From UNESCO Guide for Generative AI in Education and Research (2023):
“Generative AI (GenAI) is an artificial intelligence (AI) technology that automatically generates content in response to prompts written in natural-language conversational interfaces. Rather than simply curating existing webpages by drawing on existing content, GenAI actually produces new content. The content can appear in formats that comprise all symbolic representations of human thinking: texts written in natural language, images (including photographs, digital paintings and cartoons), videos, music and software code.
GenAI is trained using data collected from webpages, social media conversations and other online media. It generates its content by statistically analysing the distributions of words, pixels or other elements in the data that it has ingested and identifying and repeating common patterns (for example, which words typically follow which other words). While GenAI can produce new content, it cannot generate new ideas or solutions to real-world challenges, as it does not understand real-world objects or social relations that underpin language. Moreover, despite its fluent and impressive output, GenAI cannot be trusted to be accurate” (UNESCO, 2023, p. 8).
Terms & Jargon
Algorithm - A sequence of instructions for solving a problem or performing a task. Algorithms define how an artificial intelligence system processes input data to recognize patterns, make decisions, and generate outputs.
Artificial Intelligence (AI) - Computer systems designed to perform tasks associated with human intelligence, such as pattern recognition or decision making.
Bias - In regards to large language models, errors resulting from the training data. This can result in falsely attributing certain characteristics to certain races or groups based on stereotypes.
Chatbot - A program that communicates with humans through text in a written interface, built on top of a large language model. Examples include ChatGPT by OpenAI, Bard by Google, and more. While many people refer to chatbots and LLMs interchangeably, technically the chatbot is the user interface built on top of an LLM.
Data Mining: Data mining is a process of discovering new knowledge from large amounts of data. It is a subset of AI that uses machine learning & mathematical techniques to extract knowledge from data that can be used for fraud detection, risk management, & more.
Deep Learning - A method of AI, and a subfield of machine learning, that uses multiple parameters to recognize complex patterns in pictures, sound and text. The process is inspired by the human brain and uses artificial neural networks to create patterns.
Emergent Behavior - When an AI model exhibits unintended abilities.
Ethical/Responsible AI: Ethical and thoughtful development and use of AI systems so that fairness, transparency, privacy, and societal impact are primary considerations. This is to ensure that AI benefits society while minimizing potential harms.
Generative Artificial Intelligence (GAI) - A subfield of Artificial Intelligence, referring to models capable of generating content (such as language, images, or music). The output of GAI models is based on patterns learned from extensive training datasets.
Hallucination - In the context of AI, a falsehood presented as truth by a large language model. For example, the model may confidently fabricate details about an event, provide incorrect dates, create false citations, or dispense incorrect medical advice.
Language Learning Model (LLM) - A type of neural network that learns skills — including generating prose, conducting conversations and writing computer code — by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.
Machine Learning - A field of computer science in which a system learns patterns or trends from underlying data. Machine learning algorithms perform tasks like prediction or decision making.
Natural Language Processing (NLP): NLP is a branch of AI that deals with the interaction between computers and human (natural) languages. It encompasses a wide range of tasks, including text analysis, chatbots, speech recognition, and more.
Neural Network - A mathematical system, modeled on the human brain, that learns skills by finding statistical patterns in data. It consists of layers of artificial neurons: The first layer receives the input data, and the last layer outputs the results. Even the experts who create neural networks don’t always understand what happens in between.
Pattern Recognition: A field of computer science that deals with the automatic discovery of predictive information from data. It is an area of machine learning that develops algorithm that can recognize data patterns & use them to make predictions
Plugins: Software components or modules that can be added to existing programs or systems to extend their functionality, allowing for customization without altering the core software.
Prompt - In the context of AI, it is the input text written by a human that is given to a generative AI model. The prompt often describes what you are looking for, but may also give specific instructions about style, tone, or format.
Training Data - The content used to teach a machine learning system how to perform a particular task. Training data gives the system a knowledge base from which the model can make predictions or identify patterns. Training data might include images, text, code, or other types of media.
Adapted from: Glossary of Terms, Artificial Intelligence (A.I.) & Information Literacy, Wolfram Memorial Library, Accessed 24 Feb. 2025; AI Jargon/Terms, Student Guide to Generative AI, UNC University Libraries, Accessed 24 Feb. 2025.
Source: Park, J., & Choo, S. (2024). Generative AI Prompt Engineering for Educators: Practical Strategies. Journal of Special Education Technology. https://doi.org/10.1177/01626434241298954
