Yükseköğretimde Kalite Güvencesi İçin Bir Model Önerisi: Açıklanabilir Kalite Güvencesi Modeli

Abstract

Bu çalışma, kalite güvence süreçlerinde yapay zekâ kullanımının yalnızca performans ve hız ekseninde değil; açıklanabilirlik, insan denetimi ve kurumsal meşruiyet ekseninde tasarlanması gerektiğini ileri sürmektedir. Yükseköğretimde kalite güvencesi, dijital dönüşümün etkisiyle göstergelere ve veri akışına daha fazla dayanan bir yönetişim alanına dönüşmüştür. Kurum iç değerlendirme raporları, performans göstergeleri, öğrenci geri bildirimleri, mezun izleme verileri ve karar panelleri artık yalnızca bilgi depolayan araçlar değil; kurumsal öncelikleri belirleyen ve kararların gerekçelendirme biçimini etkileyen mekanizmalardır. Bu dönüşüm verimlilik, karşılaştırılabilirlik ve erken uyarı kapasitesi üretirken, aynı zamanda karar süreçlerinin şeffaflığı ve hesap verebilirliği bakımından yeni bir sorun alanı doğurmaktadır. Yükseköğretimde yapay zekâ destekli kalite güvencesi uygulamalarının başarı ölçütü sadece daha hızlı raporlama ve daha geniş veri işleme kapasitesi olmamalıdır. Üretilen kararların kurumsal aktörler tarafından anlaşılabilir, gerekçelendirilebilir ve itiraz edilebilir olması gerekir. Bu nedenle çalışmada Açıklanabilir Kalite Güvencesi başlığı altında yedi bileşenli bir model önerilmektedir. Modelin çekirdeğini veri şeffaflığı, gösterge gerekçelendirmesi, algoritmik izlenebilirlik, insan denetimi, bağlamsallık, etik uyum ve itiraz edilebilirlik ilkeleri oluşturmaktadır. Çalışma, YÖKAK’ın güncel kalite güvencesi mimarisini ESG 2015, UNESCO’nun üretken yapay zekâ rehberliği, NIST’in açıklanabilir yapay zekâ yaklaşımı, ENQA’nın yapay zekâ ve dış kalite güvencesi metinleri ile Avrupa Birliği Yapay Zekâ Tüzüğü bağlamında değerlendirmektedir. Sonuç olarak yapay zekâ destekli kalite güvencesinin geleceğinin teknik otomasyonda değil; açıklanabilir, denetlenebilir ve insan muhakemesiyle dengelenmiş bir algoritmik yönetişim düzeninde aranması gerektiği savunulmaktadır.

Keywords

• Yükseköğretimde Kalite Güvencesi
• YOKAK
• Yapay Zekâ
• Açıklanabilir Kalite Güvencesi
• Algoritmik Yönetişim
• Meşruiyet
• Dijital Dönüşüm.

A Model Proposal For Quality Assurance in Higher Education: The Explainable Quality Assurance Model

Abstract

This study argues that the use of artificial intelligence in quality assurance processes should be designed not only along the axis of performance and speed, but also along the axes of explainability, human oversight, and institutional legitimacy. Under the impact of digital transformation, quality assurance in higher education has turned into a governance domain that relies increasingly on indicators and data flows. Institutional self-evaluation reports, performance indicators, student feedback, graduate tracking data, and decision dashboards are no longer merely tools for storing information; they are mechanisms that shape institutional priorities and influence the way decisions are justified. While this transformation generates efficiency, comparability, and early-warning capacity, it also creates a new problem area in terms of the transparency and accountability of decision processes. The success criterion for AI-supported quality assurance practices in higher education should not be limited to faster reporting and broader data-processing capacity. The decisions produced must be understandable, justifiable, and contestable for institutional actors. Accordingly, this study proposes a seven-component model under the heading of Explainable Quality Assurance. The core of the model consists of the principles of data transparency, indicator justification, algorithmic traceability, human oversight, contextuality, ethical compliance, and contestability. The study evaluates the current quality assurance architecture of the Turkish Higher Education Quality Council (THEQC/YÖKAK) in relation to ESG 2015, UNESCO’s guidance on generative AI, NIST’s approach to explainable AI, ENQA’s texts on AI and external quality assurance, and the European Union Artificial Intelligence Act. As a result, it argues that the future of AI-supported quality assurance should be sought not in technical automation, but in an explainable, auditable, and humanly balanced regime of algorithmic governance.

Keywords

• Quality Assurance in Higher Education
• YOKAK
• Artificial Intelligence
• Explainable Quality Assurance
• Algorithmic Governance
• ; Legitimacy
• Digital Transformation

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