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.
Chiba City, Japan
Chiba University
Chiba University has 5 source-backed AI policy claims from 1 official source attribution. Review state: agent reviewed; 5 reviewed claims. Last checked May 20, 2026.
Chiba University AI policy short answer
v1 public contract
Chiba University has 5 source-backed AI policy claims from 1 official source attribution, including 5 reviewed claims. The record review state is agent reviewed; original-language evidence snippets, source URLs, confidence, and public JSON are preserved for citation. Last checked May 20, 2026. Discovery context: Chiba University is listed as QS 2026 rank 791-800.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Chiba University as an agent-reviewed AI policy record last checked on May 20, 2026 and last changed on May 20, 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/chiba-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 Privacy claims.
- Evidence includes Teaching claims.
- Evidence includes AI tool treatment 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
Chiba University has 4 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
Chiba University has 4 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
Chiba University has 1 source-backed public claim for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
No source-backed public claim about academic-integrity treatment of AI use is present in this profile.
The current public tracker record does not contain claim evidence about AI use under academic integrity, misconduct, dishonesty, plagiarism, or cheating rules.
Approved tools
Chiba University has 1 source-backed public claim for approved tools; deterministic analysis status: blocked.
Named AI services
Chiba University has 2 source-backed public claims for named ai services; deterministic analysis status: blocked.
Teaching guidance
Chiba University has 4 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Chiba University has 1 source-backed public claim for research guidance; deterministic analysis status: restricted.
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
Chiba University's guideline tells students not to enter personal information or confidential information into generative AI and notes the information-leakage risk of entering unpublished research plans or research results.
Review: Agent reviewed
Normalized value: students_no_personal_confidential_unpublished_research_info_in_ai
Original evidence
Evidence 1学生は、生成 AI を教育・学習で利用する際には、以下の諸点について留意する。生成 AI には個人情報や機密情報は入力しないこと（公開されても差し支えない情報のみ入力すること）。生成 AI に未公表の研究計画や研究成果などを入力すると、情報漏洩につながる危険性があること。
Localized display only
Students should keep these points in mind: do not enter personal or confidential information into generative AI; entering unpublished research plans or results may lead to information leakage.
Teaching
Chiba University's guideline says each course instructor judges whether generative AI use is consistent with class objectives and may prohibit or restrict generative AI use where it would significantly undermine achievement of educational objectives.
Review: Agent reviewed
Normalized value: instructors_may_restrict_ai_when_objectives_undermined
Original evidence
Evidence 1生成 AI の授業での利用は、それが授業の目的に合致することが前提であり、合致するかどうかは、それぞれの授業の担当教員が判断する。生成 AI の利用によって、教育目標の達成が大きく損なわれる授業においては、教員は受講生に生成 AI の利用を禁止・制限することができる。
Localized display only
Generative AI use in class must align with class objectives; each instructor judges alignment and may prohibit or restrict use where it would significantly undermine educational objectives.
Ai Tool Treatment
Chiba University's teaching-and-learning guideline says the university does not uniformly prohibit generative AI use in teaching and learning and promotes learning about, with, and aside from generative AI according to purpose.
Review: Agent reviewed
Normalized value: generative_ai_not_uniformly_prohibited_teaching_learning
Original evidence
Evidence 1千葉大学は生成 AI の教育・学習での利用を一律に禁止することはせず、その目的に応じて、「生成 AI についての学び」「生成 AI を用いた学び」「生成 AI によらない学び」をそれぞれ推進する。
Localized display only
Chiba University does not uniformly prohibit generative AI use in teaching and learning and promotes learning about, with, and aside from generative AI according to purpose.
Teaching
Chiba University's guideline says that when instructors prohibit or restrict generative AI use in a class, they should clearly state that in the syllabus or other course handouts and explain the reason to students.
Review: Agent reviewed
Normalized value: ai_restrictions_should_be_stated_in_syllabus_or_handouts
Original evidence
Evidence 1授業において生成 AI の利用を禁止・制限する際には、教員はその旨を当該授業のシラバス等に明記する。また、なぜ利用を禁止・制限するのかを受講生にわかりやすく説明することが望まれる。
Localized display only
When prohibiting or restricting generative AI use in class, instructors should state that in the syllabus or similar materials and explain the reason clearly to students.
Teaching
Chiba University's guideline says that when instructors allow students to use generative AI in class, instructors should consider fairness because outputs may differ depending on external-service registration and paid or free versions.
Review: Agent reviewed
Normalized value: instructors_should_consider_fairness_when_ai_allowed
Original evidence
Evidence 1授業において学生に生成 AI の利用を認める場合には、外部サービス利用登録の有無、有料版か無料版かなどにより、成果物等に差が生まれ得るため、教員は公平性に配慮する。
Localized display only
When allowing students to use generative AI in class, instructors should consider fairness because differences may arise depending on service registration or paid/free versions.
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
千葉大学の教育・学習における生成 AI の利用についての指針
drive.google.com
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.