Overview

In this unit, our goal is to introduce you to some common roles generative AI may play in creating OER. We will also offer philosophical and practical questions for you to consider when defining the role of generative AI in your publishing program.

Learning Goals

Define AI and related key terms
Identify the primary ways authors may leverage common AI tools
Analyze what your publishing program’s position may be on using AI to develop, write and publish open textbooks

Glossary

Artificial Intelligence (AI): The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this. In later use also: software used to perform tasks or produce output previously thought to require human intelligence, esp. by using machine learning to extrapolate from large collections of data. Also as a count noun: an instance of this type of software; a (notional) entity exhibiting such intelligence. Abbreviated AI.¹
Generative Artificial Intelligence (Generative AI): 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; abbreviated generative AI.²
Large Language Model (LLM): A statistical language model, trained on a massive amount of data, that can be used to generate and translate text and other content, and perform other natural language processing (NLP) tasks.³
ChatGPT: ChatGPT is a generative artificial intelligence chatbot developed by OpenAI and launched in 2022. It is currently based on the GPT-4o large language model (LLM). ChatGPT can generate human-like conversational responses and enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language.⁴
Hallucinations: AI hallucinations are incorrect or misleading results that AI models generate. These errors can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. AI hallucinations can be a problem for AI systems that are used to make important decisions, such as medical diagnoses or financial trading.⁵
Prompts: A prompt is a natural language request submitted to a language model to receive a response back. Prompts can contain questions, instructions, contextual information, few-shot examples, and partial input for the model to complete or continue. After the model receives a prompt, depending on the type of model being used, it can generate text, embeddings, code, images, videos, music, and more.⁶
Weights: The numerical values that determine the strength and direction of connections between neurons in artificial neural networks. These weights are akin to synapses in biological neural networks and play a crucial role in the network's ability to learn and make predictions. They are essential parameters that control the influence of input data on the output by setting the standards for signal strength within the network. Typically initialized randomly, weights are learned traits that guide the propagation of signals through the network, ultimately impacting the accuracy of the model's predictions.⁷

^{1 Oxford English Dictionary.
2 Oxford English Dictionary.} ^{3 Language Learning Models in Large Language Models from Google Cloud.
4 Chat GPT from Wikipedia.}^{5 What are AI Hallucinations? in AI Hallucinations from Google Cloud.}^{6 Prompting}^{in Introduction to Prompting from Google Cloud.}^{7 Weights from Weights in TED AI San Francisco.}