Policy presence
Imperial College London has 3 source-backed public claims for policy presence; deterministic analysis status: unclear.
London, United Kingdom
Imperial College London
Imperial College London has 10 source-backed AI policy claims from 14 official source attributions. Review state: agent reviewed; 10 reviewed claims. Last checked May 6, 2026.
Imperial College London AI policy short answer
v1 public contract
Imperial College London has 10 source-backed AI policy claims from 14 official source attributions, including 10 reviewed claims. The record review state is agent reviewed; original-language evidence snippets, source URLs, confidence, and public JSON are preserved for citation. Last checked May 6, 2026. Discovery context: Imperial College London is listed as QS 2026 rank 2.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Imperial College London as an agent-reviewed AI policy record last checked on May 6, 2026 and last changed on May 6, 2026. The record contains 10 source-backed claims, including 10 reviewed claims, from 14 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/imperial-college-london.json. The entity-level confidence is 95%. 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 Academic integrity claims.
- Evidence includes Teaching claims.
- Evidence includes Privacy claims.
- Evidence includes Research claims.
- Evidence includes Other policy claims.
- Named AI services detected in public claims: Microsoft Copilot.
- Disclosure, acknowledgment, citation, or attribution language appears in the public claim text.
- Teaching, assessment, coursework, or syllabus-related 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
Imperial College London has 3 source-backed public claims for ai disclosure; deterministic analysis status: required.
Coursework
Imperial College London has 5 source-backed public claims for coursework; deterministic analysis status: blocked.
Exams
Imperial College London has 5 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
Imperial College London has 5 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
Imperial College London has 2 source-backed public claims for academic integrity; deterministic analysis status: conditionally_allowed.
Approved tools
Imperial College London has 2 source-backed public claims for approved tools; deterministic analysis status: restricted.
Named AI services
Imperial College London has 2 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
Imperial College London has 3 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Imperial College London has 3 source-backed public claims for research guidance; deterministic analysis status: restricted.
Security and procurement
Imperial College London has 2 source-backed public claims for security and procurement; deterministic analysis status: restricted.
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
10 reviewed evidence-backed public claim
Academic Integrity
Unless explicitly authorised, using generative AI to create assessed work may be treated as an academic offence such as contract cheating under Imperial's Plagiarism, Academic Integrity & Exam Offences regulations. Improper use of AI can be investigated under the University's Academic Misconduct procedures.
Review: Agent reviewed
Evidence originale
Evidence 1Unless explicitly authorised, using AI to create assessed work may be treated as an offence such as contract cheating under Imperial's Plagiarism, Academic Integrity & Exam Offences regulations. Familiarise yourself with Imperial's Academic Misconduct Policy for rules on appropriate AI usage.
Teaching
Individual departments at Imperial may allow or prohibit the use of generative AI for specific assessments. Local (team/department/faculty) instructions take precedence over university-wide guidance. Students should check their department's current policy on using and disclosing generative AI in academic work and follow their module leader's instructions.
Review: Agent reviewed
Evidence originale
Evidence 1Individual departments may allow or prohibit GenAI for specific assessments; local (team/department/faculty) instructions take precedence. Always check the brief and acknowledge permitted AI use as directed.
Privacy
Imperial's dAIsy AI platform uses University SSO authentication with auditing. Prompts and metadata are logged for operational monitoring, and AI model providers are configured not to train on user data. Users' prompts and responses are not used to train external AI models. dAIsy is approved for use with unrestricted data within Imperial's secure infrastructure.
Review: Agent reviewed
Evidence originale
Evidence 1dAIsy uses University SSO and auditing. Logs (prompts/metadata) are retained for operational monitoring. Model providers are configured to not train on your data.
Academic Integrity
Breaches of Imperial's dAIsy Use Policy may lead to action under Academic Misconduct procedures for students and HR/disciplinary processes for staff, as well as under Information Security and Data Protection policies. Sanctions may include removal of access, grade penalties, or formal disciplinary measures.
Review: Agent reviewed
Evidence originale
Evidence 1Breaches may lead to action under Academic Misconduct (students) and HR/disciplinary processes (staff), as well as Information Security and Data Protection policies. Sanctions may include removal of access, grade penalties, or formal disciplinary measures.
Teaching
Students should include a statement acknowledging their use of generative AI tools for all assessed work, specifying the tool name and version, publisher, URL, a brief description of how it was used, and confirmation that the work is their own. Further requirements such as prompts used, date of output, the output obtained, and how it was modified may also be required by individual departments.
