Policy presence
University of Maryland, College Park has 5 source-backed public claims for policy presence; deterministic analysis status: unclear.
College Park, United States
University of Maryland, College Park
University of Maryland, College Park has 11 source-backed AI policy claims from 5 official source attributions. Review state: agent reviewed; 11 reviewed claims. Last checked May 15, 2026.
University of Maryland, College Park AI policy short answer
v1 public contract
University of Maryland, College Park has 11 source-backed AI policy claims from 5 official source attributions, including 11 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 15, 2026. Discovery context: University of Maryland, College Park is listed as QS 2026 rank =207.
Citation-ready summary
As of this public record, University AI Policy Tracker lists University of Maryland, College Park as an agent-reviewed AI policy record last checked on May 15, 2026 and last changed on May 15, 2026. The record contains 11 source-backed claims, including 11 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/university-of-maryland-college-park.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 Research claims.
- Evidence includes Privacy claims.
- Evidence includes Teaching claims.
- Evidence includes Procurement claims.
- Evidence includes Security review claims.
- No specific AI service name is highlighted by the current 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 Maryland, College Park has 1 source-backed public claim for ai disclosure; deterministic analysis status: required.
Coursework
University of Maryland, College Park has 5 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
University of Maryland, College Park has 5 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
University of Maryland, College Park has 3 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
University of Maryland, College Park has 2 source-backed public claims for academic integrity; deterministic analysis status: restricted.
Approved tools
University of Maryland, College Park has 5 source-backed public claims for approved tools; deterministic analysis status: restricted.
Named AI services
University of Maryland, College Park has 3 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
University of Maryland, College Park has 5 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
University of Maryland, College Park has 2 source-backed public claims for research guidance; deterministic analysis status: restricted.
Security and procurement
University of Maryland, College Park has 3 source-backed public claims for security and procurement; deterministic analysis status: conditionally_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
11 reviewed evidence-backed public claim
Ai Tool Treatment
UMD publishes GenAI guidelines that apply to faculty, staff, students, and affiliates using GenAI tools in academic, research, or administrative activities.
Review: Agent reviewed
Normalized value: university-wide_genai_guidelines
原始证据
Evidence 1These guidelines apply to all UMD faculty, staff, students, and affiliates using GenAI tools and technologies in academic, research, or administrative activities.
Academic Integrity
For coursework, UMD tells students to assume GenAI use for assignments and assessments is not allowed unless the syllabus or assignment instructions specify otherwise.
Review: Agent reviewed
Normalized value: students_assume_disallowed_unless_specified
原始证据
Evidence 1Students should assume that the use of GenAI tools to complete course assignments and assessments is not allowed unless otherwise specified in the course syllabus or assignment/assessment instructions.
Research
UMD guidance tells researchers not to input federal, state, or UMD data into externally sourced GenAI tools and not to upload unpublished research data or other confidential information into tools that have not undergone proper review.
Review: Agent reviewed
Normalized value: external_genai_research_data_restriction
原始证据
Evidence 1Researchers should not input federal, state, or UMD data into externally sourced GenAI tools due to the high risk of exposing sensitive information to public or open-source domains.
Privacy
For administrative work, UMD guidance says administrative staff should not input non-public institutional data into externally sourced GenAI platforms and faculty or staff should not put moderate-risk Level 2 or higher data into public external GenAI platforms.
Review: Agent reviewed
Normalized value: administrative_external_genai_data_restriction
原始证据
Evidence 1Administrative staff should not input any institutional data that is not publicly available into externally sourced platforms (free or paid) using GenAI tools.
Teaching
UMD strongly encourages instructors to establish course-specific policies defining appropriate and inappropriate GenAI use.
Review: Agent reviewed
Normalized value: course_specific_policy_encouraged
原始证据
Evidence 1Instructors are strongly encouraged to establish a course-specific policy that defines the appropriate and inappropriate use of GenAI tools.
Academic Integrity
UMD's Division of Academic Affairs advises against incorporating GenAI detection tools into course policies and says detection results should be treated only as potential indicators, not definitive proof or the sole basis for grading decisions.
Review: Agent reviewed
Normalized value: ai_detection_not_definitive_proof
原始证据
Evidence 1The Division of Academic Affairs advises against incorporating GenAI detection tools into course policies. Results from GenAI detection tools should be treated only as potential indicators of misconduct, not definitive proof.
Procurement
UMD guidance says individuals intending to use UMD credentials to access or purchase products with GenAI functionality should contact the Division of IT before signing up, including for free or open-source products.
Review: Agent reviewed
Normalized value: dit_contact_before_genai_tool_signup
原始证据
Evidence 1Individuals intending to use UMD credentials to access or purchase products or tools with GenAI functionality should contact the Division of IT at itsupport@umd.edu before signing up.
Security Review
UMD's AI services page lists DIT AI services and states that listed DIT tools have gone through SRM review and are approved for university community members to use.
Review: Agent reviewed
Normalized value: dit_ai_services_srm_reviewed
原始证据
Evidence 1All software that will be used with university data or for educational activities must go through the university's Software Risk Management (SRM) review process.
Security Review
UMD's AI software page identifies a list of software with AI capabilities approved by DIT Security and Compliance.
Review: Agent reviewed
Normalized value: ai_capable_software_dit_security_compliance
原始证据
Evidence 1The following is a list of software with AI capabilities approved by DIT Security and Compliance. For a complete list of software approved for use, visit the Software Catalog.
Teaching
UMD's TLTC guidance encourages instructors to communicate expectations for AI-based tools and to assess privacy/security risks, including using UMD-approved tools when possible.
Review: Agent reviewed
Normalized value: tltc_instructor_ai_expectations_and_risk
原始证据
Evidence 1Speak openly and frequently with your students about your expectations for technology use - specifically, for AI-based tools.
Teaching
UMD's TLTC sample syllabus page gives instructors non-binding example AI course-policy language spanning prohibited, limited, and broad AI use, with citation or attribution expectations where applicable.
Review: Agent reviewed
Normalized value: sample_syllabus_language_non_binding
原始证据
Evidence 1The University of Maryland strongly encourages instructors to establish a course-specific policy that defines the appropriate and inappropriate use of Generative AI (GenAI) tools.
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
Artificial Intelligence (AI)
tltc.umd.edu
Available Services
ai.umd.edu
Guidelines for Use
ai.umd.edu
Sample Syllabus Language: AI Course Policies
tltc.umd.edu
Software
ai.umd.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.