Policy presence
University of Leicester has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Leicester, United Kingdom
University of Leicester
University of Leicester is listed as QS 2026 rank 326. University of Leicester has 5 source-backed AI policy claim records from 2 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Short answer
v1 public contract
University of Leicester is listed as QS 2026 rank 326. University of Leicester has 5 source-backed AI policy claim records from 2 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 University of Leicester as an agent-reviewed AI policy record last checked on May 16, 2026 and last changed on May 16, 2026. The record contains 5 source-backed claims, including 5 reviewed claims, from 2 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-leicester.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.
Policy signals in this record
- Evidence includes AI tool treatment claims.
- Evidence includes Privacy claims.
- Evidence includes Academic integrity claims.
- Evidence includes Teaching claims.
- No specific AI service name is highlighted by the current 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.
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
No source-backed public claim about AI disclosure or acknowledgement is present in this profile.
The current public tracker record does not contain claim evidence about disclosing, acknowledging, citing, or declaring AI use.
Coursework
University of Leicester has 4 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
University of Leicester has 4 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
University of Leicester has 1 source-backed public claim for privacy and data entry; deterministic analysis status: blocked.
Academic integrity
University of Leicester has 2 source-backed public claims for academic integrity; deterministic analysis status: blocked.
Approved tools
University of Leicester has 2 source-backed public claims for approved tools; deterministic analysis status: required.
Named AI services
University of Leicester has 2 source-backed public claims for named ai services; deterministic analysis status: blocked.
Teaching guidance
University of Leicester has 3 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
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.
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.
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
5 reviewed evidence-backed public claim
Ai Tool Treatment
The University of Leicester's Generative AI policy provides a university-level framework for appropriate AI use in learning, teaching, and assessment activities.
Review: Agent reviewed
Normalized value: university_level_ai_learning_teaching_assessment_framework
Original evidence
Evidence 11.3 This policy provides a university-level framework, for staff and students for how and where it is appropriate to utilise AI for learning, teaching and assessment activities.
Privacy
The University of Leicester policy states that staff and students must not use generative AI in ways that collect, store, access, or share personal data without explicit consent.
Review: Agent reviewed
Normalized value: no_personal_data_without_explicit_consent
Original evidence
Evidence 17.2 Staff and students must not use Generative AI for any purpose that would result in personal data being collected, stored, accessed, and shared without the explicit consent of the people whose data is being processed.
Academic Integrity
For assessment, the University of Leicester uses three categories for generative AI use: red where AI must not be used, amber where AI may support assessment but not generate submitted content, and green where AI may generate assessment content within the brief.
Review: Agent reviewed
Normalized value: red_amber_green_assessment_categories
Original evidence
Evidence 19.4 The University has identified three broad categories of assessment for the purposes of determining whether students may use Generative AI in the process of completing the assessment. Students will be informed which assessments fall into which categories.
Teaching
The University of Leicester policy says AI is not normally used to generate marks or feedback, and any approved AI-supported marking or feedback must be reviewed by academic staff.
Review: Agent reviewed
Normalized value: ai_marking_feedback_requires_staff_review_if_approved
Original evidence
Evidence 19.20 Where a school wishes to request that the marking for an assessment could be supported by AI, this will be reviewed and subject to approval by the University. In all cases, any Generative AI supported marking must be subject to review and validation by members of academic staff.
Teaching
The University of Leicester policy says academic integrity training should include the appropriate use of generative AI, including ethical considerations, environmental impact, and critical engagement with AI outputs.
Review: Agent reviewed
Normalized value: student_training_ai_in_academic_integrity
Original evidence
Evidence 18.4 Training for students on the appropriate use of Generative AI should be included within the standard Academic Integrity training delivered to students.
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
2 source attribution
Academic quality and standards
le.ac.uk
Policy on Generative Artificial Intelligence in Learning, Teaching and Assessment
le.ac.uk
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.