AI and Information Literacy: Assess Content

Introduction: analyzing AI-generated information

Although many responses produced by AI text generators are accurate, AI also often generates misinformation. Oftentimes, the answers produced by AI will be a mixture of truth and fiction. If you are using AI-generated text for research, it will be important to be able to verify its outputs. You can use many of the skills you’d already use to fact-check and think critically about human-written sources, but some of them will have to change. For instance, we can’t check the information by evaluating the credibility of the source or the author, as we usually do. We have to use other methods, like lateral reading, which we’ll explain below.

Remember, the AI is producing what it believes is the most likely series of words to answer your prompt. This does not mean it’s giving you the ultimate answer! When choosing to use AI, it’s smart to use it as a beginning and not an end. Being able to critically analyze the outputs that AI gives you will be an increasingly crucial skill throughout your studies and your life after graduation.

When AI gets it wrong

As of summer 2023, a typical AI model isn't assessing whether the information it provides is correct. Its goal when it receives a prompt is to generate what it thinks is the most likely string of words to answer that prompt. Sometimes this results in a correct answer, but sometimes it doesn’t – and the AI cannot interpret or distinguish between the two. It’s up to you to make the distinction.

AI can be wrong in multiple ways:

- - It can give the wrong answer
  - It can omit information by mistake
  - It can make up completely fake people, events, and articles
  - It can mix truth and fiction

Explore each section below to learn more

It can give a wrong or misleading answer

Sometimes an AI will confidently return an incorrect answer. This could be a factual error, or – like in the example below – inadvertently omitted information. Vanuatu and Vatican City are both real countries, but these are not all the countries that start with the letter V.

It can make up false information

Sometimes, rather than simply being wrong, an AI will invent information that does not exist. Some people call this a “hallucination,” or, when the invented information is a citation, a “ghost citation.”

These are trickier to catch, because often these inaccuracies contain a mix of real and fake information. In the screenshot above, none of the listed sources on The Great Gatsby exist. While the authors are all real people, and the collections are all real books, none of the articles listed here are actually real.

When ChatGPT gives a URL for a source, it often makes up a fake URL, or uses a real URL that leads to something completely different. It’s key to double-check the answers AI gives you with a human-created source. You can find out how to fact-check AI text in sections and video at the bottom of this page.

It cannot accurately produce its sources

Currently, if you ask an AI to cite its sources, the results it gives you are very unlikely to be where it is actually pulling this information. In fact, neither the AI nor its programmers can truly say where in its enormous training dataset the information comes from.

As of summer 2023, even an AI that provides real footnotes is not providing the places information is from, just an assortment of webpages and articles that are roughly related to the topic of the prompt. If prompted, the AI will provide the exact same answer but footnote different sources.

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For example, the two screenshots above are responses to the same prompt. In the screenshot on the left, the user specified to use only peer-reviewed sources. When you compare the two, you can see that the AI cites different sources for word-for-word identical sentences. This means that these footnotes are not where the AI sourced its information. (Also note that the sources on the right are all either not peer-reviewed or not relevant. Plus, artsy.net, history.com, and certainly theprouditalian.com are not reliable enough for you to source from in your assignments.)

This matters because an important part of determining a human author’s credibility is seeing what sources they draw on for their argument. You can go to these sources to fact-check the information they provide, and you can look at their sources as a whole to get insight into the author’s process, potentially revealing a flawed or biased way of information-gathering.

You should treat AI outputs like fact-checking a text that provides no sources, like some online articles or social media posts. You’ll determine its credibility by looking to outside, human-created sources (see lateral reading below).

It can interpret your prompts in an unexpected way

AI can accidentally ignore instructions or interpret a prompt in a way you weren’t expecting. A minor example of this is chatbots returning a 5-paragraph response when prompted to give a 3-paragraph response or ignoring a direction to include citations throughout a piece of writing. In more major ways, though, it can make interpretations that you might not catch. If you’re not too familiar with the topic you’re asking an AI-based tool about, you might not even realize that it’s interpreting your prompt inaccurately.  The way you ask the question and which chatbot you use can also skew the response you get. Any assumptions you make in your prompt will likely be fed back to you by the AI. 

