Lumami (Nagaland): Researchers at Nagaland University have developed a cutting-edge ‘Flexible Learning System’ (FLS) aimed at revolutionizing personalised learning for students in higher education institutions.
The newly proposed framework integrates Multi-Access Edge Computing (MEC) to facilitate real-time adaptive learning within Intelligent Tutoring Systems (ITS), an AI-driven educational model designed to provide individualised instruction.
The research, conducted by Er. Ramesh Singh, Er. Chenlep Yakha Konyak, and Er. Akangjungshi Longkumer from the Department of Computer Science and Engineering, seeks to bridge gaps in ITS by enhancing real-time adaptability.
Their findings, published in the peer-reviewed International Journal of Information Technology, highlight the system’s potential to significantly improve student engagement and learning outcomes.
Field trials conducted by the researchers at Nagaland University’s School of Engineering & Technology demonstrated a remarkable 94% reduction in task processing time, allowing for real-time responses that optimize the learning experience.
The FLS categorizes learners based on their performance and tailors content delivery accordingly, ensuring that students progress through courses with easy, moderate, or advanced content suited to their individual capabilities.
“This proposed system will help students navigate courses effectively by providing content aligned with their level of understanding,” said Prof. Jagadish K. Patnaik, Vice Chancellor of Nagaland University.
“It will also assist those who need additional guidance. Looking ahead, the model will incorporate the Internet of Things (IoT) to further enhance learning insights. Additionally, augmented reality and virtual reality integration will enable immersive educational experiences.”
The research team emphasised that the FLS not only optimizes network bandwidth and processing tasks but also fosters stronger interactions between students and instructors.
Er. Chenlep Yakha Konyak noted that existing ITS platforms often lack real-time adaptive learning capabilities, limiting their ability to cater to individual learning needs.
“Our system addresses this gap by leveraging MEC to process tasks efficiently while delivering tailored content,” he explained.
Er. Ramesh Singh highlighted the system’s role in improving accessibility, particularly in regions with limited infrastructure.
“By utilising edge computing, we ensure that students in remote areas have access to personalised learning experiences without latency issues,” he said.
Er. Akangjungshi Longkumer underscored the broader implications of the research, stating that the system has the potential to transform educational methodologies at scale.
“With ongoing refinements and further testing, we aim to expand the FLS’s capabilities and explore potential commercialization to benefit educational institutions nationwide.”
The research represents a significant advancement in educational technology, offering students a more flexible, efficient, and personalised learning experience.
As further developments unfold, the framework is expected to play a key role in shaping the future of intelligent tutoring systems in India and beyond.
