University College London (UCL)

For Category 1, you might use GenAI tools to help you revise and prepare, but the work you complete for the assessment should be your own. In Category 2, you could use GenAI to get ideas, help draft your work, or even proofread what you've written. Just make sure you keep a note of how you've used GenAI, so that you can acknowledge it afterwards. You won't lose marks just for having used GenAI. Category 3 is a lot like category 2, but there will also be some essential use of GenAI as part of the assessment.

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However, use of the three categories is not a formal policy, but rather a resource to help staff and students communicate and understand expectations around use of GenAI in assessments. The final decision about how GenAI may or may not be used is up to the staff member setting the assessment.

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Students reading this page should note that instructions given in assessment briefs take precedence over the information on this page, as some disciplines may have unique approaches or requirements.

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Students with Reasonable Academic Adjustments may still be permitted to use other assistive technology required. Staff should clarify if there are GenAI tools that are exempt in each case.

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Students are permitted to use GenAI tools in a limited, critical and responsible way. The student should still be the author of their own work – in other words, GenAI should be limited to supporting and assisting the student in completing the assessment, and should not be completing the assessment (entirely or only in part) on the student’s behalf.

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Please note: These categories help staff to design, set and advise students on their assignments in a way that will enhance student learning. Staff can use these categories to help discuss how, how much, and where students can use GenAI in the assessment. However, use of the three categories is not a formal policy, but rather a resource to help staff and students communicate and understand expectations around use of GenAI in assessments.

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The final decision about how GenAI may or may not be used is up to the staff member setting the assessment. Staff may, for example, specify a category and then apply further conditions, restrictions or permissions, but these must be clearly communicated to students (see Academic Manual 9.2.6c ). Students reading this page should note that instructions given in assessment briefs take precedence over the information on this page, as some disciplines may have unique approaches or requirements.

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Students are permitted to use GenAI tools in a limited, critical and responsible way. The student should still be the author of their own work – in other words, GenAI should be limited to supporting and assisting the student in completing the assessment, and should not be completing the assessment (entirely or only in part) on the student’s behalf. Students may not use GenAI to gain unfair academic advantage (see below).

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Students with Reasonable Academic Adjustments may still be permitted to use other assistive technology required. Staff should clarify if there are GenAI tools that are exempt in each case.

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Academic integrity is about fairness. If somebody gets an unfair advantage over other students, we consider this academic misconduct. This means that there's no single list of fair and unfair uses of GenAI – it always depends on the category of the assessment.

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For Category 1, you might use GenAI tools to help you revise and prepare, but the work you complete for the assessment should be your own. In Category 2, you could use GenAI to get ideas, help draft your work, or even proofread what you've written. Just make sure you keep a note of how you've used GenAI, so that you can acknowledge it afterwards. You won't lose marks just for having used GenAI. Category 3 is a lot like category 2, but there will also be some essential use of GenAI as part of the assessment.

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It's okay to use GenAI to help with spelling and grammar, and even with the tone of your language. However, make sure it isn't changing the content and meaning of what you've written. You should always understand the work you submit.

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1. Language and Writing review: defined as having a third-party or software check areas of academic writing such as structure, fluency, presentation, grammar, spelling, punctuation, and language translation.

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CLIE’s policy is based on UCL’s three categories of GenAI use in assessment. CLIE oral assessments and invigilated in-person assessments are category 1 unless otherwise stated: generative AI tools cannot be used. This is because the use of generative AI is wholly inappropriate for the skills to be assessed and the format in which they are assessed.

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CLIE non-invigilated written assessments are category 2 : generative AI tools may only be used in an assistive role in the research and writing process (for example, brainstorming, in the background research stage, to help you recognise different perspectives and approaches to a subject, where output can be verified by authoritative sources) and should not undermine your learning. This means that generative AI should not be used to produce the substantive structure and content of your work.

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However, for CLIE Modern Foreign Language modules, use of software (or any other third-party) for Language and Writing review is not permitted, because you are being assessed on your ability to translate or use a language other than English.

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For UCL Summer School modules, the policy is set by the department teaching the module.

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Please familiarise yourself with the UCL Library†library-guides.ucl.ac.uk guidance on acknowledging use of generative AI. This guidance explains the limitations of generative AI, why it is not an authoritative source, and sets out considerations when using generative AI. You must acknowledge any use of generative AI tools e.g.

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The use of GenAI must be acknowledged in any piece of academic work where it has been used as a functional tool to assist in the process of creating the academic work, such as drafting ideas and planning or structuring written materials.

