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The rapid advancement of technology has transformed education, with Intelligent Tutoring Systems (ITS) emerging as a leading innovation in personalized learning. However, the recent developments in ITS highlight the need for real-time responses to enable seamless interactions and feedback between learners and educators for enhanced learning experiences. To address this, we propose a real-time recommendation system within ITS offering adaptive and personalized learning through multi-access edge computing (MEC) for low-latency and localized processing. Experiments demonstrate the successful implementation of this system, which dynamically suggests personalized notes based on student performance, enhancing learning focus and outcomes.

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Recommendation System to Assist Learner: Intelligent Tutoring System

  • 会议论文

  • 发表日期：

    2025年

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  • Ramesh Singh,
  • Chenlep Yakha Konyak,
  • Akangjungshi Longkumer,
  • Anand Kumar,
  • Akash Sarkar,
  • Ashim Paul,
  • Gaurab Thapa

摘要

The rapid advancement of technology has transformed education, with Intelligent Tutoring Systems (ITS) emerging as a leading innovation in personalized learning. However, the recent developments in ITS highlight the need for real-time responses to enable seamless interactions and feedback between learners and educators for enhanced learning experiences. To address this, we propose a real-time recommendation system within ITS offering adaptive and personalized learning through multi-access edge computing (MEC) for low-latency and localized processing. Experiments demonstrate the successful implementation of this system, which dynamically suggests personalized notes based on student performance, enhancing learning focus and outcomes.