Review: Agent reviewed
Evidence originale
Evidence 1You should include a statement to acknowledge your use of generative AI tools for all assessed work, in accordance with guidelines from your department or course team. This statement should be written in complete sentences and include the following information: Name and version of the generative AI tool e.g. Copilot, ChatGPT-5; Publisher (name of company that provides the AI system) e.g. Microsoft, OpenAI; URL of the AI tool; Brief description (single sentence) of the way in which the tool was used; Confirmation that the work is your own.
Research
Research at Imperial that involves people, personal data, or sensitive topics may require ethics approval, a Data Protection Impact Assessment (DPIA), and data-governance controls before using any AI tool. Researchers must verify whether their use of AI in research requires special approval, particularly when uploading private or confidential research data.
Review: Agent reviewed
Evidence originale
Evidence 1Research that involves people, personal data, or sensitive topics may require ethics approval, a Data Protection Impact Assessment (DPIA), and data-governance controls before using any AI tool.
Other
All Imperial staff and students have access to Microsoft Copilot with Commercial Data Protection when signed in using their Imperial credentials. Microsoft Copilot has no access to organizational data in the Microsoft 365 Graph. Chat results are not saved or made available to Microsoft, and data does not pass outside the organisation.
Review: Agent reviewed
Evidence originale
Evidence 1All Imperial staff and students have access to Microsoft Copilot. You should ensure you sign in to use Copilot so that you are using the secure version of Microsoft Copilot with Commercial Data Protection. MS Copilot has no access to organizational data in the Microsoft 365 Graph. Your data is protected and the chat results are NOT saved or made available for Microsoft, so the data does not pass outside of the organisation.
Other
Users of Imperial's dAIsy AI platform must always apply critical judgment to AI outputs. Generative AI can produce inaccurate or biased outputs ('hallucinate'), omit context, or reflect training-data biases. Users remain accountable for the accuracy, legality, and appropriateness of any content they submit or share through the platform.
Review: Agent reviewed
Evidence originale
Evidence 1Always apply critical judgement. GenAI can 'hallucinate', omit context, or reflect training-data biases. Users remain accountable for the accuracy, legality, and appropriateness of any content they submit or share.
Other
Users of Imperial's dAIsy platform must not upload third-party content they are not permitted to share. Reuse of AI outputs must comply with licensing and academic citation norms. When communicating externally, dAIsy outputs must not be presented as Imperial's position without approval.
Review: Agent reviewed
Evidence originale
Evidence 1Do not upload third-party content you are not permitted to share. Ensure that any reuse of AI outputs complies with licensing and academic citation norms. When communicating externally, do not present dAIsy outputs as Imperial's position without approval.
Teaching
Imperial College London has established five Generative AI Principles (aligned with Imperial Values: Respect, Collaboration, Excellence, Integrity, Innovation) to provide a foundational framework for approaches to using generative AI in teaching, learning and assessment university-wide. The principles cover promoting critical use of AI, adopting a consistent ethical approach, and building a proactive research community around AI in education.
Review: Agent reviewed
Evidence originale
Evidence 1These principles are intended to provide a starting point for approaches to using generative AI in teaching, learning and assessment at Imperial. Imperial supports the use of the principles to frame and underpin activities university-wide as we as a community explore the use of generative AI, progress and develop policy, and establish guidelines. The principles are underpinned by Imperial's core Values. ... Promoting the critical use of generative AI in teaching, learning and assessment. Adopting a consistent ethical approach to the use of generative AI. Building a proactive research community around the use of generative AI.
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
14 source attribution
Academic Misconduct Policy and Procedure v1.3
imperial.ac.uk
CfAE guidance on GenAI
imperial.ac.uk
dAIsy - gen AI platform
imperial.ac.uk
dAIsy Use Guidance and Process Flows
imperial.ac.uk
dAIsy Use Policy
imperial.ac.uk
DPA Code of Practice 08 - Use of Artificial Intelligence (AI)
imperial.ac.uk
Generative AI & Education Guidance Hub
imperial.ac.uk
Generative AI and Study Guidance Hub
imperial.ac.uk
Generative AI guidance
imperial.ac.uk
Generative AI Principles
imperial.ac.uk
ICT Code of Practice 06 - Conditions of Use of IT Resources
imperial.ac.uk
Microsoft Copilot
imperial.ac.uk
Plagiarism, Academic Integrity & Exam Offences
imperial.ac.uk
Use of generative AI for teaching, learning and assessment
imperial.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.