Policy presence
Université de Montpellier has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Montpellier, France
Université de Montpellier
Université de Montpellier is listed as QS 2026 rank =430. Université de Montpellier has 6 source-backed AI policy claim records from 3 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Short answer
v1 public contract
Université de Montpellier is listed as QS 2026 rank =430. Université de Montpellier has 6 source-backed AI policy claim records from 3 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Université de Montpellier as an agent-reviewed AI policy record last checked on May 16, 2026 and last changed on May 16, 2026. The record contains 6 source-backed claims, including 6 reviewed claims, from 3 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/universite-de-montpellier.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 Privacy claims.
- Evidence includes Academic integrity claims.
- Evidence includes Teaching claims.
- Evidence includes Source status claims.
- Evidence includes AI tool treatment claims.
- Named AI services detected in public claims: Microsoft Copilot.
- 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
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
Université de Montpellier has 4 source-backed public claims for coursework; deterministic analysis status: recommended.
Exams
Université de Montpellier has 4 source-backed public claims for exams; deterministic analysis status: recommended.
Privacy and data entry
Université de Montpellier has 1 source-backed public claim for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
Université de Montpellier has 1 source-backed public claim for academic integrity; deterministic analysis status: recommended.
Approved tools
Université de Montpellier has 1 source-backed public claim for approved tools; deterministic analysis status: recommended.
Named AI services
Université de Montpellier has 2 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
Université de Montpellier has 3 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Université de Montpellier has 1 source-backed public claim for research guidance; deterministic analysis status: recommended.
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
6 reviewed evidence-backed public claim
Privacy
UM's AI-in-training principles say confidentiality, privacy, personal data, and intellectual property protections should be guaranteed, and no nominative, confidential, or sensitive data should be used in a public AI tool.
Review: Agent reviewed
Normalized value: no_nominative_confidential_sensitive_data_in_public_ai_tool
Original evidence
Evidence 1Le respect de la confidentialite et de la vie privee, ainsi que la protection des donnees personnelles et de la propriete intellectuelle, doivent toujours etre garantis. Aucune donnee nominative, confidentielle ou sensible ne doit etre utilisee dans un outil d'IA public.
Localized display only
The source says confidentiality, privacy, personal data, and IP protections should be guaranteed, and no nominative, confidential, or sensitive data should be used in a public AI tool.
Academic Integrity
UM's AI-in-training principles say student AI use should respect university regulations, rules for each teaching activity, and assessment-specific instructions; expectations should be clarified in course and assessment materials.
Review: Agent reviewed
Normalized value: students_follow_regulations_course_rules_assessment_instructions
Original evidence
Evidence 1L'utilisation de l'IA par les etudiantes et etudiants doit respecter les reglements en vigueur a l'Universite de Montpellier, les regles propres a chaque enseignement et les consignes specifiques a chaque evaluation. Les attentes concernant l'utilisation de l'IA sont explicitees dans les programmes de cours et expliquees aux etudiantes et etudiants.
Localized display only
The source says student AI use should respect UM regulations, teaching-specific rules, and assessment instructions; expectations should be explained in courses.
Teaching
UM's AI-in-training principles say decisions to use or address AI in training activities rest with teaching staff and should align with learning objectives.
Review: Agent reviewed
Normalized value: teacher_decision_learning_objectives
Original evidence
Evidence 1La decision d'utiliser ou d'aborder l'IA dans les activites de formation releve des personnels enseignants, et s'appuie sur une exploration des possibilites que l'IA offre dans leur domaine disciplinaire, en coherence avec les objectifs d'apprentissage.
Localized display only
The source says decisions to use or address AI in training activities belong to teaching staff and should align with learning objectives.
Source Status
Universite de Montpellier has official AI-in-training principles, but this staged crawl found no separate central binding AI policy page.
Review: Agent reviewed
Normalized value: official_ai_training_principles_found_no_separate_central_binding_ai_policy
Original evidence
Evidence 1Face aux enjeux lies a l'essor de l'IA, l'Universite de Montpellier a defini 7 principes pour l'usage de l'intelligence artificielle dans la formation. Ils ont ete votes par la Commission Formation et Vie Universitaire du 28 janvier 2025 et presentes au Conseil d'Administration du 3 fevrier 2025.
Localized display only
UM says it defined seven AI-in-training principles, voted by CFVU on 2025-01-28 and presented to the board on 2025-02-03.
Ai Tool Treatment
UM's digital-services guidance says the university provides institutional Microsoft Copilot access through A1 and A3 licences for staff and students, with data-protection advantages.
Review: Agent reviewed
Normalized value: institutional_copilot_access_a1_a3_staff_students_data_protection
Original evidence
Evidence 1L'Universite de Montpellier a un acces institutionnel a Copilot avec les licences A1 et A3 mises a disposition des personnels et etudiants. Cela signifie que vous beneficiez d'avantages, comme celui de la protection de vos donnees.
Localized display only
UM says it has institutional Copilot access through A1/A3 licences for staff and students, with advantages such as data protection.
Teaching
UM's official AI hub says it offers multiple pathways to help teachers, researchers, staff, and students discover and appropriate AI uses, including AI tools/platforms, resources, self-training, and student acculturation.
Review: Agent reviewed
Normalized value: ai_hub_training_resources_for_community
Original evidence
Evidence 1Cet espace propose plusieurs parcours pour accompagner enseignants, chercheurs, personnels et etudiants dans la decouverte et l'appropriation des usages de l'IA. Contenu disponible : Outils & plateformes IA; Ressources & replays; Formations en autoformation; Acculturation des etudiants.
Localized display only
The UM AI hub says it offers multiple pathways and resources for teachers, researchers, staff, and students.
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
3 source attribution
Intelligence Artificielle
numerique.umontpellier.fr
Principes pour l'usage de l'IA dans la formation a l'Universite de Montpellier
numerique.umontpellier.fr
Utiliser Copilot avec votre compte institutionnel UM
numerique.umontpellier.fr
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.