Policy presence
University of Toronto has 1 source-backed public claim for policy presence; deterministic analysis status: unclear.
Toronto, Canada
University of Toronto
University of Toronto has 8 source-backed AI policy claims from 8 official source attributions. Review state: agent reviewed; 8 reviewed claims. Last checked May 23, 2026.
University of Toronto AI policy short answer
v1 public contract
University of Toronto has 8 source-backed AI policy claims from 8 official source attributions, including 8 reviewed claims. The record review state is agent reviewed; original-language evidence snippets, source URLs, confidence, and public JSON are preserved for citation. Last checked May 23, 2026. Discovery context: University of Toronto is listed as QS 2026 rank 29.
Citation-ready summary
As of this public record, University AI Policy Tracker lists University of Toronto as an agent-reviewed AI policy record last checked on May 23, 2026 and last changed on May 23, 2026. The record contains 8 source-backed claims, including 8 reviewed claims, from 8 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/university-of-toronto.json. The entity-level confidence is 98%. 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 AI tool treatment claims.
- Evidence includes Research claims.
- Evidence includes Teaching claims.
- Evidence includes Privacy claims.
- Named AI services detected in public claims: Microsoft Copilot.
- Disclosure, acknowledgment, citation, or attribution language appears in the public claim text.
- Teaching, assessment, coursework, or syllabus-related 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
University of Toronto has 2 source-backed public claims for ai disclosure; deterministic analysis status: required.
Coursework
University of Toronto has 4 source-backed public claims for coursework; deterministic analysis status: conditionally_allowed.
Exams
University of Toronto has 4 source-backed public claims for exams; deterministic analysis status: conditionally_allowed.
Privacy and data entry
University of Toronto has 2 source-backed public claims for privacy and data entry; deterministic analysis status: required.
Academic integrity
University of Toronto has 2 source-backed public claims for academic integrity; deterministic analysis status: conditionally_allowed.
Approved tools
University of Toronto has 2 source-backed public claims for approved tools; deterministic analysis status: required.
Named AI services
University of Toronto has 2 source-backed public claims for named ai services; deterministic analysis status: required.
Teaching guidance
University of Toronto has 3 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
University of Toronto has 4 source-backed public claims 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
8 reviewed evidence-backed public claim
Academic Integrity
University of Toronto considers use of generative AI tools on marked assessments without instructor permission to be use of an unauthorized aid under the Code of Behaviour on Academic Matters.
Review: Agent reviewed
Normalized value: ai_use_requires_instructor_permission
Oryginalny dowod
Evidence 1By failing to comply with the instructor's specific instructions that you not use generative AI tools in writing your term paper, you have knowingly used an unauthorized aid in preparing your assignment. Any student using an unauthorized aid has committed an offence under the University's Code of Behaviour on Academic Matters
Widok lokalny only
By failing to comply with the instructor's specific instructions that you not use generative AI tools... you have knowingly used an unauthorized aid
Ai Tool Treatment
University of Toronto teaching guidance says Microsoft Copilot is the recommended generative AI tool to use at U of T and, when signed in with University credentials, conforms to U of T privacy and security standards for use with up to level 3 data.
Review: Agent reviewed
Normalized value: microsoft_copilot_recommended_protected_tool_up_to_level_3_data
Oryginalny dowod
Evidence 1Currently, Microsoft Copilot is the recommended generative AI tool to use at U of T. When a user signs in using University credentials, Microsoft Copilot conforms to U of T’s privacy and security standards (i.e., does not share any data with Microsoft or any other company) for use with up to level 3 data .
Widok lokalny only
Currently, Microsoft Copilot is the recommended generative AI tool to use at U of T
Research
University of Toronto School of Graduate Studies recommends that graduate students seek and document unambiguous approval from their supervisor(s) and supervisory committee before using generative AI tools in research, writing, or other scholarly activities relevant to graduate academic milestones.
