Postmodern epistemik galat paradoksu: Üretken yapay zekâ kullanımı için bir etiketleme çerçevesi

Öz

Üretken Yapay Zekâ (ÜYZ) kullanımının artmasıyla birlikte şeffaflık, doğrulanabilirlik ve akademik dürüstlük sorunları çevrim içi ortamlarda daha görünür hâle gelmiştir. ÜYZ araçları kişiselleştirme, öğrenci desteğini ölçekleme ve öğrenme analitiği açısından önemli fırsatlar sunarken, kaynağı belirsiz sentetik içeriklerin yayılımını hızlandırarak bilginin güvenilirliğini de zayıflatabilmektedir. Bu durum, bilginin doğruluk iddiasıyla dolaşıma girip zamanla hatalı biçimde yeniden kurulmasını ifade eden “epistemik galat anlatıları” kavramı çerçevesinde ele alınmaktadır. Çalışma, ÜYZ kullanımının etik ve sorumlu biçimde raporlanmasına yönelik iki katmanlı bir şeffaflık çerçevesi önermektedir. Bu kapsamda, katkı türünü nitel olarak sınıflandıran aiTARAS yaklaşımı ile katkı yoğunluğunu 0–6 arasında sayısallaştıran aiLE ölçeği birlikte sunulmaktadır. Sonuç olarak çalışma, çevrim içi ortamlarda ÜYZ kullanımını görünür, karşılaştırılabilir ve denetlenebilir kılmaya yönelik ölçeklenebilir bir kavramsal katkı sunmaktadır.

Anahtar Kelimeler

• üretken yapay zekâ
• akademik dürüstlük
• yapay zekâ etiketi

The postmodern epistemic fallacy paradox: A framework for labelling generative artificial intelligence use

Öz

As the use of Generative Artificial Intelligence (GenAI) increases, concerns related to transparency, verifiability, and academic integrity have become more visible in online environments. While GenAI tools offer important opportunities for personalisation, scaling student support, and learning analytics, they may also weaken the reliability of knowledge by accelerating the spread of synthetic content with uncertain origins. This issue is discussed through the concept of “epistemic fallacy narratives,” which refers to the circulation of knowledge under claims of accuracy and its gradual reconstruction in misleading ways over time. The study proposes a two-layered transparency framework for the ethical and responsible reporting of GenAI use. In this framework, the aiTARAS approach, which qualitatively classifies the type of contribution, is presented together with the aiLE scale, which quantifies contribution intensity from 0 to 6. Overall, the study offers a scalable conceptual contribution to making GenAI use in online environments visible, comparable, and auditable.

Anahtar Kelimeler

• generative artificial intelligence
• academic integrity
• AI label

Kaynakça

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