University AI Policy Tracker

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

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Change log

Université de Montpellier

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 16, 2026Last changedMay 16, 2026Claims6Sources3Release records0
University pagePublic JSONMonthly reportUniversity index

Change summary

Current public record freshness and review state.

Université de Montpellier currently has 6 source-backed claim records and 3 official source attributions. Latest tracked changed date: May 16, 2026. No tracker diff rows are recorded in the latest public release.

This page combines all public release diffs for Université de Montpellier. 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.

Université de Montpellier combined release diff

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

+12-0
11 # Université de Montpellier AI policy record
2+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.
3+Evidence (fr, 268d1366e75f): Le 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.
4+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.
5+Evidence (fr, 268d1366e75f): L'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.
6+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.
7+Evidence (fr, 268d1366e75f): La 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.
8+source_status: Universite de Montpellier has official AI-in-training principles, but this staged crawl found no separate central binding AI policy page.
9+Evidence (fr, 268d1366e75f): Face 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.
10+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.
11+Evidence (fr, cfd6ea215023): L'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.
12+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.
13+Evidence (fr, 81283164d517): Cet 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.

Release history

0 public release diffs

Claim changes

6 claim records

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 reviewedConfidence84%Evidence1Languagesfr

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 reviewedConfidence88%Evidence1Languagesfr

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 reviewedConfidence93%Evidence1Languagesfr

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 reviewedConfidence95%Evidence1Languagesfr

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 reviewedConfidence92%Evidence1Languagesfr

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 reviewedConfidence90%Evidence1Languagesfr

Source snapshots

3 source attributions