University AI Policy Tracker

Dossiers ouverts et fondes sur des preuves sur les politiques IA universitaires.

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Change log

Technical University of Munich

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.

Review: Agent reviewedLast checkedMay 10, 2026Last changedMay 10, 2026Claims5Sources1Release records0
University pagePublic JSONMonthly reportUniversity index

Change summary

Current public record freshness and review state.

Technical University of Munich currently has 5 source-backed claim records and 1 official source attribution. Latest tracked changed date: May 10, 2026. No tracker diff rows are recorded in the latest public release.

This page combines all public release diffs for Technical University of Munich. 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

Semantic classification for this release diff.

Policy text0Newly extracted0Evidence0Source snapshots0Source text0Source added0Source removed0

Combined release diff

Unified tracker diff generated from all public release snapshots for this university.

Technical University of Munich combined release diff

Initial tracked release. Lines represent public claim/evidence records entering the release snapshot.

+10-0
11 # Technical University of Munich AI policy record
2+teaching: TUM ProLehre guidance says instructors at TUM have broad discretion when deciding whether and how AI is used in teaching, and that related rules should be didactically grounded and communicated transparently to students.
3+Evidence (de, aac26801e1a9): Dozierende an der TUM haben dabei einen großen Entscheidungsspielraum (siehe TUM AI Strategy). Damit geht zugleich für Sie als Dozierende die Verantwortung einher, Regelungen didaktisch fundiert zu treffen und für Studierende nachvollziehbar zu kommunizieren.
4+teaching: When AI use is restricted, TUM ProLehre guidance tells instructors to clearly define what AI may be used for, what it may not be used for, and to discuss this with students.
5+Evidence (de, aac26801e1a9): Falls eine Anpassung des Formats nicht möglich ist, legen Sie klar fest, o wofür KI genutzt werden darf, o wofür nicht und o reden Sie mit Ihren Studierenden darüber.
6+teaching: TUM ProLehre guidance recommends starting AI-use decisions from the intended learning outcomes and whether AI use supports, complements, or hinders those competencies.
7+Evidence (de, aac26801e1a9): Nehmen Sie als Ausgangspunkt für Ihre Entscheidung die angestrebten Lernergebnisse, die Ihre Studierenden in Ihrer Lehrveranstaltung erwerben sollen – und ob der Einsatz von KI das Erreichen dieser Kompetenzen fördert, ergänzt oder behindert.
8+academic_integrity: TUM ProLehre guidance says reliable control of AI use is difficult to impossible, and recommends designing assessments so unauthorized AI use does not provide a decisive advantage.
9+Evidence (de, aac26801e1a9): Eine zuverlässige Kontrolle der KI-Nutzung ist schwierig bis unmöglich. Wird die KINutzung trotzdem eingeschränkt oder verboten werden, sollten Prüfungen so gestaltet werden, dass eine unerlaubte Nutzung keinen entscheidenden Vorteil bietet.
10+teaching: TUM ProLehre guidance says students often need targeted training in competent AI use, including AI functions, limits, common errors, biases, misinformation, and critical evaluation of AI outputs.
11+Evidence (de, aac26801e1a9): In vielen Fällen ist es wichtig, Studierende gezielt darin zu schulen, wie ein kompetenter Umgang mit KI aussieht - insbesondere dann, wenn es ein explizites Lernergebnis des Moduls ist. Üben Sie in diesem Fall mit Ihren Studierenden u.a. folgende Aspekte: o o o die Funktionsweise, Grenzen und häufige Fehler von KI benennen und beschreiben, Biases und Fehlinformationen erkennen, sowie KI-Ergebnissen kritisch bewerten und überarbeiten können.

Release history

0 public release diffs

Claim changes

5 claim records

teaching

TUM ProLehre guidance says instructors at TUM have broad discretion when deciding whether and how AI is used in teaching, and that related rules should be didactically grounded and communicated transparently to students.

Review: Agent reviewedConfidence94%Evidence1Languagesde

teaching

When AI use is restricted, TUM ProLehre guidance tells instructors to clearly define what AI may be used for, what it may not be used for, and to discuss this with students.

Review: Agent reviewedConfidence93%Evidence1Languagesde

teaching

TUM ProLehre guidance recommends starting AI-use decisions from the intended learning outcomes and whether AI use supports, complements, or hinders those competencies.

Review: Agent reviewedConfidence92%Evidence1Languagesde

academic_integrity

TUM ProLehre guidance says reliable control of AI use is difficult to impossible, and recommends designing assessments so unauthorized AI use does not provide a decisive advantage.

Review: Agent reviewedConfidence91%Evidence1Languagesde

teaching

TUM ProLehre guidance says students often need targeted training in competent AI use, including AI functions, limits, common errors, biases, misinformation, and critical evaluation of AI outputs.

Review: Agent reviewedConfidence90%Evidence1Languagesde

Source snapshots

1 source attribution