Policy presence
Colorado School of Mines has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Golden, United States
Colorado School of Mines
Colorado School of Mines has 4 source-backed AI policy claims from 1 official source attribution. Review state: agent reviewed; 4 reviewed claims. Last checked May 16, 2026.
Colorado School of Mines AI policy short answer
v1 public contract
Colorado School of Mines has 4 source-backed AI policy claims from 1 official source attribution, including 4 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 16, 2026. Discovery context: Colorado School of Mines is listed as QS 2026 rank =571.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Colorado School of Mines as an agent-reviewed AI policy record last checked on May 16, 2026 and last changed on May 17, 2026. The record contains 4 source-backed claims, including 4 reviewed claims, from 1 official source attribution. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/colorado-school-of-mines.json. The entity-level confidence is 94%. 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 AI tool treatment claims.
- Evidence includes Academic integrity claims.
- Evidence includes Source status claims.
- Evidence includes Teaching claims.
- No specific AI service name is highlighted by the current public claim text.
- 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
Colorado School of Mines has 1 source-backed public claim for ai disclosure; deterministic analysis status: required.
Coursework
Colorado School of Mines has 3 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
Colorado School of Mines has 3 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
Colorado School of Mines has 1 source-backed public claim for academic integrity; deterministic analysis status: required.
Approved tools
Colorado School of Mines has 1 source-backed public claim for approved tools; deterministic analysis status: restricted.
Named AI services
Colorado School of Mines has 1 source-backed public claim for named ai services; deterministic analysis status: restricted.
Teaching guidance
Colorado School of Mines has 3 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
4 reviewed evidence-backed public claim
Ai Tool Treatment
In the absence of contrary written instructor directions, Mines guidance says students may not submit evaluation work generated in whole or in part by GenAI, while GenAI use for learning, studying, proofreading, and brainstorming is permitted and encouraged unless instructors state otherwise.
Review: Agent reviewed
Normalized value: default_no_submission_without_permission_learning_uses_allowed
Original evidence
Evidence 1Unless instructors provide explicit permission or instruction to the contrary, students may not submit content for evaluation that was generated, in whole or in part, by genAI tools. Using genAI tools when unauthorized would be considered a potential violation of Mines' policy on academic integrity.
Academic Integrity
Mines sample syllabus language says students who include AI-generated ideas, text, code, or images in submitted work must document and credit the source, and that failure to properly cite AI tools would be considered academic misconduct.
Review: Agent reviewed
Normalized value: ai_generated_content_requires_credit_when_included
Original evidence
Evidence 1If you include content (e.g., ideas, text, code, images) that was generated, in whole or in part, by Generative Artificial Intelligence tools (including, but not limited to, ChatGPT and other large language models) in work submitted for evaluation in this course, you must document and credit your source.
Source Status
Colorado School of Mines Academic Affairs maintains official generative-AI guidance, but the page frames the material as guidelines rather than a universal policy prescribing when GenAI must be allowed or disallowed.
Review: Agent reviewed
Normalized value: official_guidance_not_universal_policy
Original evidence
Evidence 1While the intent of this resource is not to prescribe universal policies of when to allow or disallow generative AI, the text below provides some guidelines for the use of genAI in connection with academic work at the University.
Teaching
Mines guidance expects faculty to state course GenAI expectations explicitly and in writing, including permitted and prohibited uses, and encourages instructors to design assignments that reduce unethical GenAI use.
Review: Agent reviewed
Normalized value: faculty_write_course_genai_expectations
Original evidence
Evidence 1Faculty are expected to state explicitly and affirmatively their expectations regarding student use of genAI tools. Instructors should specify in writing the permitted and prohibited uses of genAI tools in their courses, and should seek to clarify any expectations if they differ from one assessment to another.
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
Guidelines for Using Generative Artificial Intelligence at Mines
academicaffairs.mines.edu
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.