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
Change log
ETH Zurich
Source-backed change history with no release-to-release policy diff rows recorded yet; current claims, official sources, review state, and freshness remain visible across 0 public release records.
Change summary
Current public record freshness and review state.
ETH Zurich currently has 8 source-backed claim records and 5 official source attributions. Latest tracked changed date: May 5, 2026. No tracker diff rows are recorded in the latest public release.
This page combines all public release diffs for ETH Zurich. Individual release snapshots remain available from their release-specific URLs.
No release-to-release policy diff rows are recorded for this university yet. The page still tracks current source-backed claims, official source attributions, review state, source freshness, and public JSON for discovery and citation.
This tracker 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.
Newly extracted claims are tracker additions and are not necessarily newly published by the university. Source snapshot changes show hash changes for the same source URL and are not by themselves policy changes.
Diff categories
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Combined release diff
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ETH Zurich combined release diff
Initial tracked release. Lines represent public claim/evidence records entering the release snapshot.
+16-0
11 # ETH Zurich AI policy record
2+teaching: ETH Zurich advocates a proactive approach to the use of generative AI in educational contexts, emphasising responsible use among students and lecturers.
3+Evidence (en, b8b7707c1be2): ETH 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.
4+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.
5+Evidence (en, b8b7707c1be2): Responsibility: 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.
6+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.
7+Evidence (en, b8b7707c1be2): As 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.
8+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.
9+Evidence (en, 92d97c5c89e0): Legal 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.
10+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.
11+Evidence (en, 976363c22960): The 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.
12+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.
13+Evidence (en, 3042c683ebe3): It 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.
14+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.
15+Evidence (en, b8b7707c1be2): Transparency: 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.
16+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.
17+Evidence (en, 3042c683ebe3): Technical 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.
Release history
0 public release diffs
Claim changes
8 claim records
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
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
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
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
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
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
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
Source snapshots
5 source attributions
Academic Integrity | ETH Zurich
official_guidance Tracker checked at May 5, 2026, 5:43 PM
AI in Teaching and Learning — ETH Zurich
official_guidance Tracker checked at May 5, 2026, 5:43 PM
AI Tools & Licenses | ETH Zurich
official_guidance Tracker checked at May 5, 2026, 5:43 PM
ETH AI Ethics Policy Network
official_guidance Tracker checked at May 5, 2026, 5:43 PM
Generative AI in Teaching and Learning — Guidelines (ETH Zurich)
official_policy_page Tracker checked at May 5, 2026, 5:43 PM