Wafaa Adnan Sajid

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: UAV-assisted communication networks have emerged as vital tools for disaster recovery, offering rapid deployment and scalability in dynamic environments. However, challenges such as regulatory compliance, data security, energy efficiency, and real-time adaptability limit their widespread implementation. Objective: This study aims to develop a multi-objective optimization framework for UAV-assisted networks that enhances coverage efficiency, reduces latency, and optimizes energy consumption while addressing regulatory and data security challenges. Methods: The proposed framework integrates k-means clustering, genetic algorithms, and real-time adaptation mechanisms. Key metrics: coverage, latency, energy efficiency, and regulatory compliance, were evaluated across urban, suburban, and rural disaster scenarios. Dynamic geofencing, end-to-end encryption, and anomaly detection were incorporated to ensure compliance and secure operations. Results: The framework achieved significant improvements: coverage efficiency increased by 8%, latency reduced by 43%, and battery life extended by 33%. Regulatory compliance rose from 75% to 95%, and data security was enhanced with a 50% improvement in threat detection. The framework demonstrated robust scalability, maintaining high performance across diverse user densities. Conclusion: The study presents a scalable and adaptable UAV-assisted communication framework that addresses operational, regulatory, and security challenges. Its results validate its potential for real-world disaster recovery, paving the way for further innovations in this critical domain.