Austin, United States

University of Texas at Austin

University of Texas at Austin is listed as QS 2026 rank 68. University of Texas at Austin has 11 source-backed AI policy claim records from 6 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.

Short answer

v1 public contract

University of Texas at Austin is listed as QS 2026 rank 68. University of Texas at Austin has 11 source-backed AI policy claim records from 6 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.

Policy statusReviewed evidence-backed recordReview: Agent reviewedEvidence-backed claims11Reviewed11Candidate0Official sources6

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.

Coverage score100/100Coverage labelbroad public coverageReview: Machine candidateAnalysis confidence80%

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 Texas at Austin has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence77%Evidence1Sources1

Security and procurement

University of Texas at Austin has 2 source-backed public claims for security and procurement; deterministic analysis status: restricted.

RestrictedMachine candidateConfidence81%Evidence2Sources2

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

11 reviewed evidence-backed public claim

Privacy

UT Austin acceptable-use guidance says published university information may be used freely with AI tools, while controlled or confidential university information can be used only with university-managed AI tools covered by contracts that protect university data and disable web search functionality.

Review: Agent reviewedConfidence96%

Normalized value: Published data allowed; controlled/confidential data limited to contracted university-managed AI tools with protections and disabled web search.

Original evidence

Evidence 1
Public or Published Data: Data that is publicly available or classified as Published university information ... may be used freely with AI tools. Controlled or Confidential Data ... can be used with AI tools that are managed by the university and covered by contracts explicitly protecting university data ... and web search functionality must be disabled.

Privacy

UT Austin acceptable-use guidance says unauthorized AI tools are not approved for controlled or confidential university information, including student records subject to FERPA, health information, proprietary information, and other controlled or confidential data.

Review: Agent reviewedConfidence96%

Normalized value: Unauthorized AI tools not approved for controlled/confidential university information.

Original evidence

Evidence 1
AI tools that lack a university contract and appropriate data-sharing controls are not approved for use with Controlled or Confidential university information. This includes free or non-UT-managed versions of AI tools like ChatGPT and Copilot.

Procurement

UT Austin AI detection guidance prohibits third-party AI detection software from being used to evaluate student work or assignments unless a university contract or purchase order is in place.

Review: Agent reviewedConfidence96%

Normalized value: AI detection software for student-work evaluation requires university contract or purchase order.

Original evidence

Evidence 1
The University prohibits the use of all third-party software, including AI Detection Software ... to evaluate student work or assignments unless a University contract or purchase order is in place.

Security Review

UT Austin acceptable-use guidance says the CISO must review AI tools before procurement, development, deployment, or use when the tools are intended to autonomously make, or be a controlling factor in making, consequential decisions.

Review: Agent reviewedConfidence95%

Normalized value: CISO review required for consequential-decision AI uses.

Original evidence

Evidence 1
UT Austin’s CISO must review AI tools to be used for any of these purposes prior to their procurement, development, deployment, or use.

Teaching

UT Austin responsible-adoption guidance defines responsible AI use in teaching and learning as adopting AI in ways that facilitate learning outcomes and foster human development for campus community members.

Review: Agent reviewedConfidence94%

Normalized value: Responsible AI in teaching and learning is tied to learning outcomes and human development.

Original evidence

Evidence 1
At UT Austin, we define responsible use of AI in teaching and learning as the adoption of AI that facilitates the achievement of learning outcomes and fosters human development for all members of the campus community.

Privacy

UT Austin AI detection guidance says submitting student work into AI detection or other third-party software without a university contract or purchase order may violate student copyright, intellectual property, or FERPA privacy rights.

Review: Agent reviewedConfidence94%

Normalized value: AI detection uploads without university contract may implicate student IP/copyright/FERPA rights.

Original evidence

Evidence 1
Submitting student work (even if it is anonymized) into any AI Detection Software (or any other third-party software) without a University contract or purchase order in place may be a violation of that student’s copyright and intellectual property rights.

Academic Integrity

UT Austin responsible-adoption guidance includes academic integrity as a principle for AI use, linking responsible use to the honor code, scholarly values, ownership, and appropriate authorship of tool outputs.

Review: Agent reviewedConfidence92%

Normalized value: Academic integrity is an explicit responsible-adoption principle.

Original evidence

Evidence 1
Academic Integrity: Use AI in alignment with our honor code and fundamental scholarly values such honesty, respect and authenticity, taking ownership and claiming authorship of the output of tools when appropriate.

Research

UT Austin graduate-education recommendations state that graduate students and mentors must use only vetted, university-contracted generative AI platforms for work involving non-public research data.

Review: Agent reviewedConfidence91%

Normalized value: Graduate AI recommendations restrict non-public research data to vetted university-contracted platforms.

Original evidence

Evidence 1
Graduate students and their mentors must only use vetted, University-contracted generative AI platforms for work involving non-public research data.

Academic Integrity

UT Austin CTL teaching-policy guidance says using generative AI tools to create course-assignment responses in a way the instructor does not accept may be considered academic dishonesty by the university.

Review: Agent reviewedConfidence91%

Normalized value: Unacceptable instructor-disallowed AI-generated assignment responses may be academic dishonesty.

Original evidence

Evidence 1
The use of generative AI tools to create responses to course assignments in a way that is unacceptable to the course instructor may be considered a case of academic dishonesty by the university.

Teaching

UT Austin classroom guidance recommends that instructors requiring generative AI understand and abide by UT acceptable-use guidance, identify syllabus policies clearly, and submit specific software requirements through the University Co-Op.

Review: Agent reviewedConfidence91%

Normalized value: Classroom-required AI use guidance addresses acceptable use, syllabus notice, and course-material reporting.

Original evidence

Evidence 1
Ensure that your syllabus clearly identifies Syllabus Policy statements regarding the use of generative AI in your class. If you plan to encourage or require the use of generative AI in your class, ensure that requirement is clearly listed in the Required Course Materials section of your syllabus.

Research

UT Austin graduate-education recommendations say students should remain accountable as the sole intellectual author of milestone work and should never cite AI as an author.

Review: Agent reviewedConfidence90%

Normalized value: Graduate milestone recommendations keep student authorship/accountability central and reject AI authorship.

Original evidence

Evidence 1
Students should remain accountable as the sole intellectual author of milestone work and should never cite AI as an author.

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

6 source attribution

Acceptable Use of Generative AI Tools | UT Austin Information Security Office

security.utexas.edu

Snapshot hash
f65f66948048f0c455452d6d095d831a0fe2b04c8b543234f058f86af5ddd094

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.

Last checkedMay 13, 2026Last changedMay 13, 2026Open change log

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.

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