Paths to Technology's Home page

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How to create accessible pie charts for low vision - Perkins School for the Blind

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How to create accessible pie charts and donut charts for audiences with vision loss, including visual and non visual A11Y.

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What is MathML?

MathML is a low-level specification for mathematical and scientific content on the Web and beyond.

These pages provide you with information on how to learn and use MathML, on available software, and news from the MathML community.

Here is an example of Presentation MathML: a b c d ⁢ x y = e f and another: a⁢x+b⁢y = e c⁢x+d⁢y = f

Highlights

Software

Many browsers support MathML and support in math&science applications is ubiquitous. To write MathML, you don't need more than a text editor, but there are many tools available that make it even easier. The MathWG keeps a non-exhaustive list on its public wiki, including browser support, JavaScript polyfills, authoring&conversion tools and more general MathML-enabled software.

Of course, nearly all software has bugs. And some programs are further ahead implementing MathML 3 than others.

The Math Working Group

The Math Working Group is one of the oldest W3C Working Groups. During its first period of activity (1996-2004), it created and maintained versions 1 and 2 of MathML. During its second period (2006-2016), it created, among other things, MathML version 3 and saw it become an ISO standard.

Since April 2021, the group is developing a new revision of MathML, MathML version 4, as well as MathML Core, a subset that can be reliably displayed in web browsers.

Details of the Math WG "