Policy presence
Queen Mary University of London has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
London, United Kingdom
Queen Mary University of London
Queen Mary University of London is listed as QS 2026 rank =110. Queen Mary University of London has 7 source-backed AI policy claim records from 6 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Short answer
v1 public contract
Queen Mary University of London is listed as QS 2026 rank =110. Queen Mary University of London has 7 source-backed AI policy claim records from 6 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Queen Mary University of London as an agent-reviewed AI policy record last checked on May 14, 2026 and last changed on May 14, 2026. The record contains 7 source-backed claims, including 7 reviewed claims, from 6 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/queen-mary-university-of-london.json. The entity-level confidence is 96%. This tracker is not legal advice, not academic integrity advice, and not an official university statement unless the linked source is the university's own official page.
Policy signals in this record
- Evidence includes Privacy claims.
- Evidence includes Academic integrity claims.
- Evidence includes Research claims.
- Evidence includes Source status claims.
- Evidence includes Procurement claims.
- Named AI services detected in public claims: Microsoft Copilot.
- Teaching, assessment, coursework, or syllabus-related language appears in the public claim text.
- Privacy, sensitive-data, or security language appears in the public claim text.
Review: Agent reviewed
This reference record summarizes visible public data only. Official sources and original-language evidence remain canonical; confidence is separate from review state.
This page is not legal advice, not academic integrity advice, and not an official university statement unless a linked source is the university's own official page.
Policy profile
Deterministic source-backed dimensions derived from this record's public claims.
Review: Machine candidate
Policy profile rows are machine-candidate derived metadata. They are not final policy conclusions; inspect the linked claim evidence before reuse.
Analysis page-quality metadata is available at /api/public/v1/analysis/page-quality.json.
AI disclosure
Queen Mary University of London has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
Coursework
Queen Mary University of London has 2 source-backed public claims for coursework; deterministic analysis status: required.
Exams
Queen Mary University of London has 2 source-backed public claims for exams; deterministic analysis status: required.
Privacy and data entry
Queen Mary University of London has 3 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
Queen Mary University of London has 3 source-backed public claims for academic integrity; deterministic analysis status: required.
Approved tools
Queen Mary University of London has 2 source-backed public claims for approved tools; deterministic analysis status: restricted.
Named AI services
Queen Mary University of London has 3 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
No source-backed public claim about teaching guidance is present in this profile.
The current public tracker record does not contain claim evidence about instructor, classroom, assessment-design, or syllabus guidance.
Research guidance
Queen Mary University of London has 2 source-backed public claims for research guidance; deterministic analysis status: recommended.
Security and procurement
Queen Mary University of London has 1 source-backed public claim for security and procurement; deterministic analysis status: required.
Coverage score measures breadth of public, source-backed coverage only. It is not a policy quality, strictness, legal adequacy, safety, or compliance score.
Evidence-backed claims
7 reviewed evidence-backed public claim
Privacy
Queen Mary staff guidance says open-access GenAI tools are not secure for institutional data, and directs staff to use institutional Microsoft Copilot for sensitive, confidential, or internal-use-only information while not sharing internal-use Queen Mary data with external AI tools.
Review: Agent reviewed
Normalized value: Staff guidance directs sensitive/internal Queen Mary data to institutional Copilot, not external AI tools
Original evidence
Evidence 1It’s important to remember that data entered into open-access GenAI tools - including widely used platforms such as ChatGPT, Gemini, Claude, and others - is not secure. Use Microsoft Copilot when working with sensitive, confidential, or internal-use-only information. Do not share any Queen Mary data designated for internal use with external AI tools.
Localized display only
For staff, sensitive, confidential, or internal-use Queen Mary information should use institutional Copilot; internal-use data should not be shared with external AI tools.
Academic Integrity
Queen Mary student guidance says any use of Generative AI must align with the Academic Integrity and Misconduct Policy, and that AI use may be acceptable in some scenarios but not all.
Review: Agent reviewed
Normalized value: Student GenAI use must align with Academic Integrity and Misconduct Policy; acceptable uses vary by scenario
Original evidence
Evidence 1Any use of Generative AI must align with Queen Mary’s Academic Integrity and Misconduct Policy. AI use may be acceptable in some scenarios, but not all.
Localized display only
Students are told GenAI use must align with the Academic Integrity and Misconduct Policy and may be acceptable only in some scenarios.
