AI disclosure policy claim records

UNSW uses a Levels of AI Assistance framework with six categories for assessments: No Assistance, Simple Editing Assistance, Planning or Design Assistance, Assistance with Attribution, Generative AI Software-based Assessments, and Not Applicable.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UNSW defines six high-level categories for permitted AI use in assessments: No Assistance, Simple Editing Assistance, Planning/Design Assistance, Assistance with Attribution, Generative AI Software-based Assessments, and Not Applicable.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At UNSW, the unauthorised or unacknowledged use of AI in assessments is classified as cheating and considered student misconduct under the Code of Conduct and Values.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

ANU prohibits using AI to collect, use, store, or disclose personal information without express consent from the individual(s).

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Students retain IP ownership of their assignments at ANU; staff may not upload student work to AI platforms without express student consent.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At ANU, using AI-generated content when not permitted and claiming authorship without acknowledgment constitutes a breach of academic integrity.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Cornell's committee report recommends three policy approaches for generative AI use: prohibit GAI where it interferes with foundational learning, allow with attribution where it supports higher-level thinking, and encourage use where it enables exploration and creative thinking.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Cornell recommends that faculty clearly communicate their generative AI policies in their syllabus, in assignment instructions, and verbally in class to support student learning and reduce academic integrity violations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Cornell's committee report recommends that the Code of Academic Integrity be updated with clear and explicit language on the use of generative AI, indicating that individual faculty have authority to determine when AI use is prohibited, attributed, or encouraged.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Penn requires all community members (educators, staff, researchers, and students) to be transparent about the use of AI and to disclose when a work product was created wholly or partially using an AI tool.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Users of AI at Penn are accountable for AI-generated content and should validate its accuracy with trusted first-party sources, being wary of misinformation or hallucinations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Instructors must not require students to enter their own work into unlicensed AI tools or use such tools in assignments; unlicensed tools may be used optionally by students at the instructor's discretion, but Penn-licensed tools should be used for mandatory coursework components.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Stanford's BCA issued guidance on generative AI use, and the Office of Community Standards recommends that instructors give advance notice to students when using AI detection software.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

For Stanford Graduate School of Business (GSB) MBA and MSx courses, instructors may not ban student use of AI tools for take-home coursework, including assignments and exams. Instructors may choose whether to allow AI for in-class work. For PhD and undergraduate courses, GSB follows the university-wide Generative AI Policy Guidance from the Office of Community Standards.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Stanford School of Medicine MD and MSPA programs have a formal AI policy: students may use AI for learning, clarification, and grammar/style editing unless contrary to assignment instructions. AI use for closed-book exams or assignments where internet is restricted is prohibited unless explicitly authorized by faculty. Students are responsible for all AI-generated content they submit, must disclose and cite substantial AI contributions, and violations may result in disciplinary action.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

The UC Berkeley Academic Senate recommends that all faculty include a clear statement on their syllabus about course expectations regarding the use of Google Gemini or any other generative AI tool for course-related work. In the absence of such a statement, students may be more likely to use these technologies inappropriately.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

The UC Berkeley Academic Senate provides three sample syllabus statement frameworks for faculty: 'Full AI' (GenAI required), 'Some AI' (limited permitted use with restrictions), and 'No AI' (all GenAI use prohibited). Faculty should modify these to fit their course requirements.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

The UC Berkeley Academic Senate recommends that for assignments where GenAI is not permitted, instructors should adopt enforcement mechanisms such as in-person proctored exams, an additional oral exam component, or a written statement of academic integrity, since no validated GenAI detection tools exist.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

SNU's AI Guidelines require transparent disclosure of AI use, fact and source verification, copyright/privacy/information security compliance, bias correction, and awareness of accountability.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

SNU Library cites COPE's position that AI tools cannot be listed as authors because they cannot take responsibility for research results and lack legal personality.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

SNU Library cites COPE requiring authors to clearly disclose in their methods section which AI tools were used and how, covering manuscript writing, image creation, and data analysis.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

