AI Tools at University: Introduction

This guide will introduce you to the potential benefits of AI tools. If you can apply critical thinking and evaluation skills, these tools could benefit your learning. This guide covers a range of uses, including how to use AI tools effectively within the ARU regulations. There is a glossary which explains some key terminology, as well as a section on referencing AI use.

If you have any doubts about how to use AI for a particular assignment, please check with your Module Leader (or academic supervisor) that you are using AI tools in an acceptable way.  

More information and tips on how to use AI tools effectively and ethically in your studies within the ARU regulations are available from the Using AI in Your Studies and Assessment pages on MyARU. You may also find it useful to book on one of our 'AI Tools at University' workshops: Study Skills Plus Workshops.

The use of AI in education is a fast moving area of technology, research and ethics. This guidance is current as of September 2025.

Artificial Intelligence (AI) refers to technology that simulates human tasks such as problem solving, comprehension and decision making. It is a broad term, which was first used as early as 1956 (Stryker and Kavlakoglu, 2024). 

There are many different kinds of AI technologies, some of which you may find helpful at university and are covered in this guide. This is a glossary of some of the common terms used when talking about AI. 

Bias: In the context of AI, this relates to potential bias in the data that is used to train the AI model. For example if there is historical or representational bias in the dataset this could lead to skewed outputs in the responses generated by the model.

Bot: A software application that is programmed to perform automated tasks.

Chatbot: A bot that is designed to respond to human questions in a conversational style. An example is the AskARU chatbot or digital assistant that can provide answers to frequently asked questions about ARU.

Generative AI: AI systems that can generate new content in response to prompts by the user based on the data it was trained on.

Hallucinations: The term given to incorrect or misleading results generated by an AI model, for example a non-existent reference/citation.

Large Language Model (LLM): Artificial intelligence systems that are trained on enormous datasets of human language and are good at tasks such as language translation and answering questions.

Literature Search: The process of searching for information (books, journal articles etc) on a given topic using tools such as a library catalogue, subject databases and search engines. 

Machine Learning: A sub-field of artificial intelligence that involves enabling computers to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.

Natural Language: This refers to a search option that uses everyday language instead of keywords and Boolean operators (AND/OR/NOT). 

NLP or Natural Language Programming: This is the complex process that underlines natural language searches and uses algorithms and computer systems that can analyse and interpret human language.

Prompt: A prompt, in the context of using AI, is the question, statement, or instruction that you provide to the AI. This could be a direct question or a more open-ended instruction.

Glossary adapted from The University of British Columbia 'Glossary of AI Terms' and 'What is artificial intelligence?' (Stryker and Kavlakoglu, 2024)