Policy presence
No source-backed public AI policy or guidance record is present in this profile.
The current public tracker record does not contain a source-backed claim that establishes a policy or guidance source.
Zürich, Switzerland
ETH Zurich
ETH Zurich has 8 source-backed AI policy claims from 5 official source attributions. Review state: agent reviewed; 8 reviewed claims. Last checked May 5, 2026.
ETH Zurich AI policy short answer
v1 public contract
ETH Zurich has 8 source-backed AI policy claims from 5 official source attributions, including 8 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 5, 2026. Discovery context: ETH Zurich is listed as QS 2026 rank 7.
Citation-ready summary
As of this public record, University AI Policy Tracker lists ETH Zurich as an agent-reviewed AI policy record last checked on May 5, 2026 and last changed on May 5, 2026. The record contains 8 source-backed claims, including 8 reviewed claims, from 5 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/eth-zurich.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 Teaching claims.
- Evidence includes Academic integrity claims.
- Evidence includes AI tool treatment claims.
- Evidence includes Privacy claims.
- Named AI services detected in public claims: Microsoft Copilot, Gemini.
- 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.
- 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
ETH Zurich has 3 source-backed public claims for ai disclosure; deterministic analysis status: required.
Coursework
ETH Zurich has 5 source-backed public claims for coursework; deterministic analysis status: required.
Exams
ETH Zurich has 5 source-backed public claims for exams; deterministic analysis status: required.
Privacy and data entry
ETH Zurich has 2 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
ETH Zurich has 3 source-backed public claims for academic integrity; deterministic analysis status: required.
Approved tools
ETH Zurich has 1 source-backed public claim for approved tools; deterministic analysis status: recommended.
Named AI services
ETH Zurich has 2 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
ETH Zurich has 4 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
No source-backed public claim about research AI use is present in this profile.
The current public tracker record does not contain claim evidence about research use, publication ethics, research data, grants, or human-subjects compliance.
Security and procurement
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.
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
8 reviewed evidence-backed public claim
Teaching
ETH Zurich advocates a proactive approach to the use of generative AI in educational contexts, emphasising responsible use among students and lecturers.
Review: Agent reviewed
Evidence originale
Evidence 1ETH Zurich advocates a proactive approach to the use of generative AI (GenAI) within educational contexts, emphasising the responsible use of this technology among students and lecturers.
Academic Integrity
Students are responsible for the content of work they submit. Performance assessments must be conducted independently and personally; GenAI may serve a supplementary role but not replace student efforts.
Review: Agent reviewed
Evidence originale
Evidence 1Responsibility: You are responsible for the content of work you submit... Performance assessments must be conducted independently and personally. GenAI may serve a supplementary role, helping you but not replacing your efforts.
Teaching
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.
Review: Agent reviewed
Evidence originale
Evidence 1As a lecturer you are responsible for the content you provide to your students. This means that teaching materials created with GenAI must be subjected to quality control by you... You determine whether and how GenAI may be used in your courses.
Academic Integrity
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.
Review: Agent reviewed
Evidence originale
Evidence 1Legal aspects of GenAI uses are covered by the existing rules for performance assessments and the 'declaration of originality'. Violations such as use of unauthorised aids or non-disclosure of their use will continue to be subject to disciplinary action.
Ai Tool Treatment
ETH Zurich recommends Microsoft Copilot, Google Gemini, and NotebookLM for teaching purposes, as they offer data-protected access via ETH accounts where personal data is not used for training models.
Review: Agent reviewed
Evidence originale
Evidence 1The Gemini and NotebookLM offerings from Google, as well as Copilot from Microsoft, which are accessible via an ETH cloud subscription, are available to all ETH employees and students via secure access. It is therefore recommended that these tools be used primarily for teaching purposes.
Privacy
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.
Review: Agent reviewed
Evidence originale
Evidence 1It is important to respect the privacy and copyright of the content being worked with... If data is passed on to AI-based tools or released for them, it must be clarified in advance that no rights are being violated.
Teaching
ETH Zurich requires transparency about GenAI use: students must declare which tools they used and for which parts of their work; lecturers must communicate when GenAI use is permitted and make their own GenAI use visible.
Review: Agent reviewed
Evidence originale
Evidence 1Transparency: Be transparent about your use of GenAI. Clearly state which tools you have used for which part of your work using the correct style guide.
Academic Integrity
ETH Zurich states that technical recognition of AI-generated output is currently unreliable and will probably remain so; trust in such methods is not appropriate.
Review: Agent reviewed
Evidence originale
Evidence 1Technical recognition of output generated using generative AI is currently unreliable and will probably remain so in the future. Trust in such a method is therefore not appropriate.
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
5 source attribution
Academic Integrity | ETH Zurich
ethz.ch
AI in Teaching and Learning — ETH Zurich
ethz.ch
AI Tools & Licenses | ETH Zurich
ethz.ch
ETH AI Ethics Policy Network
aiethicspolicy.ethz.ch
Generative AI in Teaching and Learning — Guidelines (ETH Zurich)
ethz.ch
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.