For instance, when Bard (now known as Gemini) was prompted: 

“Does the University of Nebraska-Lincoln's sports culture involve a mammoth named Archie? Give a detailed answer explaining your reasoning. Be sure to only include factual information. Provide a list of sources at the end and cite throughout to support your claims.”

it created an entire story about Archie’s deep-woven presence in “the fabric of Nebraska athletics.” Including a brief, bulleted history that merges truth with fiction, or “hallucination.”

First screenshot of a Bard conversation — Bard (now known as Gemini) Prompt:

Second screenshot of a Bard conversation — Bard (now known as Gemini) prompt Archie the mascot

Though Bard (now known as Gemini) includes UNL’s real team nickname in the concluding paragraph of this response, it does not make any reference to the Cornhusker’s official mascots: Herbie Husker and Lil’ Red.

When prompted:

“Tell me about Archie, that mammoth at the University of Nebraska-Lincoln. Provide a list of sources at the end and include in-text citations.”

Bard (now known as Gemini) performed better at characterizing the significance of Archie at UNL and included no reference to Archie as a mascot or part of the University’s sports culture. In both cases, it generated a list of sources supporting its claims; however, some of the citations were not precise enough to track down specific articles or webpages.

Asking ChatGPT our original question **(Does the University of Nebraska-Lincoln’s sports culture involve a mammoth named Archie?)** returned drastically different results than those from Bard (now known as Gemini) when given the same prompt.

Depending on how we phrased the prompt, Bard (now known as Gemini) either reinforced a mistake we made in the prompt or stuck closer to the facts, whereas ChatGPT corrected that same mistake in the first prompt. Paying attention to your prompt phrasing can make a key difference!

You can see all three conversations in full here:

Nebraska’s Mammoth Mascot: Archie the Mighty Husker (Bard, now known as Gemini)

Archie the Mammoth (Bard, now known as Gemini)

Nebraska Sports: No Mammoth (ChatGPT)

Third screenshot of a Bard conversation — Bard (now known as Gemini) prompt Archie the mascot

Though Bard (now known as Gemini) includes UNL’s real team nickname in the concluding paragraph of this response, it does not make any reference to the Cornhusker’s official mascots: Herbie Husker and Lil’ Red.

When prompted:

“Tell me about Archie, that mammoth at the University of Nebraska-Lincoln. Provide a list of sources at the end and include in-text citations.”

Bard (now known as Gemini) performed better at characterizing the significance of Archie at UNL and included no reference to Archie as a mascot or part of the University’s sports culture. In both cases, it generated a list of sources supporting its claims; however, some of the citations were not precise enough to track down specific articles or webpages.

Asking ChatGPT our original question **(Does the University of Nebraska-Lincoln’s sports culture involve a mammoth named Archie?)** returned drastically different results than those from Bard (now known as Gemini) when given the same prompt.

Depending on how we phrased the prompt, Bard (now known as Gemini) either reinforced a mistake we made in the prompt or stuck closer to the facts, whereas ChatGPT corrected that same mistake in the first prompt. Paying attention to your prompt phrasing can make a key difference!

You can see all three conversations in full here:

Nebraska’s Mammoth Mascot: Archie the Mighty Husker (Bard, now known as Gemini)

Archie the Mammoth (Bard, now known as Gemini)

Nebraska Sports: No Mammoth (ChatGPT)

Lateral reading: your #1 analysis tool

If you cannot take AI-cited sources at face value and you (or the AI programmers) cannot determine where the information is sourced from, how are you going to assess the validity of what AI is telling you? Here you should use the most important method of analysis available to you: lateral reading. Lateral reading is done when you apply fact-checking techniques by leaving the AI output and consulting other sources to evaluate what the AI has provided based on your prompt. You can think of this as “tabbed reading”, moving laterally away from the AI information to sources in other tabs rather than just proceeding “vertically” down the page based on the AI prompt alone.

Watch: how lateral reading helps you sort fact from fiction

Diagram representing the concept of lateral reading. A tall icon representing an online resource is labeled “vertical” and a horizontal rectangle labeled “lateral” overlaps it and an icon representing a second online resource.

What does this process look like specifically with AI-based tools? Learn more in the sections below.

Lateral reading and AI

Lateral reading can (and should) be applied to all online sources, but you will find fewer pieces of information to assess through lateral reading when working with AI. While you can typically reach a consensus about online sources by searching for a source’s publication, funding organization, author or title, none of these bits of information are available to you when assessing AI output. As a result, it is critical that you read several sources outside the AI tool to determine whether credible, non-AI sources can confirm the information the tool returned.

