What is generative AI?
Generative artificial intelligence (AI) or Large Language Models (LLMs) are tools that use deep learning models to produce new content such as text, visual content (images/moving image), and audio. It does this by analysing large data sets, learning patterns and relationships, and generating new content that aligns with these patterns. It is important to understand its potential while also considering its limitations and potential risks as more tools adopt generative AI. It is also important to remember that generative AI does not understand concepts and meanings as humans do, but it mimics human behaviour based on probability.
Academic misconduct and generative AI
What is academic misconduct?
Academic misconduct refers to any form of academic cheating. This includes any act which gains, attempts to gain, or helps others in gaining or attempting to gain unfair academic advantage.
Why should I think about academic misconduct?
If using generative AI tools, you should be aware of whether your use of the tool(s) may constitute academic misconduct via plagiarism. Plagiarism is defined as stealing another person's ideas and presenting them as though they were your own. You can use generative AI in the developmental process of your work (see section ‘Keep track of how you use generative AI’).
You may not use it to generate your work unaltered that you submit for assessment as if it was your own work. The only exception to this, is where the course content/assessment brief permits the use of AI generated work. If you have questions, please speak to your tutor.
How can I avoid plagiarism?
Use the tips below in Keep track of how you use generative AI in your work and in particular referencing your work.
Where can I learn more?
Review UAL’s Academic Misconduct webpage for more information on what academic misconduct is and how to avoid it.
Speak to your course team (course leader, year leader, or unit leader, tutor, etc.)
How you might use generative AI
There is much excitement around this rapidly developing area, and you may find yourself considering how to you might use this in your studies.
Talk to your tutors
If you are considering using generative AI as part of your assessment you should talk to your tutors during crits, tutorials and/or other feedback sessions. They will have the best understanding of the unit you are studying and how generative AI may and may not be used.
Keep track of how you use generative AI
If you do use generative AI, it is important to show how you have used it. You must do this in two ways unless otherwise agreed with your tutor.
Keep a log
Process is one of the marking criteria at UAL. It is about your journey of learning. In assessments this can take the form of a learning log. Keeping a log may also help you to demonstrate how you are addressing the other UAL assessment criteria, for example analysis and evaluation of Enquiry.
Logs can include:
- primary and secondary research log;
- diary;
- drafts;
- process log.
You will often show these to your tutors in the development of your assessments during formative assessment points. In some cases, you may even submit your log for summative assessment (work you submit for grading, usually at the end of a unit).
In the case of generative AI, it could be useful for you to demonstrate how you have used it to reach your final outcome by keeping a prompt log that includes:
- the questions you are inputting into AI to generate content (often called a prompt);
- what content it generated in response;
- how you have reflected on the response or used that content in your assessment.
Reference AI in your work
Like any other sources you may consider using in assessments such as a journal, book or database, you must properly reference (cite) AI in your work. The Cite Them Right website provides guidance on how to reference generative AI in work.
Consider your course and unit learning outcomes
Each assessment at UAL is different. A good starting point for considering how you might use generative AI in your assessment is to think about the learning outcomes on your unit.
The learning outcomes of the units you study at UAL tell you what you will be expected to demonstrate when you have completed the unit.
You should think about what you are meant to be learning (your goal) and how you achieve it. You may consider how generative AI tools can help you achieve or potentially impact your learning. If you choose to use generative AI, are you bypassing the skills you should be developing in completing your assessment?
After you have reflected on this, speak to your tutor about potential appropriate uses of generative AI and follow the guidance above on how to keep track of how you have used generative AI.
Things to think about if using generative AI
Be curious about content generated by AI
Just with any other research you do, you should be curious and critical, and evaluate the suitability, accuracy, and source of materials in line with guidance from your course.
Generative AI tools provide responses based on the dataset(s) that it is trained on. This could include data that is out of date and/or biased. In some cases, generative AI can provide responses that are factually incorrect.
Specifically with AI generated text, how do you critically reflect on and ensure that the text responses generated are accurate? If you’re unsure of where the source of the response comes from, then how will you know it’s real, simply made up or reproduce biases?
Be curious about where the data you input is going
Be cautious when inputting data into generative AI tools through prompts and uploading of images, etc. This information may be added to datasets, saved by the tool and further distributed by the models. To protect your privacy and the privacy of others, please avoid entering any private or personal information, or research data, into generative AI tools without prior permission of the author or copyright owner and without knowing how data will be stored and re-used.
Acknowledgements
Thanks to the following authors for writing the initial version of this guide:
Sheldon Chow, David Hopwood, Andy Lee, Luis Parada, Mark Robinson & David White.
In addition, thanks to UAL colleagues who participated in the review of the initial version ahead of final approval through various committee structures such as College Academic Committees and Educational Enhancement Committee.
Learn more
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2017 MA Design Management and Cultures, London College of Communication, UAL | Photograph: UAL
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2017 Information and Interface Design, London College of Communication, UAL | Photograph: UAL