Generative AI tools are programs that can generate new content like text, images, audio, and video. They do this in an automated way based on given inputs and parameters.
There are many different kinds of AI tool with multiple uses:
Generate human-like text and conduct conversational dialogues (e.g. ChatGPT, Copilot, Claude and Gemini)
Assist researchers by finding and summarising research papers (e.g. Elicit, Semantic Scholar etc.)
Create images from text descriptions (e.g. DALL-E and Stable Diffusion )
Generate human-like voices and convert text into natural speech (e.g. Jasper and Whisper)
Generate new music compositions and songs based on different genres, instruments, etc. (e.g. MuseNet and Amper Music)
Deepfakes (algorithms that can swap faces in images and video or generate fabricated video/audio that resembles real people)
Your next step is to learn how AI tools may help to support your work.
Part seven of our self-enrol Study Skills Canvas module focuses on generative AI. There is guidance on different kinds of generative AI tools, information about things you should think about before using generative AI, and information about how you can learn more about using generative AI tools.
Ethical issues. Many ethical issues have been raised about the potential for AI tools to reproduce the biases in their training data, to be exploited to spread misinformation or disinformation, to be used to create deceptive content that threatens privacy, to endanger intellectual property rights, to displace jobs in industries that rely on content generation.
Environmental costs. Generative AI systems consume a substantial amount of energy, require large amount of water to cool their processors and generate electricity, and lead to significant carbon emissions.
While fully AI-generated outputs can seem impressive on the surface, they can often contain factual errors, lack nuance, critical engagement, and depth of expression and understanding.
Importantly, overreliance on AI tools simply to generate written content, software code or analysis reduces your opportunity to develop and practice key skills (e.g. writing, critical thinking, evaluation, analysis, coding, reasoning). These are all important aspects of your learning at university and will continue to be required in your working life.
Written work is a key way of demonstrating critical thinking and deep engagement with your course material, much of which happens during the process of writing. Relying on AI-generated output will prevent you from developing the skills you acquire when you are doing it yourself. A vital aspect of your learning at university is about developing these advanced skills, learning how to think and build an argument through writing. Generative AI is no substitute for this.
While generative AI can be useful for some tasks, it is essential that you are aware of its many limitations that include the following:
This guide is a modified version of the University of Edinburgh Libguide 'Using generative AI tools in academic work' created by Ishbel Leggat, Anna Richards, and Robert O’Brien.