University AI Policy Tracker
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Munich, Germany

Technical University of Munich

Last checkedMay 10, 2026Last changedMay 10, 2026Review: Agent reviewedConfidence94%
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Record status

Policy statusReviewed evidence-backed recordReview: Agent reviewedClaim coverage5 reviewedEvidence-backed claims5Reviewed5Candidate0Official sources1Source languagedePublic JSON/api/public/v1/universities/technical-university-of-munich.json

Policy profile

Analysis JSONAnalysis review
Coverage score60/100Coverage labelmoderate public coverageReview: Machine candidateAnalysis confidence78%

AI disclosure

No source-backed public claim about AI disclosure or acknowledgement is present in this profile.

The current public tracker record does not contain claim evidence about disclosing, acknowledging, citing, or declaring AI use.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

Privacy and data entry

No source-backed public claim about privacy or data-entry restrictions is present in this profile.

The current public tracker record does not contain claim evidence about personal, confidential, sensitive, regulated, or student data entry into AI tools.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

Academic integrity

Technical University of Munich has 1 source-backed public claim for academic integrity; deterministic analysis status: conditionally_allowed.

Conditionally AllowedMachine candidateConfidence77%Evidence1Sources1
Claim tum-claim-4

Approved tools

No source-backed public claim identifying approved or licensed AI tools is present in this profile.

The current public tracker record does not contain claim evidence that identifies institutionally approved, licensed, procured, or enterprise AI tools.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

Named AI services

No source-backed public claim naming a specific AI service is present in this profile.

The current public tracker record does not contain claim evidence naming a specific AI service.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

Teaching guidance

Technical University of Munich has 4 source-backed public claims for teaching guidance; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence78%Evidence4Sources1
Claim tum-claim-1Claim tum-claim-3Claim tum-claim-2

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.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

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.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

AI tools

Tools directory
Derived tool records0

No tool-level evidence is published for this record yet. Broad AI tool mentions are not expanded into named tool conclusions.

Evidence-backed claims

5 reviewed evidence-backed public claim

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%

Normalized value: instructors_decide_ai_use_in_teaching

Last checked
May 10, 2026
Last changed
May 10, 2026

Original evidence

Evidence 1
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.
Source language
de
Snapshot hash
aac26801e1a951ed2e23aa8936a98f510f00c21d933811221f0edaf4ee24d96f
Retrieved
May 10, 2026
Evidence locator
Introduction

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%

Normalized value: clearly_specify_allowed_and_disallowed_ai_use

Last checked
May 10, 2026
Last changed
May 10, 2026

Original evidence

Evidence 1
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.
Source language
de
Snapshot hash
aac26801e1a951ed2e23aa8936a98f510f00c21d933811221f0edaf4ee24d96f
Retrieved
May 10, 2026
Evidence locator
Schritt 2: Prüfungsaufgaben

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%

Normalized value: learning_outcomes_first_ai_assessment_design

Last checked
May 10, 2026
Last changed
May 10, 2026

Original evidence

Evidence 1
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.
Source language
de
Snapshot hash
aac26801e1a951ed2e23aa8936a98f510f00c21d933811221f0edaf4ee24d96f
Retrieved
May 10, 2026
Evidence locator
Schritt 1: Lernergebnisse

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%

Normalized value: ai_use_control_difficult_design_assessments

Last checked
May 10, 2026
Last changed
May 10, 2026

Original evidence

Evidence 1
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.
Source language
de
Snapshot hash
aac26801e1a951ed2e23aa8936a98f510f00c21d933811221f0edaf4ee24d96f
Retrieved
May 10, 2026
Evidence locator
Allgemeine Empfehlungen für die Umsetzung

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%

Normalized value: train_students_on_ai_limits_biases_misinformation

Last checked
May 10, 2026
Last changed
May 10, 2026

Original evidence

Evidence 1
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.
Source language
de
Snapshot hash
aac26801e1a951ed2e23aa8936a98f510f00c21d933811221f0edaf4ee24d96f
Retrieved
May 10, 2026
Evidence locator
Allgemeine Empfehlungen für die Umsetzung

Candidate claims

0 machine or needs-review claim

Official sources

1 source attribution

Change log

Last checkedMay 10, 2026Last changedMay 10, 2026Open change log

Corrections

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