Student-Developed AI Academic Coordination Project

Supporting Responsible AI Research Discussion and Academic Writing Practice

G-AISC is independently maintained by individual student participants currently associated with the Data Science and Big Data Technology program at Yibin University. The project supports AI research discussion, academic writing practice, study coordination, and dataset governance learning. Any university, campus, program, or advisor reference is background context only and does not imply institutional endorsement.

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Transparency Notice: This is an independent student-developed project website. It is not affiliated with, endorsed by, operated by, funded by, or authorized by Yibin University or any academic unit, does not represent official positions, and does not issue formal academic, administrative, admission, employment, or publication decisions. Any school, faculty, campus, program, or advisor reference is provided only as background context and does not imply endorsement.

About G-AISC

G-AISC is being developed as a student academic coordination project for AI research discussion, academic writing practice, reading groups, and research practice documentation.

The project focuses on responsible AI systems, model evaluation, data governance, reproducibility, and research integrity. Any external collaboration or public affiliation listing is handled only after written confirmation from the relevant person or organization.

The project is non-commercial and does not operate as a university office, institute, center, committee, admissions channel, publication service, employment service, or paid academic service.

Project Scope

This website is maintained independently by individual student participants currently associated with the Data Science and Big Data Technology program at Yibin University. Project coordinator: Hejun. Advisor, guidance, volunteer, or partner information is published only after separate written public-listing consent and identity verification. The name G-AISC is used only as a project label for this website and related student notes.

Yibin University official website reference

Project Focus Areas

AI Safety Evaluation

Benchmarking, controlled safety testing, multilingual safety review, and reproducible model assessment.

Dataset Governance

Data provenance, consent review, dataset documentation, privacy standards, and controlled access workflows.

Academic Writing Discussion

Draft organization discussion, source citation checks, study documentation, and academic writing practice.

Service Boundary

G-AISC does not provide ghostwriting, paid editing, paid authorship, publication guarantees, admission guarantees, employment guarantees, paid recommendation letters, or official peer review services. Feedback is educational and non-binding.

Inquiry & Feedback Process

Step 1

Inquiry Submission

Students or collaborators may submit a project summary, research area, contact details, and basic disclosure statements for initial discussion. This is not a formal application or enrollment process for any university program.

Step 2

Scope Screening

The student project team checks topic scope, basic originality concerns, ethics issues, formatting needs, and potential conflicts before arranging any non-binding feedback support.

Step 3

Feedback Coordination

Confirmed discussion or feedback volunteers may comment on methodology, reproducibility, contribution, and research limitations after consent and conflict checks. This is not journal or conference peer review.

Step 4

Feedback & Next Steps

Participants receive educational feedback, revision suggestions, and possible next-step references where appropriate. No publication, admission, funding, or acceptance outcome is promised.

Project Ethics & Good Practice

Research Integrity

Shared drafts may require authorship confirmation, citation checks, conflict disclosures, and plagiarism screening.

Guidance and Feedback Consent

Any guidance participant or feedback volunteer must consent to participation and disclose conflicts before commenting on drafts.

Data Responsibility

Dataset-related projects are checked for privacy, consent, provenance, licensing, and potential misuse.

Privacy and Data Handling

The contact form opens an email draft only. This website does not store messages, passwords, identity documents, payment information, or unpublished research files.

Privacy Notice

Information Used

The inquiry form may include name, email address, role, and message content. It opens an email draft from the visitor's own email client and does not create a website database submission.

Purpose and Retention

Information is used only to respond to inquiries, coordinate student academic discussion, and handle consent or affiliation checks. Email records are normally retained for up to 12 months, unless longer retention is needed for consent records, dispute prevention, or legal compliance.

Technical Access Data

This page is designed to load its frontend code from this same website only. Hosting providers may keep standard technical access logs such as IP address, browser type, and request time.

Withdrawal and Deletion

A sender may request correction, withdrawal, or deletion of their inquiry record by emailing the project coordinator at postmaster@g-aisc.com from the same contact address where possible.

Guidance & Contributor Listings

Advisors, feedback volunteers, editors, and contributors will be listed only after written approval and identity verification. This protects scholars from unauthorized endorsement claims.

Verification Standard

Public profiles require confirmed name, role, affiliation if applicable, research area, and consent to publish.

Conflict Policy

Contributors and feedback volunteers should disclose academic, financial, supervisory, or institutional conflicts.

Research Reference Areas

These areas are used for student study, topic classification, and feedback coordination. They are not official degree programs, funding programs, or institutional review categories.

Model Evaluation

Reference area: benchmarking and reproducibility

Evaluation protocols, benchmark design, failure analysis, reproducible experiments, and reporting standards for AI systems.

AI Safety

Reference area: responsible AI development

Risk assessment, controlled safety testing, alignment discussions, multilingual safety behavior, and responsible deployment practices.

Human-Centered AI

Reference area: ethics and societal impact

Research ethics, privacy, accessibility, data consent, human oversight, and social impact evaluation for AI applications.

News & Updates

2026.04 - G-AISC opens preliminary inquiry contact for student researchers and feedback volunteers.

2026.03 - Draft research ethics and disclosure checklist prepared for community feedback.

2025.12 - Student project focus areas drafted for AI safety evaluation, dataset governance, and reproducibility.

Contact

For student research discussion, feedback volunteer interest, collaboration conversation, or general coordination requests, please contact the student project coordinator. Do not send passwords, payment information, identity documents, or confidential research files through this form.

Project Coordinator: Hejun
Academic Guidance: Advisor identity and profile details are not published on this website without separate written public-listing consent and identity verification. Any guidance or feedback participation is educational and does not imply endorsement by the advisor, university, or academic unit.
Email: postmaster@g-aisc.com
General Location: Lingang Campus, Yibin University, Yibin, Sichuan, China
University Website: https://www.yibinu.edu.cn/
Project Relationship: G-AISC is an independently maintained academic coordination project by individual student participants currently associated with the Data Science and Big Data Technology program at Yibin University. It is not an official university website, administrative office, faculty project, or university-operated service.
Affiliation Verification: Advisor, feedback volunteer, and partner affiliations are verified before public listing.