AI-Driven Telecommunications for Smart Classrooms: Transforming Education Through Personalized Learning and Secure Networks

Telecom, Journal Year: 2025, Volume and Issue: 6(2), P. 21 - 21

Published: March 27, 2025

Advances in telecommunications and artificial intelligence (AI) are reshaping modern educational spaces. Drawing upon diverse resources, this systematic literature review examines how these new advances including 5G, Internet of Things (IoT), AI-based analytics can transform conventional classrooms into adaptive, secure, highly interactive environments. Real-time data collection personalized feedback systems found to significantly enhance engagement accessibility for learner populations. Furthermore, emerging security architectures, such as zero-trust frameworks AI-driven intrusion detection, mitigate cyber threats strengthen confidentiality. Nevertheless, it is that broader adoption limited due practical hurdles, which include budget allocation, professional development, regulatory compliance. In response, strategic recommendations provided guide the planning implementation intelligent different contexts while noting need responsible governance equitable access. By illustrating AI-assisted connectivity instruction safeguarding privacy, study offers a forward-looking perspective on pedagogical approaches balance technological innovation with ethical considerations.

Language: Английский

A Method for Sharing English Education Resources in Multiple Virtual Networks Based on 6G

International Journal of Network Management, Journal Year: 2024, Volume and Issue: 35(1)

Published: Dec. 23, 2024

ABSTRACT The rapid advancement of communication technologies, particularly in English language learning, is sharing education with the implementation sixth‐generation (6G) networks, offering immersive and interactive learning experiences. purpose research to establish an advanced method for resources across multiple virtual networks enabled by 6G technology. Traditional resource‐sharing systems lack effectiveness optimization requirement large‐scale instructional assignments, especially settings various user demands. To address this, study proposed a novel Dynamic Tunicate Swarm Refined Graph Neural Networks (DTS‐RGNN) model optimize resource allocation improve efficiency among educational tasks. approach uses TSO scalable through technology GNN task assignment according previous performances interaction students balance utilization. experimental group performed writing (90%), (91%), listening (85%), reading (75%), finishing 5.5 s at 1000 GB. Throughput increased 5.0 GBps utilization improved (96%) student outcomes showed high satisfaction (93%), retention (89%), engagement (90%). findings demonstrated significantly improves online resources, promoting more effective experiences networks.

Language: Английский