Policy presence
No source-backed public AI policy or guidance record is present in this profile.
The current public tracker record does not contain a source-backed claim that establishes a policy or guidance source.
Nagoya, Japan
Nagoya University
Nagoya University is listed as QS 2026 rank 164. Nagoya University 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
Nagoya University is listed as QS 2026 rank 164. Nagoya University 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 Nagoya University as an agent-reviewed AI policy record last checked on May 14, 2026 and last changed on May 14, 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/nagoya-university.json. The entity-level confidence is 97%. 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 Privacy claims.
- Evidence includes Academic integrity claims.
- Evidence includes AI tool treatment claims.
- Evidence includes Research claims.
- No specific AI service name is highlighted by the current 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
Nagoya University has 3 source-backed public claims for coursework; deterministic analysis status: conditionally_allowed.
Exams
Nagoya University has 3 source-backed public claims for exams; deterministic analysis status: conditionally_allowed.
Privacy and data entry
Nagoya University has 1 source-backed public claim for privacy and data entry; deterministic analysis status: blocked.
Academic integrity
Nagoya University has 3 source-backed public claims for academic integrity; deterministic analysis status: conditionally_allowed.
Approved tools
Nagoya University has 1 source-backed public claim for approved tools; deterministic analysis status: recommended.
Named AI services
Nagoya University has 2 source-backed public claims for named ai services; deterministic analysis status: blocked.
Teaching guidance
Nagoya University has 1 source-backed public claim for teaching guidance; deterministic analysis status: recommended.
Research guidance
Nagoya University has 2 source-backed public claims for research guidance; deterministic analysis status: recommended.
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
Privacy
Nagoya University states that students must not enter personal information or other confidential information into generative AI because information entered as prompts may leak.
Review: Agent reviewed
Normalized value: Personal or confidential information must not be entered into generative AI.
Original evidence
Evidence 1利用の際に質問として入力した情報は流出する危険があるので、個人情報など秘密とすべき情報を絶対に入力してはいけません。
Localized display only
Because prompts can leak, personal or otherwise confidential information must not be entered.
Academic Integrity
Nagoya University tells students to use AI output only as a reference for their own writing and not to use output unchanged, even in part; penalties may be imposed in such cases.
Review: Agent reviewed
Normalized value: AI output should be reference-only and not submitted unchanged.
Original evidence
Evidence 1AIの出力情報は、あくまでも自身の文書作成の参考に留めるべきです。出力情報の一部であっても、そのまま利用することはやめてください。なお、その場合にはペナルティを課すことがあります。
Localized display only
AI output should only be a writing reference; using output as-is, even partially, may lead to penalties.
Academic Integrity
Nagoya University asks students to follow the instructions of the instructor in charge when using generative AI in classes.
Review: Agent reviewed
Normalized value: Class generative-AI use follows instructor instructions.
Original evidence
Evidence 1本学における生成AIへの見解については、「教育研究における生成系人工知能技術（生成AI）の利活用について」に示されているとおりですが、学生の皆さんには、特に以下のことをお願いします。このようなAIの利用が自身の学習に与える効果や弊害について考え、ひとえに皆さん自身の学習を深めるという観点から向き合ってください。なお、授業における生成AIの利用については、担当教員の指示に従ってください。
Localized display only
For class use of generative AI, students are asked to follow the instructions of the instructor in charge.
Ai Tool Treatment
Nagoya University says it should engage with and make positive use of generative AI as a responsible education and research institution, while confirming the technology’s benefits and problems for appropriate use.
Review: Agent reviewed
Normalized value: Positive but risk-aware institutional stance toward generative AI.
Original evidence
Evidence 1名古屋大学は責任ある教育研究機関として、生成AIと向き合い前向きに利活用すべきであると考えます。但し、生成AIの利点と問題点とを改めて確認し、この技術を適切に利活用することが将来的な一層の飛躍に繋がると考えます。
Localized display only
Nagoya University frames generative AI positively, while emphasizing confirmation of benefits and problems for appropriate use.
Research
For research activities, Nagoya University asks users to understand generative AI risks, consider use while avoiding research misconduct, and coordinate treatment with partner institutions where possible in joint research or industry-academia collaboration.
Review: Agent reviewed
Normalized value: Research use should be risk-aware, avoid misconduct, and coordinate with partners where possible.
Original evidence
Evidence 1これらの問題点をよく理解したうえで、研究不正を生じさせないように配慮しつつ研究活動の効率化と質の向上に生成AIの利用を検討してください。共同研究や産学連携活動に生成AIを利用する場合には、可能であれば連携機関も含めて統一的に対応するように留意してください。
Localized display only
For research, users are asked to understand risks, avoid research misconduct, and coordinate use in joint or industry-academia work where possible.
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
教育研究における生成AIの利活用について
nagoya-u.ac.jp
生成AIの活用について
nagoya-u.ac.jp
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.