We strictly monitor the use of generative AI in manuscript preparation; while we support the ethical use of AI for language refinement, unauthorized or excessive reliance on AI to generate original content is prohibited and subject to detection scrutiny.
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The International Review of Multidisciplinary Research (IRMR) acknowledges the transformative potential of artificial intelligence (AI) in scholarly workflows. However, to safeguard the intellectual provenance and rigor of the research we publish, the IRMR maintains a strict policy of transparency regarding the utilization of large language models (LLMs) and generative AI. We operate under the premise that scholarly contribution is an inherently human endeavor, and while AI may serve as a subordinate instrument, it cannot substitute for original authorial cognition or research design.
1. Ethical Framework for AI Utilization
Authors are permitted to utilize AI-assisted technologies (e.g., ChatGPT, Claude, Grammarly AI) strictly for supportive functions, such as language refinement, stylistic polishing, or statistical data analysis.
Prohibitions: The utilization of AI to synthesize core research narratives, develop conceptual frameworks, or generate primary data points is strictly prohibited. AI tools may not be listed as co-authors under any circumstances, as they lack the legal capacity to assume accountability for the integrity of the research.
Mandatory Disclosure: Any usage of AI must be transparently documented within the "Materials and Methods" section or a dedicated "AI Disclosure Statement," outlining the specific tool employed and the scope of its contribution.
2. The Multi-Tiered Detection Protocol
To ensure compliance during Phase 1 (Screening), all submissions are subjected to an advanced AI-detection architecture:
Quantitative Heuristics: We utilize high-fidelity detection engines, to measure the probability that text has been generated by non-human systems.
Qualitative Analysis: Editorial teams assess submissions for "AI-generated textures," which include:
Linguistic Uniformity: An over-reliance on repetitive phrasing or unnaturally generic, smooth sentence structures.
Epistemological Fragility: The presence of "hallucinated" references, phantom DOIs, or fabricated data patterns that characterize synthetic generation.
Tonal Inconsistency: Disjunctions between highly sophisticated, jargon-heavy sections and simplistic analytical segments.
3. Thresholds and Administrative Adjudication
The IRMR adheres to the following thresholds to manage AI-mediated content risk:
4. Critical Red Flags and Rejection Criteria
The editorial team retains the absolute authority to reject manuscripts that exhibit evidence of synthetic content manipulation, including:
Fabricated Bibliographies: Inclusion of references or persistent identifiers that do not exist or are contextually irrelevant.
Synthetic Content Saturation: Where content reflects the characteristic "texture" of LLMs, suggesting that core argumentation was machine-derived rather than human-conceived.
Undisclosed AI Reliance: The concealment of substantive AI assistance that accounts for >30% of the manuscript's narrative structure.
Manipulated Media: AI-generated images, figures, or visual data utilized without explicit methodological disclosure or attribution.
5. Appellate Process and Remediation
Submissions that fail the automated screening phase are not immediately categorized as "dishonest" but are subject to a "Refutation of Authenticity" process.
Remediation: Authors may appeal a rejection by providing raw data logs, version history (track-changes logs), and detailed process documentation that demonstrates human-led manuscript development.
Integrity Enforcement: The IRMR prioritizes human intellect. Failure to provide satisfactory evidence during the appellate process will result in the sustained rejection of the manuscript and may result in permanent exclusion from the journal’s submission registry.
Effective June 1, 2026, these guidelines ensure that the IRMR remains a venue for human-led, authentic scholarly inquiry, providing a clear path for authors to integrate supportive technologies with academic integrity.
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