From Classrooms to Code Realms: Engineering Pedagogy Transformation through Metaverse-Based Instructional Design
Intelligent Exoskeletons for Personalized Rehabilitation using Machine Learning
Synergistic Neuro-Symbolic Multi-Agent Architecture for Robust and Transparent Autonomous Systems
A Comprehensive Survey on AI-Powered Desktop Automation (COSMO)
A Comprehensive Review of Robotics and Autonomous Systems: Evolution, Technologies, Applications, and Future Directions
Autonomous Robots for Hospital Logistics and Patient Care: An Effective Way for Elderly Care and Monitoring
A Comprehensive Review of Augmented Reality in Education and Medical Fields
Augmented Reality and Virtual Reality Technologies in Surgical Operating Systems
Virtual Lab Design for Active Filter Circuits
Immersive Virtual Reality to Improve Functional Capabilities in People with Multiple Sclerosis Disorder
Intelligent Personal Assistants: A Brief Overview
Robot Artist and XY Plotter for the Generation of Modern Art
Nanorobots and its Advancements in Medical Field
A Comprehensive Survey on AI-Powered Desktop Automation (COSMO)
From Classrooms to Code Realms: Engineering Pedagogy Transformation through Metaverse-Based Instructional Design
The rapid growth of immersive educational technology has opened up new possibilities in higher education, especially in engineering education, where hands-on and experiential learning are very important. This study looks at how using the metaverse to design lessons could change the way engineering is taught in schools. The study provides a full educational framework that includes gamified homework, working together with avatars, 3D virtual worlds, and real-time simulations of engineering processes. It is based on theories of constructivist and experiential learning. The study used a mix of methods and included undergraduate engineering students from core fields like electronics and computer science. The participants used Unity and spatial platforms to learn in a metaverse-based environment that replaced traditional lectures with coding worlds, virtual labs, and problem-solving scenarios. The results showed that students were much more motivated, engaged, and understood than when they used traditional classroom methods. Students said that using avatars made it easier for them to work together, think more clearly, and pay more attention. This study adds to the growing field of metaverse-driven pedagogy by presenting a flexible and scalable way to design engineering curricula. It gives schools that want to use immersive environments in their regular classes’ helpful advice. The results show that the metaverse can help bridge the gap between theory and practice. This will make engineers more creative, flexible, and ready for the job market.
The integration of robotics and artificial intelligence has opened new horizons in personalized rehabilitation therapy. This research focuses on the development of intelligent exoskeleton systems that leverage machine learning algorithms to provide adaptive and patient-specific support during physical rehabilitation. Traditional rehabilitation approaches often rely on standardized protocols that may not accommodate individual patient needs, leading to suboptimal recovery outcomes. The proposed system uses real-time sensor data, including kinematic and physiological signals, to monitor patient movement and dynamically adjust the exoskeleton’s assistance levels. Machine learning models analyze the patient’s progress and predict optimal movement patterns, enabling personalized therapy plans that evolve over time. Experimental evaluations demonstrate that the intelligent exoskeleton improves mobility, reduces rehabilitation duration, and enhances patient engagement compared to conventional methods. This study highlights the potential of combining AI and wearable robotics to revolutionize rehabilitation practices, offering scalable, adaptive, and data-driven therapeutic solutions.
This paper introduces a hybrid neuro-symbolic multi-agent architecture designed to strengthen the safety, interpretability, and reliability of autonomous systems. The proposed framework integrates neural perception and planning with symbolic reasoning, enabling agents to generate flexible action hypotheses while maintaining strict logical coherence through rule-based verification. The system is evaluated in robotic coordination and diagnostic environments, demonstrating improved task success, reduced unsafe behaviors, faster learning convergence, and clearer reasoning traces compared to neural-only and symbolic-only baselines. This approach offers a scalable foundation for next-generation autonomous systems requiring accountability, adaptability, and multi-agent consistency.
This paper presents an overview of AI-powered desktop automation and its evolution toward intelligent assistants capable of performing real-time tasks using natural language interaction. COSMO (Conversational Smart Machine Operator) is a lightweight desktop assistant designed to execute system level operations, manage files, interact with external APIs, and enhance user productivity. We review existing intelligent assistants, RPA tools, and NLP-driven automation systems, identify persistent challenges (e.g., UI variability, noise, dependence on cloud APIs), and motivate COSMO’s design choices that favor local execution, privacy, and modularity.
Robotics and Autonomous Systems (RAS) have progressed from early mechanical manipulators in the 1960s to intelligent, AI-driven systems capable of autonomous decision-making across industrial, medical, agricultural, aerial, underwater, and space environments. This review consolidates six decades of robotics development, drawing from more than 120 scholarly sources, including seminal contributions in industrial automation, SLAM and mobile robotics, humanoid robotics, soft robotics, swarm systems, and autonomous vehicles. The paper synthesizes historical foundations, current technologies, domain-specific advancements, and future trends (2030–2050). By integrating sensing, perception, control, SLAM, learning, human–robot interaction, and ethics, this review provides a comprehensive, human-centered perspective on robotics systems suitable for researchers, engineers, and policymakers.
