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Generative artificial intelligence (Gen-AI)

Using Gen-AI in your studies

Generative AI, also called Gen-AI is a type of artificial intelligence that allows machines to generate new content based on a series of prompts entered by its user. Outputs can include text, images, videos, sound, code and other media.

This guide provides resources to help you understand Gen-AI, its limitations, and how to use it ethically in your studies. 

 

How Gen-AI works

 

Training

Gen-AI systems are trained on large data sets, learning patterns and features from existing data. 

Input and Output

Gen-AI systems are often prompted with natural language from a human, which can be text or voice, or copied text from elsewhere. From these inputs (prompts) Gen-AI systems generate new content that resembles what they have learned. This could be text, images, videos, music, or code. 

Examples of Gen-AI 

Chatbots (ie Large Language Models or LLMs): ChatGPT, Copilot, Gemini, and LLaMA
Text-to-image systems: Stable Diffusion, Midjourney, and DALL-E
Text-to-video generators: Sora

Not all Gen-AI is equal

Different Gen-AI models have varying capabilities depending on their training data, architecture, and specific design, leading to differences in output quality, accuracy, and suitability for different tasks, meaning some Gen AI models will perform better than others depending on the situation. 

Why Gen-AI varies

Training data diversity: The quality and variety of data used to train a Gen-AI model significantly impacts its output. 
Model complexity: Different architectures and algorithms used in building Gen-AI models can result in varying levels of sophistication and performance. 
Bias in data: If the training data contains biases, the generated outputs will reflect those biases. Like any source the output of Gen-AI needs to be scrutinised and evaluated.
Application specific: Some Gen-AI models are optimized for specific tasks like text generation, image creation, or code writing, making them less suitable for other tasks. 

Basic terminology 

Artificial intelligence is a broad term and involves the simulation of human intelligence by machines, especially computer systems.​ Machine Learning (ML) is a branch of artificial intelligence which uses advanced algorithms to learn from data by identifying patterns.​ ​ ML allows machines to analyse data to make informed predictions and decisions.​ ​ Examples:​ Email filtering​ Fraud detection​ Personalised online advertisements​. Deep Learning is an advanced subset of ML that enables machines to perform tasks using artificial neural networks inspired by the human brain. ​ ​ Examples:​ Facial recognition​ Online customer service chatbots​ Driverless vehicles​ Virtual assistants like Siri, Alexa & Google Assistant. The term generative artificial intelligence (Gen-AI) refers to a type of artificial intelligence that creates new & original content by using large language models (LLMs) and data sets to generate human-like outputs in response to user prompts. This can include generation of text, images, sound, coding and video.​ Examples of this type of artificial intelligence include Open AI’s ChatGPT, Dall-E, Claude AI and Google’s Gemini. ​

Further reading

Policies and Procedures

Student Support Guides

You must consult your course outline or Blackboard site to confirm whether the use of Gen-AI has been explicitly allowed or is required in your assessment task and how you may use it. Using Gen-AI to complete your assessment without explicit authorisation is a breach of academic integrity under The University of Notre Dame's Academic Integrity Policy. 

 

Citing Gen-AI 

When approved to use Gen-AI in assessments, adhere to the relevant referencing guidelines for advice on how to cite the use of Gen-AI in your in-text and end-text references. 

Click on your school's referencing style guide to see the rules relating to citing approved use of Gen-AI use in your assignments. 

Referencing Guides:
AGLC | APA | Chicago | AMA (Vancouver)

Statement of acknowledgement

A written statement of acknowledgement is also required when using Gen-AI in your assignments. Your course coordinator will provide guidance on how to acknowledge your use of Gen-AI in your assessment.  

In your acknowledgement you should provide: 

  • A written statement acknowledging the use of Gen-AI 

  • Specify what Gen-AI tools and technology were used 

  • Include a list of prompts used 

  • Explain how the outputs were used in your work 

For example:  

I acknowledge the use of [insert name of AI tool] to [insert description of usage]. The prompts used were [insert list of prompts]. The outputs generated from these prompts were used to XXX. 

Gen-AI tools

If using any Gen-AI tools you should:

  • Check with your lecturer or supervisor that use of these tools is permitted and appropriate.
  • Document your usage of Gen-AI search tools and use with transparency.
  • Critically evaluate the resources you have found using the CRAAP test
Whether using Gen-AI for your studies or for any other purpose, remember that it does not guarantee the quality, accuracy or appropriateness of anything it produces.


Gen-AI can:

  • Be biased because of the way it's been trained.

Types of biases in content generated by Gen-AI include:

  • Cultural bias
  • Racial bias
  • Stereotypes
  • Gender bias
  • Age bias
  • Sometimes make up completely untrue information (this is known as 'hallucinating')

To try and mitigate AI hallucinations you should use an AI tool designed for the purpose you intend to use it for and provide clear, specific direction in your prompts. 

Refer to Crafting useful prompts for further advice.

  • Does not usually include references, so the information can't be checked.

Most of the free Gen-AI tools don't provide the sources used to generate the response, making it difficult to fact check the validity of the outputs. 

If a reference is provided this could be made up or a mash up from different sources to create a fake reference. 

You will need to do your own research to ensure the outputs you receive are factually correct

  • May not protect the data or images you upload

There is no reassurance that your uploaded data is protected. There is potential for your data to be collected and on-sold.

  • Use huge amounts of energy, contributing to global warming

The carbon footprint of Gen-AI models is significant. Data centres used to develop and deploy Gen-AI technologies consume massive amounts of energy and produce high levels of carbon emission. 
 

Open Educational Resources (OERs)

While OERs can be valuable learning materials, they may not always align with Notre Dame's policies or academic standards. Always cross-check information with the University's current policies and guidelines for using Gen-AI in assignments or research. What is permitted at one institution may not be allowed at another.

Training modules

Credo

On campus you will have direct access to the modules from the links; off campus, you may be asked to login with your student number and password if you have not already authenticated. 

Sage Campus

This is a a free course on how to master interacting with ChatGPT. Click on demo hub in the module linked below and register your details to launch the course.

Linked-in Learning

Curated content, updated regularly on the most vital AI topics and technologies, from emerging tools to responsible AI.

Further assistance

  • Try the Library’s online FAQ service AskUs for general enquiries.
  • If you are unsure whether your use of Gen-AI is allowed in your assessment check with your lecturer or course coordinator.
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