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Instances where use of GenAI may not require acknowledgement include: familiarising oneself with a topic before refining your research process; reading an AI-generated summary of information from search results in a library research database to determine whether to proceed to consult the original sources, or using generative AI to summarise long or complex information to make it easier to review; helping to improve your grammar and writing structure.

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Minimum requirement to include in acknowledgement: Name and version of the GenAI system used; e.g. Microsoft Copilot (version GPT-4); ChatGPT-3.5 Publisher (company that made the |GenAI system); e.g. Microsoft; OpenAI URL of the GenAI system. Brief description (single sentence) of context in which the tool was used. For example: I acknowledge the use of Microsoft Copilot (version GPT-4, Microsoft, https://m365.cloud.microsoft/chat) to summarise my initial notes and to proofread my final draft.

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Further requirements may be stipulated by a department, academic programme or individual teaching staff, or for a particular assignment, and must be made clear to students when an assignment is set. Additional requirements may include expanded description in an ‘Appendix’ or ‘Methods’ section, such as: If relevant, the prompt(s) used to generate a response in the GenAI system. The date the output was generated. The output obtained (e.g. a ‘link to chat’ if ChatGPT, or a compilation of all output generated as an appendix). How the output was changed for use or incorporation into a piece of work (e.g. a tracked-changes document or a descriptive paragraph).

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A GenAI tool cannot be classed as an author – it cannot take responsibility for its work, nor does it generate original ideas but reproduces ideas found elsewhere. A primary function of a reference list is so the reader can refer to the original source, which is not possible with AI generated content. This proposal therefore favours the approach of most academic publishers (1-3), which stipulates GenAI systems should not be cited as an author nor included as a source in the reference list.

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Where a student or researcher is required to use a standard referencing style which has specific rules for citing and referencing GenAI, the rules of the citation style should be followed, in addition to acknowledging the use of GenAI as outlined above. This may require the use of in-text citations and reference list entries where the GenAI tool is listed as an author. E.g. APA, Chicago.

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In cases where authorship is in question (normally allegations of Contract Cheating and Misuse of GenAI), the Department/Faculty will organise an Investigatory Viva and the Secretary will prepare the paperwork and invite the student to attend.

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3. The purpose of the Investigatory Viva is to establish the student’s authorship of the submitted work and make a decision on the case when it is Minor. For Major cases it is to determine whether there is prima facie evidence to support lack of authorship for further consideration at an Academic Misconduct Panel (via the Casework Team).

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4. The Secretary must provide the student with the following at least five working days before the scheduled Investigatory Viva:

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1. An invitation to the Investigatory Viva, outlining the date, time, and location.

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2. A completed Student Academic Misconduct Report Form.

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3. A copy of all documentary evidence relating to the case (i.e. the student’s submission).

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5. A student’s attendance at an Investigatory Viva is mandatory and cannot be conducted in their absence. Failure to attend will result in the actions under section 5 paragraph 16 and 17 being taken.

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6. An Investigatory Viva should be organised within 20 working days from the date of the Stage 1 outcome, unless there are reasonable circumstances that prevent this.

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7. The Investigatory Viva should be constituted as follows:

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1. Chair of the Board of Examiners owning the affected module, or appropriate nominee where there is a conflict of interest (acting as Chair).

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2. At least two other members of staff from the Department not directly involved in the teaching of the affected module(s).

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8. The quorum for the Investigatory Viva must be three including the Chair.

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9. Where deemed necessary, a Departmental Representative who is competent to assess the student’s knowledge and understanding of the work or the allegation may be invited to attend in an advisory capacity. The Departmental Representative may, at the discretion of the Chair, ask the student questions, but will not be involved in the panel’s decision making.

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10. A decision will be made by the Investigatory Viva after their private deliberations and decide one of the following outcomes:

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1. No Further Action

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2. To apply a penalty in line with Section 6 (where the allegation is Minor)

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3. To refer to an Academic Misconduct Panel via Casework (where the Investigatory Viva is investigating a Major case)

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11. The decision of the Investigatory Viva will be by majority vote, and in the case of a tie, the Chair of the Investigatory Viva will have the casting vote.

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12. The Secretary should record the rationale for the decision and for any penalty applied.

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13. The Secretary should inform the student of the Investigatory Viva decision within five working days and record this on the module minutes in Portico.

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14. Students may appeal an Investigatory Viva penalty under Section 7: Stage 3 (Appeal Review).