CUHK requires students to declare in each assignment that they have read and understood the University's policy on AI use, complied with course teacher instructions on AI tools, and consent to AI content detection software review.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

CUHK defines four approaches to AI use in courses: (1) prohibit all use, (2) use only with prior permission, (3) use only with explicit acknowledgement, and (4) free use without acknowledgement requirement.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

CUHK penalties for academic dishonesty involving AI tools may include reviewable/permanent demerits, failure grade, suspension, lowering degree classification, and termination of studies.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

The University of Manchester does not ban generative AI. The university's position is that when used appropriately, AI tools have the potential to enhance teaching and learning, and can support inclusivity and accessibility.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Manchester has adopted five core principles for AI use: transparency, accountability, competence, responsible use, and respect. All staff and students using or developing AI are personally responsible for adhering to these.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Students at Manchester must cite or acknowledge the outputs of generative AI tools when they use them in their work, including quoting, summarising, paraphrasing, editing, translating, data processing, re-writing, and idea generation.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Staff setting summative assessment must: declare whether/how students can use AI; review assessment design for alignment with permitted AI use; ensure equality of baseline AI tool provision where authorised; specify declaration forms for student AI use; only identify suspected unauthorised AI use through marking or university-endorsed detection tools (none currently endorsed); and handle misconduct under usual disciplinary regulations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Oxford requires postgraduate research students to include a statement on their use of generative AI in their final thesis submission.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Students undertaking summative assessment must: complete assessment in line with the AI use declaration for each assignment; acknowledge their AI use via a formal declaration in the prescribed format; and understand that submitting work breaching AI specifications constitutes cheating and may constitute plagiarism, handled under usual disciplinary regulations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

In Caltech's Humanities and Social Sciences (HSS) division, students may use generative AI tools only in ways explicitly allowed by the course instructor in the course materials. Any usage not specifically allowed should be assumed to be disallowed.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

The Caltech HSS generative AI policy applies to all assignments including major papers, exams, discussion board posts, reflections, and problem sets.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Caltech HSS students are expected to follow specific course guidance for documenting any permitted use of generative AI tools.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Teachers College provides five example syllabus statements ranging from no AI use permitted to generally permitted with attribution, allowing instructors to choose their stance.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Teachers College example syllabus statements require citations or disclosure detailing specific AI tools and models used when AI use is permitted.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

All Teachers College example syllabus statements include provisions for students with disabilities who have AI-related accommodations through OASID.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Lecturers determine whether and how GenAI may be used in their courses and for respective assessments. Teaching materials created with GenAI must be subjected to quality control by the lecturer.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Violations of GenAI guidelines such as use of unauthorised aids or non-disclosure of their use are subject to disciplinary action under existing performance assessment rules and the declaration of originality.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Students must refrain from disclosing copyrighted, private, or confidential information to commercial GenAI clients unless expressly permitted, and must respect privacy and copyright of content they work with.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

HGSE (Harvard Graduate School of Education) school-level policy: Unless otherwise specified by the instructor, using generative AI to create all or part of an assignment (e.g., paper, memo, presentation, short response) and submitting it as one's own work violates the HGSE Academic Integrity Policy. Permissible uses include seeking clarification on concepts, brainstorming ideas, or generating scenarios that help contextualize learning.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

HGSE (Harvard Graduate School of Education) school-level policy: For any permitted use of generative AI tools, students must acknowledge and document that use in their assignment submission by explaining what tool(s) were used, prompts provided, and how the output was integrated into the work. Direct citations must use proper citation format.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

HGSE (Harvard Graduate School of Education) school-level policy: It is forbidden to make personal recordings of any course meetings, with or without AI tool integrations. Uploading substantial course content is only allowable through the Harvard-approved AI Sandbox.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

NUS policy states that representing AI output as one's own work without acknowledgement is plagiarism; students who submit AI-generated work without acknowledging its use can be sanctioned for academic dishonesty.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

NUS states that instructors should be transparent about where and how they deploy AI in courses, including for generating content, virtual tutoring, and assessment feedback.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

NUS policy sets the default assumption that AI tool use is permitted for unsupervised (take-home) assessments, provided use is duly acknowledged; assessments forbidding AI must be conducted in-person and instructor-supervised.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Melbourne, using GenAI tools to produce work submitted for assessment without acknowledgement constitutes academic misconduct under cl. 4.13 of the Student Academic Integrity Policy (MPF1310).

