Şeffaflıktan Güvene: Eğitim Sistemlerinde Açıklanabilir Yapay Zeka Üzerine Bir Literatür İncelemesi

Year 2025, Volume: 21 Issue: 3, 999 - 1018, 22.12.2025

Hasan Tınmaz

https://doi.org/10.17860/mersinefd.1739347

https://izlik.org/JA29ZM63LH

Abstract

Yapay zekâ (YZ) sistemleri eğitim deneyimlerini giderek daha fazla şekillendirdikçe, algoritmik karar alma süreçlerinde şeffaflık ve güvene olan talep, Açıklanabilir Yapay Zeka'ya (YZ) olan ilginin artmasına yol açmıştır. Bu literatür incelemesi, YZ'nin farklı ülkelerdeki eğitim ortamlarında nasıl uygulandığını inceleyerek hem pedagojik vaatlerini hem de etik gerilimlerini vurgulamaktadır. Gerçek dünya uygulamalarından ve öğrenci görüşlerinden yola çıkan çalışma, açıklanabilirliğin geri bildirim sistemlerini nasıl geliştirdiğini, öğrenci özerkliğini nasıl desteklediğini ve öğretimsel uyumu nasıl teşvik ettiğini araştırmaktadır. Bununla birlikte, bilişsel aşırı bağımlılık, eğitimci yetersizliği ve adalet yanılsaması gibi kalıcı zorlukları da belirlemektedir. Küresel vaka çalışmalarının bir sentezi aracılığıyla bu inceleme, eğitimde YZ için tasarım hususları, politika çerçeveleri ve kullanıcı merkezli uygulamalar hakkında kapsamlı bir analiz sunmaktadır. Çalışma, etik dağıtım için ayrıntılı bir kontrol listesi ve Açıklanabilir Yapay Zeka'nın eşitlikçi, insan merkezli öğrenme ortamlarına anlamlı bir şekilde katkıda bulunmasını sağlamayı amaçlayan bir dizi ileriye dönük öneriyle sonuçlanmaktadır.

Keywords

açıklanabilir yapay zekâ , XAI , etik yapay zekâ , eğitim , öğrenci merkezli tasarım

From Transparency to Trust: A Literature Review on Explainable AI in Educational Systems

https://izlik.org/JA29ZM63LH

Abstract

As artificial intelligence (AI) systems increasingly shape educational experiences, the demand for transparency and trust in algorithmic decision-making has led to a growing interest in Explainable AI (XAI). This literature review examines how XAI is being implemented in educational contexts across different countries, highlighting both its pedagogical promises and ethical tensions. Drawing from real-world applications and student reflections, the study explores how explainability enhances feedback systems, supports learner autonomy, and fosters instructional alignment. However, it also identifies persistent challenges, including cognitive over-reliance, educator deskilling, and the illusion of fairness. Through a synthesis of global case studies, this review offers a comprehensive analysis of design considerations, policy frameworks, and user-centered practices for XAI in education. The study concludes with a detailed checklist for ethical deployment and a set of forward-looking recommendations aimed at ensuring that XAI contributes meaningfully to equitable, human-centered learning environments.

Keywords

explainable artificial intelligence , XAI , ethical AI , education , student-centered design

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