Policy presence
University of Murcia has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Murcia, Spain
University of Murcia has 5 source-backed AI policy claims from 1 official source attribution. Review state: agent reviewed; 5 reviewed claims. Last checked May 24, 2026.
v1 public contract
University of Murcia has 5 source-backed AI policy claims from 1 official source attribution, including 5 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 24, 2026. Discovery context: University of Murcia is listed as QS 2026 rank 1001-1200.
As of this public record, University AI Policy Tracker lists University of Murcia as an agent-reviewed AI policy record last checked on May 24, 2026 and last changed on May 24, 2026. The record contains 5 source-backed claims, including 5 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/university-of-murcia.json. The entity-level confidence is 84%. 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.
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.
Deterministic source-backed dimensions derived from this record's public claims.
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.
University of Murcia has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
University of Murcia has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
University of Murcia has 2 source-backed public claims for coursework; deterministic analysis status: required.
University of Murcia has 2 source-backed public claims for exams; deterministic analysis status: required.
University of Murcia has 2 source-backed public claims for privacy and data entry; deterministic analysis status: restricted.
University of Murcia has 2 source-backed public claims for academic integrity; deterministic analysis status: required.
University of Murcia has 1 source-backed public claim for approved tools; deterministic analysis status: restricted.
University of Murcia has 1 source-backed public claim for named ai services; deterministic analysis status: restricted.
University of Murcia has 1 source-backed public claim for teaching guidance; deterministic analysis status: recommended.
University of Murcia has 1 source-backed public claim for research guidance; deterministic analysis status: restricted.
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.
5 reviewed evidence-backed public claim
Academic Integrity
Normalized value: library_guidance_for_citing_ai_generated_content
Evidence originale
Evidence 1Pautas: Descripción en Metodología. Hay que describir como se han empleado en el apartado de metodología del trabajo. Entrada y Respuesta. Hay que incluir el prompt que se ha utilizado y el fragmento más relevante de la respuesta. Verificación de la fuente IA. Hay que citar las fuentes fiables.
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Guidelines include describing how AI was used in methodology, including the prompt and the most relevant response fragment, and citing reliable sources.
Academic Integrity
Normalized value: library_guidance_requires_instructor_confirmation_for_tfg_tfm_ai_use
Evidence originale
Evidence 1Aprobación docente. El docente debe informar al alumno que para la realización de trabajos (TFG/TFM) necesita la confirmación del docente antes de usar herramientas de IA.
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Instructor approval. The instructor should inform the student that, for TFG/TFM work, instructor confirmation is needed before using AI tools.
Privacy
Normalized value: responsible_ai_use_privacy_ip_ethics_confidentiality
Evidence originale
Evidence 1Uso responsable que tenga en cuenta la privacidad de los datos, propiedad intelectual, ética, y confidencialidad.
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Responsible use should account for data privacy, intellectual property, ethics, and confidentiality.
Source Status
Normalized value: official_library_ai_page_lists_tools_without_approved_prohibited_status
Evidence originale
Evidence 1Principales herramientas basadas en IA de interés para la Comunidad Universitaria por categorías: Análisis y Ciencia de datos - aprendizaje automático - Asistentes - Búsqueda - Cursos y talleres - Alfin crítica - Digitalización - Diseño.
Affichage localise only
The page lists main AI-based tools of interest to the university community by categories such as data analysis, machine learning, assistants, search, courses, critical information literacy, digitalization, and design.
Research
Normalized value: library_page_summarizes_european_research_generative_ai_guidance
Evidence originale
Evidence 1Los investigadores deben de abstenerse de utilizar herramientas de IA generativa en actividades sensibles como revisiones por pares o evaluaciones. Deben utilizar la IA generativa respetando la privacidad, la confidencialidad y los derechos de propiedad intelectual.
Affichage localise only
Researchers should abstain from using generative AI tools in sensitive activities such as peer review or evaluations, and should use generative AI respecting privacy, confidentiality, and IP rights.
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.
1 source attribution
um.es
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