{"id":36773,"date":"2025-08-27T14:38:07","date_gmt":"2025-08-27T13:38:07","guid":{"rendered":"https:\/\/blogs.qub.ac.uk\/digitallearning\/?page_id=36773"},"modified":"2025-11-19T12:46:33","modified_gmt":"2025-11-19T12:46:33","slug":"qub-guidance-on-responsible-use-of-ai-in-research","status":"publish","type":"page","link":"https:\/\/blogs.qub.ac.uk\/digitallearning\/ai\/ai-in-research\/qub-guidance-on-responsible-use-of-ai-in-research\/","title":{"rendered":"Queen\u2019s Guidance on the Responsible Use of AI in Research"},"content":{"rendered":"\t\t
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Queen\u2019s Guidance on the Responsible Use of AI in Research<\/h1>\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Queen\u2019s is committed to supporting the responsible, ethical, and innovative use of Artificial Intelligence (AI) tools in research. AI technologies hold transformative potential to enhance research productivity and address complex challenges. Equally, we must ensure their use aligns with our principles of research integrity, ethics, and accountability. This guidance outlines essential principles and practical advice to help researchers integrate AI responsibly into their research activities.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This guidance applies to all staff and postgraduate researchers involved in research at Queen\u2019s, with an aim to cover aspects of AI use across the research lifecycle<\/em>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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For broader guidance on generative AI, including teaching and general staff guidance, please see the Queen\u2019s Staff Guidance on Generative AI<\/a><\/strong>, and explore the resources available at the Queen\u2019s AI Hub<\/a><\/strong> and the Library<\/a><\/strong>. Please also be aware that some Schools may issue additional guidance to reflect discipline-specific contexts\/needs.<\/p>

For detailed ethical guidelines, researchers should consult the Queen\u2019s webpages on Research Integrity<\/a><\/strong> and ensure compliance with the University\u2019s Data Protection <\/a><\/strong>\u00a0policies, and consider relevant Export Control<\/strong><\/a> requirements where applicable if the use of AI may result in the export of data\/technology outside the UK.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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What is AI in Research?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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AI broadly refers to computer systems capable of performing tasks that normally require human intelligence. Within research contexts, AI is increasingly used to automate tasks, process and interpret data, and generate new insights.<\/p>

Two primary types of AI are relevant for researchers:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Predictive AI (Machine Learning) <\/h3>\t\t\t\t

Uses algorithms trained on historical data to forecast trends, classify data, and identify patterns. This includes comparative models (e.g. ranking algorithms, similarity networks) which support evaluation, prioritisation, and selection tasks in both STEM and humanities research.<\/p>\n\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Generative AI <\/h3>\t\t\t\t

Creates new content\u2014e.g. text, images, audio, or code\u2014by learning from existing data patterns. Tools based on Gen AI include large language models (LLMs), such as Microsoft Copilot, Gemini, and ChatGPT.<\/p>\n\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t<\/div>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Scope of this Guidance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This guidance specifically addresses the responsible use of commonly available AI tools in research activities \u2013 particularly general-purpose generative AI platforms such as large language models (LLMs). It does not cover the process of developing new AI technologies or systems. If you are developing or creating AI as a primary research goal, you should refer directly to the Queen\u2019s Code of Conduct and Integrity in Research<\/a><\/strong>.<\/p>

This guidance does not seek to address well-established ML techniques that are routinely used for data analysis within disciplines. Researchers using such techniques should continue to follow standard ethics, data protection, and research integrity protocols and ensure compliance with export control requirements pertinent to their research area.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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This guidance applies to:<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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