With AI, instead of asking “who’s behind this information?” we have to ask “who can confirm this information?” In the video above, lateral reading is applied to an online source with an organization name, logo, URL, and authors whose identities and motivations can be researched and fact checked from other sources. AI content has no identifiers and AI output is a composite of multiple unidentifiable sources. This means you must take a look at the factual claims in AI content and decide on the validity of the claims themselves rather than the source of the claims.

Since AI output is not a single source of information but rather drawn from multiple sources that could be both factual and false, you will find it useful to break apart AI output into smaller components of information that can be evaluated independent of each other.

For instance, let’s see what happens when we ask ChatGPT to write an essay on Johnny Carson's undergraduate studies at The University of Nebraska - Lincoln.

Johnny Carson's undergraduate journey at the University of Nebraska - Lincoln ChatGPT

The AI correctly states that Carson graduated with a BA degree in Radio and Speech, but is incorrect in stating that Carson was in the Pershing Rifles, a military drill team (third full paragraph). No mention of Carson being a member shows up in reputable websites about the Pershing Rifles. ChatGPT seems to have hallucinated a fact that sounds quite plausible, but is unfounded upon some quick lateral reading.

see full ChatGPT transcript

Links to an external site.

Instructions: tackle an AI fact-check

Here's how to fact-check something you got from ChatGPT or a similar tool:
1. Break down the information. Take a look at the response and see if you can isolate specific, searchable claims. This is called fractionation.
2. Then it’s lateral reading time! Open a new tab and look for supporting pieces of information. Here are some good sources to start with:
  - When searching for specific pieces of information: Google results or Wikipedia
  - When seeing if something exists: Google Scholar, WorldCat, or Wikipedia
  - Tip: Some things to watch out for – is the AI putting correct information in the wrong context (like when it said that Texas A&M’s tradition was a UMD one)? Is it attributing a fake article to a real author?
3. Next, think deeper about what assumptions are being made here
  - What did your prompt assume?
  - What did the AI assume?
  - Who would know things about this topic? Would they have a different perspective than what the AI is offering? Where could you check to find out?
4. Finally, make a judgment call. What here is true, what is misleading, and what is factually incorrect? Can you re-prompt the AI to try and fix some of these errors? Can you dive deeper into one of the sources you found while fact-checking? Remember, you’re repeating this process for each of the claims the AI made – go back to your list from the first step and keep going!
For an example of this in action, take a look at the video at the bottom of the page.

Beyond fact-checking

Critical thinking about AI responses goes beyond determining whether the specific facts in the text are true or false. We also have to think about bias and viewpoint – two things we keep in mind when reading human authors, but you might be surprised to learn we have to keep in mind with AI as well.

Any text implicitly contains a point of view, influenced by the ideologies and societal factors the author lives with. When we critically think about news articles, books, or social media posts out in the wild, we think about the author’s viewpoint and how that might affect the content we’re reading. These texts that all of us produce every day are the foundation of generative AI’s training data. While AI text generators don’t have their own opinions or points of view, they are trained on datasets full of human opinions and points of view, and sometimes those viewpoints surface in its answers.

AI can be explicitly prompted to support a particular point of view (for instance, “give a 6-sentence paragraph on ramen from the perspective of someone obsessed with noodles”). But even when not prompted in any particular way, AI is not delivering a “neutral” response. For many questions, there is not one “objective” answer. This means that for an AI tool to generate an answer, it must choose which viewpoints to represent in its response. It’s also worth thinking about the fact that we can’t know exactly how the AI is determining what is worth including in its response and what is not.

AI also often replicates biases and bigotry found in its training data (as you learned on page 1 of this module). It is very difficult to get an AI tool to arrive at the fact that people in positions of authority, like doctors or professors, can be women, without explicit prompting from a human. AI image editing tools have edited users to be white when prompted to make their headshot look “professional,” and can sexualize or undress women, particularly women of color, when editing pictures of them for any purpose.

AI also replicates biases by omission. When asked for a short history of 16th-century art, ChatGPT and Bing AI invariably only include European art. This is the case even if you ask in other languages, like Chinese and Arabic, so the AI tool is not basing this response on the user’s presumed region. China and the Arabic-speaking world were certainly producing art during the 16th century, but the AI has decided that when users ask for “art history,” they mean “European art history,” and that users only want information about the rest of the world if they specifically say so.

