University AI Policy Tracker

Open, evidence-backed AI policy records for public reuse.

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Gwangju Institute of Science and Technology (GIST)

Source-check timeline, source snapshot hashes, claim review state, and a diff-style preview of current source-backed claim evidence.

Review: Agent reviewedLast checkedMay 16, 2026Last changedMay 16, 2026Claims4Sources3
University pagePublic JSON

Change summary

Current public record freshness and review state.

Gwangju Institute of Science and Technology (GIST) currently has 4 source-backed claim records and 3 official source attributions. Latest tracked changed date: May 16, 2026.

This tracker 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.

Claim/evidence diff preview

Diff-style preview built from current public claim/evidence records. Full old/new source diffs require paired historical snapshots.

Gwangju Institute of Science and Technology (GIST) current policy evidence

Inserted lines represent current public claim and evidence records in the source-backed dataset.

+8-0
11 # Gwangju Institute of Science and Technology (GIST) AI policy record
2+academic_integrity: GIST's research misconduct reporting page lists reportable targets including fabrication, falsification, plagiarism, improper authorship, duplicate publication, investigation obstruction, serious departures from accepted academic norms, and coercing or proposing misconduct.
3+Evidence (ko, 9d419160ee48): 연구부정행위 신고대상 ‘위조’, ‘변조’, ‘표절’, ‘부당한 논문저자 표시’, ‘부당한 중복게재’, ‘연구부정 행위에 대한 조사 방해 행위’, 각 학문 분야에서 통상적으로 용인되는 범위를 심각하게 벗어난 행위, 타인에게 부정행위를 행할 것을 제안·강요·협박하는 행위
4+privacy: For the NRF-scoped guidance relayed by GIST, the listed recommendations include not uploading evaluation materials to AI tools, indicating AI tool use, verifying reliability and validity, and caution about research-data leakage.
5+Evidence (ko, 2319b3383bad): ○ 권고 내용 - (비밀유지) AI 도구에 평가자료 업로드 금지 - (정보공개) AI 도구 사용 내역 표기 권장 ※ 사용 시 표기 방식(예시) 마련 - (검증책임) AI 도구 사용 시 자료의 신뢰성, 타당성 확보를 위한 검증 책임 명시 - (보안책임) AI 도구에 연구자료 업로드 시 정보 유출 주의, 유출 시 연구자 책임 명시
6+research: GIST's Research Office notice relays NRF guidance for NRF project applicants, performers, and evaluators to use generative AI tools ethically and responsibly in related work.
7+Evidence (ko, 2319b3383bad): 한국연구재단에서 재단 과제의 신청·수행 및 평가 업무에 참여하는 연구자가 생성형 인공지능(AI) 도구를 "윤리적이고 책임있게" 사용하도록 『생성형 인공지능(AI) 도구의 책임 있는 사용을 위한 권고사항』 (개정판)을 마련하여 붙임과 같이 배포하오니 관련 업무시 활용하여 주시기 바랍니다.
8+academic_integrity: GIST's undergraduate academic handbook lists academic and research-related disciplinary categories including exam-score manipulation or question leakage, cheating during exams, class disruption, academic warning, and research ethics violations.
9+Evidence (ko, afef7c5932e1): 2. 학업/연구 관련 가. 시험성적 조작 및 문제 유출 나. 시험 중 부정행위 다. 수업방해 라. 학사경고 마. 연구윤리 위반

Claim changes

4 claim records

academic_integrity

GIST's undergraduate academic handbook lists academic and research-related disciplinary categories including exam-score manipulation or question leakage, cheating during exams, class disruption, academic warning, and research ethics violations.

Review: Agent reviewedConfidence78%Evidence1Languagesko

academic_integrity

GIST's research misconduct reporting page lists reportable targets including fabrication, falsification, plagiarism, improper authorship, duplicate publication, investigation obstruction, serious departures from accepted academic norms, and coercing or proposing misconduct.

Review: Agent reviewedConfidence86%Evidence1Languagesko

privacy

For the NRF-scoped guidance relayed by GIST, the listed recommendations include not uploading evaluation materials to AI tools, indicating AI tool use, verifying reliability and validity, and caution about research-data leakage.

Review: Agent reviewedConfidence84%Evidence1Languagesko

research

GIST's Research Office notice relays NRF guidance for NRF project applicants, performers, and evaluators to use generative AI tools ethically and responsibly in related work.

Review: Agent reviewedConfidence82%Evidence1Languagesko

Source snapshots

3 source attributions