Navigating the Regulatory Labyrinth: Challenges of Artificial Intelligence for Higher Education Quality Assurance in China

Abstract

China's ambitious integration of artificial intelligence (AI) into higher education represents a strategic imperative aligned with national development goals, yet it simultaneously exposes profound and evolving tensions between technological innovation, regulatory compliance, and educational quality assurance (QA). This article examines the multi-layered and increasingly complex regulatory challenges confronting Chinese universities as they deploy AI systems within an expanding governance framework. Drawing on analysis of national policies, institutional practices, and emerging regulatory instruments—including the Interim Measures for Generative AI Services (2023), algorithm registration requirements, and data security legislation—we identify six critical challenge domains: (1) regulatory fragmentation across cybersecurity, data protection, and education-specific mandates; (2) tensions between algorithmic transparency requirements and proprietary innovation; (3) inadequacy of existing QA metrics for AI-enhanced pedagogy; (4) academic integrity crises precipitated by generative AI tools; (5) institutional capacity disparities in regulatory compliance; and (6) ethical governance gaps in rapidly evolving AI applications. We argue that China’s command-and-control regulatory model, while effective in scaling AI deployment, inherently conflicts with the iterative and experimental nature of educational AI innovation. Rigid and often prescriptive compliance requirements may significantly limit institutional flexibility and discourage meaningful pedagogical experimentation and innovation. Therefore, we propose adaptive, risk-based regulatory frameworks that enable controlled and context-sensitive innovation while ensuring accountability, educational quality, and academic integrity.

Keywords

• artificial intelligence
• quality assurance
• higher education regulation
• China
• generative AI
• algorithmic governance
• academic integrity
• Double First-Class Initiative

DÜZENLEYİCİ LABİRENTTE YOL ALMAK: ÇİN’DE YÜKSEKÖĞRETİMDE KALİTE GÜVENCESİ AÇISINDAN YAPAY ZEKÂNIN YARATTIĞI ZORLUKLAR

Abstract

Çin’in yapay zekâyı (YZ) yükseköğretime iddialı biçimde entegre etmesi, ulusal kalkınma hedefleriyle uyumlu stratejik bir zorunluluğu temsil etmekte; ancak aynı zamanda teknolojik yenilik, düzenleyici uyum ve eğitimde kalite güvencesi (KG) arasında derin ve giderek artan gerilimleri de açığa çıkarmaktadır. Bu makale, genişleyen bir yönetişim çerçevesi içinde YZ sistemlerini uygulamaya koyan Çin üniversitelerinin karşı karşıya kaldığı çok katmanlı ve giderek karmaşıklaşan düzenleyici zorlukları incelemektedir. Ulusal politikalar, kurumsal uygulamalar ve ortaya çıkan düzenleyici araçların—2023 tarihli Üretken Yapay Zekâ Hizmetlerine İlişkin Geçici Önlemler, algoritma kayıt gereklilikleri ve veri güvenliği mevzuatı dâhil—analizine dayanarak altı kritik zorluk alanı tespit edilmektedir: (1) siber güvenlik, veri koruma ve eğitime özgü düzenlemeler arasında düzenleyici parçalanma; (2) algoritmik şeffaflık gereklilikleri ile mülkiyet temelli yenilik arasındaki gerilimler; (3) YZ destekli pedagojiyi değerlendirmede mevcut kalite güvencesi ölçütlerinin yetersizliği; (4) üretken YZ araçlarının tetiklediği akademik dürüstlük krizleri; (5) düzenleyici uyum kapasitesinde kurumlar arası farklılıklar; ve (6) hızla gelişen YZ uygulamalarında etik yönetişim boşlukları. Bu çalışma, Çin’in komuta ve kontrol temelli düzenleyici modelinin, YZ uygulamalarını ölçeklendirmede etkili olmakla birlikte, eğitimde YZ yeniliğinin doğasında bulunan yinelemeli ve deneysel süreçlerle yapısal olarak çeliştiğini ileri sürmektedir. Katı ve çoğu zaman kuralcı uyum gereklilikleri, kurumsal esnekliği önemli ölçüde sınırlayabilir ve anlamlı pedagojik deneyim ile yeniliği caydırabilir. Bu nedenle, hesap verebilirliği, eğitim kalitesini ve akademik dürüstlüğü güvence altına alırken kontrollü ve bağlama duyarlı yeniliğe imkân tanıyan uyarlanabilir ve risk temelli düzenleyici çerçeveler önerilmektedir.

Keywords

• Yapay zekâ
• kalite güvencesi
• yükseköğretim düzenlemesi
• Çin
• üretken yapay zekâ
• algoritmik yönetişim
• akademik dürüstlük
• Çift Birinci Sınıf Girişimi

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