Preus Library's AI Literacy Guide

AI Literacy means understanding Artificial Intelligence (AI)—what it is, how it works, and what it can and can't do—so you can engage with it responsibly, thoughtfully, and ethically, and make informed decisions about its role in your life and the world around you.

You don't need to be a digital expert to understand enough to be "AI literate".
This AI Literacy guide will:

• encourage a nuanced approach that considers both the advantages and risks of AI
• provide a general overview of AI, and some of the popular tools that are available
• offer guidance for deciding how, if, and when to use AI in your writing and research
• share advice for crafting prompts so that AI-generated material is higher quality
• provide guidelines for evaluating and citing AI-generated material
• help you approach AI critically, ethically, and responsibly

Note: this guide will not tell you whether you are allowed to use AI for a particular task. This guide is not policy. Before using AI for school or work, look for policies provided by your instructor, supervisor, or publisher.

Artificial Intelligence (AI): The ability of a computer system to perform tasks that typically require human intelligence, such as learning, problem-solving, and "understanding" language.

Chatbot: A computer program designed to simulate conversation with human users, especially over the internet. It's like talking to a robot through text.

Deep learning: A type of machine learning that uses complex artificial "neural networks" inspired by the human brain. It's especially good at recognizing patterns in things like images and speech.

Deepfake: A fake image, audio, or video that has been digitally altered to replace one person's likeness convincingly with another's, often using AI.

Generative AI (GenAI): A type of AI that can create new content, such as text, images, music, or even code, rather than just analyzing existing data.

Hallucination: When an AI system, especially a language model, generates information that sounds plausible but is actually incorrect or fabricated.

Large language model (LLM): A type of AI program trained on vast amounts of text data, allowing it to interpret, generate, and respond to human language in a sophisticated way.

Machine learning: A method where computers learn from data without being explicitly programmed. Instead of being given exact instructions, they figure out patterns on their own.

Natural Language Processing (NLP): A field of AI that focuses on enabling computers to interpret and generate human language.

Output: The information or result that an AI system produces after processing a prompt or input.

Prompt: The input or question given to an AI system, especially a generative AI, to guide its response or creation.

Prompt engineering: The art and science of crafting effective prompts to get the desired output from an AI model, especially large language models.

RAG (Retrieval-Augmented Generation) layer: An extra component added to some (but not all) LLMs that allows them to access and use information from outside their original training data. These tools can look up new data--like a search engine--before generating their AI output. 

Scraping: The automated process of extracting information from websites to gather large datasets for training AI models or for analysis.

All definitions on this page were generated by Gemini, independently evaluated for accuracy, and edited for clarity. The prompt used was: "Provide concise definitions of the following AI terms. The definitions should be written in a way that can be understood by a layperson." This information was accessed on July 8, 2025 and more terms were added on August 14, 2025. 

For guidance on acknowledging and citing AI use, see the Citing AI section of this guide.