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<p>Intelligent tutorial systems (ITS) lack real-time adaptive learning features to provide better learning experiences to the learners. To address this problem, a novel flexible learning system (FLS) is proposed which supports real-time adaptive learning in ITS using a multi-access edge computing (MEC) approach. The adaptive features create personalized learning styles and content tailored to each student’s context, facilitating one-to-one learning. The MEC environment allows the FLS to process tasks for content generation with real-time responses while optimizing network bandwidth usage. The proposed FLS reduced task processing time by 94%, enabling real-time responses that facilitate flexible learning experiences.</p>

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A Multi-Access Edge Computing Approach to Intelligent Tutoring Systems for Real-Time Adaptive Learning

  • 期刊论文

  • 发表日期：

    2025年2月27日

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  • Ramesh Singh,
  • Chenlep Yakha Konyak,
  • Akangjungshi Longkumer

摘要

Intelligent tutorial systems (ITS) lack real-time adaptive learning features to provide better learning experiences to the learners. To address this problem, a novel flexible learning system (FLS) is proposed which supports real-time adaptive learning in ITS using a multi-access edge computing (MEC) approach. The adaptive features create personalized learning styles and content tailored to each student’s context, facilitating one-to-one learning. The MEC environment allows the FLS to process tasks for content generation with real-time responses while optimizing network bandwidth usage. The proposed FLS reduced task processing time by 94%, enabling real-time responses that facilitate flexible learning experiences.