Policy presence
Xiamen University has 1 source-backed public claim for policy presence; deterministic analysis status: unclear.
Xiamen, China (Mainland)
Xiamen University
Xiamen University is listed as QS 2026 rank 341. Xiamen University has 5 source-backed AI policy claim records from 1 official source attribution. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Short answer
v1 public contract
Xiamen University is listed as QS 2026 rank 341. Xiamen University has 5 source-backed AI policy claim records from 1 official source attribution. 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 Xiamen University 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 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/xiamen-university.json. The entity-level confidence is 95%. 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 Teaching claims.
- Evidence includes Academic integrity claims.
- Evidence includes Privacy 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.
- 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
Xiamen University has 1 source-backed public claim for ai disclosure; deterministic analysis status: required.
Coursework
Xiamen University has 4 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
Xiamen University has 4 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
Xiamen University has 1 source-backed public claim for privacy and data entry; deterministic analysis status: blocked.
Academic integrity
Xiamen University has 2 source-backed public claims for academic integrity; deterministic analysis status: required.
Approved tools
No source-backed public claim identifying approved or licensed AI tools is present in this profile.
The current public tracker record does not contain claim evidence that identifies institutionally approved, licensed, procured, or enterprise AI tools.
Named AI services
Xiamen University has 1 source-backed public claim for named ai services; deterministic analysis status: blocked.
Teaching guidance
Xiamen University has 5 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
Teaching
Xiamen University's AI teaching guideline applies to Xiamen University teachers and students using generative AI services for on-campus course teaching activities.
Review: Agent reviewed
Normalized value: guideline applies to teachers and students using generative AI in on-campus course teaching
Original evidence
Evidence 1第二条 厦门大学在校师生利用生成式人工智能服务从事校内课程教学的相关活动，适用本指南。
Localized display only
Article 2 says the guideline applies when Xiamen University teachers and students use generative AI services for on-campus course teaching activities.
Teaching
Xiamen University's AI teaching guideline says teachers should state in their courses which generative AI services students may use or are prohibited from using.
Review: Agent reviewed
Normalized value: teachers state permitted or prohibited student generative AI services in courses
Original evidence
Evidence 1第九条 教师应在所负责课程的教学过程中明确规定学生可使用或禁止使用的生成式人工智能服务。
Localized display only
Article 9 says teachers should clearly state in the courses they teach which generative AI services students may use or are prohibited from using.
Academic Integrity
Xiamen University's AI teaching guideline says students using generative AI services in course learning should actively disclose that use to the teacher and accurately mark the contribution of generative AI when required by the teacher.
Review: Agent reviewed
Normalized value: students disclose course AI use and mark AI contributions when required
Original evidence
Evidence 1第十三条 学生在课内学习中使用生成式人工智能服务时，应主动向教师披露。学生应遵守相关引用规范，根据教师要求，在作业或者作品中准确、完整地标注生成式人工智能的贡献，维护学术诚信和避免抄袭（详见附件）。
Localized display only
Article 13 says students should actively disclose course generative AI use to teachers and, when teachers require it, accurately and completely mark generative AI contributions.
Privacy
Xiamen University's AI teaching guideline says students should strictly observe data protection principles and must not enter sensitive data into generative AI tools.
Review: Agent reviewed
Normalized value: students must not enter sensitive data into generative AI tools
Original evidence
Evidence 1第十四条 学生应严格遵守数据保护原则，不得将敏感数据输入生成式人工智能工具。
Localized display only
Article 14 says students should strictly observe data protection principles and must not enter sensitive data into generative AI tools.
Academic Integrity
Xiamen University's AI teaching guideline says the university may handle improper use of generative AI under higher-education academic misconduct rules, Xiamen University academic misconduct rules, and student disciplinary rules.
Review: Agent reviewed
Normalized value: improper generative AI use may be handled under academic misconduct and disciplinary rules
Original evidence
Evidence 1第十五条 对违规使用生成式人工智能的，学校可以依据《高等学校预防与处理学术不端行为办法》《厦门大学学术失范与学术不端行为处理办法》《厦门大学学生违纪处分规定》等规定予以处理。
Localized display only
Article 15 says improper generative AI use may be handled under higher-education academic misconduct rules, Xiamen University academic misconduct rules, and student disciplinary rules.
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
1 source attribution
厦门大学关于教学中使用人工智能的指南
med.xmu.edu.cn
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.