If you're going to use a GenAI tool, be sure to use your critical thinking skills. The following list of things to keep in mind, adapted from an NPR article from June 2023, is a good starting point:
Privacy: Be cautious about sharing any personal information with AI tools. These platforms may use your input for training AI models, and companies developing these models may have access to what you enter. Are you comfortable with your input data or prompts being shared?
Purpose: What are you using the tool to create? Are you asking an image generator to copy the style of a living artist? Or using it in a class without your teacher's knowledge? Consider the ethical implications of your use case and refer to your instructor's guidance.
Consent: If you're creating an image, who or what are you depicting? Could the subject be harmed by the portrayal? What is your intent behind creating this image?
Disclosure: If you're sharing your AI-generated content on social media, have you made it clear this content is synthesized? What would happen if your generated work was shared further without that disclosure?
Fact check: Generative AI get things wrong and it is important to double-check any important information before you post or share it.
Some AI chatbots that have real-time access to the Internet. These work by using AI to transform a question into a set of queries used to search the Internet, and then generate a text-based response based on the information that it finds.
Even though they have access to the Internet, internet-connected chatbots can still have "hallucinations" and deliver false information. These systems frequently generate responses that contain unsupported statements and inaccurate citations. And, of course, the Internet itself is full of false or biased information. This information is also used to train the AI models used when interacting with GenAI.
Search engines retrieve text that already exists and connect users to it in a fairly linear fashion, while generative chatbots create new text based on highly complex language models that attempt to provide the most likely sequence of words based on the information it has been trained on.
GenAI chatbots like ChatGPT or Gemini do not refer to, quote from, or recall information from specific or actual sources. A GenAI chatbot will generate sources or appear to cite sources if you ask it to, but it's important to keep in mind that it does not actually "read" or "understand" these sources. In some situations, the chatbot will make up entirely fake citations which are called "hallucinations".
Although GenAI can deliver answers to questions in a way that feels similar to Google, Bing, Kagi, or other search engines, it's important to understand these tools operate very differently from the search engines.
A traditional Google, Bing, or Kagi search helps users find and retrieve information that already exists and is published on the Internet. It does this by identifying words and phrases in your search (known as keywords), looking for existing sources that match these terms, and applying a ranking algorithm (which is not public) to identify the results that are likely to be most relevant.
Google has recently added AI summaries to search results, which work more like ChatGPT than traditional Google searching. The same information literacy rules apply for these AI summaries, which are created via a subset of Google's Gemini genAI toolset.
ChatGPT is not searching an existing body of text for matches. Rather than pointing users to information that is already published on the Internet, it creates new text in response to a query in a conversational tone, based on the information that it was trained on. GenAI such as ChatGPT and Copilot can access the internet and infer information from online sources; however, these tools will "interpret" the information for you, potentially misconstruing or incorrectly attributing information.
There are real concerns with search engine algorithms and how they curate information to users. Different searchers can find different results for the same search criteria because a search engine's algorithm is tailoring its results to the user, data associated, and cookies associated. While search engines are flawed, GenAI such as Perplexity Search do not function in the same fashion.
Publishers of major scientific databases, such as JSTOR, EBSCOhost, and ProQuest Research Library are also experimenting with AI-enabled search tools to help researchers discover sources in new ways. With these tools, publishers are attempting to increase the accuracy of responses by limiting the AI-generated responses to information that is retrieved using a more traditional search engine search. De Anza has these features currently disabled for student use, but may open up in the future.
Generative AI text models attempt to create human-like text based on large amounts of sample training data, often taken from the internet. These systems mimic human communication (without any actual intelligence) by acting like highly complex predictive models of what words would appear, given millions upon millions of example texts.
It should be noted that these models do not “understand” what they are saying, and are merely repeating patterns of associations found across the millions upon millions of documents powering their output.
Examples of generative AI that can create text content include: ChatGPT, Google Gemini, Perplexity AI, and Microsoft Copilot.
Much like text, AI has famously been applied to the realm of image creation. Models are exposed to billions of images downloaded from the internet and labeled by humans. Then new synthetic images are created via text input based upon the patterns found in those massive data sets.
Keep in mind that ethical considerations remain important when working with AI-created images, as they can sometimes raise questions about authenticity and copyright. It's advisable to be aware of these issues and use AI-generated images responsibly in your work
Examples of generative AI that can create imagery include: DALL·E, Midjourney, and Stable Diffusion.
Generated by Microsoft Copilot on 04/15/25.
AI music generators analyse music tracks and metadata (artist name, albums name, genre, year song was released, associated playlists) to identify patterns and features in particular music genres.
Keep in mind, if it has only been exposed to one type of music such as Mozart, then the music it generates will sound somewhat similar to his works.
Examples of generative AI that can create audio content include: AIVA and Soundful.
Creating a video typically requires the use of audio, visual, and text elements. Some generative AI video programs have harvested existing videos to learn how to create new ones, while others have sourced the three elements to create video from audio, visual, and text sources.
There are even generative AI video programs that have been trained to use video editing software, so they can apply effects to a video that you have created, such as adding captions, transitions, and animations.
Examples of generative AI that can create videos include: Runway Gen-1 and Invideo.