Elaf Sabah Abbas

Background: The integration of artificial intelligence (AI) with augmented reality (AR) has significantly revolutionized educational practices. By blending digital content with the physical environment, AR enhances student engagement, while AI-driven tools personalize learning experiences. Objective: This article aims to explore the future of AI-powered AR in education, analyzing its potential to transform traditional learning environments by improving student interaction, knowledge retention, and personalized learning. Methods: A comprehensive literature review was conducted, examining current AI-AR applications in educational settings. Additionally, case studies from early adopters of this technology in classrooms were analyzed. Interviews with educators and experts were conducted to gain insights into the challenges and opportunities associated with AI-enhanced AR. Results: The findings indicate that AI-AR systems significantly enhance student engagement, promote interactive learning experiences, and offer personalized feedback based on individual learning styles. However, challenges such as high implementation costs, technical expertise requirements, and the need for curriculum alignment were identified. Conclusion: AI-AR has the potential to reshape educational practices by fostering a more interactive, engaging, and tailored learning experience. Future efforts should focus on addressing the technical and pedagogical challenges to ensure successful adoption across various educational contexts.

Background: Software-Defined Networking (SDN) is widely considered a new paradigm shift in today’s telecommunication evolving method of centralized control, program interface, and dynamic resource configuration. Members of such a network can be reached through single-hop or multi-hop communication and is, however, still faced with inexhaustible challenges in scalability, security, energy consumption as well as Quality of Service (QoS). Objective: Specifically, the article will seek to compare both SDN enabled network as well as legacy networks as regards to established parameters like scalability, security, power consumption, traffic control and path finding. The research aims to fill these gaps by employing state-of-art methods and offer useful recommendations of SDN implementation. Methods: Both simulation and analytical modeling were used to evaluate the proposed SDN architectures under different loads. Metrics were assessed with the congestion control based on the neural network, optimization involved the multiple objectives, and security assessment via game theory. Analyses for statistical significance further supported the performance enhancements determined. Results: The results show 44% improved latency, 33% better energy consumption, and better load balancing in SDN-enabled network. Neural network-based mechanisms were able to reroute 95% of the time under low traffic conditions, while distributed controller-based strategy had high scalability and security. Conclusion: This study points to the capacity of SDN to revolutionize the contemporary telecommunication with strong techniques for comprehensive problems. For the future work it is recommended to conduct validations in operational conditions, and include underdevelopment technologies into the system hierarchy to improve its flexibility and operation characteristics.