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<p>This article proposes a novel conceptual framework that integrates Artificial Intelligence (AI) with Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML) to enhance Open Distance eLearning (ODeL) systems. By bridging the gap between traditional cognitive theories and cutting-edge AI technologies, this framework supports adaptive cognitive load management, AI-mediated schema creation, and human-AI collaborative learning. A refined critique of Twabu's (2023) PhD study, based on conceptual analysis of its theoretical scope and limitations, highlights the absence of AI integration. This article presents a unique contribution by synthesizing established cognitive theories with the dynamic potential of AI, a perspective underrepresented in current literature. Practical examples, policy recommendations, and ethical considerations are consolidated to offer a comprehensive path forward. The study proposes an expanded framework incorporating <i>AI-enhanced cognitive load management, AI-mediated schema creation, and human-AI collaborative learning</i>. The Literature review and theoretical framework emphasises AI's role in simplifying cognitive processes by dynamically adjusting content presentation based on learner needs and performance. This includes AI-mediated adjustments to manage cognitive load and enhance schema development through personalised feedback and tailored content delivery. Such integration promises to create a more effective learning environment by aligning multimedia content with individual learner profiles, thus addressing the challenge of cognitive overload and improving knowledge retention and understanding.</p>

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Enhancing the cognitive load theory and multimedia learning framework with AI insight

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月7日

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  • Khanyisile Twabu

摘要

This article proposes a novel conceptual framework that integrates Artificial Intelligence (AI) with Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML) to enhance Open Distance eLearning (ODeL) systems. By bridging the gap between traditional cognitive theories and cutting-edge AI technologies, this framework supports adaptive cognitive load management, AI-mediated schema creation, and human-AI collaborative learning. A refined critique of Twabu's (2023) PhD study, based on conceptual analysis of its theoretical scope and limitations, highlights the absence of AI integration. This article presents a unique contribution by synthesizing established cognitive theories with the dynamic potential of AI, a perspective underrepresented in current literature. Practical examples, policy recommendations, and ethical considerations are consolidated to offer a comprehensive path forward. The study proposes an expanded framework incorporating AI-enhanced cognitive load management, AI-mediated schema creation, and human-AI collaborative learning. The Literature review and theoretical framework emphasises AI's role in simplifying cognitive processes by dynamically adjusting content presentation based on learner needs and performance. This includes AI-mediated adjustments to manage cognitive load and enhance schema development through personalised feedback and tailored content delivery. Such integration promises to create a more effective learning environment by aligning multimedia content with individual learner profiles, thus addressing the challenge of cognitive overload and improving knowledge retention and understanding.