Research
Queen Mary Doctoral College guidance tells postgraduate researchers to decide with supervisors which GenAI systems to use and why, keep detailed records of inputs and outputs, and reference and declare GenAI use in their work.
Review: Agent reviewed
Normalized value: PGR GenAI use should be planned with supervisors, recorded, referenced, and declared
Original evidence
Evidence 1With your supervisors, decide which system(s) you will use, why you will use them, and how you will use them. Keep detailed records of your inputs and outputs. Reference GenAI information and declare its use in your work.
Localized display only
PGRs are guided to plan GenAI use with supervisors and keep records of inputs and outputs.
Privacy
Queen Mary IT FAQs say staff using built-in Copilot with a Queen Mary account get Microsoft 365 environment protections, and inputs are not stored or used for external model training.
Review: Agent reviewed
Normalized value: Built-in Copilot with Queen Mary account keeps data in Microsoft 365 environment and does not use inputs for external model training
Original evidence
Evidence 1The green shield icon confirms you’re using the Queen Mary secure version of Copilot. This means: Your data stays within the Queen Mary’s Microsoft 365 environment. It meets institutional security and data protection requirements. Inputs are not stored or used for external model training.
Localized display only
Queen Mary describes the signed-in, green-shield Copilot experience as keeping data within its Microsoft 365 environment and not using inputs for external model training.
Source Status
Queen Mary publishes an official AI education guidance page that links AI principles, staff guidance, student guidance, PGR guidance, academic integrity resources, and the Policy Zone rather than presenting the guidance page itself as a standalone binding AI policy.
Review: Agent reviewed
Normalized value: Official AI education guidance hub links staff, student, PGR, academic integrity, and policy-zone resources
Original evidence
Evidence 1Our innovative use of AI in education is supported by policy and guidance for educators and students. Guidance for staff and students: Staff Guide to Generative AI; Student Guide to Generative AI; Academic Integrity at Queen Mary; AI for student learning and research; AI guidance for PGRs.
Localized display only
Queen Mary frames AI in education as supported by policy and guidance, and links staff, student, PGR, academic integrity and Policy Zone resources.
Procurement
Queen Mary IT FAQs say staff intending to use Queen Mary data with a free AI tool must submit the tool for approval through the Ideas portal before using it.
Review: Agent reviewed
Normalized value: Free AI tools involving Queen Mary data require pre-use approval via Ideas portal
Original evidence
Evidence 1If you intend to use Queen Mary data (such as staff, student, research, or confidential information) with a free tool, you must submit the tool for approval via the Ideas portal – Software Request before you start using the tool
Localized display only
For free AI tools involving Queen Mary data, the FAQ requires approval through the Ideas portal before use.
Academic Integrity
Queen Mary’s Academic Integrity and Misconduct Policy applies to all Queen Mary students and says actions undermining academic integrity may be misconduct in assessment or learning activities, including formative assessment.
Review: Agent reviewed
Normalized value: Academic Integrity and Misconduct Policy applies to all students and can cover assessment or learning activities including formative assessment
Original evidence
Evidence 1The Academic Integrity & Misconduct Policy applies to all students at Queen Mary. Academic Integrity is essential in all areas of academic life. Actions that undermine integrity may be considered misconduct in any assessment or activity, including formative assessment or learning activities.
Localized display only
The academic integrity policy applies to all students and may cover misconduct in assessments or learning activities, including formative work.
Candidate claims
0 machine or needs-review claim
Candidate claims are not final policy conclusions. They preserve source URL, source snapshot hash, evidence, confidence, and review state so the record can be audited before review.
Official sources
6 source attribution
Academic Integrity & Misconduct Policy 2025-26
qmul.ac.uk
AI FAQs - IT Services
qmul.ac.uk
AI Guidance for PGRs - Doctoral College
qmul.ac.uk
Staff Guide to Generative AI - Queen Mary Academy
qmul.ac.uk
Student Guide to Generative AI - Library Services
qmul.ac.uk
University AI principles, guidance and policies - Queen Mary Academy
qmul.ac.uk
Change log
Source-check timeline and diff-style claim/evidence preview.
View the public change record for this university, including source snapshot hashes, claim review states, and a diff-style preview of current source-backed evidence.
Corrections and missing evidence
Corrections create review tasks and do not directly change this public record.
If an official source is missing, stale, moved, blocked, or incorrectly summarized, submit a source URL, policy change report, or institution correction for review. Corrections must preserve source URLs, source language, original evidence, review state, and audit history.