Screenshot of a Bing AI conversation in Chinese: "User: 您能給我簡單介紹一下 16 世紀藝術的歷史嗎？Bing AI: Searching for: 16世紀藝術. Generating answers for you…16 世紀是歐洲藝術成就的一個時期。文藝復興高峰期（大約是 16 世紀前兩個十年）和曼尼爾主義是最常與這個時期相關的運動。文藝復興文化和思想從意大利傳播到整個大陸，產生了許多新風格。曼尼爾主義不是一種單一風格，而是以其知識分子的精神和人工特質為特徵。它通常被看作是對文藝復興高峰期藝術的理想化自然主義的反應。 [1]

希望這有幫助！

Learn more: 1. hi-in.facebook.com 2. ar-ar.facebook.com 3. ko-kr/facebook/com

These are more obvious examples, but they also reveal the decision-making processes that the AI is using to answer more complex or subtle questions. The associations that an AI has learned from its training data are the basis of its “worldview,” and we can’t fully know all the connections AI has made and why it has made those connections. Sometimes these connections lead it to decisions that reinforce bigotry or give us otherwise undesirable responses. When this happens in ways we can see, it prompts the question: how is this showing up in ways that aren’t as obvious?

Instructions: go beyond fact checking

Now let’s try lateral reading for a second time, with a focus on the response’s perspective:

We can start with fractionation again, but this time we’re thinking about what claims and perspectives are being represented in the AI response.
- Brainstorm the groups who might be invested in this issue and who might have a discrete perspective (stakeholders, corporations, governments, demographic groups, nationalities, regions, etc.)
- Think about the argument as a whole. What perspective(s) can you find here? Which might be missing?
- Now break down the response into individual claims. What perspective(s) can you find in these claims? Which might be missing?
Time to start your lateral reading. Think about what sources might provide the perspectives above, both the ones in the AI’s response and the ones missing from it.
- Try publications like newspapers or well-established magazines, like the Atlantic or Scientific American.
- You can find perspectives you’re looking for in news articles, opinion pieces, speeches, etc. Remember to think critically about these perspectives – some may be based on incorrect facts or a distortion of the issue.
- Check Wikipedia to get a sense of each publication’s reputation.
Next, think deeper about what assumptions are being made here.
- What did your prompt assume?
- What did the AI assume?
- Who would know things about this topic? Would they have a different perspective than what the AI is offering? Where could you check to find out?
Finally, make a judgment call. What here is true, what is misleading, and what is factually incorrect? Can you re-prompt the AI to try to get a different perspective? Can you dive deeper into one of the sources you found while fact-checking?

Again, the key is remembering that the AI is not delivering you the one definitive answer to your question.

Example: let's check an AI-generated response!

Check out the videos below to see these lateral reading strategies in action.

Watch AI Fact-checking Text and Links

Learn how to use lateral reading strategies to check AI generated text and links (7:05).

