Cognitive Load Optimization In AI-Assisted Educational Technologies: A Comprehensive And Systematic Literature Review

Öz

This systematic literature review comprehensively synthesizes the existing knowledge base in the field of cognitive load optimization in artificial intelligence-assisted educational technologies between 2020 and 2025. Conducted in accordance with the PRISMA protocol, this study analyzes 48 peer-reviewed academic articles and reveals four main themes: the evolution of cognitive load measurement methods, the effects of different artificial intelligence modalities on cognitive load, evidence-based optimization strategies, and specific application areas and theoretical developments. The findings show that traditional subjective measurements such as NASA-TLX are gradually being replaced by objective methods based on EEG, eye tracking, and multimodal sensor data. While Intelligent Tutoring Systems and adaptive platforms are successful in proactively managing cognitive load, generative artificial intelligence tools such as ChatGPT create paradoxical effects such as cognitive offloading and the atrophy of critical thinking skills. Personalization according to expertise level, real-time adaptive feedback, and multimodal measurement emerge as evidence-based design principles. In this context, the ability of AI systems to monitor cognitive load in real time and provide adaptive task difficulty makes the learning experience more efficient. However, there is a need for larger-sample studies comparing the effectiveness of these approaches across different learner profiles and disciplines. This study emphasizes that artificial intelligence offers revolutionary potential for cognitive load management in education, but this potential must be supported by careful pedagogical approaches and ethical frameworks. 

Anahtar Kelimeler

• Adaptive Learning
• Artificial Intelligence
• Cognitive Load Theory
• Educational Technologies
• Systematic Literature Review

Proje Numarası

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Yapay Zeka Destekli Eğitim Teknolojilerinde Bilinçsel Yük Optimizasyonu: Kapsamlı ve Sistematik Bir Literatür Taraması

Öz

Bu sistematik literatür taraması, 2020-2025 yılları arasında yapay zeka destekli eğitim teknolojilerinde bilişsel yük optimizasyonu alanındaki mevcut bilgi birikimini kapsamlı bir şekilde sentezlemektedir. PRISMA protokolüne uygun olarak yürütülen bu çalışma, 48 hakemli akademik makaleyi analiz ederek, bilişsel yük ölçüm yöntemlerinin evrimi, farklı yapay zeka modalitelerinin bilişsel yük üzerindeki etkileri, kanıta dayalı optimizasyon stratejileri ve özel uygulama alanları ile teorik gelişmeler olmak üzere dört ana temayı ortaya koymaktadır. Bulgular, NASA-TLX gibi geleneksel öznel ölçümlerin yerini giderek EEG, göz izleme ve multimodal sensör verilerine dayalı nesnel yöntemlerin aldığını göstermektedir. Akıllı Öğretim Sistemleri ve adaptif platformlar, bilişsel yükü proaktif olarak yönetmede başarılı olurken, ChatGPT gibi üretken yapay zeka araçları bilişsel boşaltma ve eleştirel düşünme becerilerinin körelmesi gibi paradoksal etkiler yaratmaktadır. Uzmanlık düzeyine göre kişiselleştirme, gerçek zamanlı adaptif geri bildirim ve multimodal ölçüm, kanıta dayalı tasarım prensipleri olarak öne çıkmaktadır. Bu kapsamda, yapay zekâ sistemlerinin bilişsel yükü gerçek zamanlı olarak izlemesi ve uyarlanabilir görev zorluk düzeyi sunması, öğrenen deneyimini daha verimli hale getirmektedir. Bununla birlikte, farklı öğrenen profilleri ve disiplinlerde bu yaklaşımların etkinliğini karşılaştıran daha geniş örneklemeli çalışmalara ihtiyaç duyulduğu görülmektedir. Bu çalışma, yapay zekanın eğitimde bilişsel yük yönetimi için devrim niteliğinde bir potansiyel sunduğunu, ancak bu potansiyelin dikkatli bir pedagojik yaklaşım ve etik çerçevelerle desteklenmesi gerektiğini vurgulamaktadır.

Anahtar Kelimeler

• Adaptif Öğrenme
• Bilişsel Yük Teorisi
• Eğitim Teknolojileri
• Sistematik Literatür Taraması
• Yapay Zeka

Proje Numarası

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Kaynakça

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