International Literature of Muslim Understanding (ILMU) is committed to protecting originality, academic integrity, responsible authorship, and transparency in scholarly publishing.

Submitted manuscripts may therefore be evaluated through similarity screening, editorial assessment, and where appropriate, AI-generated content detection before or during the editorial process.

All manuscripts submitted to ILMU must represent original scholarly work. Authors must appropriately acknowledge all ideas, arguments, data, translations, quotations, interpretations, and textual materials obtained from other sources.

Plagiarism may involve direct copying, inappropriate paraphrasing, unattributed translation, excessive reproduction of previously published material, or presenting another person's intellectual contribution as one's own.

Manuscripts may be screened using Turnitin, iThenticate, or other recognized similarity-checking systems available to the journal.

A similarity percentage is an editorial screening indicator and is not automatically equivalent to plagiarism. Editors may examine individual matching sources, quotations, references, methodology sections, common academic terminology, and other relevant factors before making a decision.

The editorial team may return the manuscript to the author with a request to:

• rewrite excessively similar passages;
• improve paraphrasing;
• add missing citations;
• correct quotation practices;
• reduce unnecessary reproduction of previous publications;
• resubmit the manuscript for similarity rechecking.

Authors must properly disclose and cite their own previously published work. Substantial reuse of previous text, data, arguments, translations, or research findings without appropriate acknowledgment may be considered self-plagiarism or redundant publication.

Authors must not submit or publish substantially the same manuscript, dataset, analysis, or scholarly argument in more than one publication without appropriate disclosure and legitimate academic justification.

ILMU recognizes that Artificial Intelligence (AI) and generative AI tools may support certain aspects of scholarly writing and research. However, their use must remain transparent, responsible, academically appropriate, and subject to human verification.

AI detection tools are probabilistic technologies and may produce false positives or false negatives. Therefore, an AI Detection Score will not be treated as conclusive evidence of misconduct. Editorial decisions will consider the manuscript itself, author clarification, academic context, and other relevant evidence.

AI systems, chatbots, large language models, and generative AI tools cannot be listed as authors or co-authors because they cannot assume responsibility, provide consent, manage conflicts of interest, or be accountable for the integrity of published work.

If generative AI materially contributes to manuscript preparation, analysis, translation, organization, or other substantive activities, authors should disclose the use transparently.

Because ILMU publishes research involving the Qur'an, Hadith, tafsir, classical Islamic literature, manuscripts, and turath, authors must exercise particular caution when using AI tools in textual and historical research.

Any AI-generated information concerning Arabic texts, Qur'anic verses, Hadith attribution, classical scholars, manuscript metadata, translations, or historical claims must be independently verified against authoritative sources.

Authors must manually verify all bibliographic references suggested or generated by AI tools. Fictitious, inaccurate, unverifiable, or hallucinated references are unacceptable.

AI-generated or AI-modified images must not be used to fabricate, distort, or misrepresent research evidence, manuscript documentation, historical materials, inscriptions, archival records, fieldwork, or other scholarly evidence.

When AI tools contribute to data processing or analysis, authors must ensure methodological transparency, validate outputs independently, and retain full responsibility for the reliability and interpretation of the results.

Authors must not upload confidential research data, identifiable participant information, unpublished manuscripts, restricted archival materials, private correspondence, or sensitive documents to public AI platforms when doing so could compromise privacy, confidentiality, copyright, or research ethics.

Reviewers must protect manuscript confidentiality. Unpublished manuscripts, figures, data, or confidential review materials should not be uploaded to public generative AI systems where confidentiality or intellectual property may be compromised.

Automated technologies may assist editors with technical or administrative screening. However, decisions concerning originality, ethical compliance, peer review, revision, acceptance, or rejection remain under human editorial responsibility.

The editorial team may request authors to:

• clarify how AI tools were used;
• substantively rewrite relevant sections in the author's own academic voice;
• strengthen original analysis and scholarly argumentation;
• verify quotations, translations, data, citations, and references;
• submit a revised version for additional screening.

Regardless of the tools used during research or writing, authors remain fully accountable for the accuracy, originality, integrity, interpretation, citations, ethical compliance, and final content of the manuscript.

International Literature of Muslim Understanding (ILMU) supports responsible technological innovation while maintaining originality, transparency, scholarly judgment, and human accountability as essential principles of academic publishing.