University of Arizona Plagiarism and AI Policy
Last verified October 24, 2026 · AI-assisted research and drafting · Maintained by Plagiarism Checker Online, operated by Markus Nick
The short answer: According to the published guidance checked October 24, 2026: The university's AI Guidelines & Principles says students may use AI for assignments only with explicit instructor approval. Instructors set syllabus expectations; the guidance describes no single university-wide AI-specific policy and refers to the Code of Academic Integrity. Departmental rules may add requirements.
Key facts, with sources
| Topic | What the school publishes | Source |
|---|---|---|
| School | Public, four-year institution in Tucson, AZ | Source |
| Official policy | Academic integrity policy | Source |
| Honor code | Code of Academic Integrity | Source |
| Generative AI | Instructor decides. The university's AI Guidelines & Principles says students may use AI for assignments only with explicit instructor approval. Instructors set syllabus expectations; the guidance describes no single university-wide AI-specific policy and refers to the Code of Academic Integrity. Departmental rules may add requirements. | Source |
| Sanctions | Published options, not a mandatory sequence or an individual outcome; see the linked policy. | Source |
| Appeals | The faculty-decision appeal goes to the Dean of the College. Certain severe sanctions automatically receive that appeal. A later University Hearing Board route is limited to specified decisions, failure to act and a discretionary failing-grade situation. Check the exact route and notice; the two filing triggers differ. Deadline stated: Dean appeal: within10 academic days of the electronic or postal postmark of the Record of Faculty-Student Conference. Eligible Hearing Board appeal: within10 academic days of receipt of the decision or the Dean's failure to act. Academic Days normally exclude weekends and holidays during regular fall/spring semesters; special break-session and extension rules apply.. | Source |
| Reporting office | Dean of Students Office The code identifies the office as a source of process advice; this is not representation or a prediction of an outcome. | Source |
| Earlier coursework | The code addresses previously or simultaneously submitted work without fair citation of the original or authorization from the supervising faculty member. Faculty must communicate their submission rules; ask which conditions apply before reusing earlier work. | Source |
| Student rights and scope | Students have rights to fair consideration, seeing the evidence and confidentiality subject to the stated limits, and may have an advisor. The code excludes the Colleges of Law and the MD program in Medicine, which have their own honor codes and procedures. | Source |
| Summary scope | Independent AI-assisted summary of Arizona's Code of Academic Integrity and AI Guidelines & Principles, not affiliated with the university. Law and MD-program exceptions and route-specific academic-day appeals are explicit; no individual result or universal professional-school rule inferred. | Source |
Published outcomes and review levels
These are the published options or review limits below, not a predicted outcome or an automatic sequence. Follow the linked policy for the applicable process.
- Case-specific sanctions. Possible sanctions include warnings, credit or grade consequences, transcript notation and more serious academic-status outcomes. Faculty consider seriousness, state of mind, harm and mitigating or aggravating factors. Multiple violations may lead to additional sanctions at the Academic Dean's discretion, not automatic expulsion.
What students at University of Arizona should know
At the University of Arizona, the AI Guidelines & Principles page tells students to obtain explicit instructor approval before using AI for class assignments. It also explains that course materials and department-specific guidance can set expectations. The presence of university AI tools therefore does not itself authorize their use on an assessment. Start with the actual syllabus and ask before using a tool where permission is unclear. The Code of Academic Integrity has important scope limits: it does not govern the Colleges of Law or the MD program in Medicine, which use their own honor codes and procedures. Its appeal clock also needs careful reading. The first appeal to the Dean of the College runs from the electronic or postal postmark of the Record of Faculty-Student Conference, not simply the day a student first speaks with an instructor. A later University Hearing Board appeal is available only in specified situations and uses a different trigger. The code defines Academic Days around the regular fall and spring semesters and provides special rules for breaks and extensions, so ten days should not be casually converted to ten calendar days. Students may see the evidence and have an advisor under the code's stated protections. Sanctions depend on the case and may be combined; the published options are not a guaranteed warning-first ladder. Contact the Dean of Students Office for help understanding the process.
Frequently asked questions
No. The code expressly excludes the Colleges of Law and the MD program in the College of Medicine, which have their own honor codes and procedures. Check the policy applicable to your specific program.
For the faculty-decision appeal to the Dean of the College, the code uses the electronic or postal postmark of the Record of Faculty-Student Conference. It specifies ten academic days, with Academic Day definitions and special break-session or extension provisions. The later Hearing Board route has separate eligibility and timing triggers.
No. The university AI guidelines require explicit instructor approval for assignment use. Read the syllabus and any department-specific rules; availability of a tool is not permission for a particular assessment.
Every row links to its source. Independent summary, not affiliated with University of Arizona. Found a mistake? Report a correction.
For background, read the overview of university AI policies.
A related topic we cover in depth: our article on AI writing in academic papers.
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