Policy presence
Queen's University Belfast has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Belfast, United Kingdom
Queen's University Belfast has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Queen's University Belfast has 2 source-backed public claims for ai disclosure; deterministic analysis status: required.
Queen's University Belfast has 5 source-backed public claims for coursework; deterministic analysis status: blocked.
Queen's University Belfast has 5 source-backed public claims for exams; deterministic analysis status: blocked.
Queen's University Belfast has 3 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Queen's University Belfast has 2 source-backed public claims for academic integrity; deterministic analysis status: recommended.
Queen's University Belfast has 2 source-backed public claims for approved tools; deterministic analysis status: blocked.
Queen's University Belfast has 4 source-backed public claims for named ai services; deterministic analysis status: restricted.
Queen's University Belfast has 3 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Queen's University Belfast has 4 source-backed public claims for research guidance; deterministic analysis status: restricted.
No source-backed public claim about AI security review or procurement is present in this profile.
The current public tracker record does not contain claim evidence about security review, procurement, vendor approval, risk assessment, authentication, SSO, or enterprise licensing.
12 reviewed evidence-backed public claim
Research
Normalized value: research AI guidance scope
Original evidence
Evidence 1This guidance applies to: All Queen’s researchers (staff, postgraduate research students, visiting researchers, and contractors) conducting research under the auspices of the University. All types of research activities across the research lifecycle including planning, funding proposal development, data collection and analysis, publication and dissemination, peer review and evaluation, and research management.
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The research guidance applies to Queen’s researchers across the research lifecycle.
Academic Integrity
Normalized value: module-level clarification for AI use in assessment
Original evidence
Evidence 1This academic year, those delivering modules will clarify if and how AI can be used when completing assessment. If students have any doubt about how AI can be used, they should consult with their tutor.
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Module staff clarify if and how AI may be used; students should ask tutors if unsure.
Teaching
Normalized value: RAISE principles frame staff and student AI guidance
Original evidence
Evidence 1This section brings together tailored QUB guidance for staff and students. This includes recommendations on how to get started as well as more specific guidance on the use of AI, for example within assessment. Our guidance is based on the following principles: R esponsible use, A I best practice, I ntegrity, S upport and E quitable Access – collectively RAISE.
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QUB describes its staff and student AI guidance as based on Responsible use, AI best practice, Integrity, Support and Equitable Access.
Academic Integrity
Normalized value: AI misuse subject to academic misconduct regulations
Original evidence
Evidence 1Students need to be fully aware of when and how they can use AI in assessments, including any limitations on certain tools or the need to cite or document how AI has been used. If students misuse AI, they will be subject to the University's academic misconduct regulations .
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AI misuse in assessment is linked to the University’s academic misconduct regulations.
Research
Normalized value: document acknowledge and validate material AI use in research
Original evidence
Evidence 1All use of AI in research must be clearly documented and acknowledged, especially where it constitutes material use in the research process/output. Researchers are expected to validate AI-generated content and uphold the standards of academic honesty, avoiding misrepresentation or plagiarism.
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Material research AI use should be documented and acknowledged, and AI-generated content validated.
Ai Tool Treatment
Normalized value: text-based AI detectors not recommended
Original evidence
Evidence 1Current tools that attempt to detect AI generated text – whether by analysing writing styles, using machine learning classification, or watermarking – cannot definitively identify AI-authored content. Worryingly, these systems often produce an unacceptably high rate of false positives. In the future, with the integration of AI writing tools into platforms like Microsoft Word and Google Workplace, it is anticipated that much of our writing will include AI-generated elements. This will be similar to how we currently benefit from algorithm-driven spell checkers and grammar tools. Considering these factors, the use of text-based AI detectors is not recommended.
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The assessment page says current AI text detectors cannot definitively identify AI-authored content and are not recommended.
Research
Normalized value: AI use involving participants or sensitive data in ethics applications
Original evidence
Evidence 1Researchers using AI in projects involving human participants, personal data, or sensitive information must explicitly outline AI usage in their ethics applications. Ethics applications must include clear details about how AI will be used in data collection, analysis, or management, and how participants’ data privacy will be protected.
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AI use with human participants, personal data, or sensitive information must be outlined in ethics applications.
Ai Tool Treatment
Normalized value: Copilot Chat available to faculty and staff with QUB login
Original evidence
Evidence 1Microsoft Copilot Chat is available for use by Queens University Faculty and staff. To use Copilot, please log in using your @qub.ac.uk email address. Copilot Chat is supported officially on Microsoft Edge and Chrome (using the latest Stable Channel update).
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Microsoft Copilot Chat is available for Queen’s faculty and staff with a @qub.ac.uk login.
Privacy
Normalized value: ethical judgment and permission before submitting data to AI tools
Original evidence
Evidence 1When using an AI tool, it is necessary to make an ethical judgment about the data or information that you put into the system when you use it to complete a task. Any information that is submitted to an AI tool then becomes part of the data that the tool draws upon to complete future tasks for anyone who uses the tool. You need to consider whether you have permission to submit the information that you do.
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Users are told to judge whether they have permission before submitting information to AI tools.
Research
Normalized value: do not present AI responses as own in research
Original evidence
Evidence 1Whilst work is ongoing within the University to develop guidance on its use, the fundamental principle is NOT to present any responses from AI as if they were your own, be clear, open and transparent in your use.
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The Research Integrity page says not to present AI responses as your own and to be clear, open, and transparent.
Privacy
Normalized value: listed AI tools for exploration with publicly available data only
Original evidence
Evidence 1Users are advised to use Artificial Intelligence tools responsibly. It is important to emphasise that the AI tools featured on these pages are intended solely for exploration, and exclusively with publicly available data.
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The tools page limits listed AI tools to exploration with publicly available data.
Teaching
Normalized value: student AI support resources available
Original evidence
Evidence 1These dedicated resources include an AI Glossary for understanding foundational AI concepts, a guide titled, ‘How to use AI for Academic Success’, and a guide on ‘How to U se Generative AI in your studies at Queen’s’. More resources will be added over time, so stay tuned for future updates. AI Glossary How to use Generative AI in your studies How to use AI for Academic Success Top AI Tools for Students AI Myth Busting AI and Creativity AI and Accessibility Acceptable and Unacceptable AI Use at QUB Sustainable AI Use: Top Tips for Students
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The student support page lists guides for AI academic success, generative AI in studies, citing AI, and the Student RAISE guide.
0 machine or needs-review claim
8 source attribution
blogs.qub.ac.uk
blogs.qub.ac.uk
qub.ac.uk
blogs.qub.ac.uk
blogs.qub.ac.uk
blogs.qub.ac.uk
blogs.qub.ac.uk
blogs.qub.ac.uk