AI Fact-checking Text and Links Transcript

[slide 1] Hello! Now, that we've gone over some fact-checking strategies, let's put them into practice. [slide 2] I asked ChatGPT about mammoths at the University of Nebraska-Lincoln, and got this answer in return: saying that UNL's connection to mammoth-related discoveries centers around Ashfall Fossil Beds and that UNL unearthed a wooly mammoth with evidence of human butchering in 2006. Let's apply the lateral reading method to investigate these claims. So, let's start with breaking down some of these claims with fractionation. So, this first claim that mammoth research at UNL centers around Ashfall Fossil Beds jumps out for me. I know that Ashfall Fossil Beds is a real place and that there have been fossils from a variety of species found there, so there are some true components of this answer. However, I can't remember if mammoths are among those species found here. To start off, I'm just going to Google: [slide 3] "were mammoths found in Ashfall Nebraska?" So, our first result is from a PBS show, NOVA Science Now. [slide 4] This episode, Mammoth Mystery, that tells the story of two mammoth skeletons found with their tusks interlocked together. We could watch the episode, which looks like it runs just over 11 minutes. [slide 5] The PBS also page includes an interview with vertebrate paleontologist, Mike Voorhies. I'm not sure I want to spend the time watching the 11-minute video for fact-checking purposes at this point, so I'm going to see if the interview provides any clues—it's simply more easily skim-able and searchable. [slide 6] The interview mentions Ashfall in a couple places, stating that "dozens of other fossil skeletons have come out of what is now known as Ashfall Fossil Beds," but there is nothing that specifically states that a mammoth discovery was made at that particular location. I'm still not sure I want to spend the time to watch that video, so I'm going to go back to my Google search results and see what other things came up. [slide 7] The third result is from the University of Nebraska's State Museum-Trailside. [slide 8] It tells the same story of the mammoth fossil skeletons found interlocked together. Turns out, the mammoths in question were found, not at Ashfall Fossil Beds, but in the Little Badlands near Crawford. So far neither source we've found corroborates Ashfall Fossil Beds as a center for mammoth research at UNL. But, just because these particular mammoths weren't found at Ashfall doesn't mean that there were NO mammoths found there. So, I'm going to do a little further digging. [slide 9] My next step is going to be to Google Ashfall Fossil Beds Wikipedia to see what I can find on that page that might be helpful. Interestingly, the second result of this search points to the Wikipedia entry for Mike Voorhies, that paleontologist featured in the PBS interview we just looked at. [slide 10] This Wikipedia entry is too long to read closely, so I'm going to look for the word "mammoth" using the find function in the browser. Looks like there are no mentions of mammoths in this article. I'm beginning to think that, while Ashfall Fossil Beds are a key location for the study of paleontology in Nebraska, they might not be that central for the study of mammoths. There's just one more thing I'd like to check before I decide that this part of the ChatGPT response isn't entirely accurate. [slide 11] This time, I'm just going to search for "Ashfall Fossil Beds" and go to the website for the Ashfall Fossil Beds State Park. [slide 12] On the Ashfall website, under "About Ashfall," there is a page that lists "Ashfall Animals." This page notes that of the 18 species of vertebrates recovered from the Ashfall site, twelve are mammals. Among the most common are a species of rhino and some horses. Other species include deer, dogs, turtles, and birds; but there is no mention on this page of a mammoth of any kind being among the species found here. At this point, I think it's safe to say that mammoth research at UNL does not, in fact, center around the Ashfall Fossil Beds site. [slide 13] I'm going to take a look at another claim in the response that I'm not sure about: the claim that "In 2006, researchers at UNL unearthed a mammoth specimen with evidence of human butchering marks, suggesting that humans played a role in the demise of these majestic creatures." Again, I'm going to go straight to Google. [slide 14] This time, I'm going to search: "mammoth specimen human butcher marks UNL". The first result that comes up is from the news archive at the University of Texas-Austin, the headline of which reads: "New Mexico Mammoths Among Best Evidence for Early Humans in North America." [slide 15] According to this article, bones were found that show evidence of a butchering site in New Mexico, not Nebraska, and it states that researchers at the University of Texas-Austin were involved in the discovery, not UNL researchers. [slide 16] Another article from CNN also locates this discovery of mammoth bones showing evidence of butcher marks in New Mexico, and not Nebraska. So, ChatGPT has mashed up a real claim with a fictional claim to support its argument. [slide 17] But, hold on. ChatGPT also gave us some sources (as we'd asked it to do). So let's take a look at the sources ChatGPT gave us. [slide 18] The only link ChatGPT included is for the Ashfall Fossil Beds website, but that link doesn't work. We get a "Not Found" error when trying to go to that page. Not a huge deal, since we can easily find the Ashfall site with a simple search (we did it already). But, let's take a look at the other sources that ChatGPT gave us. [slide 19] None of them seem to speak specifically to any of the details or claims in the response. But, let's check out the only one that mentions Nebraska explicitly. [slide 20] Searching for the title of the last reference, by D.C. Fisher and D.L. Fox doesn't yield any promising results. It's possible that this citation isn't real. In our next video, we'll dive a little deeper into strategies for fact-checking scholarly sources from AI.

Watch AI Fact-checking: Scholarly Sources

Learn how to use lateral reading strategies to fact check scholarly sources in AI generated text (4:17).