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15. For Major Cases being investigated at an Investigatory Viva, if prima facie evidence of lack of authorship is found to exist, the findings and all the relevant documentation must be forwarded to the Secretary of the Academic Misconduct Panel via the Casework Team for adjudication.

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16. Where a student fails to attend a Viva, they may be referred to the Mandatory Faculty Interview process for a meeting with the Faculty. The purpose of the meeting is to outline the importance of engagement with the Academic Misconduct process. Following this meeting, the student will be sent a new date, time, and location of the rescheduled Viva. Alternatively, where the allegation is Minor and the student accepts the allegation, the Department may apply a penalty in line with Section 6.

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17. Continued non-engagement with the Viva process may result in Suspension or Termination of Studies via the Mandatory Faculty Interview process.

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5. A student’s attendance at an Investigatory Viva is mandatory and cannot be conducted in their absence. Failure to attend will result in the actions under section 5 paragraph 16 and 17 being taken.

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6. An Investigatory Viva should be organised within 20 working days from the date of the Stage 1 outcome, unless there are reasonable circumstances that prevent this.

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4. The Secretary must provide the student with the following at least five working days before the scheduled Investigatory Viva:

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An Appeal Review is a paper-based review of the decision made at either Stage 1 or Stage 2. An appeal against an Academic Misconduct decision will only be heard if it is based on one or more of the following grounds:

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1. The decision or panel process was not conducted in accordance with the procedures.

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2. Fresh evidence has become available which was not available and could not reasonably be available for consideration during the decision or panel process.

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3. The judgement of misconduct was not reasonable given the circumstances of the case.

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4. The penalty will have a significant impact on the student given their specific circumstances (this applies to decisions that lead to non-progression or suspension/termination of studies).

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##### Student Submission and Eligibility

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2. Students must, within 10 working days of receiving formal notification of the decision, submit an appeal via the ‘UCL Student Academic Misconduct Procedure - Appeal Form†forms.office.com ’.

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3. The Casework Team will conduct an eligibility review of the request. If it is determined that the request review does not fall within the grounds for review and/or does not meet the on-time criterion of 10 working days, the Casework Team will notify the student that the review request will not be taken forward. This means that a case will not progress to considerations at Stage 3 and a Completion of Procedures Letter will be issued.

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##### Consideration of Appeal Review

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4. If it is determined that the request review falls within the grounds for review, the case will be allocated to a member of the Casework Team, who is normally a Senior Casework Officer, for consideration.

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5. The paper-based review will be considered on the basis of the student’s written statement and supporting evidence.

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##### Appeal Review Outcome

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6. Following the investigation, the Stage 3 Appeal Review may be upheld, partially upheld or not upheld. This decision will normally be issued within 20 working days of the student having been informed that the case is eligible for review.

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7. If any aspect of the Stage 3 Appeal Review has been found upheld in full or in parts, then the outcome letter will make recommendations for what UCL will do to put things right. This decision may not be in line with the desired outcome as requested by the student. Recommendations by the Casework Team are subject to confirmation from the relevant Exam Board or equivalent academic body before implementation.

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8. Where a review request is not upheld, then the outcome letter will explain how that decision has been reached.

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9. If an appeal outcome leads to a necessary revision in a student’s certificate or transcript, the student must return any originals before updated versions can be issued.

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10. The outcome of the Stage 3 Review will conclude UCL’s consideration of the case and will be confirmed in the form of a Completion of Procedures letter.

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UCL offers a service called Studiosity to support students with their academic writing and referencing skills. The platform uses GenAI and is available 24/7 to all current students, at all levels of study. Staff can explore the Studiosity Resource Centre for support resources including How To guides, frequently asked questions and use cases.

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Studiosity can review your work and give you feedback to help improve the structure, language, spelling and grammar, and use of sources in your writing. This service is available 24/7 and there is no limit to the number of times you can use it.

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This means that, unlike other AI tools, you can use Studiosity with confidence that it is supporting your learning. Studiosity, uses a ‘closed system’ AI, so your work remains your own and will not be used to train AI.

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All current UCL students can access Studiosity via Moodle, where you'll also find frequently asked questions and links to further resources

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UCL is the Controller of your personal data for the purposes of providing academic skills support. Studiosity Pty Ltd (Studiosity) acts as a Processor when providing services on behalf of UCL. Additionally, Studiosity acts as an independent Controller for data collected directly from you to manage your user account and platform improvements.

