Policy presence
Osaka University has 4 source-backed public claims for policy presence; deterministic analysis status: unclear.
Osaka City, Japan
Osaka University
Osaka University is listed as QS 2026 rank 91. Osaka University has 7 source-backed AI policy claim records from 4 official source attributions. The public record preserves original-language evidence snippets, source URLs, snapshot hashes, confidence, and review state.
Short answer
v1 public contract
Osaka University is listed as QS 2026 rank 91. Osaka University has 7 source-backed AI policy claim records from 4 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 Osaka University as an agent-reviewed AI policy record last checked on May 14, 2026 and last changed on May 14, 2026. The record contains 7 source-backed claims, including 7 reviewed claims, from 4 official source attributions. Original-language evidence snippets and source URLs remain canonical, with public JSON available at https://eduaipolicy.org/api/public/v1/universities/osaka-university.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 Source status claims.
- Evidence includes Privacy claims.
- Evidence includes Teaching claims.
- Evidence includes AI tool treatment claims.
- Evidence includes Academic integrity 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
Osaka University has 1 source-backed public claim for ai disclosure; deterministic analysis status: recommended.
Coursework
Osaka University has 5 source-backed public claims for coursework; deterministic analysis status: restricted.
Exams
Osaka University has 5 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
Osaka University has 1 source-backed public claim for privacy and data entry; deterministic analysis status: restricted.
Academic integrity
Osaka University has 1 source-backed public claim for academic integrity; deterministic analysis status: conditionally_allowed.
Approved tools
Osaka University has 1 source-backed public claim for approved tools; deterministic analysis status: recommended.
Named AI services
Osaka University has 2 source-backed public claims for named ai services; deterministic analysis status: restricted.
Teaching guidance
Osaka 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
7 reviewed evidence-backed public claim
Source Status
Osaka University's TLSC generative AI education guide is instructor-facing and explicitly says it is not a university-wide official view.
Review: Agent reviewed
Normalized value: tlsc_guide_is_instructor_facing_not_university_wide_official_view
Original evidence
Evidence 1生成AIの教育利用を検討している教員向けに、生成AIの基本や注意事項、教育評価における生成AIの影響、生成AIを活用した授業づくりの実践例などについて紹介しています。＊本ページは、大阪大学の教員ならびに生成AIの活用を検討している教員向けに情報をまとめて提供しているものです。そのため、大阪大学の全学的な公式見解を示すものではありません。
Localized display only
TLSC says the guide is for Osaka University instructors and instructors considering generative AI use, and that it is not a university-wide official view.
Privacy
Osaka University's student-facing generative AI message warns that prompts and input content may be stored or learned by the system and tells students to avoid providing personal or confidential information.
Review: Agent reviewed
Normalized value: avoid_entering_personal_or_confidential_information
Original evidence
Evidence 1生成AIへ投げかけた質問事項やその記載内容が、システムに蓄積・学習される可能性があり、情報の漏洩に繋がる恐れがあります。個人情報や機密情報を提供しないように注意してください。
Localized display only
Osaka University warns students that prompts and input content may be stored or learned and could lead to leaks, and tells them not to provide personal or confidential information.
Teaching
Osaka University's TLSC guidance says generative AI output is not always accurate and that fact-checking generated information must be performed by people.
Review: Agent reviewed
Normalized value: tlsc_guidance_requires_human_fact_checking_of_generated_information
Original evidence
Evidence 1生成AIは内容を理解して回答しているわけではないので、生成AIからの情報が常に正確であるとは限りません。特に生成AIは理路整然とした文章の中に間違った情報が入る、つまりは平気で間違えるために、警戒が必要です。このような生成AIがつく間違いを「ハルシネーション（幻覚：Hallucination）」といいます。生成された情報の真偽を確かめるファクトチェックは、必ず人の手で行う必要があります。
Localized display only
TLSC says generative AI information is not always accurate and that fact-checking generated information must be performed by people.
