What is generative AI? 

Generative AI is a type of technology that can generate content based on prompts provided by humans. Currently, some of the the most commonly used AI tools include ChatGPT (text-based) and DALL-E 3 (image-based). Others are available, which produce slides, videos and computer code. Major companies such as Microsoft and Google are beginning to integrate AI tools into some of their products.  

Microsoft's CoPilot and Google's Gemini are further examples. 

This guide will help you find resources to understand these new tools and offer insights into how they may be used in education. 

Content from this box has been modified from Generative Artificial Intelligence (ecu.edu.au)

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Artificial Intelligence (AI): AI is a branch of computer science. AI systems use hardware, algorithms, and data to create “intelligence” to do things like make decisions, discover patterns, and perform some sort of action.

Black Box: We call things we don’t understand, “black boxes” because what happens inside the box cannot be seen. Many machine learning algorithms are “black boxes” meaning that we don’t have an understanding of how a system is using features of the data when making their decisions.

Deep Learning: Deep learning models are a subset of neural networks. With multiple hidden layers, deep learning algorithms are potentially able to recognize more subtle and complex patterns. Like neural networks, deep learning algorithms involve interconnected nodes where weights are adjusted

Large language models (LLMs) Large language models (LLMs) Large language models form the foundation for generative AI (GenAI) systems. GenAI systems include some chatbots and tools including OpenAI’s GPTs, Meta’s LLaMA, xAI’s Grok, and Google’s PaLM and Gemini. LLMs are artificial neural networks. At a very basic level, the LLM detected statistical relationships between how likely a word is to appear following the previous word in their training.

Machine Learning (ML): Machine learning is a field of study with a range of approaches to developing algorithms that can be used in AI systems. AI is a more general term. In ML, an algorithm will identify rules and patterns in the data without a human specifying those rules and patterns. 

Neural Networks (NN): Neural networks also called artificial neural networks (ANN) and are a subset of ML algorithms. They were inspired by the interconnections of neurons and synapses in a human brain. In a neural network, after data enter in the first layer, the data go through a hidden layer of nodes where calculations that adjust the strength of connections in the nodes are performed, and then go to an output layer.

Natural Language Processing (NLP): Natural Language Processing is a field of Linguistics and Computer Science that also overlaps with AI. NLP uses an understanding of the structure, grammar, and meaning in words to help computers “understand and comprehend” language

Training Data: This is the data used to train the algorithm or machine learning model. It has been generated by humans in their work or other contexts in their past. While it sounds simple, training data is so important because the wrong data can perpetuate systemic biases.

Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Ruiz, P, and Fusco, J.,(2024). Glossary of Artificial Intelligence Terms for Educators. Educator CIRCLS Blog. Retrieved from https://circls.org/educatorcircls/ai-glossary