There are a variety of descriptions of AI and they shift depending on context.

The term AI emerged with the advent of computers in the 1950s as part of efforts to make machines intelligent. Since then it has evolved into processing and algorithms that enable computers to perform cognitive tasks such as learning, adapting and problem solving. Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. Modern AI systems, like large language models and neural networks, learn patterns from vast amounts of data to generate human-like text, recognize images, make predictions, and assist with complex tasks. While current AI excels at specific tasks through pattern recognition and statistical learning, it differs from human intelligence in fundamental ways, lacking true understanding, consciousness, or general intelligence.

AI has been developing over peaks and troughs since the 1950s. To understand the broader context of developments see this timeline: The brief history of artificial intelligence, from Our World in Data (2022).

Recognising where AI is already embedded in our daily lives can be a useful starting point. AI based tools, such as maps, voice assistants, and spell checkers have facilitated, accelerated, and streamlined our daily tasks and are integrated into the ways we live, work and study.

For example, AIs suggest words to complete our text messages or email messages, act as opponents in apps and games, make suggestions on books, music and films we might like, help us drive safely and stay healthy.

AI based tools also have application for a range of study-related purposes and, where appropriately integrated in the learning process, can assist educators and students in their efforts to personalise learning and sustain engagement.

There are a variety of descriptions of GenAI and they shift depending on context. The following provides a brief overview:

AI is underpinned by algorithms and models. GenAI builds on new models and increasing computational power.

Some GenAI, such as ChatGPT, Claude, Copilot and Gemini, involve Large Language Models (LLMs) that have been trained on vast amounts of textual data. They use patterns to predict what words might follow in a sequence, using the content of large datasets to generate new sentences and paragraphs. These AI are based on Transformer models that learn from patterns in data and then use context and assigning different values to different inputs to enable prediction of likely next items. T is for transformer in GPT (Generative Pre-trained Transformer).

For more detail, watch Simon Angus’ presentation on Large Language Models and ChatGPT.

Think of it this way: if the current auto-completion on our smartphones can predict the next word and auto-completion in our email client can predict a sentence, then LLMs can predict words to fill paragraphs and pages.

The capacities of GenAI are shaped by the amounts and kinds of data that they are trained on. Therefore it is important to be cognisant of the data sources and potential biases within the particular training data used.

Many interactions with LLMs continue to expand the training data and capacities of the models. Therefore, be careful to check the data and privacy provisions of the AI tools that you are using and be very cautious not to provide any personal data when using such systems.

Warwick recommends the use of MS Co Pilot which protects material uploaded and inputted and does not use data for training.

LLMs with broad knowledge of language can produce seemingly coherent sentences and information. To date, the patterns that the neural network machine learning has learned are based on language syntax (how we put words together – nouns, verbs, adjectives etc.) and with some understanding of pragmatic tone and style but with very limited understanding of semantic meaning.

Therefore, a LLM can produce grammatically correct sentences that are not necessarily accurate or unbiased and can recombine accurate information to generate inaccurate content. False information is currently referred to as a “hallucination” and includes making up names of people, facts or events that have no proof of existence. Although the interaction with a chatbot interface LLMs may seem like knowledgeable human communication, the content generated by the AI tool needs to be carefully evaluated and critically considered.

LLM interfaces are primarily text-based, like a dialogue or a chat. Making a request for something is called prompting. Formulating the question/request and the precise syntax of the request guides the responses/outputs, which can be iteratively revised through further prompting. Like any conversation or collaboration, we have to frame, phrase and rephrase things to negotiate the outcomes with our partner(s).

In addition to using text prompts to generate more text (prose, poetry, computer code etc.), text prompts can also be used to generate images, sounds, videos, 3D-objects, even virtual worlds. Midjourney and DALL-E are well-known examples of GenAI that produce images. There are text to video generators such as Runway, Sora and Hour one. There are audio generators such as Elevenlabs or transcribers such as Otter.ai, to name a few. Interfaces are also evolving with the ability to upload reference files. Unlike transformer models that predominate LLM and text generation, diffusion AI models are typically used for creating images by diffusing pixels from random positions until they are distributed in a way that forms a coherent image that matches the interpretation of a prompt.

Collaborating with machine intelligences to generate a range of media expands creative possibilities but it is important to use these technologies responsibly (with critical awareness of the data, privacy, biases, limitations, inaccuracies etc.) and to acknowledge the contributions of AI partners in the collaborative production of work.

Generative AI (GenAI) generates texts, images, etc. which can appear as if they had been produced by humans. Therefore acknowledging GenAI use in collaborative productions (e.g. research papers, web pages or student assessments) is essential, so that audiences can recognise where multiple intelligences have contributed to the creation/production of a specific output.

GenAI is a specific form of AI that is designed to ‘generate’ content. However, other forms of AI can also produce a range of outputs (e.g. the responses from a Google search, or the next move in a game of chess with a computer) without generating ‘new’ content. Further, GenAI functionality is increasingly being integrated into AI tools such as Copilot Link opens in a new windowin Microsoft 365, Google Gemini Link opens in a new window, Apple Intelligence or Copilot and Perplexity Link opens in a new windowsearch engines and in writing assistants, such as GrammarlyLink opens in a new window.

Making clear distinctions then is quite difficult. As a result, the guidance provided here to educators and students refers to both the larger umbrella category of AI and the specific variations of GenAI that are enabling the automated rapid production of texts, images, sounds, videos, data analyses, websites, code, etc.