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Your personal data is processed by Studiosity in Australia and the United States. To ensure your data receives an adequate level of protection, these transfers are governed by the UK Approved International Data Transfer Addendum.†ico.org.uk

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* Service Content: Submitted academic drafts for feedback, "Connect Live" session transcripts (including audio recordings), and chat histories.

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We have put in place appropriate security measures to prevent data loss or unauthorized access. This includes AES-256 encryption at rest, TLS 1.2+ encryption in transit, and Multi-Factor Authentication (MFA) for administrative access. We retain your data only as long as necessary to fulfill the purposes of the service or as required by the UCL Records Retention Schedule .

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UCL is not investing in GenAI detection software. Staff must not submit student work into such tools, including GenAI themselves, as this may compromise students' intellectual property and personal data rights.

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In addition to concerns about students’ personal data rights, there is not yet sufficient evidence that GenAI is able to contribute effectively and without bias to the process of marking student work. As a result, staff should not use GenAI to mark student work.

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UCL does not use GenAI detectors when marking. Instead, if a marker has any concerns about your work, they’ll usually request a quick chat to get more information. Please don’t be offended if you’re asked to have a talk with your marker – it doesn’t mean you’re under suspicion or in trouble.

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In the event that you suspect student work has made inappropriate use of GenAI, you should report it in the same way as any other Academic Misconduct, using the Student Academic Misconduct Report Form (download) , to your Exam Board Chair who may initiate an Investigatory Viva.

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CLIE’s policy is based on UCL’s three categories of GenAI use in assessment. CLIE oral assessments and invigilated in-person assessments are category 1 unless otherwise stated: generative AI tools cannot be used. This is because the use of generative AI is wholly inappropriate for the skills to be assessed and the format in which they are assessed.

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CLIE non-invigilated written assessments are category 2 : generative AI tools may only be used in an assistive role in the research and writing process (for example, brainstorming, in the background research stage, to help you recognise different perspectives and approaches to a subject, where output can be verified by authoritative sources) and should not undermine your learning. This means that generative AI should not be used to produce the substantive structure and content of your work.

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However, for CLIE Modern Foreign Language modules, use of software (or any other third-party) for Language and Writing review is not permitted, because you are being assessed on your ability to translate or use a language other than English.

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For UCL Summer School modules, the policy is set by the department teaching the module.

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Please familiarise yourself with the UCL Library†library-guides.ucl.ac.uk guidance on acknowledging use of generative AI. This guidance explains the limitations of generative AI, why it is not an authoritative source, and sets out considerations when using generative AI. You must acknowledge any use of generative AI tools e.g.

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If you sign into Microsoft Copilot with your UCL credentials you can be confident that:

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your prompts, questions and results are protected

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the chat data is not saved

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Microsoft has no eyes-on access.

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This means no one can view your data and your data is not used to train the AI models.

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Microsoft Copilot is grounded in data from the public web and provides verifiable answers with citations, along with visual answers that include graphs and charts. It is designed in line with Microsoft’s AI principles.

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Although data is more secure when using Microsoft Copilot, staff should still be careful of what they enter into the tool. You should not enter personal, confidential or special category data into the tool. We advise that you only enter content for which you have the copyright.

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This policy outlines how to acknowledge human authors and contributors in published work. We don’t allow AI or LLM tools to be listed as an author on any research-based work published by UCL Press.

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The monograph programme currently doesn’t permit the use of Gen AI in the creation and manipulation of images and figures , or original research data for use in our publications. The term ‘images and figures’ includes pictures, charts, data tables, medical imagery, snippets of images, computer code and formulas.

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Editors and peer reviewers shouldn’t upload proposal or manuscripts into Gen AI tools, as our review process is single-blind and the materials we send for review may contain personal information, such as email addresses and bios.

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We therefore do not allow AI or LLM tools to be listed as an author on any research-based work published by UCL Press.

Evidence locator: Continuous extracted-text characters 26278–26396; extracted text SHA256 479028ab551b3f540d23df04e812d201b205d0cb02509e255e18c46a02d612d4; raw source SHA256 3512c3c6e2292ab0d9df628f6ae43bc9b6516a8c6c75e0baad43cbc778280fdb; Lines 290–290; characters 26278–26396; excerpt offset 0–118

Broadly, the use of AI-assisted technologies in the creation, editing, manipulating, enhancing, adjusting, obscuring, moving, removing or introducing any new specific feature within an image or figure, is not accepted.

