This guide provides information and recommended resources on the applications of Generative Artificial Intelligence (GenAI) in research.
Artificial Intelligence, or AI is a very broad term often used these days to describe any advanced machine learning system.
Generative AI refers to deep learning models that can generate text, images, and other media in response to user prompts. Those creations are not spontaneous: behind the scenes, there is an algorithm that has been trained with massive amounts of data. As a result of this training, a GenAI model can associate user input (prompts) with the knowledge learned from the training data and provide an output according to the instructions given.
It depends on the type of AI system you are using.
Popular AI chatbots and virtual assistants such as ChatGPT, Claude 3, Google Gemini or HuggingChat are based on Large Language Models (LLMs) that are trained to generate realistic text in a natural language or Vision Language Models (VLMs) that can analyse and generate both images and text. Therefore, they are particularly good at text-centric tasks, for example:
generating text by description;
spelling and grammar correction;
suggesting synonyms;
rewording or reformulating text;
rewriting text in a particular style;
applying formatting, e.g. LaTeX, to text;
tasks that require generating new text based on a template;
keyword extraction;
image captioning (VLMs only).
Apart from general-purpose AI chatbots and virtual assistants, there are AI-based tools optimised for particular tasks and connected to relevant knowledge bases, e.g. Elicit or Consensus for research literature review. See more examples of AI-based tools in the AI Tools for Research section of this guide.
Never assume the information generated by AI is accurate or true! While AI can produce logical and confident responses to most scenarios, it lacks any real ability to analyse information, and ultimately produce original thought. It is limited by the data it is trained upon and prone to hallucinations: generating false or misleading statements, presented as if it were a fact. See the section on AI Concerns for more information.
In most circumstances, yes, the use of generative AI tools should be cited. Your publisher may have specific guidance on how to attribute the AI tools you’ve used. Please see the section on Citing AI for more information.
For generating emails and editing grammar, there is no need to credit an AI unless you're simply copying and pasting its response to an inquiry. Remember that you are still responsible for all your communication even if it was generated by AI.
While some platforms have claimed to be able to detect AI usage, none have been able to consistently prove it. However, AI-written content can often stray significantly from our own writing styles, which is easily noticeable to the human eye. Additionally, AI-written content often contains factual errors and non-existent references that appear due to AI hallucinations and a limited knowledge base. An expert on the subject will detect these kinds of errors and understand their nature immediately.
European Approach to Artificial Intelligence. (2024). European Commission.
First EDPS Orientations for EUIs using Generative AI. (2024). European Data Protection Supervisor.
Guidance for generative AI in education and research. (2024). UNESCO.
Artificial Intelligence: A Reading List. (2024) House of Commons Library.
Schwamm, H. (2023). Navigating the AI Landscape in Research. Blog for the University of Galway Library.
Cummings, M. (2024). Doing more, but learning less: The risks of AI in research. Yale News.
Thais, S. (2024). Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community. arXiv:2408.15244
Cardon, P., Fleischmann, C., Aritz, J., Logemann, M., & Heidewald, J. (2023). The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age. Business and Professional Communication Quarterly, 86(3), 257-295.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16).
Australian National University. (2023). Chat GPT and other generative AI tools: What ANU academics need to know.
Salvaggio, E. (2024). Challenging The Myths of Generative AI. Tech Policy Press.
Rose, D. (2023). Generative AI vs. Traditional AI: Explore generative AI vs. traditional AI. [Video] LinkedIn Learning.
Riedl, M. (2023). A Very Gentle Introduction to Large Language Models without the Hype. Medium.
University of Galway Research Integrity Policy (QA514) (updated 12/2021)
Research Integrity Guidance. The University of Galway.
Milmo, D., Hern, A. (2024). What will the EU’s proposed act to regulate AI mean for consumers? The Guardian.
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