Review: Agent reviewed
Normalized value: graduate_ai_use_recommended_supervisor_approval
Oryginalny dowod
Evidence 1It is recommended that students seek and document in writing unambiguous approval from their supervisor(s) and supervisory committee in advance of the use of generative AI tools in research, writing, or other scholarly activities relevant to graduate academic milestones.
Widok lokalny only
It is recommended that students seek and document in writing unambiguous approval from their supervisor(s) and supervisory committee in advance
Teaching
University of Toronto recommends that instructors include a statement on their syllabus that informs students about expectations with respect to the use of AI, and provides sample syllabus statements for instructors to use.
Review: Agent reviewed
Normalized value: syllabus_ai_statement_recommended
Oryginalny dowod
Evidence 1The University also recommends that you include a statement on your syllabus that informs students about your expectations with respect to the use of AI. We have created sample statements for instructors to include in course syllabi and course assignments to help shape the message to students about what AI technology is, or is not, allowed.
Widok lokalny only
The University also recommends that you include a statement on your syllabus that informs students about your expectations with respect to the use of AI
Privacy
University of Toronto Information Security guidelines state that no data should be provided to generative AI if any part of that data should not be included in results produced by that system, and users must verify AI tools have been assessed by the university as suitable for the data classification level before sharing personal information or university data classified as level 2, 3 or 4.
Review: Agent reviewed
Normalized value: ai_data_sharing_requires_classification_check
Oryginalny dowod
Evidence 1No data should be provided to generative AI if any part of that data should not be included in results produced by that system. If multiple people are using the system, one person’s data may potentially be revealed to someone else. Be aware of whether the system shares data with other systems and platforms. Before sharing personal information or university data classified as level 2, 3 or 4 with any AI tool, verify that the tool has been assessed by the university as suitable for that data type and classification level .
Widok lokalny only
No data should be provided to generative AI if any part of that data should not be included in results produced by that system
Research
University of Toronto School of Graduate Studies says use of generative AI tools in any aspect of researching or writing a doctoral thesis must have prior approval from the supervisor or supervisory committee.
Review: Agent reviewed
Normalized value: University of Toronto School of Graduate Studies says use of generative AI tools in any aspect of researching or writing a doctoral thesis must have prior approval from the supervisor or supervisory committee.
Oryginalny dowod
Evidence 1The use of generative AI tools in any aspect of researching or writing of the thesis must be done with the prior approval of the supervisor(s) and supervisory committee.
Research
University of Toronto graduate students must clearly describe and cite any generative AI tools used in thesis research or writing.
Review: Agent reviewed
Normalized value: University of Toronto graduate students must clearly describe and cite any generative AI tools used in thesis research or writing.
Oryginalny dowod
Evidence 1Careful attention must be paid in the thesis to appropriately citing and describing any use of generative AI tools in the research process. It must be clear to the reader which generative AI tools were used, as well as how and why they were used.
Academic Integrity
University of Toronto states that unauthorized use of generative AI tools for graduate scholarly work may be considered an academic or research misconduct offence.
Review: Agent reviewed
Normalized value: University of Toronto states that unauthorized use of generative AI tools for graduate scholarly work may be considered an academic or research misconduct offence.
Oryginalny dowod
Evidence 1Unauthorized use of generative AI tools for scholarly work at the University of Toronto may be considered an offence under the Code of Behaviour on Academic Matters, and research misconduct as defined in the Policy on Ethical Conduct in Research.
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
8 source attribution
Generative AI Tools
teaching.utoronto.ca
Generative Artificial Intelligence in the Classroom: FAQ's
viceprovostundergrad.utoronto.ca
Guidance on the Appropriate Use of Generative Artificial Intelligence for Graduate Academic Milestones
sgs.utoronto.ca
Guidance on the Appropriate Use of Generative Artificial Intelligence for Graduate Academic Milestones
sgs.utoronto.ca
Guidelines - Toward an AI-ready University
ai.utoronto.ca
Teaching with Generative AI at U of T
teaching.utoronto.ca
Use artificial intelligence intelligently
security.utoronto.ca
Using ChatGPT or other generative AI tool on a marked assessment
academicintegrity.utoronto.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.