Digital Technologies Librarian
Credits
This guide was originally developed by Amanda Izenstark and Amanda Crego-Emley from the University of Rhode Island University Libraries in spring 2024. It was adapted by Allison Martel in June 2025 and is licensed under Creative Commons CC-BY 4.0 International license.
Introduction
Generative artificial intelligence is rapidly reshaping the academic landscape, and it'll likely be a heated topic of debate (if it hasn't been already) in courses you take or teach during your time at Springfield College.
This guide will give you a brief introduction to AI tools, covering how they work and how to use them effectively as well as their potential pitfalls to help you be a savvy and ethical user of AI.
Check with your instructor for their policies on AI tools prior to using them as part of your coursework.
What is generative AI?
AI tools are not search engines. While they might feel similar when you use them - you can type a question and get an answer from both - they actually function very differently.
AI tools generate output based on training data (the images and text used to train the tool enables it to shape a likely response), while search engines crawl the web to find sources that contain potential matches for a search query. The crucial difference being that a search engine connects you to published material that exists online while generative AI creates a new result based on the data it was trained on. Because AI tools are creating new content rather than quoting or referencing information from a specific, verifiable source, it's essential to critically evaluate AI output before using it in any way.
To break things down further, Wikipedia provides a brief definition that is helpful to understanding artificial intelligence tools:
Generative artificial intelligence (generative AI, GAI, or GenAI[1]) is artificial intelligence capable of generating text, images, or other media, using generative models.[2][3][4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.[5][6] [see "Generative artificial intelligence," Wikipedia, The Free Encyclopedia, accessed August 5, 2025]
How does generative AI work?
Generative AI tools for writing, generating images, or creating music work fairly similarly.
First, developers of these tools collect text, images, music, or audio from various sources to use as training data. Sometimes, this collection is called a "corpus."
Next, developers create programs and interfaces that allow users to explore the contents of the corpus. Some popular interfaces are chatbots such as ChatGPT, DALL-E, and Midjourney, where the user provides instructions as though conversing with another person.
Finally, the user prompts the tool with what they're looking for, for example, asking for ideas for topics to research for a class or creating an image that would illustrate a point for a presentation. The AI tool then provides output for the user based on the prompt, the corpus, and the algorithms used in the chatbot program.
Key Definitions
You're likely to run into the following terms as you grow your knowledge of generative AI. Use the menu items below to learn a bit about each concept.
Artificial intelligence designed to produce output, esp. text or images, previously thought to require human intelligence, typically by using machine learning to extrapolate from large collections of data; (also) a system, piece of software, etc., used to create content in this way.
Oxford University Press. (n.d.). Generative AI, n. In Oxford English dictionary. Retrieved August 5, 2025.
A software tool capable of corpus-based linguistic analysis and prediction. In later use esp.: an artificial intelligence system that processes written prompts and is capable of generating natural language text.
Oxford University Press. (n.d.). Large language model, n. In Oxford English dictionary. Retrieved August 5, 2025.
The capacity of computers to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and infer from patterns in data; the field of artificial intelligence concerned with this.
Oxford University Press. (n.d.). Machine learning, n. In Oxford English dictionary. Retrieved August 5, 2025.
A form of computational linguistics in which natural-language texts are processed by computer (for automatic machine translation, literary text analysis, etc.); abbreviated NLP.
Oxford University Press. (n.d.). Natural language processing, n. In Oxford English dictionary. Retrieved August 5, 2025.
A type of machine learning considered to be in some way more dynamic or complete than others; esp. machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively more features from data.
Oxford University Press. (n.d.). Deep learning, n. In Oxford English dictionary. Retrieved August 5, 2025.