Shanghai, China (Mainland)

Tongji University

Record status

Policy statusReviewed evidence-backed recordReview: Agent reviewedClaim coverage3 reviewedEvidence-backed claims3Reviewed3Candidate0Official sources3Source languagezhPublic JSON/api/public/v1/universities/tongji-university.json

Policy profile

Coverage score100/100Coverage labelbroad public coverageReview: Machine candidateAnalysis confidence77%

AI disclosure

Tongji University has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence75%Evidence1Sources1

Privacy and data entry

Tongji University has 1 source-backed public claim for privacy and data entry; deterministic analysis status: restricted.

RestrictedMachine candidateConfidence77%Evidence1Sources1

Academic integrity

Tongji University has 1 source-backed public claim for academic integrity; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence75%Evidence1Sources1

Approved tools

Tongji University has 1 source-backed public claim for approved tools; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence80%Evidence1Sources1

Teaching guidance

Tongji University has 1 source-backed public claim for teaching guidance; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence77%Evidence1Sources1

Security and procurement

Tongji University has 1 source-backed public claim for security and procurement; deterministic analysis status: recommended.

RecommendedMachine candidateConfidence80%Evidence1Sources1

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

3 reviewed evidence-backed public claim

Security Review

Tongji University's information-system construction standard says projects involving artificial intelligence services should register with the Informatization Office in advance, ensure related filing is completed, and in principle use the school standard platform designated by the managing unit for agent design and development.

Review: Agent reviewedConfidence94%

Normalized value: ai_service_information_system_projects_require_prior_registration_filing_and_designated_standard_platform

Original evidence

Evidence 1
若项目内容涉及人工智能服务,建设单位应提前向管理单位登记,并确保相关内容已完成备案。备案范围与方式可参考《生成式人工智能服务管理暂行办法》《互联网信息服务算法推荐管理规定》等相关国家规范要求。智能体设计与开发原则上应使用管理单位指定的学校规范平台;

Localized display only

If a project involves AI services, the construction unit should register with the managing unit in advance, complete filing, and use the school standard platform designated by the managing unit for agent design and development in principle.

Teaching

Tongji University Library's 2025 fall information-literacy lecture plan includes an artificial-intelligence-literacy series for students and staff, covering AI-assisted academic research, AIGC-era research integrity, DeepSeek use, AI-assisted patents, and AIGC-assisted research graphics.

Review: Agent reviewedConfidence90%

Normalized value: library_ai_literacy_series_covers_ai_research_support_integrity_deepseek_patents_and_graphics

Original evidence

Evidence 1
图书馆信息素养培训以满足师生科研和学习需求为出发点,针对学习科研周期,设计了新生入学教育、学位论文开题、人工智能素养等不同模块,内容围绕图书馆资源和服务、信息检索、数据库检索与利用、数据处理与可视化分析、论文选题挖掘、常用软件工具的使用、专利基础知识、AI工具辅助学术研究、AIGC时代的科研诚信等主题。

Localized display only

Tongji University Library's information-literacy training includes an artificial-intelligence-literacy module and topics such as AI-assisted academic research and research integrity in the AIGC era.

Academic Integrity

Tongji University Library guidance summarizes AI-generated content labeling as a traceable and verifiable explicit-plus-implicit labeling system, and frames clear disclosure of where and how AI was used, and who bears responsibility, as the way to make AI a reliable research and learning assistant.

Review: Agent reviewedConfidence88%

Normalized value: library_guidance_ai_generated_content_labeling_and_disclosure

Original evidence

Evidence 1
构建了“显式+隐式”的双重标识体系,要求AI生成内容在传播全链条中实现可追溯、可验证。结语:AI不是学术诚信的“对立面”,而是需要被正确理解和使用的新型工具。当我们学会在作品中清晰地告诉读者:“哪里用了AI、怎么用的、责任由谁承担”,AI才能真正成为研究和学习的可靠助手,而不是潜在的风险源。

Localized display only

The library article presents explicit and implicit labeling for traceability and frames clear disclosure of where and how AI was used, and who is responsible, as necessary for responsible use.

Candidate claims

0 machine or needs-review claim

Official sources

3 source attribution

Change log

Last checkedMay 15, 2026Last changedMay 15, 2026Open change log

Corrections

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