Ai Tool Treatment
In a message to students, Osaka University says generative AI can be useful when used appropriately, but students should recognize that AI-generated text can contain errors and that presenting AI answers as one's own without judging truth carries risks.
Review: Agent reviewed
Normalized value: students_should_check_ai_outputs_and_recognize_risks
Original evidence
Evidence 1適切に使うことができれば、大変有用なツールになります。しかしながら、このようなツールは、さまざまな問題点に留意しながら利用しなければなりません。まず、インターネット上の情報は、正しいものばかりではなく、生成AIで作られた文章には誤りが含まれることもあります。生成AIから得られた回答を、その真偽を正しく判断せずに自分の言葉として発信した場合、さまざまなリスクをはらむことがあります。
Localized display only
Generative AI can be useful when used appropriately, but Osaka University warns students that AI text can contain errors and that presenting answers as one's own without checking truth carries risks.
Teaching
Osaka University's TLSC assessment guidance says instructors need to choose assessment methods that respond to generative AI, share precautions with students in advance, and clearly state permitted and prohibited AI tool use.
Review: Agent reviewed
Normalized value: tlsc_guidance_for_assessment_design_and_communication_of_ai_rules
Original evidence
Evidence 1教員は生成AIに対応した評価方法を選んでいく必要があります。今後、課題作成の際に多くの学生が生成AIを使用することが予想されます。たとえ生成AIを禁止したとしても、現実問題として学生の生成AI利用を止めることはできないでしょう。そのため教員は、従来の評価方法を再検討し、生成AIの不適切な利用を防ぐ対策を講じる必要があります。また、学生と教員間で生成AIの使用上の注意点を事前に共有しておき、生成AIの不正使用を予防することも大切です。試験で許可する・しないツールを明記する。AIの利用に関する方針をシラバスに明記する。
Localized display only
TLSC says instructors need to select assessment methods that respond to generative AI, share precautions with students, and state allowed/prohibited tools and AI-use policy.
Teaching
Osaka University's student-facing message says that merely creating outputs with generative AI does not deepen learning and asks students to value each process of learning.
Review: Agent reviewed
Normalized value: student_learning_process_should_not_be_replaced_by_ai_output_generation
Original evidence
Evidence 1生成AIツールで成果物を作成するだけでは、学びは深まりません。高等教育の意義は、さまざまな情報を活用し、自らの考えを創り上げ、さらには、自らと異なる意見や考えに耳を傾けて、人と人との対話を通して独創的な考え方やアイデアを生み出すところにあります。学びの一つ一つのプロセスを大切にしてください。
Localized display only
The message says learning is not deepened merely by creating outputs with generative AI and asks students to value each process of learning.
Academic Integrity
Osaka University's TLSC assessment guidance advises instructors to tell students that inappropriate generative AI use may be treated as academic misconduct, AI use should be limited to learning support, and AI use for analysis should be clearly stated.
Review: Agent reviewed
Normalized value: tlsc_guidance_for_student_ai_use_disclosure_and_academic_integrity_notice
Original evidence
Evidence 1生成AIの適切な使用について学生に早めに伝えることで、トラブルを未然に防ぐことが期待できます。以下に事前に学生に伝えておくことについて挙げます（浦田 2023）。・生成AIを適切に使用しない場合は、学業上の不正行為とみなされること・生成AIの使用は、あくまでも学習サポートに限ること・情報の分析などで生成AIを使用した場合は、その旨を明記すること
Localized display only
TLSC advises early student communication that inappropriate generative AI use may be considered academic misconduct, AI use should be limited to learning support, and AI use for analysis should be stated.
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
4 source attribution
生成AI（Generative AI）の利用について
osaka-u.ac.jp
生成AIに関する注意点
tlsc.osaka-u.ac.jp
生成AI教育ガイド
tlsc.osaka-u.ac.jp
評価における生成AIの影響
tlsc.osaka-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.