Reviewer Management

Reviewer Management Policy At HEISJ, our reviewer management system is designed to bridge the gap between technical innovation and pedagogical application through expert, ethical evaluation.

1. Reviewer Recruitment and Selection

• Expertise: Selected from specialists in AI, Machine Learning, and Educational Technology with a strong record of technical publication.

• Diversity: We engage a global pool of computer scientists and educators to ensure diverse views on digital transformation.

2. Reviewer Assignment Process

• Double-Blind Review: Ensures that technical assessments of algorithms and systems remain objective and unbiased.

• Workload Management: Assignments are balanced to prevent fatigue among experts in high-demand tech fields.

3. Reviewer Expectations and Guidelines

• Criteria: Focus on algorithmic integrity, data privacy standards, and the relevance of intelligent systems to higher education.

• Ethics: Reviewers must report any suspected data manipulation or misuse of automated research tools.

4. Communication and Support

• Recognition: Top-performing reviewers are invited to join the Editorial Board and receive formal certificates of recognition.

• Support: The office provides technical assistance for evaluating code or complex datasets within the review portal.