Generative Artificial Intelligence is having a major impact on the landscape of academia. This guide is intended to inspire conversation about generative AI tools, support informed decision making regarding their use, and encourage critical thinking and transparency when they are used. Generative AI tools can be powerful, but they come with myriad technical and ethical challenges, requiring critical human oversight, expertise, and active participation.

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Artificial intelligence

AI is a type of technology related to data science and machine learning. It includes "machines that imitate some features of human intelligence, such as perception, learning, reasoning, problem-solving, language interaction and creative work” (UNESCO).  

You likely encounter AI on a regular basis. Some examples include when you use a customer service chatbot for support online, when you ask questions to Alexa or Siri, when you receive custom recommendations on streaming service platforms, and when you receive directions from Google Maps.  

Learn more about different kinds of AI and related technology in this “family tree” created by The LibrAIry.

Generative artificial intelligence

Generative AI is a specific kind of AI that uses machine learning algorithms to produce or remix content, generating text, images, audio, video, or other formats. Commonly used tools such as ChatGPT, Microsoft 365 Copilot, Meta AI, and Google Gemini use generative AI.  

Large Language Models, or LLMs, are one kind of generative AI. These use algorithms to analyze and predict language patterns, generating a likely textual output in response to a prompt. They work like predictive text features that you may be familiar with when texting or emailing, but on a larger scale. 

The definitions on this page are adapted from the University of Alberta Library’s Using Generative AI guide. This material licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.