Beirut, Lebanon

Saint Joseph University of Beirut (USJ)

Record status

Policy statusReviewed evidence-backed recordReview: Agent reviewedClaim coverage6 reviewedEvidence-backed claims6Reviewed6Candidate0Official sources4Source languagefrPublic JSON/api/public/v1/universities/saint-joseph-university-of-beirut-usj.json

Policy profile

Coverage score100/100Coverage labelbroad public coverageReview: Machine candidateAnalysis confidence74%

AI disclosure

Saint Joseph University of Beirut (USJ) has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence75%Evidence1Sources1

Research guidance

No source-backed public claim about research AI use is present in this profile.

The current public tracker record does not contain claim evidence about research use, publication ethics, research data, grants, or human-subjects compliance.

Not MentionedMachine candidateConfidence0%Evidence0Sources0

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

AI tools

Derived tool records0

No tool-level evidence is published for this record yet. Broad AI tool mentions are not expanded into named tool conclusions.

Evidence-backed claims

6 reviewed evidence-backed public claim

Ai Tool Treatment

USJ's AI guiding principles advise users to systematically verify data and information sources provided by generative AI.

Review: Agent reviewedConfidence90%

Normalized value: verify_generative_ai_sources

Original evidence

Evidence 1
Vérifier systématiquement les données et sources d’information données par l’IA générative.

Localized display only

Verify information sources: systematically check the data and sources of information provided by generative AI to ensure accuracy and reliability.

Academic Integrity

USJ's ChatGPT-3 guidance says using ChatGPT is not necessarily an academic-integrity violation, but unauthorized use to create content submitted as one's own is considered plagiarism and a violation of academic-integrity policy.

Review: Agent reviewedConfidence90%

Normalized value: unauthorized_chatgpt_submission_plagiarism

Original evidence

Evidence 1
L'utilisation de ChatGPT n'est pas nécessairement considérée comme une violation de l'intégrité académique, cependant, son utilisation non autorisée pour créer du contenu soumis comme étant le sien propre est considérée comme du plagiat et une violation de la politique d'intégrité académique.

Localized display only

Using ChatGPT is not necessarily considered an academic-integrity violation, but unauthorized use to create content submitted as one's own is considered plagiarism and a violation of academic-integrity policy.

Teaching

USJ's CINIA page presents guiding principles intended to support effective, responsible, and ethical use of artificial intelligence in higher education teaching and learning, and says the principles were approved and validated by USJ's Committee for Digital and Artificial Intelligence Strategic Orientation.

Review: Agent reviewedConfidence88%

Normalized value: guiding_principles_approved_validated

Original evidence

Evidence 1
Quelques principes directeurs pour un usage efficace, responsable et éthique de l’intelligence artificielle dans l’enseignement et l’apprentissage au supérieur. Note: The Committee for Digital and Artificial Intelligence Strategic Orientation at USJ has approved and validated these guiding principles.

Localized display only

Guiding principles for effective, responsible, and ethical AI use in higher education teaching and learning; the page notes that USJ's digital and AI strategic-orientation committee approved and validated them.

Academic Integrity

USJ's AI guiding principles frame academic integrity as appropriately incorporating AI-altered or manipulated digital content and citing it accordingly.

Review: Agent reviewedConfidence88%

Normalized value: cite_ai_altered_content

Original evidence

Evidence 1
Maintain Academic Integrity: Demonstrate academic integrity by appropriately incorporating AI-altered or manipulated digital content into one's work and citing it accordingly.

Localized display only

Maintain academic integrity by appropriately incorporating AI-altered or manipulated digital content into one's work and citing it accordingly.

Privacy

USJ's CINIA guidance warns that free AI tools may carry privacy-related risks and should be used with awareness of secure and responsible-use considerations.

Review: Agent reviewedConfidence86%

Normalized value: free_ai_tools_privacy_risk

Original evidence

Evidence 1
L'utilisation d'outils d'IA gratuits peut comporter certains risques liés à la vie privée. Il est donc crucial d'être conscient de ces outils et des considérations associées pour garantir une utilisation sécurisée et responsable.

Localized display only

The use of free AI tools may entail privacy-related risks; users should be mindful of these tools and related considerations to ensure secure and responsible use.

Teaching

USJ's CINIA training catalog includes student training on proper use of AI tools, with ethical questions such as data confidentiality, AI-error responsibility, and possible discrimination from biased training data.

Review: Agent reviewedConfidence82%

Normalized value: student_training_proper_ai_use

Original evidence

Evidence 1
Formation aux étudiants au bon usage des outils de l’intelligence artificielle (IA). Soulever les questions reliées aux défis et aux implications éthiques de l’IA telles que la confidentialité des données, la responsabilité en cas d’erreurs de l’IA, et l’éventuelle discrimination liée aux données d’entraînement biaisées.

Localized display only

Student training on proper AI-tool use raises ethical challenges and implications including data confidentiality, responsibility for AI errors, and possible discrimination from biased training data.

Candidate claims

0 machine or needs-review claim

Official sources

4 source attribution

Change log

Last checkedMay 17, 2026Last changedMay 17, 2026Open change log

Corrections

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