Using Generative AI: A Guide for Students

Welcome to the MSU Library Generative AI Guide for Students

This guide is designed to help students:

• Understand key terms in the Generative AI conversation
• Learn about Generative AI applications in academic work
• Consider ethical implications of AI usage
• Engage with Generative AI systems through prompt engineering
• Cite Generative AI results in your work

"Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on" (MIT Tech News).

Before we started talking about tools like ChatGPT (which is an example of generative AI), artificial intelligence generally referenced machine-learning models that could make predictions based on their dataset. An example is an email spam filter that is predicting that some of the messages may not be relevant to you and directs that email to your Junk folder. This type of AI is performing predictive tasks based on the data it was trained on.

Generative AI creates new content (images, data, text, etc.) based on learning patterns and structures of the datasets and information inputs. See below for more terms to know!

Terms to Know

• Artificial Intelligence (AI): The broad field of computer science that aims to create machines capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and understanding language. Generative AI is a subset of AI.
• Generative AI (GenAI): A type of artificial intelligence that can create new, original content (text, images, audio, video, code, etc.) by learning patterns from vast amounts of existing data.
• Machine learning: "The ability of computers to identify patterns, learn from data, and make inferences or decisions, without having been explicitly programmed to do so" (National Research Council Canada).
• Large Language Model (LLM): A type of generative AI model specifically designed to understand and generate human-like text. LLMs are trained on massive datasets of text and code, allowing them to learn the statistical relationships and patterns in language.
• Deep Learning: A subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to process complex data and learn intricate patterns, often inspired by the structure and function of the human brain.
• Hallucination: False or misleading information generated by an AI tool in response to a prompt

Some of these definitions were created with help from Google Gemini. A citation for this material might look like:

• Gemini. (2025, May 12). Response to "Define key terms related to generative AI" [Large language model]. Google.

Want to continue to develop a more discerning lens for AI technologies?

Consider the ROBOT Test, developed by two librarians from McGill University, the ROBOT test is meant to help you engage with a myriad AI technologies through active learning and a critical lens. The creators describe it as a "tool you can use when reading about AI applications to help consider the legitimacy of the technology."