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

University of Melbourne students must appropriately cite any use of GenAI tools in the preparation of assessment submissions.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Melbourne, generative AI tools can only be used in research outputs where the material generated or substantially altered by these tools is acknowledged according to the University's policy and the Australian Code for the Responsible Conduct of Research.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

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.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

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.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

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.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Institut Polytechnique de Paris's 2025-2026 Master programs academic regulations prohibit the use of generative AI in assessments for those programs unless explicitly authorized by the instructor in written instructions. Unauthorized use constitutes academic misconduct.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Le règlement des études 2025-2026 des masters de l'Institut Polytechnique de Paris interdit l'utilisation de l'intelligence artificielle générative dans les évaluations de ces programmes, sauf autorisation explicite de l'enseignant dans ses consignes écrites. Tout manquement est considéré comme une fraude.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Under Institut Polytechnique de Paris's 2025-2026 Master programs academic regulations, when generative AI use is explicitly permitted by an instructor, students must clearly acknowledge its use in accordance with standard citation practices.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At King's College London, inappropriate use of generative AI without attribution is considered academic misconduct and can result in penalties ranging from formal warnings to expulsion.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

King's College London does not require students to reference generative AI as an authoritative source in the reference list, but does require explicit acknowledgement of AI tool use in coursework.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

King's College London permits doctoral students to use generative AI tools in their thesis writing processes for assistive purposes such as clarifying writing, provided use is declared and consistent with guidance.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

When allowed or required to use AI in an assessment, students must follow all instructions and restrictions on its use, clearly document the type of AI used and how it contributed, and provide written acknowledgment of the use of AI and its extent.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Using AI in a way not permitted in an assessment, or failing to acknowledge its use correctly, may breach assessment conditions under the Student Academic Integrity Procedure.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Monash's AI responsible use principles include community benefit, education and research excellence, fairness, transparency, integrity and accountability, and data security and privacy.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

NTU states that the use of generative AI beyond basic spelling and grammar checks should be acknowledged and cited in research outputs, publications, and presentations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

NTU states that not citing or acknowledging the use of generative AI could be considered plagiarism (a form of research misconduct), especially if GenAI was used to generate ideas or for literature reviews.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

NTU requires students to disclose the use of AI tools in their submissions and to always refer to their module's AI use policy for specific expectations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UQ course profiles must clearly state if, when, and how AI (including Machine Translation) is allowed. Two options exist: Option 1 prohibits AI in in-person assessment; Option 2 permits AI use with mandatory referencing.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At UQ, the use of AI outputs without attribution, and contrary to any direction by teaching staff, is a form of plagiarism and constitutes academic misconduct.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UQ says students must acknowledge where they used AI in assessment, including direct quotes or paraphrases of AI-generated content and use of AI tools for summarising, brainstorming, planning, editing, or proofreading.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UTokyo states it is unacceptable to present AI-generated text as one's own when submitting class assignments.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UTokyo advises faculty to test their own assignments with generative AI tools to understand how well AI can complete them, and use this understanding to inform assessment design.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UTokyo requires instructors to clearly state their AI stance per class/assignment, and when allowing AI use, to explain risks: information leakage, data concentration, copyright concerns, and potential bias.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Tsinghua University prohibits students from directly copying or mechanically paraphrasing AI-generated text, code, or other output and submitting it as academic coursework.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Tsinghua University requires teachers and students to disclose their use of AI and AI-generated content in accordance with regulations, as part of the 'compliance and integrity' principle.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Tsinghua University advises instructors to determine how AI should be used according to course objectives, clearly explain AI usage norms to students at the start of each course, and remain responsible for AI-generated teaching materials.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

A student using any unacknowledged content generated by artificial intelligence within a summative assessment as though it is their own work constitutes academic misconduct, unless explicitly stated otherwise in the assessment brief.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

All GenAI outputs must be thoroughly evaluated by a human being before they are used. Use of GenAI must be acknowledged if it makes a significant and unrevised contribution to a substantive or impactful piece of work. Staff are responsible for ensuring any use of GenAI is conducted reasonably, lawfully and in conjunction with relevant University policies.

