Mexico City, Mexico

Universidad Nacional Autónoma de México (UNAM)

Universidad Nacional Autónoma de México (UNAM) is listed as QS 2026 rank 136. Universidad Nacional Autónoma de México (UNAM) has 7 source-backed AI policy claim records from 3 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.

Short answer

v1 public contract

Universidad Nacional Autónoma de México (UNAM) is listed as QS 2026 rank 136. Universidad Nacional Autónoma de México (UNAM) has 7 source-backed AI policy claim records from 3 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.

Citation-ready summary

As of this public record, University AI Policy Tracker lists Universidad Nacional Autónoma de México (UNAM) as an agent-reviewed AI policy record last checked on May 14, 2026 and last changed on May 14, 2026. The record contains 7 source-backed claims, including 7 reviewed claims, from 3 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/universidad-nacional-autonoma-de-mexico.json. The entity-level confidence is 90%. 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.

Claim coverage7 reviewedSource languagees-MXPublic JSON/api/public/v1/universities/universidad-nacional-autonoma-de-mexico.json

Policy signals in this record

  • Evidence includes Privacy claims.
  • Evidence includes Academic integrity claims.
  • Evidence includes AI tool treatment claims.
  • Evidence includes Research claims.
  • Evidence includes Source status claims.
  • Named AI services detected in public claims: ChatGPT.
  • Teaching, assessment, coursework, or syllabus-related language appears in the public claim text.
  • Privacy, sensitive-data, or security language appears in the public claim text.
Policy statusReviewed evidence-backed recordReview: Agent reviewedEvidence-backed claims7Reviewed7Candidate0Official sources3

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 confidence74%

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.

Research guidance

Universidad Nacional Autónoma de México (UNAM) has 1 source-backed public claim for research guidance; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence71%Evidence1Sources1

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.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

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

7 reviewed evidence-backed public claim

Privacy

UNAM's 2025 education recommendations advise faculty using generative AI in teaching to avoid entering personal or sensitive information into AI platforms and to orient students and colleagues about data privacy.

Review: Agent reviewedConfidence90%

Original evidence

Evidence 1
Protección de datos y privacidad. Evita proporcionar información personal o sensible en plataformas de IAGen y asegura el cumplimiento de las normativas de privacidad. Orienta al estudiantado, así como a tus colegas, acerca de la importancia de la privacidad de los datos y cómo las distintas plataformas los procesan.

Localized display only

Data protection and privacy: avoid providing personal or sensitive information in GenAI platforms and orient students and colleagues about data privacy.

Academic Integrity

For assessment, UNAM's 2025 recommendations advise faculty to define the level of generative-AI incorporation and, when AI use is allowed, ask students to provide the prompts/instructions and generated responses for review.

Review: Agent reviewedConfidence88%

Original evidence

Evidence 1
Definir su nivel de incorporación. Es importante determinar en qué medida utilizar la IAGen en función de los conocimientos y habilidades que se desean evaluar. ... En aquellos casos en los que se permita el uso de IAGen, solicita que el estudiantado proporcione las instrucciones y respuestas generadas por la herramienta para valorarlas.

Localized display only

Define the level of GenAI incorporation for the knowledge and skills being assessed; when allowed, ask students to provide prompts/instructions and generated responses.

Academic Integrity

UNAM's 2025 recommendations advise designing strategies for undeclared generative-AI use and caution against indiscriminate use of AI detectors because their effectiveness is questioned and they raise privacy concerns.

Review: Agent reviewedConfidence88%

Original evidence

Evidence 1
Diseñar estrategias que prevengan y atiendan el uso no declarado de la IAGen. ... Evitar el uso indiscriminado de detectores de IAGen. Actualmente, su eficacia es cuestionada y plantea preocupaciones de privacidad.

Localized display only

Design strategies for undeclared GenAI use and avoid indiscriminate AI-detector use because effectiveness is questioned and privacy concerns exist.

Academic Integrity

UNAM's 2023 teaching recommendations suggest that faculty clearly specify acceptable and unacceptable ChatGPT use, require recognition and documentation of AI-tool use, and emphasize user responsibility for submitted academic products.

Review: Agent reviewedConfidence88%

Original evidence

Evidence 1
Asimismo, establecer la necesidad de reconocer y documentar cómo se utilizó la herramienta y qué acciones se tomaron para decidir la permanencia o no del contenido creado por estas herramientas, así como enfatizar la responsabilidad final del usuario sobre los productos académicos que se entreguen a revisión.

Localized display only

Establish the need to recognize and document how the tool was used and emphasize the user's final responsibility for academic products submitted for review.

Ai Tool Treatment

UNAM's 2025 GAIA-GEN document frames its generative-AI education content as recommendations for informed, ethical, and critical use, not as a rigid prescriptive framework.

Review: Agent reviewedConfidence86%

Original evidence

Evidence 1
Estas recomendaciones aspiran a ser elementos que detonen en la comunidad universitaria (profesorado, estudiantado, funcionariado) creatividad y apropiación crítica de estas tecnologías, no pretenden ser marcos rígidos que intenten prescribir el trabajo académico respecto a la IAGen.

Localized display only

These recommendations are intended to encourage critical appropriation, not to be rigid frameworks prescribing academic work with GenAI.

Research

The 2025 UNAM DGTIC ethics diagnostic frames transparency in AI-mediated research as clearly stating whether AI tools were used in questions, hypotheses, experimental design, calculations, data analysis, or research-report writing.

Review: Agent reviewedConfidence84%

Original evidence

Evidence 1
Las implicaciones descritas ... están ligadas entre sí por la necesidad de transparentar el uso de los SIA y sus herramientas, lo que obliga moralmente al investigador a explicitar con claridad si se ha apoyado en ellas, ya sea en la formulación de preguntas, hipótesis y objetivos, en el diseño de experimentos, en la ejecución de los cálculos, el análisis de los datos o la elaboración del informe de investigación.

Localized display only

The document links AI-mediated research ethics to transparency: researchers should clearly state whether they used AI tools in research questions, design, calculations, analysis, or report writing.

Source Status

A 2025 UNAM DGTIC ethics diagnostic says UNAM did not yet have an institutional framework for ethical integration of AI systems, while recommending stronger normative work and ethics-committee capacity for AI cases.

Review: Agent reviewedConfidence82%

Original evidence

Evidence 1
Por el momento, la UNAM aún no cuenta con ningún marco institucional de integración de la ética en los SIA; no obstante existen áreas universitarias, como el IIMAS o el IIJ, que han profundizado en la aplicación de la ética al proceso investigativo y en la ética del derecho.

Localized display only

The diagnostic says UNAM does not yet have an institutional ethical-integration framework for AI systems, while noting university areas working on ethics and AI topics.

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

3 source attribution

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 14, 2026Last changedMay 14, 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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