Editorial Guidelines

HEISJ is dedicated to ensuring the highest levels of technical and academic integrity in the exploration of AI and digital systems in higher education.

1. Submission and Initial Evaluation

• Scope: Research on Machine Learning in education, Learning Management Systems, and AI policy.

• Plagiarism: A strict 15% similarity threshold via Turnitin is mandatory.

• Formatting: Submissions must follow technical reporting standards, including algorithmic documentation and data availability statements.

• Licensing: Published under CC BY-NC 4.0 to facilitate knowledge sharing.

2. Peer Review Policy

• Process: Double-blind review by at least two experts in educational technology or computer science.

• Conflicts: Reviewers must disclose any affiliation with commercial software or AI developers mentioned.

3. Editorial Decision-Making

• Revision: Technical revisions may require a second round of review to verify code or statistical accuracy.

• Appeals: Written appeals are reviewed by the Editor-in-Chief and a relevant technical board member.

4. Ethical Standards

• Data Ethics: Authors must comply with data privacy regulations regarding the use of student information in intelligent systems.

• Authorship: All contributors must have provided substantial intellectual input to the study or system design.

5. Post-Publication Policy

• Transparency: Retractions are mandatory for cases of data fabrication or significant technical error.

• Preservation: Digital archiving ensures long-term citation stability for all published letters.