Policy presence
Chungnam National University has 3 source-backed public claims for policy presence; deterministic analysis status: unclear.
Daejeon, South Korea
Chungnam National University
Chungnam National University has 4 source-backed AI policy claims from 4 official source attributions. Review state: agent reviewed; 4 reviewed claims. Last checked May 21, 2026.
Chungnam National University AI policy short answer
v1 public contract
Chungnam National University has 4 source-backed AI policy claims from 4 official source attributions, including 4 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 21, 2026. Discovery context: Chungnam National University is listed as QS 2026 rank 851-900.
Citation-ready summary
As of this public record, University AI Policy Tracker lists Chungnam National University as an agent-reviewed AI policy record last checked on May 21, 2026 and last changed on May 21, 2026. The record contains 4 source-backed claims, including 4 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/chungnam-national-university.json. The entity-level confidence is 90%. 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 Academic integrity claims.
- Evidence includes Teaching 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.
- Teaching, assessment, coursework, or syllabus-related 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
Chungnam National University has 3 source-backed public claims for ai disclosure; deterministic analysis status: recommended.
Coursework
Chungnam National University has 3 source-backed public claims for coursework; deterministic analysis status: required.
Exams
Chungnam National University has 4 source-backed public claims for exams; deterministic analysis status: restricted.
Privacy and data entry
No source-backed public claim about privacy or data-entry restrictions is present in this profile.
The current public tracker record does not contain claim evidence about personal, confidential, sensitive, regulated, or student data entry into AI tools.
Academic integrity
Chungnam National University has 3 source-backed public claims for academic integrity; deterministic analysis status: restricted.
Approved tools
Chungnam National University has 1 source-backed public claim for approved tools; deterministic analysis status: conditionally_allowed.
Named AI services
No source-backed public claim naming a specific AI service is present in this profile.
The current public tracker record does not contain claim evidence naming a specific AI service.
Teaching guidance
Chungnam National University has 4 source-backed public claims for teaching guidance; deterministic analysis status: recommended.
Research guidance
Chungnam National University has 1 source-backed public claim 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
4 reviewed evidence-backed public claim
Academic Integrity
Chungnam National University's learner guide states that not following syllabus or instructor-agreed generative-AI criteria, or inappropriate AI use, may be regarded as cheating; examples include presenting AI answers as one's own, failing to disclose AI use, or using AI in a class where it is fully prohibited.
Review: Agent reviewed
Normalized value: learner guide: inappropriate generative AI use may be treated as cheating
Original evidence
Evidence 1강의계획서에 명시된 지침 또는 학기 초에 교수자와 합의된 기준을 준수하지 않거나 생성형 AI를 부적절하게 사용한 것으로 판단 될 시, 부정행위로 간주 될 수 있음을 인지한다. 생성형 AI 의 답변을 본인이 작성한 것처럼 표기한 경우, 생성형 AI 사용시 사용 여부를 표기하지 않은 경우, 생성형 AI 사용이 전면 금지된 수업에서 사용한 경우 등
Localized display only
The learner guide says students should recognize that failure to follow syllabus or instructor-agreed AI criteria, or inappropriate AI use, may be considered cheating; examples include presenting AI answers as one's own, not disclosing AI use, or using AI where it is fully prohibited.
Academic Integrity
Chungnam National University's learner guidance says learners should follow instructor-provided generative-AI criteria, use AI as a learning support tool, verify AI outputs against reliable sources, and clearly mark the source, generation method, and scope of AI-generated materials.
Review: Agent reviewed
Normalized value: learner guidance: follow instructor criteria, verify AI outputs, disclose source/method/scope
Original evidence
Evidence 1학습자는 생성형 AI 활용 시 교수자가 제시한 지침을 숙지하고, 수업 활동이나 과제 수행에서 이를 준수한다. 학습자는 생성형 AI를 활용하여 산출된 자료에 대해서는 출처와 생성 방법, 활용 범위 등을 명확하게 표시한다.
Localized display only
Learners should know and follow instructor-provided generative AI guidance, and clearly indicate the source, generation method, and scope for materials produced using generative AI.
Original evidence
Evidence 2학습자는 생성형 AI를 학습 목표 달성을 위한 보조 도구로 활용한다. 학습자는 생성형 AI를 활용한 결과물에 대해서 신뢰할 수 있는 출처를 통해 사실 여부를 반드시 확인한다. 학습자는 생성형 AI 결과물을 과제에 활용할 경우 출처를 분명하기 표기한다.
Localized display only
Learners should use generative AI as a support tool for learning goals, verify AI outputs through reliable sources, and clearly mark sources when using AI outputs in assignments.
Academic Integrity
Chungnam National University's instructor guidance says that when generative AI is used in class, instructors should state its purpose and scope in the syllabus, explain correct use and standards for cheating or plagiarism, and guide students to cite AI-generated outputs.
Review: Agent reviewed
Normalized value: instructor guidance: syllabus scope, academic integrity standards, and AI-output citation
Original evidence
Evidence 1교수자는 수업에서 생성형 AI를 활용할 경우 강의계획서에 그 목적과 범위를 명시하고, 학습자에게 올바른 사용법과 활용 기준을 구체적으로 안내한다. 교수자는 생성형 AI 사용 시 부정행위나 표절에 대한 기준을 명확히 전달하고, AI가 생성한 결과물의 출처를 반드시 표기하도록 지도한다.
Localized display only
When using generative AI in class, instructors should state its purpose and scope in the syllabus, explain correct use and standards, and guide students to mark the source of AI-generated outputs.
Teaching
Chungnam National University's Education Innovation Office publishes generative-AI teaching and learning principles saying instructors and learners should discuss AI use in teaching and learning and comply with the agreed terms.
Review: Agent reviewed
Normalized value: official guidance: agreed generative AI use principles for teaching and learning
Original evidence
Evidence 1교수자와 학습자는 생성형 AI의 교수학습 활용 방안에 대해 논의하고 합의된 사항을 준수해야 한다. 교수자와 학습자는 AI가 생성한 결과물을 비판적으로 검토하고, 편향성과 오류 가능성을 인지하며, 윤리적으로 책임감을 가지고 활용해야 한다.
Localized display only
Instructors and learners should discuss generative AI use in teaching and learning, comply with agreed terms, and critically review AI outputs while recognizing bias and error risks.
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
교육혁신본부 | 생성형AI교수학습활용 | 생성형 AI 교수학습 활용 지침 | 교수자 지침
ile.cnu.ac.kr
교육혁신본부 | 생성형AI교수학습활용 | 생성형 AI 교수학습 활용 지침 | 기본 원칙
ile.cnu.ac.kr
교육혁신본부 | 생성형AI교수학습활용 | 생성형 AI 교수학습 활용 지침 | 학습자 지침
ile.cnu.ac.kr
교육혁신본부 | 생성형AI교수학습활용 | 수업 단계별 생성형 AI 활용 가이드 | 학습자 가이드
ile.cnu.ac.kr
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.