Evidence locator: Continuous extracted-text characters 27569–27787; extracted text SHA256 479028ab551b3f540d23df04e812d201b205d0cb02509e255e18c46a02d612d4; raw source SHA256 3512c3c6e2292ab0d9df628f6ae43bc9b6516a8c6c75e0baad43cbc778280fdb; Lines 295–295; characters 27569–27787; excerpt offset 0–218

It would, however, be acceptable for AI-assisted technologies and tools that are used as part of research design or methodology (such as in AI-assisted imaging approaches to generate or interpret the underlying research data, for example in data visualisation), but this must be clearly declared as part of the methodology and full disclosure about what tools were used, how the tools were used, when this was used and explicably outline which images or figures were created using these tools. Where necessary, authors may be asked to provide pre-AI-adjusted versions of images and/or the composite raw images used to create the final submitted versions, for editorial assessment.

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Editors and peer reviewers must treat a manuscript that is under review confidentially and must not submit the manuscript through an AI-assisted technology or tool. Submitting any manuscript or data the manuscript reports on through any AI-assisted technology or tool may incur a breach in confidentiality, proprietary rights and data, including personally identifiable information.

Evidence locator: Continuous extracted-text characters 28960–29342; extracted text SHA256 479028ab551b3f540d23df04e812d201b205d0cb02509e255e18c46a02d612d4; raw source SHA256 3512c3c6e2292ab0d9df628f6ae43bc9b6516a8c6c75e0baad43cbc778280fdb; Lines 299–299; characters 28960–29342; excerpt offset 0–382

2. Any support from sources beyond the supervisory team, whether from other colleagues, family and friends, collaborators, professional proofreaders, generative AI or elsewhere, where it has been used as a functional tool to assist in the process of creating the academic work must be listed in the Doctoral Candidate Thesis Declaration Form .

Evidence locator: Continuous extracted-text characters 5905–6248; extracted text SHA256 abad884a9d3e267d2f21608d526c4cc4d6d9592a6716e04fd6c8658862c2d820; raw source SHA256 bb3df48b37cb0d363ce2f26f4798b3b71f44c27bd2aa885f6b02438f40232914; Lines 68–68; characters 5905–6248; excerpt offset 0–343

* helping to improve your grammar and writing structure [N.B. If GenAI is used to draft any text including copyediting (rather than proofreading), this must be declared].”

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4. You must maintain a critical oversight of your use of AI as with all other aspects of your research. You must be able to explain clearly what work you did and what work you received human or computational support with. You must remain responsible for all aspects of your research.

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3. In all circumstances, but especially in a fully remote viva, it is important for the examiners to ask questions that allow them to confirm that the thesis “is genuinely the work of the candidate”, and to ensure that the candidate can answer questions satisfactorily without the support of AI.

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UCL staff and students can now access Microsoft Copilot. Microsoft Copilot is a safer generative AI tool than using the consumer-oriented generative AI services, provided you log in with your UCL credentials.

Evidence locator: Continuous extracted-text characters 1226–1434; extracted text SHA256 225db72a7f327fd54b853d3dea0095f3e0655c09f0b40516f8921d5858d8336c; raw source SHA256 225db72a7f327fd54b853d3dea0095f3e0655c09f0b40516f8921d5858d8336c; Lines 69–69; characters 1226–1434; excerpt offset 0–208

Microsoft Copilot is our approved GenAI tool because of its enhanced data protection. This tool is a more secure and safer alternative to other GenAI services. Read about how you can use Microsoft Copilot easily and safely.

Evidence locator: Continuous extracted-text characters 0–223; extracted text SHA256 18a8d649f6ef529ede562cfe8f034500720b556a31107d5b5d82b5fd45ecb7d9; raw source SHA256 18a8d649f6ef529ede562cfe8f034500720b556a31107d5b5d82b5fd45ecb7d9; Lines 1–1; characters 0–223; excerpt offset 0–223

Microsoft Copilot is our approved GenAI tool because of its enhanced data protection. This tool is a more secure and safer alternative to other GenAI services. Read about how you can use Microsoft Copilot easily and safely.

Evidence locator: Continuous extracted-text characters 0–223; extracted text SHA256 18a8d649f6ef529ede562cfe8f034500720b556a31107d5b5d82b5fd45ecb7d9; raw source SHA256 18a8d649f6ef529ede562cfe8f034500720b556a31107d5b5d82b5fd45ecb7d9; Lines 1–1; characters 0–223; excerpt offset 0–223

UCL staff and students can now access Microsoft Copilot. Microsoft Copilot is a safer generative AI tool than using the consumer-oriented generative AI services, provided you log in with your UCL credentials.

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