AI-ASSISTED REWRITING AND IDEA LAUNDERING IN ACADEMIA: A PROOF-OF-CONCEPT STUDY FROM THE PERSPECTIVE OF PUBLICATION CONTROLS

Öz

This study examines the extent to which the similarity reports and AI writing reports used in academic publishing can provide assurance against the risk of “idea laundering” that may be carried out through AI-assisted rewriting. Its central claim is that AI-assisted writing does not, in itself, constitute plagiarism; the ethical problem lies in rewriting another author’s academic idea, argument, or contribution without attribution and presenting it as an original contribution. To this end, the discussion/conclusion sections of twelve English-language social science articles in the fields of internal audit, corporate governance, and risk management were used. Three separate files were prepared for each source text: the original text taken from the source article, a naive LLM rewriting produced without any specific instruction, and a structural rewriting created to observe the risk of idea laundering under controlled conditions. This yielded thirty-six core evaluation files for the twelve source articles, together with five AI-assisted control texts grounded in the authors’ own original ideas. All files were uploaded to Turnitin in “No Repository” mode. The findings show that the original texts were recognized by Turnitin with an average similarity of 99.75%, that the average similarity score fell to 44.50% under the naive rewriting condition, and that it reached 19.08% under the structural rewriting condition. Two independent expert raters found that the main idea, argument flow, and concluding claim were largely preserved across all twelve cases. While the control texts returned a similarity score of 0%, their high AI writing scores support the position that an AI score cannot be interpreted as an indicator of plagiarism. The study argues that countering the risk of idea laundering requires a multi-layered publication governance approach grounded in semantic evaluation, source–argument traceability, editorial judgment, and an internal control perspective.

Anahtar Kelimeler

• Idea laundering
• Semantic plagiarism
• AI-assisted rewriting
• Academic publication governance.

YAPAY ZEKÂ DESTEKLİ YENİDEN YAZIM VE AKADEMİDE FİKİR AKLAMA: YAYIN KONTROLLERİ AÇISINDAN BİR KAVRAM KANITI ÇALIŞMASI

Öz

Çalışma, akademik yayın süreçlerinde kullanılan benzerlik raporları ve AI (Artificial Intelligence, Yapay Zekâ) yazım raporlarının, yapay zekâ destekli yeniden yazım yoluyla gerçekleştirilebilecek “fikir aklama” riskine karşı ne ölçüde güvence sağlayabildiğini incelemektedir. Çalışmanın temel iddiası, yapay zekâ destekli yazımın tek başına intihal olmadığı; etik sorunun, başkasına ait akademik fikir, argüman veya katkının kaynak gösterilmeksizin yeniden yazılarak özgün katkı gibi sunulması olduğudur. Bu amaçla çalışmada; iç denetim, kurumsal yönetişim, risk yönetimi alanlarına ilişkin yazılmış olan 12 adet İngilizce sosyal bilimler makalesinin tartışma/sonuç bölümleri kullanılmıştır. Her kaynak metin için üç ayrı dosya hazırlanmıştır: kaynak makaleden alınan orijinal metin, özel bir yönlendirme içermeyen naif LLM (Large Language Model-Büyük Dil Modeli) yeniden yazımı ve fikir aklama riskini kontrollü biçimde gözlemlemek amacıyla oluşturulan yapısal yeniden yazım metnidir. Bu şekilde 12 kaynak makale için toplam 36 ana değerlendirme dosyası elde edilmiş, ayrıca yazarların özgün fikirlerine dayanan AI destekli 5 kontrol metni hazırlanmıştır. Tüm dosyalar Turnitin’e depolama olmaksızın yüklenmiştir. Bulgular, orijinal metinlerin Turnitin tarafından ortalama %99,75 benzerlikle tanındığını; naif yeniden yazım koşulunda ortalama benzerlik skoru %44,50’ye düştüğünü ve yapısal yeniden yazım koşulunda ise ortalama benzerlik skorunun %19,08 olarak gerçekleştiğini göstermektedir. İki bağımsız uzman değerlendirici, 12 vakanın tamamında ana fikir, argüman akışı ve sonuç iddiasının büyük ölçüde korunduğunu belirlemiştir. Kontrol metinlerinde benzerlik skoru %0 iken AI yazım skorlarının yüksek çıkması, AI skorunun intihal göstergesi olarak yorumlanamayacağını desteklemektedir. Çalışma, fikir aklama riskine karşı semantik değerlendirme, kaynak-argüman izlenebilirliği, editöryal muhakeme ve iç kontrol perspektifine dayalı çok katmanlı yayın yönetişimi yaklaşımının gerekli olduğunu savunmaktadır.

Anahtar Kelimeler

• Fikir Aklama
• Semantik İntihal
• Yapay Zekâ Destekli Yeniden Yazım
• Akademik Yayın Yönetişimi.

Kaynakça

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