Focus & Purpose

The focus of these Guidelines is on the Responsible use of Generative AI in Research. This focus reflects the rapid development and uptake of these powerful systems and the recognition that research is one of the sectors that could be most significantly disrupted by GenAI.

The UCC Guidelines for the Responsible use of Generative AI in Research signpost the complex GenAI landscape for UCC Researchers, enabling them to:

• Define and understand the meaning of key terms: Artificial Intelligence (AI), Generative AI (GenAI) and Large Language Models (LLMs) (Section 1.3).
• Provide a clearer understanding of the AI and GenAI legislative, policy & guidance landscape (Section 1.4).
• Provide a clearer understanding of the UCC’s current AI & Gen AI Landscape (Section 1.5).
• Understand GenAI (Section 2), together with its benefits and applications in research (Section 3), as well as the limitations and risks with using and applying GenAI (Section 4).
• Understand what constitutes Research Misconduct and Unacceptable Research Practices in the context of AI and GenAI use and applications in research (Section 5).
• Understand the Principles of using GenAI responsibly in research (Section 6) and how to apply these principles in research to uphold Research Integrity and mitigate risks (Section 7).
• Support the navigation and identification of further training opportunities available to UCC researchers (Section 7.5 and Appendix G).

Note: Section 8 provides a summary of requirements and recommendations for UCC Researchers for the Responsible use of Generative AI in Research.

Scope

Aligning with principles set out in the UCC Code of Research Conduct [4], these Guidelines apply to all researchers aligned with UCC. The term “Researcher” is used throughout this document to refer to any or all of the categories below, as appropriate.

• academic staff
• research assistants
• postdoctoral researchers
• research fellows
• senior research fellows
• research professors
• academic-related staff
• visiting researchers
• other staff involved in the research process (including technical, clerical, clinical and administrative staff) employed by the University, whether in the University, or while at another institution:
  • supervisors of postgraduate and undergraduate research.
  • postgraduate and undergraduate students.
  • any persons, with honorary or adjunct positions or otherwise involved in research within, or on behalf of or accommodated within, the University.
  • collaborators and sub-contractors from other institutions, government bodies and industry, whether working within the University or not.
  • all individuals engaged in the setting of research priorities and the assessment of research.

Key Definitions

Artificial Intelligence (AI) systems are machine-based systems, broadly characterised as a set of advanced computational technologies designed to enable machines to perform tasks that typically require human cognitive capabilities. AI systems vary in their levels of autonomy and adaptiveness and include, but are not limited to, natural language processing, visual perception, automated reasoning, and the generation of coherent written responses. Examples: Virtual assistants (e.g. SIRI, Alexa); smart home devices; Google Maps.

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Machine Learning, is a branch of AI that focuses on building systems that can learn from and make decisions based on data, without being explicitly programmed for every specific task . ML algorithms use data to identify patterns and improve their performance over time. Examples: Search Engines (Google Search to personalise results), Netflix's recommendation engine learns your preferences to suggest shows.

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Deep Learning, is a specialized subset of Machine Learning that uses artificial neural networks with many layers—hence the term "deep." It’s particularly powerful for handling large amounts of unstructured data like images, audio, and text. Examples: Image and speech recognition systems.

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Generative AI (Gen AI) systems constitutes a specialized domain within the broader field of AI. Most modern GenAI systems use Deep Learning. GenAI systems are capable of autonomously producing content—such as human-like dialogue, text, images, video and code. These systems generate outputs in response to user inputs or instructions, often leveraging large-scale data models to simulate human creativity and expression. Gen AI systems are used across a wide range of domains; each tailored to specific creative or functional tasks. 

Examples: common applications include text generation (e.g., drafting, summarizing), data analysis, and coding.

• DALL·E (by OpenAI): Generates images from text prompts.
• GitHub Copilot: Assists developers by suggesting code and completing functions.
• ChatGPT with Code Interpreter (Advanced Data Analysis): Analyzes data, creates visualizations, and performs calculations.
• Power BI Copilot: Helps generate reports and insights from business data.

Science & Research specific GenAI tools include

• AlphaFold (by DeepMind): Predicts protein structures from amino acid sequences.
• Elicit: Assists researchers in literature review and hypothesis generation.
• SciSpace: Summarizes and explains scientific papers.

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Large Language Models (LLMs) are a form of GenAI that has been trained through deep learning algorithms to recognize, generate, translate, and/or summarize vast quantities of written human language and textual data. LLMs are some of the most advanced and accessible natural language processing (NLP) solutions today. LLMs can be used to not only assess existing text but to generate original content based on user inputs and queries.

Examples: 

• ChatGPT Assists with writing, brainstorming, summarizing, and more.
• Microsoft (MS) Copilot (in Office apps like Word, Excel, PowerPoint) is not itself a LLM, but uses LLMs as its core technology. MS CoPilot enhances productivity by generating text, summarizing documents, creating presentations, analyzing spreadsheets, etc.

The Guiding Landscape

• EU AI Act 2024
• ERA Living Guidelines on the Responsible Use of Generative AI in Research 2025
• UCC Code of Research Conduct
• UCC Statement on AI (forthcoming)