AI Fact-checking Scholarly Sources Transcript

[slide 1] Welcome back. Now let's try fact checking for scholarly sources generated by AI. [slide 2] We have four sources in this bibliography, and all of them look like articles from essay compilations or academic journals. We have the author, the title of the article in quotes, and the title of the book or journal afterwards. Let's take a look at fact-checking the second one: "Seeing and Believing: The Great Gatsby" by Claudia Durst Johnson. [slide 3] Right away, I'm going to go to Google Scholar and search the name of the author and the title of the article. Looking at the results, there are books by Claudia Durst Johnson listed, some of which have similar titles—Understanding the Call of the Wild, Understanding the Odyssey— [slide 4] which sound like the title of the book with the article we're looking for: Understanding the Great Gatsby. [slide 5] So, let me try putting the article title in quotes to see if we can find that. No results. [slide 6] Maybe if I try without the author's name. Still no results. So, now I'm going to try to look up the author to see what reputation she has, if she’s a real person, and anything else that she's written. [slide 7] So, it looks like Claudia Durst Johnson is a known author and literary scholar. We can also see that she was an English professor at the University of Alabama. There's also a list of her books, including some in the same "student casebook" series, but I'm still not seeing the book that we're looking for: Understanding the Great Gatsby. [slide 8] I can Google and confirm that this book does exist. So, let's try to see what's inside. Specifically, I'm hoping we can find a table of contents that will show us what articles and authors are included in the book. [slide 9] One way to do this is to search on the library website. Sometimes catalog records include this kind of information. If I search our library catalog, [slide 10] the book comes right up; however, neither the table of contents [slide 11] nor a list of contributing authors is included in our catalog record. From here, there are a couple other places we can go to try to locate this information. [slide 12] We might try seeing if WorldCat has more detailed information than our catalog does. The easiest way to get to WorldCat is to click on the "Search WorldCat & Big Ten Library Catalogs" link underneath the "Quick Search" box from the Libraries' home page. [slide 13] Once we've navigated to WorldCat, we can search on the title of the book by selecting "Title" from the options on the “search for” dropdown menu. [slide 14] Once again, the book appears right away in our search results. [slide 15] Clicking into the WorldCat entry for more details, we quickly see that a list of contents is included. Scanning over the list we can see that neither Claudia Durst Johnson nor "Seeing and Believing" are listed. I would expect to see both if she had, in fact, contributed to this volume. We already found a list of contents in WorldCat, but I want to walk through one more option that can be useful in this kind of source sleuthing. [slide 16] We could also look to see if Google Books or Amazon has a "preview" option that includes the table of contents. Using Google to search for "understanding the great gatsby amazon" leads me directly to the book we're interested in. [slide 17] The Amazon listing shows a "read sample" option. I'm going to select that to see if a table of contents is included. [slide 18] We're in luck! The Amazon sample includes the table of contents. Just like with the WorldCat catalog entry, we could scan the contents for the author and chapter title we're looking for. As is no surprise, the author and article we're looking for don't show up in this table of contents, either. [slide 19] So, now we can see what happened: ChatGPT took a real book, found an author it decided was probably in that book, but wasn't, and made up an article that doesn't exist. When you're getting sources from AI, just remember that it's assembling the citations from building blocks that might not go together. And one more thing, none of these citations are formatted correctly either. Take care, and always remember to fact check. [slide 20] If you have questions, the UNL Libraries can help.

Additional resources (optional reading)

Explore these additional optional resources for more information on different topics mentioned on this page, as well as references for the content.

Learn more

For more information on lateral reading and a worksheet to track your work, see below:

References

Buell, S. (2023, July 19). An MIT student asked AI to make her headshot more ‘professional.’ it gave her lighter skin and blue eyes. Boston Globe. Retrieved August 2, 2023, from https://www.bostonglobe.com/2023/07/19/business/an-mit-student-asked-ai-make-her-headshot-more-professional-it-gave-her-lighter-skin-blue-eyes/
Heikkilä, M. (2022, December 12). The viral AI avatar app Lensa undressed me—without my consent. MIT Technology Review. Retrieved August 2, 2023, from https://www.technologyreview.com/2022/12/12/1064751/the-viral-ai-avatar-app-lensa-undressed-me-without-my-consent/
Stanford History Education Group. (2020, Jan 16). Sort fact from fiction online with lateral reading [Video]. YouTube. https://www.youtube.com/watch?v=SHNprb2hgzU
Stanford History Education Group. (2020). Teaching lateral reading. Civic Online Reasoning. https://cor.stanford.edu/curriculum/collections/teaching-lateral-reading/

Now that you know how to assess the accuracy of AI-generated work, continue onto the next page of this module to learn how to cite it in your academic work!