Evidence records: 2. Original evidence remains canonical on the linked university record and public JSON.

When using GenAI tools, users should: remain aware of privacy and data implications and not share anything personal or sensitive; understand ethical implications as tools often have limited attribution; acknowledge use of GenAI if it makes a significant contribution to substantive work; and take responsibility for ensuring use is conducted reasonably, lawfully, and in conjunction with relevant University policies.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Chicago, AI-generated content may be misleading or inaccurate, and it is the responsibility of the tool user to review the accuracy and ownership of any AI-generated content.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Chicago, AI transcription or assistant tools may not be used to secretly record or join meetings, per the Business Conduct Policy.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Chicago, entering sensitive data into AI tools without review and approval by security, privacy, and the appropriate data steward may create an unauthorized data disclosure that may violate University policy, federal and state law, sponsor or contract obligations, and data use agreements.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

JHU requires all uses of AI tools in any assignment to be disclosed, with FERPA guidelines referenced for data protection.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

JHU provides official syllabus statement templates, including options that prohibit students from using ChatGPT or other AI tools to generate written content for assignments.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Use of generative AI tools at MIT must comply with all applicable federal and state laws and orders (including FERPA, HIPAA, Massachusetts Data Protection Standards, export control laws, and the Executive Order on Safe, Secure, and Trustworthy Development and Use of AI), Institute policies (including 10.1 Academic and Research Misconduct, 11.0 Privacy and Disclosure of Personal Information, and 13.0 Information Policies), Information Protection guidelines, and the Institute's Written Information Security Program (WISP), plus any additional policies established by the user's department, lab, center, or institute (DLCI).

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

MIT advises community members to disclose the use of generative AI tools for all academic, educational, and research-related uses, and not to publish research results relying on AI-generated content without disclosing the nature of such use.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

HKUST allows faculty members the flexibility to set their own course-level policies for GenAI integration in teaching and learning.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

HKUST assessment policies require course syllabi to clearly present policies on the use of Generative AI tools and academic integrity.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Edinburgh, presenting AI outputs as your own original work, submitting AI-generated text without acknowledgment, and using AI agents within university learning platforms constitute academic misconduct.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

At the University of Edinburgh, staff presenting AI-generated content as their own original work, uploading personal data or confidential information to external AI tools, and relying on AI detection tools are listed as unacceptable uses.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Students may only use GenAI for assessed work (assignments, exams, projects, theses) if expressly permitted by their instructor, supervisor, or program.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Graduate students must obtain approval from their supervisor/committee for substantive GenAI use in research and thesis work, and must include a GenAI use statement in their thesis preface.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

U-M requires AI use in teaching and learning to align with principles of honesty, candor, openness, and integrity in scholarship and research, including appropriate disclosure and citation.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

U-M leaves GenAI policy to individual instructors, who may allow, restrict, or forbid AI use in their courses. Course policies should be clearly articulated in syllabi.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

Northwestern provides instructors with three course-level AI policy options: Open (GAI permitted), Conditional (GAI permitted when explicitly authorized), and Closed (GAI prohibited).

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

HKU states that researchers should clearly disclose generative AI tool usage in research outputs, publications, and presentations, including the type of GenAI used, data sources, and potential limitations.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.

UCL uses a 3-category assessment framework for GenAI: Category 1 requires own work only; Category 2 permits GenAI with acknowledgement; Category 3 includes essential GenAI use as part of the assessment.

Evidence records: 1. Original evidence remains canonical on the linked university record and public JSON.