Policy presence
Université de Montréal has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
Montreal, Canada
Université de Montréal
Université de Montréal is listed as QS 2026 rank 168. Université de Montréal has 7 source-backed AI policy claim records from 5 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 Montréal is listed as QS 2026 rank 168. Université de Montréal has 7 source-backed AI policy claim records from 5 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 Montréal as an agent-reviewed AI policy record last checked on May 15, 2026 and last changed on May 15, 2026. The record contains 7 source-backed claims, including 7 reviewed claims, from 5 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-montreal.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 Academic integrity claims.
- Evidence includes Privacy claims.
- Evidence includes Research claims.
- Evidence includes AI tool treatment claims.
- Evidence includes Security review claims.
- Evidence includes Teaching claims.
- Named AI services detected in public claims: DeepSeek.
- Disclosure, acknowledgment, citation, or attribution 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
Université de Montréal has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
Coursework
Université de Montréal has 4 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
Université de Montréal has 3 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
Université de Montréal has 2 source-backed public claims for privacy and data entry; deterministic analysis status: blocked.
Academic integrity
Université de Montréal has 1 source-backed public claim for academic integrity; deterministic analysis status: restricted.
Approved tools
Université de Montréal has 2 source-backed public claims for approved tools; deterministic analysis status: allowed.
Named AI services
Université de Montréal has 4 source-backed public claims for named ai services; deterministic analysis status: blocked.
Teaching guidance
Université de Montréal has 2 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Université de Montréal has 2 source-backed public claims for research guidance; deterministic analysis status: restricted.
Security and procurement
Université de Montréal has 1 source-backed public claim for security and procurement; deterministic analysis status: allowed.
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
7 reviewed evidence-backed public claim
Academic Integrity
UdeM's teaching-integrity guidance says course plans should explicitly state when generative AI tools are permitted; without explicit authorization, the tools are deemed prohibited.
Review: Agent reviewed
Normalized value: course_plans_explicit_ai_authorization_required_for_permission
Original evidence
Evidence 1Les plans de cours doivent indiquer explicitement lorsque l’utilisation de tels outils est permise ; sans autorisation explicite, ces outils sont réputés interdits.
Localized display only
Course plans should explicitly state when these tools are permitted; without explicit authorization, they are deemed prohibited.
Privacy
UdeM Directive 10.70 tells administrative staff that confidentiality level 3 and 4 information must never be entered into a generative AI tool.
Review: Agent reviewed
Normalized value: administrative_staff_no_confidential_level_3_4_into_genai
Original evidence
Evidence 1Les informations de niveaux de confidentialité 3 et 4 « Information confidentielle ou hautement confidentielle à risque élevé, critique ou majeur » ne doivent en aucun cas être saisies dans un outil d’IA générative;
Localized display only
Confidentiality level 3 and 4 information must not be entered into a generative AI tool.
Research
For UdeM graduate directed work, master's theses, doctoral essays, and dissertations, the graduate-studies guidance says generative AI use in research and writing must be transparent, and non-transparent use could be considered a disciplinary offence.
Review: Agent reviewed
Normalized value: graduate_thesis_directed_work_transparent_authorized_ai_use
Original evidence
Evidence 1l’utilisation d’outils d’IA générative dans le processus de recherche et de rédaction des travaux dirigés, des mémoires de maitrise, des essais doctoraux et des thèses de doctorat doit toujours se faire en toute transparence. ... L'utilisation non transparente d'outils d'IA générative pourrait être considérée comme une infraction au règlement disciplinaire
Localized display only
For graduate directed work, theses, doctoral essays, and dissertations, generative AI use in research and writing must be transparent; non-transparent use could be treated as a disciplinary offence.
Privacy
UdeM's graduate-studies guidance says graduate students working with human-participant research data must not submit personal or identifying participant information, or information that could identify an individual or group, to generative AI tools.
Review: Agent reviewed
Normalized value: graduate_research_no_identifying_human_participant_data_to_genai
Original evidence
Evidence 1la personne étudiante travaillant avec des données provenant de participants humains à une recherche ne doit soumettre aucune information personnelle ou d'identification sur les participantes et participants, ni aucune information qui pourrait être utilisée pour identifier un individu ou un groupe à des outils d'IA générative.
Localized display only
A student working with human-participant research data must not submit personal or identifying information, or information that could identify an individual or group, to generative AI tools.
Ai Tool Treatment
For administrative staff, UdeM Directive 10.70 says generative AI use should be disclosed, and use in a decision-making process should be disclosed to affected persons with consent obtained before use.
Review: Agent reviewed
Normalized value: administrative_staff_transparency_and_decision_process_consent
Original evidence
Evidence 1Si l’IA générative est utilisée dans le cadre d’un processus décisionnel, divulguez-le aux personnes concernées et obtenez leur consentement avant d’y avoir recours.
Localized display only
If generative AI is used in a decision-making process, disclose it to affected persons and obtain their consent before using it.
Security Review
UdeM's TI page for administrative staff says some generative AI tools are permitted if Directive 10.70 principles are respected, but identifies READ.AI and DeepSeek as proscribed tools.
Review: Agent reviewed
Normalized value: administrative_staff_readai_deepseek_proscribed
Original evidence
Evidence 1L’utilisation de certains outils d’IA générative est permise dans la mesure où les principes énoncés dans la Directive pour l’utilisation de l’intelligence artificielle (IA) générative (10.70) sont respectés. ... L’utilisation des outils d’IA générative suivants est proscrite. READ.AI ... DeepSeek
Localized display only
Some generative AI tools are permitted if Directive 10.70 is respected; READ.AI and DeepSeek are listed as proscribed.
Teaching
UdeM's teaching-integrity guidance cautions teaching staff that AI-generated-content detection tools can vary in reliability and their results should be considered carefully.
Review: Agent reviewed
Normalized value: ai_detection_results_variable_reliability_teaching_context
Original evidence
Evidence 1la fiabilité des outils de détection de contenu produit par l’IAg peut être variable, ce qui oblige à considérer avec beaucoup de prudence leurs résultats.
Localized display only
The reliability of tools that detect AI-generated content can vary, so their results should be considered with caution.
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
5 source attribution
Directive pour l'utilisation de l'intelligence artificielle (IA) générative
secretariatgeneral.umontreal.ca
Enseigner avec l'intelligence artificielle générative (IAg)
cpu.umontreal.ca
Intelligence artificielle - Intégrité
integrite.umontreal.ca
Lignes directrices pour une utilisation appropriée de l'intelligence artificielle générative aux études supérieures
esp.umontreal.ca
Utilisation de l'intelligence artificielle (IA) générative
ti.umontreal.ca
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.