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Journals / Uluslararası Bilim Teknoloji ve Tasarım Dergisi / 2026 / Cilt: 7 Sayı: 2

From Digital to Intelligent Education: The AIPER Framework for AI-Integrated Programming Education

Dijitalden Akıllı Eğitime: Yapay Zeka Entegreli Programlama Eğitimi İçin AIPER Çerçevesi

Gamze Bilen(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ)0009-0006-3736-8177
Ahmet Cançelik(MEHMET AKIF ERSOY UNIVERSITY)0009-0000-3646-047X
Pages
184–210
Publication Date
2026-08-10
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We set out six testable propositions about how the components depend on one another, together with a table of the failure modes that should appear under partial adoption, and we draw out the implications for curriculum designers, assessment practitioners, and administrators.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Generative AI coding assistants like ChatGPT and GitHub Copilot have changed the conditions under which students learn to program.
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Key Findings & Data:This paper presents AIPER (Artificial Intelligence-Integrated Programming Education Redesign), a conceptual framework synthesised from 37 studies.
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Conclusion & Impact:We set out six testable propositions about how the components depend on one another, together with a table of the failure modes that should appear under partial adoption, and we draw out the implications for curriculum designers, assessment practitioners, and administrators.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Üretici yapay zeka · kişiselleştirilmiş öğrenme · yapay zeka destekli aktif öğrenme

Abstract

Generative AI coding assistants like ChatGPT and GitHub Copilot have changed the conditions under which students learn to program. Most institutions have responded by banning the tools or quietly tolerating them, and neither does much for learning. This paper presents AIPER (Artificial Intelligence-Integrated Programming Education Redesign), a conceptual framework synthesised from 37 studies. It organises the institutional response around five interdependent components: curriculum recalibration, AI-augmented learning practices, assessment redesign, academic integrity and ethical framing, and instructor role transformation. The components are not a menu to pick from; redesigning one while leaving the others alone tends to undo the gains. Running through all of them is a single premise, that AI should personalise and support learning rather than stand in for the thinking a course is meant to develop, which calls for genuine pedagogical redesign rather than a policy patch. We set out six testable propositions about how the components depend on one another, together with a table of the failure modes that should appear under partial adoption, and we draw out the implications for curriculum designers, assessment practitioners, and administrators.

Özet

ChatGPT ve GitHub Copilot gibi üretken yapay zekâ kodlama asistanları, programlama eğitiminin yürütüldüğü koşulları değiştirmiştir; yükseköğretim kurumları ise günümüzde müfredat, ölçme-değerlendirme ve eğitmen hazırlığı süreçlerini kapsayan tasarım sorunlarıyla karşı karşıyadır. Kurumların bugüne kadarki tepkileri, yasaklama ile pasif bir izin verme eğilimi arasında gidip gelmiştir ki bu iki yaklaşım da pedagojik bir duruş teşkil etmemektedir. Bu çalışma, 37 ampirik ve teorik kaynağın bütünleştirici bir incelemesi (integrative review) yoluyla geliştirilen "Yapay Zekâ Entegreli Programlama Eğitimi Yeniden Tasarımı" (AIPER) Çerçevesini sunmaktadır. Söz konusu çerçeve birbirine bağımlı beş bileşenden oluşmaktadır: müfredatın yeniden kalibre edilmesi, yapay zekâ destekli aktif öğrenme uygulamaları, ölçme-değerlendirmenin yeniden tasarımı, akademik dürüstlük ve etik çerçeveleme ve eğitmen rolünün dönüşümü. Bu bileşenlerin birbirine bağımlı olduğu iddiası, yanlışlanabilir altı önerme ve kısmi benimseme durumunda ortaya çıkacağı öngörülen başarısızlık senaryolarını (failure modes) içeren bir tablo aracılığıyla test edilebilir kılınmıştır. AIPER, yapay zekâ araçlarını bilişsel çabanın bir alternatifi olarak değil, kişiselleştirilmiş öğrenme için yapı iskeleleri (scaffolds) olarak ele almakta ve bu araçların benimsenmesini reaktif bir politika geliştirmek yerine, bilinçli bir pedagojik yeniden tasarım fırsatı olarak çerçevelemektedir. Sonuç bölümünde müfredat tasarımcıları, ölçme-değerlendirme uygulayıcıları ve kurumsal yöneticiler için doğuracağı çıkarımlar tartışılmaktadır.

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Cite This Article

Bilen, G., Cançelik, A. (2026). From Digital to Intelligent Education: The AIPER Framework for AI-Integrated Programming Education. Uluslararası Bilim Teknoloji ve Tasarım Dergisi, Cilt: 7 Sayı: 2, 184–210. https://ajindex.com/articles/1623183

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