Swansea, United Kingdom

Swansea University

Swansea University is listed as QS 2026 rank 292. Swansea University has 6 source-backed AI policy claim records from 4 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.

Short answer

v1 public contract

Swansea University is listed as QS 2026 rank 292. Swansea University has 6 source-backed AI policy claim records from 4 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 Swansea University as an agent-reviewed AI policy record last checked on May 16, 2026 and last changed on May 16, 2026. The record contains 6 source-backed claims, including 6 reviewed claims, from 4 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/swansea-university.json. The entity-level confidence is 96%. 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 coverage6 reviewedSource languageen-GBPublic JSON/api/public/v1/universities/swansea-university.json

Policy signals in this record

  • Evidence includes Privacy claims.
  • Evidence includes Academic integrity claims.
  • Evidence includes AI tool treatment claims.
  • Evidence includes Teaching claims.
  • No specific AI service name is highlighted by the current public claim text.
  • Disclosure, acknowledgment, citation, or attribution language appears in the public claim text.
  • 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 claims6Reviewed6Candidate0Official sources4

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 confidence79%

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

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

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

6 reviewed evidence-backed public claim

Privacy

Swansea University's AI assessment policy says staff and students should not enter identifiable information into any AI application, and are not permitted to upload student assessment or identifiable data without explicit written consent.

Review: Agent reviewedConfidence96%

Normalized value: no_identifiable_information_or_student_assessment_uploaded_without_written_consent

Original evidence

Evidence 1
Staff and students should not enter any identifiable information into any AI application. Staff or students are not permitted to upload student assessment, or any identifiable data without explicit written consent, to AI applications for any reason (including attempts to check for AI generated content).

Academic Integrity

Swansea University's AI assessment policy says staff and students are not permitted to present work completed by AI applications as their own without acknowledgement.

Review: Agent reviewedConfidence95%

Normalized value: ai_work_cannot_be_presented_as_own_without_acknowledgement

Original evidence

Evidence 1
Staff and students will not be permitted to present work completed by AI applications as their own without acknowledgement.

Ai Tool Treatment

Swansea University has a policy for responsible use of AI applications, including generative AI, in assessment, applying to faculty staff, students, and professional service staff involved in assessment or AI use within the University.

Review: Agent reviewedConfidence94%

Normalized value: ai_assessment_policy_applies_to_assessment_participants

Original evidence

Evidence 1
This policy outlines the guidelines and principles for the responsible use of Artificial Intelligence (AI) applications, including generative AI, in assessment at Swansea University. This policy applies to all faculty staff, students, and professional service staff involved in assessment and/or the use of AI technologies within the University.

Academic Integrity

Swansea University's student AI guidance says AI-generated work submitted for assessment when not expressly authorised and declared could be investigated for academic misconduct.

Review: Agent reviewedConfidence93%

Normalized value: unauthorised_undeclared_ai_assessment_work_could_be_academic_misconduct

Original evidence

Evidence 1
Work generated by AI tools and submitted for assessment when not expressly authorised and declared could be investigated for academic misconduct.

Teaching

Swansea University's student AI framework asks students to apply University policies on data protection, privacy, and academic integrity when selecting and using approved academic work, and to follow assessment instructions around permitted AI use and disclosure.

Review: Agent reviewedConfidence90%

Normalized value: student_framework_links_ai_use_to_policy_awareness_and_assessment_disclosure

Original evidence

Evidence 1
Apply University policies on data protection, privacy, and academic integrity when selecting and using approved academic work, ensuring responsible and ethical practice. Follow assessment instructions around permitted AI use and disclosure.

Teaching

Swansea University's teaching-staff AI framework describes a 5P assessment-design approach intended to provide ethical and creative uses of generative AI while minimising academic misconduct risks.

Review: Agent reviewedConfidence87%

Normalized value: teaching_staff_5p_assessment_design_genai_guidance

Original evidence

Evidence 1
The teaching and assessment model provides ethical and creative uses of GenAI while minimising the risks of academic misconduct. It can be used across disciplines and as a catalyst for cross-disciplinary collaboration.

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

4 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 16, 2026Last changedMay 16, 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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