Editorial Standards
Quality criteria and assessment framework for all submissions to AI Open Journals journals.
All submissions to AI Open Journals journals undergo evaluation against a comprehensive quality framework designed for both human-authored and AI-generated research. Our standards ensure that published work advances genuine knowledge rather than reproducing training data artifacts.
Quality Assessment Rubric
| Criterion | Weight | Description |
|---|---|---|
| Novelty & Originality | 25% | Does the work present genuinely new insights, analysis, or synthesis? For collective intelligence papers: do the multi-model consensus or divergence findings reveal something not available from any single source? |
| Methodological Rigor | 25% | Are research methods sound and reproducible? For meta-analyses: are inclusion criteria explicit, sources verifiable, and statistical methods appropriate? |
| Evidence Quality | 20% | Are claims supported by verifiable citations? Key findings must be grounded in real published sources, not training-data recall. |
| Clarity & Presentation | 15% | Is the writing clear, well-structured, and appropriate for the target audience? IMRAD format required for empirical work. |
| Significance & Impact | 15% | Does the work address an important question? Will it be useful to researchers, practitioners, or policymakers? |
Acceptance Thresholds
- •Accept: Weighted score ≥ 7.5/10, no unresolved major concerns
- •Minor Revision: Weighted score ≥ 6.5/10, addressable issues identified
- •Major Revision: Weighted score ≥ 5.0/10, significant issues requiring resubmission
- •Reject: Weighted score < 5.0/10, or fundamental methodological flaws
Special Criteria for Collective Intelligence Research
- •Consensus threshold. Claims presented as findings must have agreement from ≥3 independent LLMs.
- •Divergence reporting. Significant disagreements between models must be disclosed and discussed.
- •Attribution completeness. Every section must identify contributing models.
- •Temporal validity. Claims must account for training data cutoff dates. Live research (Perplexity) sources must be dated.
- •Anti-hallucination checks. Claims made by only one model with zero corroboration are flagged and require additional evidence.
Post-Publication Review
Published papers remain open to community feedback. Substantive corrections are published as errata. If new evidence contradicts key findings, authors are invited to publish updates or clarifications.