Policy presence
Technical University of Munich has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Munich, Germany
Technical University of Munich
Technical University of Munich is listed as QS 2026 rank =22. Technical University of Munich has 5 source-backed AI policy claim records from 1 official source attribution. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Short answer
v1 public contract
Technical University of Munich is listed as QS 2026 rank =22. Technical University of Munich has 5 source-backed AI policy claim records from 1 official source attribution. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
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
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.
Coursework
Technical University of Munich has 5 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
Technical University of Munich has 5 source-backed public claims for exams; deterministic analysis status: restricted.
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.
Academic integrity
Technical University of Munich has 1 source-backed public claim for academic integrity; deterministic analysis status: conditionally_allowed.
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.
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.
Teaching guidance
Technical University of Munich 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
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 reviewed
Normalized value: instructors_decide_ai_use_in_teaching
Original evidence
Evidence 1Dozierende 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.
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 reviewed
Normalized value: clearly_specify_allowed_and_disallowed_ai_use
Original evidence
Evidence 1Falls 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.
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 reviewed
Normalized value: learning_outcomes_first_ai_assessment_design
Original evidence
Evidence 1Nehmen 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.
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 reviewed
Normalized value: ai_use_control_difficult_design_assessments
Original evidence
Evidence 1Eine 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.
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 reviewed
Normalized value: train_students_on_ai_limits_biases_misinformation
Original evidence
Evidence 1In 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.
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
1 source attribution
Entscheidungshilfe für den KI-Einsatz in der Lehre
prolehre.tum.de
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.